Point defects in monolayer WSi_2N_4 and MoSi_2N_4

Domain: biology
Published: 2026-03-23
Editorial outcome: major_revision

Abstract
Point defects critically govern the properties of two-dimensional semiconductors, yet their atomic-scale characteristics in the emerging MoSi N family remain unexplored. Here we combine atomic-resolution scanning transmission electron microscopy and first-principles calculations to systematically investigate point defects in monolayer semiconducting WSi N and MoSi N . We identify ten distinct defect types, with Si-for-top-N antisites (Si ), double-middle-N divacancies (V ), and double-top-N divacancies (V ) being the most abundant. These defects induce mobility modulation and bandgap reduction — in some cases leading to complete band closure and insulator-to-metal transitions — and, in certain configurations, give rise to spin-polarized bands with localized magnetic moments. Additionally, we observe low-dimensional assemblies formed via defect self-organization, including 2D Si networks and 1D Si2 chains. Our findings establish fundamental defect–property relationships and provide insights for defect-driven engineering of electronic and magnetic states in 2D WSi N and MoSi N semiconductors.

Introduction
Point defects, which are ubiquitous in crystalline materials, have a significant influence on the electronic, optoelectronic, thermal, magnetic, and catalytic properties, particularly for 2D materials due to the reduced dimensionality1-14. Usually, point defects act as scattering centers of electrons and phonons, leading to a great degradation of electron mobility, thermal conductivity and photoelectric properties of 2D materials1,3,4,9,12,15-19. On the other hand, some unique or improved properties could be achieved by introducing point defects. For example, S vacancies in MoS can induce localized states, enabling MoS a carrier hopping transport phenomenon10; Pt vacancies in PtSe introduce localized magnetic moments, giving rise to the Kondo effect2; the Frenkel point defects and S vacancies greatly promote the hydrogen evolution reaction activity of monolayer MoS 6,11. Thus, understanding the point defects in 2D materials is essential not only for controlled growth of perfect crystals but also for tailoring their properties to meet various applications.

The MoSi N family is an emerging class of van der Waals (vdW) layered 2D materials with a sandwich-structured septuple-atomic-layer configuration20,21. Such unique structure enables MoSi N -like materials many interesting properties21-38, such as remarkable electrical, optical and valley-contrasting properties, piezoelectricity, ferroelectricity, magnetism, and catalytic properties. Meanwhile, the complex compositions and structures of the MoSi N family enable the formation of a wider variety of point defects compared to graphene, h-BN and transition metal dichalcogenides (TMDCs), providing greater degrees of freedom for tailoring the properties. Monolayer MoSi N and WSi N are the first two materials that have been synthesized by chemical vapor deposition (CVD) in the MoSi N family20,21.

Both of them are p-type 2D semiconductors with intrinsically higher carrier mobility, higher mechanical strength, higher thermal conductivity, and better stability than most 2D semiconductors20-22,39. Moreover, monolayer WSi N has been predicted to possess higher carrier mobilities and thermal conductivity than monolayer MoSi N 21,28. Although point defects have been predicted to have a significant influence on the properties of MoSi N -like materials38,40-43, the atomic structures of point defects remain experimentally unknown, leaving a critical gap in our understanding of their role in shaping material properties.

Here, we studied the point defects in monolayer MoSi N and WSi N using aberration-corrected scanning transmission electron microscopy (STEM) combined with first-principles calculations, with a focus on monolayer WSi N because it has higher tolerance to electron beam irradiation than MoSi N (Supplementary Figure 1). Four kinds of vacancies and six kinds of antisite substitutions were identified, among which the Si-for-top-N antisites (Si ), double-middle-N divacancies (V ), and double-top-N divacancies (V ) exhibited higher concentrations. First-principles calculations show that these point defects reduce the bandgap of monolayer WSi N : some completely close the bandgap, resulting in insulator-to-metal transition; some introduce spin-polarized bands, leading to local magnetic moments. Intriguingly, two low-dimensional defect configurations of 2D Si networks and 1D double-Si-for-Mo antisite (Si2 ) chains were identified in monolayer MoSi N , both exhibiting unique electronic characteristics.

Results
Both WSi N and MoSi N monolayers were synthesized by CVD20. Atomic force microscopy (AFM) characterization of the MoSi N and WSi N samples transferred onto SiO substrates confirms their monolayer nature. The measured thicknesses are approximately 1.21 nm for MoSi N and 1.19 nm for WSi N (Supplementary Figure 2), which align with the reported values for monolayers20. X-ray photoelectron spectroscopy (XPS) characterization confirms the existence of W/Mo, Si, and N elements in WSi N /MoSi N (Supplementary Figures 3 and 4). It is worth noting that island-like multilayers can form under an increased NH flow. Such intentionally grown thick samples were used to reveal the cross-sectional atomic structure of WSi N .

Figure 1a and 1b are optical and bright-field TEM images of CVD-grown WSi N crystals, respectively, showing their triangular morphology. Figure 1c exhibits an annular dark field (ADF) - STEM image of monolayer WSi N , in which W and Si atoms are arranged into a projective hexagonal ring. Since Z-contrast ADF intensity is related to Z1.7 (Z, atomic number)44, W atoms are much brighter than Si atoms. To visualize the locations of N atoms, integrated differential phase contrast (iDPC) - STEM technique was adopted. As the intensity of iDPC-STEM image is approximately proportional to Z, the heavy and light elements can be captured simultaneously45. The iDPC-STEM image clearly demonstrates that the top N atoms are precisely positioned at the center of W-Si projective hexagonal rings (Fig. 1d). In contrast, the bilayer and trilayer WSi N display six-membered ring structures with distinct central features depending on the number of stacking layers (Supplementary Figure 5).

We have tried to acquire cross-sectional STEM images of monolayer WSi N . However, the inherent flexibility of monolayers led to severe bending and distortion during cross-sectional sample preparation, which prevented the resolution of atomic arrangements. Thus, we prepared multilayer WSi N samples to analyze the cross-sectional atomic structure of WSi N . As shown in Fig. 1e, the W, Si and N atoms form a septuple-atomic-layer configuration. The above STEM results confirm that WSi N has the same crystalline structure as the reported MoSi N monolayer20 (Supplementary Figure 6). Figure 1f presents the atomic schematic diagrams of monolayer WSi N , determined from the above characterizations.

Unlike single-atomic-layer graphene, WSi N exhibits a stereo structure with overlapped atoms of Si and N, increasing the difficulty of discerning point defect configurations. The structural determination of point defects requires the imaging of light N atoms, heavy W atoms, and Si atoms. However, iDPC-STEM is highly sensitive to variations in sample contaminants and image defocus46-49, which can introduce artifacts that may compromise the accuracy of structural analysis. Additionally, the lighter atoms result in a low signal-to-noise ratio in ADF-STEM imaging, especially when the materials contain heavier atoms50. Meanwhile, the local fluctuation of the sample also diminishes the accuracy of structural analysis for both ADF-STEM and iDPC-STEM50-56. Thus, we simultaneously employed iDPC- and ADF-STEM imaging, complemented by iDPC- and ADF-STEM simulations and first-principles calculations, to identify the point defects in monolayer WSi N .

Figures 2 and 3 show ten main point defect configurations that were observed in monolayer WSi N , including four vacancies and six antisite substitutions. The vacancies include V , V , double-Si-and-single-middle-N trivacancies (V ) and W vacancies (V ) (Fig. 2); the antisite substitutions include Si , Si-for-middle-N antisites (Si ), double-Si-for-W antisites (Si2 ), Si-for-W antisites (Si ), W-for-Si antisites (W ), and W-for-top-N antisites (W ) (Fig. 3). Notably, the ADF- and iDPC-STEM simulations of corresponding point defects are in good line with the experimental observations (Fig. 2a-d and Fig. 3a-d). Furthermore, the ADF line scan profiles across defective areas show good agreement between experimental and simulated images (Fig. 2e and Fig. 3e). These consistent results validate the reliability of our defect assignments. The obtained atomic models of point defects are presented in Fig. 2f and Fig. 3f. Similarly, identical point defects were observed in monolayer MoSi N , as shown in Supplementary Figures 7 and 8.

Notably, the absence of a single N atom in WSi N does not produce a detectable contrast change in the ADF and iDPC images, which prevented unambiguous identification of V and V (Supplementary Figure 9) and distinguishing the defect sets of V /V /V (Supplementary Figure 10) and V /V (Supplementary Figure 11). Moreover, due to the larger Z difference between W and Si/N, W exhibits similar contrast with W (Supplementary Figure 12). To identify these defects, we calculated the formation energies of various point defects by first-principles calculations under the experimentally determined Si-rich conditions (Fig. 4, Supplementary Figure 13, Supplementary Table 1 and see Supplementary Note 1 for calculation details). The obtained formation energies follow the trend V < V < V , V < V , and W < W (Supplementary Figure 14). Thus, we assign these defects as V , V , and W , rather than V (V ), V , and W , respectively. It is worth noting that adatoms are highly mobile under electron beam irradiation and thus are relatively straightforward to be distinguished compared to lattice defects.

Figures 4a and 4b show large field-of-view STEM images of monolayer WSi N , clearly demonstrating the diversity of point defect structures present within a single sample. We then counted the concentration of the ten kinds of point defects by statistically analyzing 140 atomic-resolution STEM images, encompassing 6600 defects. As shown in Fig. 4c, the most prevalent defects are Si , V , and V . This is in sharp contrast to CVD-grown MoS monolayers, where only sulfur vacancy (V ) was frequently detected9,57. The frequent occurrence of multiple types of point defects in WSi N , including vacancies and antisite substitutions, reveals its inherently more complex structural configuration. The Si antisite and V vacancy exhibit the highest defect concentrations ‒ (2.12 ± 0.002) × 1013 cm-2 and (1.14 ± 0.005) × 1013 cm-2, respectively ‒ which are comparable to that of V in CVD-grown MoS (1.2 × 1013 cm-2)9. In contrast, the concentrations of other defects, such as V and V are much lower. It should be noted that the statistical analysis of V may inadvertently include V and V due to their nearly identical image contrast, even though V configuration is thermodynamically most stable and dominant. This leads to a potential overestimation of the true V concentration. Similarly, the statistical analysis of W possibly includes the case of W .

Furthermore, it should be noted that W-containing vacancy complexes do exist. For instance, Supplementary Figure 15 shows the absence of two Si atoms and two N(t) atoms around the missing W atom. Owing to the multiple atomic configurations of W-containing vacancy complexes and their inherently low concentration, all instances were included in the statistical analysis of V . For simplicity, the term tungsten vacancies (V ) was used representatively to present the statistical results.

To understand the formation of the observed defects, we first conducted in-situ STEM experiments at 300 kV to rule out the influence of electron beam irradiation. During the observation for normal photography with only one brief irradiation event, no new point defects were formed. After ~33 s STEM image preview, only a limited number of new nitrogen vacancies emerged (Supplementary Figure 16), along with the migration of a few tungsten adatoms. Continued exposure of 102 s led to occasional ejection of W atoms from the lattice at a few sites (Supplementary Figure 17). Furthermore, electron energy loss spectroscopy (EELS) confirmed the absence of O in the WSi N (Supplementary Figure 18). These results collectively demonstrate the robust stability of monolayer WSi N under STEM imaging. Moreover, in our experiments, we frequently moved the sample to different locations. Each position was subjected to no more than 5 s preview and only one image acquisition. Therefore, the observed defects intrinsically originate from the CVD growth process rather than being caused by electron beam irradiation.

Furthermore, we systematically evaluated the stability of point defects in monolayer WSi N under various experimental conditions. First, defect concentration statistics remained consistent at different STEM beam currents (~5–20 pA), with minor variations attributable to sample heterogeneity (Supplementary Figure 19 and Supplementary Table 2). Second, defects showed no measurable change after two months of ambient exposure (Supplementary Figure 20). Third, electrical biasing at 1 V induced no detectable alteration in defect types or concentrations (Supplementary Figure 21). Finally, thermal cycling up to 500 °C likewise preserved the defect configurations (Supplementary Figure 22). Collectively, these results confirm that the identified point defects are structurally and chemically stable under the employed imaging conditions, upon ambient exposure, as well as under moderate electrical and thermal stress.

As shown in Fig. 1f, monolayer WSi N consists of seven elementally distinct atomic layers. The formation of point defects during its growth is highly sensitive to CVD atmosphere. In our experiments, a quartz tube served as Si source, creating a Si-rich environment. Meanwhile, the supply of N was limited because a higher NH flow rate would cause undesirable etching of the Cu growth substrate. Under such Si-rich and N-deficient environments, the formation of Si substitute defects and N vacancies is thermodynamically favored. This is because excess silicon can substitute atoms in various layers while maintaining coordination symmetry, and N deficiency promotes vacancy formation across all four N layers. Experimentally, the dominant point defects observed include antisites (e.g., Si , Si , Si2 , and Si ) and vacancy complexes (e.g., V and V ), consistent with these expectations. The increasing concentration of Si and Si2 defects with higher Si supply (Supplementary Figure 23) further supports the preliminary understanding on the formation mechanism of defects.

Moreover, the observed point defect concentrations correlate with their calculated formation energies (Fig. 4c). In WSi N , the formation energies of vacancies are generally higher than those of antisite substitutions, similar to the case of MoSi N 43. Thus, antisite substitutions are more frequently observed in comparison with vacancies. This is in sharp contrast to the case of MoS , where vacancies are generally more stable than antisite substitutions9,57. Notably, the concentration of V and V are relatively high despite their higher formation energy, even surpassing that of antisite substitutions with lower formation energy. Two primary factors may account for this observation. First, the formation energies of single V and V vacancies (3.89 eV and 3.21 eV, respectively) are approximately half those of their double-vacancy counterparts, V and V (8.31 eV and 6.18 eV). This near-additivity behavior suggests weak coupling between the two single vacancies in V and V . Consequently, a high concentration of single vacancies could facilitate the formation of double vacancies through statistical pairing. Second, the N(t) atoms at the outermost surface are more susceptible to damage during TEM sample preparation, including transfer and heat treatment. It is worth noting that once a V forms near an N(m) atom, the computed energy barrier for N(m) moving towards the adjacent V is relatively low compared to that for W or Si escaping into the vacuum (Supplementary Figure 24). This suggests that N(m) atoms are susceptible to environmental disturbances in the presence of N(t) vacancies, which may further contribute to an increased concentration of V defects.

In certain cases, a discrepancy arises between the experimentally derived defect concentration and the corresponding calculated formation energy, such as the case of W . There are several reasons for such discrepancy. First, the formation energies of point defects are strongly influenced by chemical potential, which may differ between experimental and calculation conditions58. Second, our computational modeling was confined to point defects within a 4 × 4 × 1 supercell (already large for the DFT calculations), which corresponds to an unrealistically high defect concentration (~ 0.74 nm-2) compared to the much lower experimental concentration (~0.01 nm-2). Third, our calculations assume charge-neutral defects, but the possibility of charged defects cannot be excluded58. Fourth, while we assume defect-induced charge distribution is delocalized across the lattice, the potential impact of charge localization on formation energy remains difficult to rule out58.

We then conducted first-principles spin-polarized density functional theory (DFT) calculations to explore the influence of the observed point defects on the electronic properties of monolayer WSi N (Fig. 5, Supplementary Table 1). Pristine monolayer WSi N is a 2D semiconductor with an indirect bandgap of 2.09 eV. The presence of point defects reduces the bandgaps to 0.64 ‒ 1.26 eV depending on the types of point defects. The presence of V , Si2 , W , and W defects induces a change in the bandgap type from indirect to direct. Notably, the introduction of V , V , Si , Si , and Si defects completely closes the bandgap, resulting in insulator-to-metal transitions. Additionally, V , V , V , V , W , and W defects introduce spin-polarized bands, leading to local magnetic moments. For vacancies, the magnetic moment in V originates from the three surrounding W atoms; similarly, the magnetic moment in V arises from the three N(t) atoms on the upper and lower surfaces; V has a magnetic moment of 0.51 μ , which is attributed to the three surrounding Si atoms; V has a magnetic moment of 1.02 μ , which stems from the three Si atoms on the upper and lower surfaces. Notably, the magnetic moment of V is twice that of V , indicating the weak coupling effect between the two aligned N(t) atoms. For antisite substitutions, W and W exhibit magnetism derived from the antisite W atoms. Particularly, W possesses the largest magnetic moment of 2.11μ among all the point defects.

We further calculated the orbital-resolved density of states (DOSs) to identify the orbital characters of in-gap states (Supplementary Figure 25). The contribution from vacancies was obtained by introducing pseudo atoms into the positions of vacancies. It is found that for V , V , V , Si , Si2 , and Si , the in-gap states are predominantly composed of W orbitals. While for V and V , the in-gap states are primarily composed of Si orbitals. For W and W , the in-gap states are mainly attributed to the defect orbitals. These in-gap states induced by point defects strongly modulate the electronic and magnetic structure of pristine WSi N (Fig. 5, Supplementary Table 1) and therefore change the associated optical properties. For instance, the presence of V decreases the bandgap by inducing the in-gap states, which introduce additional optical absorption within the bandgap (Supplementary Figure 26). In particular, these in-gap states are rather localized, which is manifested by the appearance of flat bands within the bandgap (Fig. 5). These flat bands significantly increase the hole effective mass at  from 1.5m (pristine) to 15.3 m and thus decrease the hole mobility from 411 cm2s-1V-1 (pristine) to 2 cm2s-1V-1 according to the deformation potential theory9,59. In contrast, the introduction of Si reduces the Fermi energy, resulting in an almost rigid upward shift of the band structure and triggering an insulator-to-metal transition (Fig. 5). The crossing of the maximum valence band with the Fermi energy facilitates optical transitions within the infrared regime (Supplementary Figure 26). Furthermore, the presence of Si significantly increases hole mobility, reaching a value of 740 cm2s-1V-1.

To correlate the theoretical calculation results, we used atomic-scale scanning tunneling microscopy (STM) to probe the impact of point defects on the local electronic structure of monolayer WSi N . The surface tunneling properties of STM allowed us to identify V and Si defects from topographic images (Fig. 6). Scanning tunneling spectroscopy (STS) measurements on a defect-free region (Figs. 6a, b) revealed a bandgap of ~2.05 eV, consistent with our DFT prediction (2.09 eV). In contrast, STS on defect sites showed significantly reduced bandgaps (Figs. 6c, d), with values approaching the calculated gap (0.84 eV) for V defects. The discrepancy between the easy identification of V by STM and its limited representation in STEM images is due to the distinct imaging mechanisms and sensitivities of the two techniques. An additional defect site exhibited a metallic signature, which we identify as the Si defect—consistent with our theoretical prediction of its metallic character. These results confirm the decisive influence of point defects on the electronic properties of WSi N .

We further measured the ultraviolet and visible (UV-Vis) absorption spectra of three distinct WSi N samples and obtained their corresponding Tauc plots (Supplementary Figure 27): the original sample, the sample annealed in Ar at 300 °C for 12 h, and the sample annealed in air at 300 °C for 12 h. The absorption data showed similar bandgaps for the original sample (~2.1 eV) and the air-annealed sample (~2 eV), indicating that the point defects exhibit oxidation resistance in air at the evaluated temperature. In contrast, the sample annealed in Ar exhibited a noticeable reduced bandgap of ~1.8 eV, indicating an increase in defect concentration. In our work, the annealed samples were used to obtain high-quality STEM images for defect concentration analysis. Taking the V defect as example, argon annealing led to a significantly higher defect concentration compared to air annealing (Supplementary Figure 28), consistent with the more pronounced bandgap reduction.

The comprehensive atomic-scale catalog of point defects in monolayer WSi N , together with their tunable properties, offer significant opportunities for device engineering. For example, the substantially increased hole mobility by Si defect suggests its utility for channel optimization. Defects that induce an insulator-to-metal transition, such as V , Si , Si , and Si , when strategically incorporated at contact interfaces, may lower the Schottky barrier height, reduce contact resistance, and enhance on-state current density. Similarly, the Si antisite defects introduce a shallow acceptor state near the valence band maximum, enabling effective p-type doping for threshold voltage tuning. Conversely, in-gap states induced by V defects promote trap-assisted tunneling, potentially increasing the off-state leakage current and reducing the switching ratio. Consequently, optimizing field effect transistor performance—balancing high mobility, low leakage, and low contact resistance—will require precise spatial defect engineering: suppressing channel-degrading defects, leveraging mobility-enhancing defects in the active region, and potentially incorporating beneficial defects at contact interfaces.

Furthermore, although pristine WSi N exhibits high intrinsic thermal conductivity, a trait well-suited for transistor applications, high concentrations of defects such as V and Si are expected to substantially reduce the lattice thermal conductivity through mass-contrast and strain-field scattering, as observed in other 2D systems60-62. Such reduction is highly advantageous for thermoelectric applications, where a lowered lattice thermal conductivity directly enhances the thermoelectric figure of merit ZT, particularly when coupled with defect-tuned electronic properties, for example, the metallic states introduced by Si defects.

Beyond isolated point defects in monolayer WSi N and MoSi N , we observe a propensity for defect aggregation in these materials. Figures 7a-c and Supplementary Figure 29 exhibit typical ADF images of double, triple, and multiple Si aggregations in MoSi N and WSi N monolayers, respectively. It is worth noting that Si could even form a 2D network in certain areas of MoSi N monolayer (Fig. 7c). Simulated ADF images of Si networks are in accordance with the experimental observations (Fig. 7d). Geometric phase analysis (GPA) analysis indicates the absence of lattice strain across the Si 2D networks and MoSi N matrixes (Supplementary Figure 30). Structurally, the Si 2D network is a periodic, planar crystalline defect characterized by the aggregation of collective Si antisite defects on one side of the MoSi N monolayer, analogous to a stacking fault.

Taking MoSi N as an example, we constructed defect aggregation models containing one, two, and three Si defects within a 4 × 4 × 1 configuration of the MoSi N unit cell (Supplementary Figures 31 and 32) and calculated their corresponding binding energies. The configurations with Si antisite defects aggregated on one side of the monolayer MoSi N exhibit higher stability (Supplementary Figure 33). The increased binding energies with the quantity of Si located on one side suggest that Si defect aggregation is energetically favorable (Supplementary Figures 33 and 34). Moreover, the formation of 2D Si network triggers electronic structure reconstruction in the MoSi N system (Fig. 7e). Similar to isolated Si point defects43, the introduction of 2D Si network induces bandgap closure. Systematic analysis of DOSs demonstrates that the electronic states near the Fermi level in 2D Si network are predominantly contributed by the 3p orbitals of Si atoms (Fig. 7f).

Additionally, we observed a 1D point defect chain embedded in monolayer MoSi N (Fig. 7g), where no lattice strain was detected as well (Supplementary Figure 35). Careful examination of defect configurations in an enlarged ADF-STEM image revealed a 1D Si2 chain (Fig. 7h), in good agreement with corresponding ADF image simulation (Fig. 7i). This Si2 chain is a linear crystalline defect that separates the single-crystal MoSi N monolayer into two parts without a misorientation angle, analogous to a 0° grain boundary in 2D materials. We constructed the 1D chain-like models containing one, two, three and four Si2 within a 4 × 4 × 1 configuration of the MoSi N unit cell (Supplementary Figure 36) and calculated their binding energies. Similar to the 2D Si aggregations, the binding energies of these 1D defect aggregations increase as the quantity of Si2 increases (Supplementary Figure 37), indicating the increasing energetical stability for linear assembly of Si2 . Upon introducing this 1D chain into MoSi N , the bandgap of the system significantly decreases from 1.72 eV to 0.40 eV (Fig. 7j). Further DOSs analysis reveals that the 1D chain-induced in-gap states near the Fermi level are dominated by the d-orbitals of Mo atoms (Fig. 7k).

The thermodynamic stability of the observed 2D Si networks and 1D Si2 chains, as confirmed by our binding‑energy calculations, is consistent with an in‑situ assembly mechanism during CVD growth rather than post‑growth migration. The high‑temperature growth environment provides sufficient atomic mobility and a concurrent supply of all constituent elements, allowing direct incorporation of multi‑site defects into the extending lattice. The seamless embedding and crystallographic alignment of these extended structures further indicate a growth‑governed process. First‑principles migration‑barrier calculations additionally show that post‑growth rearrangement of isolated Si or Si2 defects—even in the presence of nearby vacancies—requires overcoming high energy barriers (> 4 eV) (Supplementary Figure 38), rendering large‑scale diffusion kinetically unfeasible under ambient conditions. Collectively, the structural coherence, energetic trends, and calculated kinetic barriers support a kinetically enabled, growth‑mediated pathway for the formation of these low‑dimensional defect aggregates.

In summary, by combining atomic-resolution STEM imaging and first-principles calculations, we revealed point defect configurations in monolayer semiconductors WSi N and MoSi N and their influence on material properties. Ten distinct types of lattice point defects, comprising four vacancies and six antisite substitutions, were identified. Taking monolayer WSi N as a representative system, we demonstrated that these defects substantially modulate electronic structure, induce magnetism, and alter optical responses. Furthermore, we identified two defect aggregation configurations in monolayer MoSi N : 2D Si networks and 1D Si2 chains, which exhibit significant influence on the electronic band structure. This work provides crucial insights into the defect-property correlation of monolayer semiconducting WSi N and MoSi N , which is essential for controlled synthesis and property engineering of such materials and paves the way for their applications in nanoelectronics, spintronics, and catalytic systems.

Methods
Sample preparation
The monolayer WSi N and MoSi N samples were first grown on Cu/W and Cu/Mo substrates, respectively, by CVD method20. Then, they were transferred onto the TEM grids coated with silicon nitrogen membranes by the chemical etching method with the help of a polymethyl methacrylate (PMMA) layer. After that, the TEM grids were immersed in hot acetone to dissolve the PMMA layer and subsequently annealed at 300 ℃ for 12 hours in air to remove hydrocarbon residues from the sample surfaces before inserting them into the electron microscope.

Structural characterization and simulation
The optical microscopy image was obtained on Nikon LV100D. The thicknesses of samples were measured using an atomic force microscope (AFM, MultiMode 8, Bruker, Inc.). Optical absorption spectra were recorded using an ultraviolet and visible spectrophotometry (UV-Vis, JASCO V-770). The BF-TEM image was obtained on a FEI Tecnai F20 at 200kV. The ADF- and iDPC-STEM imaging of monolayer WSi N and MoSi N was performed on a FEI Titan Cube Themis G2 300 instrument equipped with a high-brightness field-emission gun (X-FEG), double spherical aberration corrector and a monochromator. The accelerating voltage was 300 kV. For imaging the lattice point defects, the camera length was 230 mm; the collection semi-angle was 25 - 152 mrad for ADF imaging, while 6 - 24 mrad for iDPC imaging; the major beam current was set to ~ 9 pA. The STEM image acquisition and preview were typically conducted under the conditions of 5 μs dwell time with 2048  2048 pixels and 1.32  107 e- nm-2 electron dose and 1 μs dwell time with 1024  1024 pixels and 6.6×105 e- nm-2 electron dose, respectively. For imaging the Si 2D network and Si2 1D chain in MoSi N , the camera length was 145 mm; the collection semi-angle was 37 - 200 mrad for ADF imaging; the beam current was set to ~ 22 pA.

To ensure image quality, annealing treatment was carried out for all samples to remove surface contaminants as thoroughly as possible before STEM imaging. To improve the image contrast and signal-to-noise ratio of Si substitutions in the 2D networks and 1D chains, we selected strictly flat zones in monolayer MoSi N samples and extended the image exposure time to 10 μs dwell time with 2048 × 2048 pixels. The stability comparison for monolayer WSi N and MoSi N was conducted on JEOL ARM 200F at 200 kV.

The ADF- and iDPC-STEM images were simulated using ToTEM63 or Dr. Probe64 software based on multi-slice method. The simulation parameters were in accordance with the experimental values, disregarding any aberrations. The atomic models were optimized by DFT. To compensate for structural distortions (elongation or compression) in experimental images caused by sample drift, equivalent deformations were intentionally introduced into the simulated images to achieve precise atom-to-atom alignment.

For the statistical analysis of defect concentration, we acquired a large number of STEM images – each with a typical area of 9.45 × 9.45 nm2 – from random locations across multiple WSi N samples. The types of point defects were identified by combining STEM image contrast (ADF and iDPC) with image simulations. For V and Si point defects with inherently relatively high concentration, we used CalAtom software to perform the high-throughput statistical analysis65. The defect positions were identified by analyzing the intensity variations in ADF images. Other types of point defects were directly counted manually due to the limited quantity. The defect concentration was determined by normalizing the number of defects to the corresponding imaging area. Due to the low concentration, the datasets for V and V defects exhibited a discrete distribution. Therefore, a weighted averaging method was used to estimate their concentrations. For other defects, to minimize experimental statistical errors, Gaussian fitting was applied to the datasets to obtain the mean (μ) and variance (σ), yielding the final concentration values (Supplementary Figure 39).

For the biasing- and heating-STEM experiments, an in-situ thermoelectric holder (Protochips inc.) was used. Biasing was performed in constant voltage mode using a four-probe electrical chip, with the voltage increased to 1 V in steps of 0.1 V/s. Heating was conducted with a two-probe thermal chip, where temperature was raised from 25 °C to 500 °C at a ramp rate of 0.5 °C/s. The thermal cycling procedure comprised three stages: (1) heating to 500 °C for ∼1 h, (2) cooling to room temperature for ∼0.5 h, and (3) reheating to 500 °C for ∼1 h.

STM measurements
The monolayer WSi N sample was initially introduced into a combined scanning tunneling microscope–molecular beam epitaxy (STM-MBE) system (Unisoku Co., USM1500) and annealed at ~300 °C for 12 hours under ultrahigh vacuum to achieve an atomically clean surface. The base pressure during annealing was maintained at 1 × 10⁻¹⁰ Torr. After annealing, the sample was transferred to a low-temperature STM stage without breaking the vacuum. All the STM and STS experiments were conducted at liquid nitrogen temperature (~78 K). The STM topographic images were acquired under the constant-current mode using a PtIr tip, while the STS spectra were collected via a lock-in amplification technique with an AC modulation of 60 mV at a frequency of 879 Hz.

Theoretical calculations
First-principles calculations were carried out using the Vienna Ab initio Simulation Package66,67. The generalized gradient approximation of Perdew–Burke–Ernzerhof68 was used for the exchange-correlation functional. The interaction between nuclei and valence electrons was described by the pseudopotential within the framework of the projector augmented wave method69. The plane wave cutoff was set to 450 eV. To model the point defects, a supercell slab of 4 × 4 × 1 unit cell with a vacuum width of 20 Å was employed. The electronic optimization was performed until the total energy difference between two iterations was less than 10-6 eV. The structural relaxation was terminated until the force on each atom was smaller than 0.01 eV/Å. More computational details on the calculations of defect formation energy and binding energy are provided in Supplementary Note 1.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Response to reviewers’ comments\nWe sincerely appreciate the reviewers’ insightful comments and constructive\nsuggestions, which have helped us significantly improve the quality and clarity of our\nwork. Below, we provide a point-by-point response to each reviewer’s concerns. All\nrevisions in the manuscript are highlighted in blue for easy reference.\nReviewer 1\nThe author investigated point defects in monolayer MoSi₂N₄ and WSi₂N₄ using STEM\nand first-principles calculations, with a particular focus on WSi₂N₄. The findings\nrevealed 11 distinct types of point defects in WSi₂N₄, which lead to significant\nreductions in the bandgap, including complete gap closure that facilitates insulator-to-\nmetal transitions. However, several critical issues need to be addressed before further\nconsideration:\n1. Will the concentration and types of defects be influenced by CVD parameters, such\nas precursor ratios? The author should conduct additional experiments to explore the\neffects of CVD synthesis parameters, as the current concentration calculations lack\nrigor.\nreviewer that precise control over specific defects remains challenging, yet – as with\nother 2D materials – it can be approached through systematic optimization of growth\nand post-treatment conditions. For instance, as shown in Fig. R1, modulating the Si\nsupply during CVD directly influences defect concentration. In addition, post-treatment\nsuch as Ar annealing effectively increased the concentration of V defects, leading\nN(t)2\nto a bandgap reduction from ~ 2.1 eV to ~ 1.8 eV (Figs. R6 and R7). The precise control\nover defects and properties in monolayer MoSi N and WSi N will be a central\n2 4 2 4\nobjective of our future research.\nWe have added the above discussions into the revised manuscript.\n0.20\n0.15\n0.10\n0.05\n0.00\nFigure R13. The updated concentration statistics of point defects based on the\nexpanded dataset.\n6. The calculations for defect concentration distribution lack rigor. The statement,\n“However, since VSi and VSiN(m) have similar image contrasts with VN(m)2 (Fig.S4),\n)2-mn(\nnoitartnecnoC\nthe concentration statistics for VN(m)2 might include VSi and VSiN(m), leading to an\noverestimation” needs clarification.\nReviewer 2\nThe authors give a systematic report on the atomically resolved ADF-STEM imaging\nof point defects in the 2D monolayer MoSi2N4 and WSi2N4, the image gallery is well\nprepared, and some interesting gap-closure or insulator-to-metal transition properties\nhave been found from the perspective of DFT calculation. Although the study of defects\nlooks good, investigated systematically for the first time, the significance, depth and\nespecially applications still lack in this manuscript. Therefore, the publication of the\ncurrent form manuscript is still too early.\nBelow are the points that need to be addressed carefully,\n1. How do the authors identify the monolayer? How is the distribution of the thickness\nfor the CVD grown 2D thin layers? Any Raman or AFM measurement to facilitate the\naccurate transfer of monolayer?\nReviewer 3:\nTong et al present a study of point defects in WSi2N4 and MoSi2N4 using scanning\ntransmission electron microscopy (STEM) and complementary density functional\ntheory (DFT). There is certainly value in such an imaging and DFT study and I\nrecoganise that the imaging of such systems may be challenging, especially if they are\nelectron beam sensitive (although the sensitivity of the materials is not discussed in the\nmanuscript except to qualitatively state without experimental evidence that WSi N is\n2 4\nmore stable than MoSi N ). The work presents several interesting observations that will\n2 4\nbe of interest to those exploiting the properties of this relatively new class of materials.\nHowever, there are several areas where I feel clarification and improvement to the\nSTEM data analysis presented is required before publication in Nature Comms could\nbe recommended.\nReviewer #1 (Remarks to the Author):\nThis manuscript presents a systematic study of point defects in monolayer WSi₂N₄ and\nMoSi₂N₄. The atomistic identification of ten defect types and the discovery of exotic\nlow-dimensional defect aggregates are exceptional contributions. However, the study\nremains at the level of observation and prediction. The significance and impact are\ncurrently limited by a lack of deeper mechanistic analysis (e.g., defect formation\nkinetics) and a direct connection to experimentally validated device-level properties.", "reviewer_2": "The authors give a systematic report on the atomically resolved ADF-STEM imaging of point defects in the 2D monolayer MoSi2N4 and WSi2N4, the image gallery is well prepared, and some interesting gap-closure or insulator-to-metal transition properties have been found from the perspective of DFT calculation. Although the study of defects looks good, investigated systematically for the first time, the significance, depth and especially applications still lack in this manuscript. Therefore, the publication of the current form manuscript is still too early.\nBelow are the points that need to be addressed carefully,\n1. How do the authors identify the monolayer? How is the distribution of the thickness for the CVD grown 2D thin layers? Any Raman or AFM measurement to facilitate the accurate transfer of monolayer?\n2. Fig. 1b is the imaging of a multilayer cross section FIB sample? Can the authors provide the image of monolayer sample, to meet the figure caption.\n3. In Fig.2, the second column defect V_N(t)2 of the top panel is not a simple vacancy defect, there is also a adatom nearby. So could it be a Frenkel defect (vacancy + adatom/interstitial)?\n4. For the primary defects (V_N(m)2, V_N(t)2, Si_N(t)), please give the line profiles of the ADF-STEM intensity, and compare the simulated and experimental line profiles.\n5. Why is there no iDPC-STEM image for the antisite defects in Fig. 2d,e ?\n6. For Fig.3, the vacancy defects V_N(m)2 and V_N(t)2 have a formation energy larger than 6eV, but its concentration is not low, this is out of expectation. How do the authors do the statistical analysis of the defect density?\n7. The W_Si antisite defect has the lowest formation energy in Fig. 3 and Fig. S16, while its density is quite low. Why? The formation mechanism of defects should be discussed.\n8. How is the beam irradiation effect? The images are taken at the voltage of 300kV, this high energy would cause damage, especially for N atoms. So how can we make sure the defects are pristine, not caused by the beam.\n9. In Fig.5, the Si antisite defects form a superstructure, but the central Si atoms give very weak intensity indicated by the yellow circles and arrows, this means the STEM imaging should be very high-quality and also the monolayer sample should be ultraclean to exclude the effect of possible surface contaminations or residues.\n10. Fig. S10 show the calculated absorption spectra, could the authors show the experimental absorption spectrum?\n11. Could the authors provide magnetic or transport properties of the monolayers, to compare with the DFT prediction.", "reviewer_3": "Tong et al present a study of point defects in WSi2N4 and MoSi2N4 using scanning transmission electron microscopy (STEM) and complementary density functional theory (DFT) . There is certainly value in such an imaging and DFT study and I recognise that the imaging of such systems may be challenging, especially if they are electron beam sensitive (although the sensitivity of the materials is not discussed in the manuscript except to qualitatively state without experimental evidence that WSi2N4 is more stable than MoSi2N4). The work presents several interesting observations that will be of interest to those exploiting the properties of this relatively new class of materials. However, there are several areas where I feel clarification and improvement to the STEM data analysis presented is required before publication in Nature Comms could be recommended.\nThe authors explain that simultaneous iDPC and ADF imaging modes are required to give confidence in the presence of the different defects being observed. However, their simulations are only for the ADF image mode. It is essential that the comparison is made with STEM image simulations of both iDPC and ADF images. The authors compare line scans for the simulations of defective material to line scans from the simulations of the pristine system, but they also need to directly compare line scans to equivalent line scans from the experimental data, to give confidence in the assignment of different defects and subsequent analysis. Especially, in Figure 2 the assignment of the point defects of the VN(m)2, VN(t)2 and SiN2 is not convincingly shown.\nAlso for the VW image in Figure 2 the neighboring Si columns in the experimental data do not appear to be in the same position as they are in the simulation. Were the simulations performed on DFT relaxed xyz structure files or simply by removing the vacancy atom from the lattice? Simulations should be performed on the DFT relaxed structures. If the former relaxed DFT models are already used, possibly this VW vacancy could be a larger defect than just a single W vacancy?\nIn Figure S2 it is difficult to assess the confidence of the defect assignment as even the ADF simulations are not shown alongside the experimental data. The authors should also further explain why the atoms are assumed to be substitutional point defects rather than adatoms on the surface (due to knock on damage from the electron beam). I agree that there is clearly a long defect line where W atoms are missing, visible in Figure 5g,h,i. However, I do not think that the assignment of SiN(t) locations and agglomerations in Figure 5a-c (and SI Figure S11) is sufficiently convincingly demonstrated. I also do not thing that the term “superstructure” is justified given the disorder and inconsistency of the assignment of defects.\nThe scale of the sets of images in Figure 2 and FigureS2 are also not presented with an identical scale and the scales of the experiments does not always match the simulations, which does not help with the comparison and is distracting.\nThe imaging presented here is performed at 300 kV so the defects are likely to be overwhelmingly electron beam induced. At such an accelerating voltage, knock-on damage is likely to be dominant and N is likely to be more susceptible to knock on damage than Si and Si. The authors need to consider the knock on damage cross sections of the different atomic species when interpreting their defect results. They should also explain why 300 kV was chosen over an accelerating voltage of 80kV which is commonly recommended for reducing defect creation when imaging transition metal dichalcogenides.\nGiven the complexity and ambiguity of several of their defect assignments, the manuscript lacks sufficient evidence to support the experimental evidence of defect population data in Figure 3. The authors need to show evidence of examples of labelling of defects over large STEM fields of view, a statistical analysis of the defect concentration as a function of total electron fluence and a clear statement of the number of each type of defect that was identified. Where several possible defect types have been combined in the histogram or where it is not possible to detect a particular defect type due to insufficient signal to noise ratio this should be noted more clearly in the figure caption.\nThe authors should also demonstrate how they know the materials are monolayer by comparing to image simulations for bilayer and thicker materials. Figure S2 shows many bright atoms around the defects that are circled. These should be discussed. Are these Mo or W impurity adatoms?\nThe atomic models of the point defects should be shown more clearly and not only as tiny covering up part of the experimental images. If the authors feel there is not space in the main text figures, they can be shown clearly and from several different crystallographic directions angles in the SI. I also so not understand why some of the ADF panels (in Figure 5a-c and examples in SI) are presented on a red colour look up table. I did not find it helped to follow the assignment of defect types in these images."}, "author_response": "Response to reviewers’ comments\nWe sincerely appreciate the reviewers’ insightful comments and constructive\nsuggestions, which have helped us significantly improve the quality and clarity of our\nwork. Below, we provide a point-by-point response to each reviewer’s concerns. All\nrevisions in the manuscript are highlighted in blue for easy reference.\nReviewer 1\nThe author investigated point defects in monolayer MoSi2N4 and WSi2N4 using STEM\nand first-principles calculations, with a particular focus on WSi2N4. The findings\nrevealed 11 distinct types of point defects in WSi2N4, which lead to significant\nreductions in the bandgap, including complete gap closure that facilitates insulator-to-\nmetal transitions. However, several critical issues need to be addressed before further\nconsideration:\n1. Will the concentration and types of defects be influenced by CVD parameters, such\nas precursor ratios? The author should conduct additional experiments to explore the\neffects of CVD synthesis parameters, as the current concentration calculations lack\nrigor.\nResponse: We appreciate the reviewer for the constructive suggestion.\nThe CVD parameters indeed influence the point defects. As an example, we studied\nthe influence of Si supply on the formation of defects. In our original work, we used a\nlong quartz tube as Si source. Here, a small piece of quartz plate was used to precisely\ncontrol Si supply. As shown in Fig. R1, the total concentration of silicon substituting\ntungsten antisite defects (Si and Si2 ) increased significantly from ~0.018 nm-2 to\nW W\n~0.085 nm-2 as the distance between the quartz plate and the growth substrate decreased,\nwhich corresponds to an enhanced supply of silicon. This result clearly demonstrates\nthat CVD growth parameters substantially influence the point defect concentrations.\nHowever, the defect types were not changed and consistent with those observed in the\noriginal manuscript.\nWe have added the related discussions in the revised manuscript.\nFigure R1. ADF images of monolayer WSi N with silicon substituting tungsten\n2 4\nantisite defects (Si and Si2 ), obtained under a high (a) and low (b) silicon source\nW W\nsupply.\n2. Layered materials with defects can be sensitive to air and prone to oxidation during\nSTEM analysis. The author should comment on the stability of the material. It is\nsuggested that additional in-situ dynamic analyses be performed to elucidate the defect\nformation process, which would enhance the understanding of defect concentration\ndistribution.\nResponse: We appreciate the reviewer for the constructive suggestion.\nFollowing the reviewer’s suggestion, we conducted in-situ electron-irradiation\nexperiments at 300 kV acceleration voltage and ~9 pA beam current. The STEM image\nacquisition was conducted under the condition of 5 μs dwell time with 2048  2048\npixels and 1.32  107 e- nm-2 electron dose, and the image preview was typically\nconducted under the condition of 1 μs dwell time with 1024  1024 pixels and 6.6×\n105e- nm-2 electron dose. During the observation for normal photography with only one\nbrief irradiation event, no new point defects were formed. After ~33 s STEM image\npreview, only a limited number of new nitrogen vacancies emerged (Fig. R2), along\nwith the migration of a few tungsten adatoms. Continued exposure for 102 s (~15 pA,\n1 μs dwell time with 2048  2048 pixels, 4.4  106 e- nm-2) led to occasional ejection\nof W atoms from the lattice at a few sites (Fig. R3). Furthermore, electron energy loss\nspectroscopy (EELS) confirmed the absence of O in the WSi N monolayers, showing\n2 4\nonly signals of Si and N (Fig. R4). These results collectively demonstrate the excellent\nstability of monolayer WSi N under STEM imaging. Therefore, the experimental\n2 4\nresults reflect the intrinsic defect state in as-grown monolayer WSi N .\n2 4\nWe have added the above results in the revised manuscript.\nFigure R2. (a,c) The initially obtained ADF- (a) and iDPC- (c) STEM images of\nmonolayer WSi N . (b,d) The ADF- (b) and iDPC- (d) STEM images of monolayer\n2 4\nWSi N obtained after ~33 s STEM image preview.\n2 4\nFigure R3. STEM images of monolayer WSi N underelectron beam exposure for 102\n2 4\ns STEM image preview. Note that W atom was sputtered from the lattice. Scale bars:\n5Å.\nFigure R4. EELS spectrum acquired from a monolayer WSi N sample, showing no O\n2 4\nloss edge (~532 eV).\n3. The author provides only a qualitative description of defect morphology through\nSTEM. This work lacks significance in the field due to the absence of defect\nengineering strategies aimed at enhancing material properties, such as device\nperformance. Establishing a clear connection between defects and device performance\n- such as carrier mobility and catalytic activity – would significantly elevate the impact\nof this research beyond fundamental characterization.\nResponse: We appreciate the reviewer for the constructive suggestions.\nThe electrical transport properties of a material are influenced not only by its quality\nbut also by the device fabrication process and electrical contacts. For the recently\nemerging MoSi N and WSi N materials, the device structure and fabrication\n2 4 2 4\nprocesses have not been well established. Therefore, establishing a clear correlation\nbetween defects and electrical transport properties (e.g., carrier mobility) remains a\nsignificant challenge.\nAlternatively, we used atomic-scale scanning tunneling microscopy (STM) to\ndirectly probe the impact of point defects on the local electronic structure of monolayer\nWSi N , a key prediction in our work. The surface tunneling properties of STM allowed\n2 4\nus to identify V and Si defects from topographic images (Fig. R5). Scanning\nN(t) N(t)\ntunneling spectroscopy (STS) measurements on a defect-free region (Figs. R5a, b)\nrevealed a bandgap of ~2.05 eV, consistent with our DFT prediction (2.09 eV). In\ncontrast, STS on defect sites showed significantly reduced bandgaps (Fig. R5c and R5d),\nwith values approaching the calculated gap (0.84 eV) for V defects. Further\nN(t)\nmeasurements on two additional defect sites revealed one with a small bandgap and\nanother exhibiting metallic characteristic (Fig. R5e and R5f), which we attribute to Si\nN(t)\ndefects, consistent with our prediction of their metallic characteristic. These atomic-\nscale STM/STS results strongly confirm the decisive influence of point defects on the\nelectronic properties of WSi N .\n2 4\nFigure R5. Probing the atomic and electronic structures of point defects in monolayer\nWSi N by STM and STS. (a,b) A STM topographic image (7 nm  7 nm) (a) and the\n2 4\ncorresponding dI/dV spectrum acquired on defect-free site of monolayer WSi N (b).\n2 4\nThe defect-free site is highlighted by a red circle. U = +2.3 V, I = 500 pA. (c,d) A STM\ntopographic image (8 nm  8 nm) (c) and the corresponding dI/dV spectra acquired on\nfour defect sites (d), which are highlighted by different colored arrows in (c). U = +2.3\nV, I = 500 pA. (e,f) A STM topographic image (8 nm  8 nm) (e) and the corresponding\ndI/dV spectra acquired on two defect sites (f), which are highlighted by different\ncolored arrows in (e). U = +1.5 V, I = 1000 pA. The spectra were offset vertically for\nbetter visualization. Scale bars: 2nm.\nWe further measured the ultraviolet and visible (UV-Vis) absorption spectra of three\ndistinct WSi N samples (Fig. R6): the original sample, the sample annealed in Ar at\n2 4\n300 °C for 12 h, and the sample annealed in air at 300 °C for 12 h. The absorption data\nshowed similar bandgaps for the original sample (~2.1 eV) and the air-annealed sample\n(~2 eV), indicating that the point defects exhibit oxidation resistance in air at the\nevaluated temperature. In contrast, the sample annealed in Ar exhibited a noticeable\nreduced bandgap of ~1.8 eV, indicating an increase in defect concentration. In our work,\nto obtain high-quality STEM images, the annealed samples were used for defect\nconcentration analysis. Taking the V defect as example, argon annealing led to a\nN(t)2\nsignificantly higher defect concentration compared to air annealing (Fig. R7),\nconsistent with the more pronounced bandgap reduction.\nFigure R6. UV-Vis absorption spectra of pristine monolayer WSi N (black line) and\n2 4\nmonolayer WSi N treated by air annealing (red line) and Ar annealing (blue line). The\n2 4\njump at ~1.4 eV in the spectra is caused by the switch of the optical path in the\nspectrophotometer at 800 nm wavelength.\nFigure R7. Statistical concentration of V defects in monolayer WSi N under\nN(t)2 2 4\ndifferent conditions. (a) Annealing in air at 300 °C for 12h. (b) Annealing in Ar at\n300 °C for 12h. The values of μ and σ denote mean and variance, respectively.\nWe also evaluated the electrocatalytic performance of defective monolayer WSi N .\n2 4\nCompared with the pristine sample, the Ar-annealed sample showed no performance\nimprovement (Fig. R8). This may be attributed to the insufficient defect concentrations.\nPrevious theoretical studies have predicted that high catalytic activity could be achieved\nwhen the N(t) vacancy concentration reached 5.6% or 12.5% [Surf. Sci. 739, 122395\n(2024)], which is substantially higher than our case (~1%). A similar trend has been\nobserved for MoS , where sulfur vacancy concentration must exceed a critical level, 7–\n2\n10%, to significantly enhance catalytic performance [J. Am. Chem. Soc. 138, 16632\n(2016)].\nFigure R8. Electrocatalytic performances of pristine monolayer WSi N (black line)\n2 4\nand Ar-annealed monolayer WSi N (red line).\n2 4\nCollectively, these additional experiments confirm that point defects can effectively\nmodulate the electronic and optical properties of monolayer WSi N , laying a\n2 4\nfoundation for future defect engineering aimed at tailoring the material for different\napplications.\nWe have added the above results in the revised manuscript.\nMinor Issues\n1. In the introduction, the author states that WSi2N4 was studied due to its higher\ntolerance to electron beam irradiation compared to MoSi2N4. The advantages of WSi2N4\nover other layered materials should be further elaborated, especially given the excellent\nproperties of MoSi2N4, such as high thermal conductivity and unique electrical\ncharacteristics.\nResponse: We appreciate the reviewer for the constructive suggestion.\nFollowing the reviewer’s suggestion, we have expanded the discussion on the\nadvantages of WSi N over other layered materials. First, monolayer WSi N is\n2 4 2 4\npredicted to exhibit an outstanding thermal conductivity approaching 500 W/mK [Nano\nEnergy 82, 105716 (2021)], surpassing those of other reported 2D semiconductors,\nincluding MoSi N . Second, excitons in monolayer WSi N possess higher stability\n2 4 2 4\nthan those in MoS , phosphorene, graphdiyne, and h-BN, making it suitable for room-\n2\ntemperature optoelectronics [Phys. Scr. 96, 125826 (2021)]. Third, as a p-type\nsemiconductor, it exhibited outstanding predicted electron and hole mobilities (320 and\n2026 cm2 V-1 s-1, respectively) [Nano Energy82, 105716 (2021)], exceeding the values\nfor monolayer MoSi N and many other 2D p-type semiconductors.\n2 4\nWe have added the above information into the revised manuscript.\n2. The author synthesized monolayer WSi2N4 and MoSi2N4 via CVD, with an optical\nimage of WSi2N4 provided in Figure 1(a). However, to accurately confirm the material’s\ncomposition and structure, additional characterization techniques, such as X-ray\nphotoelectron spectroscopy (XPS), should be employed. The Supporting Information\nwould also benefit from expanded characterization data for MoSi2N4 (e.g., XPS) to\nensure a thorough comparison between the two materials.\nResponse: We appreciate the reviewer for the constructive suggestion.\nFollowing the reviewer’s suggestion, we conducted X-ray photoelectron\nspectroscopy (XPS) characterization on monolayer WSi N and MoSi N samples\n2 4 2 4\ntransferred onto Al O substrates. As shown in Figs. R9 and R10, XPS spectra confirm\n2 3\nthe existence of W/Mo, Si, and N elements in WSi N /MoSi N .\n2 4 2 4\nWe have added these results in the revised manuscript.\nFigure R9. XPS spectra of monolayer WSi N film transferred onto a Al O substrate.\n2 4 2 3\n(a) Survey XPS spectrum, (b) W 4f and W 5p spectrum, (c) Si 2p spectrum, and (d) N\n1s spectrum.\nFigure R10. XPS spectra of monolayer MoSi N film transferred onto a Al O substrate.\n2 4 2 3\n(a) Survey XPS spectrum, (b) Mo 3d spectrum, (c) Si 2p spectrum, and (d) N 1s and\nMo 3p spectra.\n3. A detailed discussion is needed to clarify whether the observed disorder arises from\nintrinsic defects (e.g., vacancies or grain boundaries), strain-induced distortions, or\nartifacts from the synthesis process.\nResponse: We appreciate the reviewer for the constructive suggestion.\nThe disorders observed in our experiments originate from intrinsic defects. As\nrepresentative examples, the Si 2D networks and Si2 1D chains have been\nN(t) Mo\nconfirmed to be intrinsically stable in monolayer MoSi N materials through DFT-\n2 4\nbased optimization and corresponding ADF-simulations, as presented in our original\nmanuscript. To rule out the influence of lattice strain, we performed geometric phase\nanalysis (GPA) on these two structures embedded in the MoSi N matrixes (Fig. R11).\n2 4\nThe results confirm the absence of lattice strain. Structurally, the Si 2D network is a\nN(t)\nperiodic, planar crystalline defect characterized by the aggregation of collective Si\nN(t)\nantisite defects on one side of the MoSi N monolayer, analogous to a stacking fault.\n2 4\nIn contrast, the Si2 1D chain is a linear crystalline defect that separates the single-\nMo\ncrystal MoSi N monolayer into two parts without a misorientation angle, analogous to\n2 4\na 0° grain boundary in 2D materials. Therefore, both Si 2D networks and Si2 1D\nN(t) Mo\nchains are intrinsic crystalline defects rather than strain-induced distortions or artifacts\nfrom the synthesis process.\nWe have added the GPA results in the revised manuscript.\nFigure R11. GPA analysis on the Si 2D networks and Si2 1D chains in the\nN(t) Mo\nMoSi N matrixes. (a-c) ADF image of monolayer MoSi N containing Si 2D\n2 4 2 4 N(t)\nnetwork domains and the corresponding ε and ε strain fields.\nxx yy\n(d-f) ADF image of\nmonolayer MoSi N containing Si2 1D chain and the corresponding ε and ε strain\n2 4 Mo xx yy\nfields. The typical areas of Si 2D networks and Si2 1D chains are highlighted by\nN(t) Mo\nyellow shadow and dashed box in (a) and (d), respectively.\n4. Figure 5(d) presents an observed superstructure, but its atomic configuration remains\nambiguous. To facilitate a comprehensive understanding, the authors should provide a\nclear atomic model of the superstructure, ideally accompanied by simulated STEM\nimages for direct comparison with experimental data.\nResponse: We appreciate the reviewer for the constructive suggestion.\nThe atomic models of Si aggregation configurations with different aggregation\nN(t)\nnumbers are shown in Fig. S28 in the revised Supporting Information (also see Fig. R12\nbelow). Figures 7c and 7d in the revised manuscript directly compare the experimental\ndata and the simulated STEM image.\nFigure R12. The aggregation models containing one (a), two (b), three (c) and sixteen\n(2D network) (d) Si within a 4 × 4 × 1 configuration of the MoSi N unit cell. The\nN(t) 2 4\ncorresponding cross-sectional models are presented below each model.\n5. Additional explanations are needed for the following points. The study primarily\nfocuses on defect types and concentrations, lacking a dynamic analysis to clarify the\ndefect formation process. This oversight limits readers' understanding of changes in\ndefect concentration. For instance, the reason why the calculated formation energy for\nWSi defects is the lowest in the chart, yet the statistical concentration is the lowest,\nneeds to be explicitly addressed. The current explanations lack rigor. The authors\ndemonstrate that defect types and concentrations impact band gap reduction, which\nsubsequently affects electrical properties. However, the experimentally measured\ndefect concentrations do not fully align with calculated results, suggesting that defect\nformation is uncontrollable and maybe influenced by synthesis parameters. Thus, while\nDFT calculations indicate that the bandgap can be adjusted, controlling this adjustment\nvia defects remains a challenge. The uncontrolled nature of defect concentration\nunderscores the importance of understanding the growth kinetics model in the defect\nformation process.\nResponse: We appreciate the reviewer for the constructive comments.\nThe van der Waals-layered material WSi N consists of seven elementally distinct\n2 4\natomic layers. The formation of point defects during its growth is highly sensitive to\nCVD atmosphere. In our original experiments, a long quartz tube served as Si source,\ncreating a Si-rich environment. Meanwhile, the supply of N was limited because a\nhigher NH flow rate would cause undesirable etching of the Cu growth substrate.\nand N vacancies is thermodynamically favored. This is because excess silicon can\nsubstitute atoms in various layers while maintaining coordination symmetry, and N\ndeficiency promotes vacancy formation across all four N layers. Experimentally, the\ndominant point defects observed include antisites (e.g., Si , Si , Si2 , and Si ) and\nN(t) N(m) W W\nvacancy complexes (e.g., V and V ), consistent with these expectations.\nN(m)2 N(t)2\nSeveral factors may account for the discrepancy between the calculated formation\nenergy and the statistical concentration for W defects. First, the formation energies of\nSi\npoint defects are strongly influenced by chemical potential, which may differ between\nexperimental and calculation conditions. Second, our computational modeling was\nconfined to point defects within a 4 × 4 × 1 supercell (already large for the DFT\ncalculations), which corresponds to an unrealistically high defect concentration (~ 0.74\nnm-2) compared to the much lower experimental concentration (~0.01 nm-2). Third, our\ncalculations assume charge-neutral defects, but the possibility of charged defects\ncannot be excluded. Fourth, while we assume defect-induced charge distribution is\ndelocalized across the lattice, the potential impact of charge localization on formation\nenergy remains a possible factor. To rule out the possible influence of statistical scope\nand data volume, we expanded our statistical dataset from the initial 78 images\n(comprising 3700 defects) to 140 images (encompassing 6600 defects). The updated\nresults (Fig. R13) align with the statistical trends presented in our original manuscript,\nconfirming the robustness of our conclusions.\nThe primary goal of this study is to establish a foundational understanding of the\ntypes and configurations of intrinsic defects generated during CVD growth and to\npredict their influence on material properties. These results will guide future efforts to\nimprove the growth and properties of MoSi N and WSi N . We agree with the\n2 4 2 4\nreviewer that precise control over specific defects remains challenging, yet – as with\nother 2D materials – it can be approached through systematic optimization of growth\nand post-treatment conditions. For instance, as shown in Fig. R1, modulating the Si\nsupply during CVD directly influences defect concentration. In addition, post-treatment\nsuch as Ar annealing effectively increased the concentration of V defects, leading\nN(t)2\nto a bandgap reduction from ~ 2.1 eV to ~ 1.8 eV (Figs. R6 and R7). The precise control\nover defects and properties in monolayer MoSi N and WSi N will be a central\n2 4 2 4\nobjective of our future research.\nWe have added the above discussions into the revised manuscript.\n0.20\n0.15\n0.10\n0.05\n0.00\nFigure R13. The updated concentration statistics of point defects based on the\nexpanded dataset.\n6. The calculations for defect concentration distribution lack rigor. The statement,\n“However, since VSi and VSiN(m) have similar image contrasts with VN(m)2 (Fig.S4),\nthe concentration statistics for VN(m)2 might include VSi and VSiN(m), leading to an\noverestimation” needs clarification.\nResponse: We appreciate the reviewer for the constructive suggestion.\nAs shown in the structural model of monolayer WSi N (Fig. R14a), the atomic\n2 4\ncolumns marked by the red box comprises four atoms: Si, N(m), N(m), and Si. The\nvacancy configuration in such atomic column may exhibit multiple possibilities (Figs.\nR14b-d). By combining experimental imaging and simulations, we found that the image\ncontrast shows negligible differences among three defect types: V , V and V\nSi SiN(m) N(m)2\n(see Fig. S10 in the revised manuscript). Since formation energy calculations revealed\nthat the V configuration is thermodynamically most stable and thus dominant, our\nN(m)2\ndefect concentration quantification primarily accounts for V configuration.\nN(m)2\nHowever, it is important to note that the statistical analysis of V may inadvertently\nN(m)2\ninclude V and V defects due to their nearly identical image contrast, leading to a\nSi SiN(m)\npotential overestimation of the true V concentration.\nN(m)2\nWe have added the above details in the revised manuscript.\nFigure R14. Cross-sectional atomic models of defective monolayer WSi N .\n2 4\n(a) The\nboxed atomic column contains two Si atoms and two N(m) atoms. (b-d) Atomic models\nof V , V , and V .\nSi SiN(m) N(m)2\nReviewer 2\nThe authors give a systematic report on the atomically resolved ADF-STEM imaging\nof point defects in the 2D monolayer MoSi2N4 and WSi2N4, the image gallery is well\nprepared, and some interesting gap-closure or insulator-to-metal transition properties\nhave been found from the perspective of DFT calculation. Although the study of defects\nlooks good, investigated systematically for the first time, the significance, depth and\nespecially applications still lack in this manuscript. Therefore, the publication of the\ncurrent form manuscript is still too early.\nBelow are the points that need to be addressed carefully,\n1. How do the authors identify the monolayer? How is the distribution of the thickness\nfor the CVD grown 2D thin layers? Any Raman or AFM measurement to facilitate the\naccurate transfer of monolayer?\nResponse: We appreciate the reviewer for the insightful comments.\nAs previously reported [Science369, 670-674 (2020)], MoSi N and WSi N exhibit\n2 4 2 4\na self-limited surface growth mode across a broad growth window. AFM\ncharacterization of the MoSi N and WSi N samples transferred onto SiO substrates\n2 4 2 4 2\nconfirms their monolayer nature. The measured thicknesses are approximately 1.21 nm\nfor MoSi N and 1.19 nm for WSi N (Fig. R15), which align with the reported values\n2 4 2 4\nfor monolayers [Science 369, 670-674 (2020)]. Extensive measurements indicate that\nthe samples consist exclusively of monolayers. It is worth noting that island-like\nmultilayers only form under an increased NH flow. Such intentionally grown thick\n3\nsamples were used in our study to demonstrate the layered structure and reveal the\ncross-sectional atomic structure of WSi N .\n2 4\nWe have added the above results in the revised manuscript.\nFigure R15. Typical AFM images of MoSi N (a) and WSi N (b), showing monolayer\n2 4 2 4\ncharacteristics.\n2. Fig. 1b is the imaging of a multilayer cross section FIB sample? Can the authors\nprovide the image of monolayer sample, to meet the figure caption.\nResponse: We appreciate the reviewer for the constructive suggestion.\nWe have made extensive efforts and numerous attempts to obtain cross-sectional\nimages of monolayer WSi N and MoSi N . However, the inherent flexibility of\n2 4 2 4\nmonolayers led to severe bending and distortion during FIB milling for cross-sectional\nsample preparation and subsequent STEM imaging. As illustrated in Fig. R16 for\nmonolayer WSi N , these artifacts prevented the resolution of atomic arrangements\n2 4\nwithin the layers. To overcome this challenge, we thus prepared multilayer WSi N\n2 4\nsamples to analyze the cross-sectional atomic structure of WSi N .\n2 4\nWe have revised the figure caption to match the results shown in this figure.\nFigure R16. Cross-sectional ADF-STEM images of monolayer WSi N . (a) Low-\n2 4\nmagnification ADF image showing bending morphology with poor rigidity. (b) High-\nmagnification ADF image showing the unresolved atomic structure.\n3. In Fig.2, the second column defect V_N(t)2 of the top panel is not a simple vacancy\ndefect, there is also an adatom nearby. So could it be a Frenkel defect (vacancy +\nadatom/interstitial)?\nResponse: We appreciate the reviewer for the insightful comment.\nA Frenkel defect consists of an atom displaced from its lattice site into an interstitial\nposition, thereby creating a vacancy-interstitial pair within the same lattice. In contrast,\nthe defect labeled as V in Fig. 2 consists of a V vacancy and a nearby W adatom.\nN(t)2 N(t)2\nNotably, this W adatom is not situated on an interstitial site of the original lattice but\nresides on the surface. Since no vacancy-interstitial pair is formed, this configuration\ndoes not meet the criteria for a Frenkel defect.\nTo avoid misunderstanding, we have updated the corresponding figure in the revised\nmanuscript, as shown in Fig. R17.\nFigure R17. The point defects in monolayer WSi N . Scale bars: 5Å.\n2 4\n4. For the primary defects (V_N(m)2, V_N(t)2, Si_N(t)), please give the line profiles\nof the ADF-STEM intensity, and compare the simulated and experimental line profiles.\nResponse: We appreciate the reviewer for the constructive suggestion.\nFollowing the reviewer’s suggestion, we have provided both experimental and\nsimulated line profiles of ADF-STEM intensity for V , V , and Si in Fig. R18.\nN(m)2 N(t)2 N(t)\nNotably, the experimental line profiles show good agreement with their simulated\ncounterparts, confirming the reliability of our assignments. The experimental ADF-\nSTEM images used here are the same as those presented in Fig. R17.\nWe have added these results in the revised manuscript.\nFigure R18. ADF-STEM images (a) and the corresponding simulated ADF-STEM\nimages (b) of monolayer WSi N with V , V , and Si . (c) Comparison of\n2 4 N(m)2 N(t)2 N(t)\nintensity line profiles of experimental and simulated ADF-STEM images across V ,\nN(m)2\nV , and Si . The defect positions are marked by red circles (a,b) and arrows (c).\nN(t)2 N(t)\nScale bars: 5 Å.\n5.Why is there no iDPC-STEM image for the antisite defects in Fig. 2d,e ?\nResponse: We appreciate the reviewer for the insightful comment.\nAntisite defects in monolayer WSi N , mainly formed by substitutions between\n2 4\ntungsten and silicon atoms, can be clearly resolved in ADF-STEM imaging due to the\natomic number-dependent contrast. The resulting intensity differences allow direct\nidentification of specific antisite defect types. Following the review’s suggestion, we\nhave also incorporated iDPC-STEM images of all antisite defects in the revised\nmanuscript (Fig. R19), which provide information about the presence or absence of\nnitrogen atoms at specific atomic sites.\nFigure R19. The ADF and the corresponding iDPC images of antisite point defects in\nmonolayer WSi N . Scale bars: 5 Å.\n2 4\n6.For Fig.3, the vacancy defects V_N(m)2 and V_N(t)2 have a formation energy larger\nthan 6 eV, but its concentration is not low, this is out of expectation. How do the authors\ndo the statistical analysis of the defect density?\nResponse: We appreciate the reviewer for the insightful comment.\nTwo primary factors may account for the relatively high concentration of V and\nN(t)2\nV , despite their higher formation energy. First, the formation energies of single\nN(m)2\nV and V vacancies (3.89 eV and 3.21 eV, respectively) are approximately half\nN(t) N(m)\nthose of their double-vacancy counterparts, V and V (8.31 eV and 6.18 eV).\nN(t)2 N(m)2\nThis near-additivity behavior suggests weak coupling between the two single vacancies\nin V and V . Consequently, a high concentration of single vacancies could\nN(t)2 N(m)2\nfacilitate the formation of double vacancies through statistical pairing. Second, the N(t)\natoms at the outermost surface are more susceptible to damage during TEM sample\npreparation, including transfer and heat treatment. It is worth noting that once a V\nN(t)\nforms, the computed energy barrier for the adjacent N(m) moving towards V is\nN(t)\nrelatively low compared to that for W or Si escaping into the vacuum (Fig. R20).\nThis\nsuggests that N(m) atoms are susceptible to environmental disturbances in the presence\nof N(t) vacancies, which may further contribute to an increased concentration of V\nN(m)2\ndefects.\nFor the statistical analysis of defect concentration, we acquired a large number of\nSTEM images – each with a typical area of 9.45 × 9.45 nm2 - from random locations\nacross multiple WSi N samples. The types of point defects were identified by\n2 4\ncombining STEM image contrast (ADF and iDPC) with image simulations. For V\nN(m)2\nand Si point defects with inherently relatively high concentration, we used CalAtom\nN(t)\nsoftware to perform the high-throughput statistical analysis [Ultramicroscopy202, 114-\n120 (2019)]. The defect positions were identified by analyzing the intensity variations\nin ADF images. Other types of point defects were directly counted manually due to the\nlimited quantity. The defect concentration was determined by normalizing the number\nof defects to the corresponding imaging area. Due to the low concentration, the datasets\nfor V and V defects exhibited a discrete distribution. Therefore, a weighted\nW Si2N(m)\naveraging method was used to estimate their concentrations. For other defects, to\nminimize experimental statistical errors, Gaussian fitting was applied to the datasets to\nobtain the mean (μ) and variance (σ), yielding the final concentration values (Fig. R21).\nWe have added the relative contents into the revised manuscript.\nFigure R20. Migration pathways and energy barriers for W, Si, N(m), and N(t) atoms\nin monolayer WSi N . We deliberately placed a V near the N(m), and N(m) migrated\n2 4 N(t)\ninto V as the final site.\nN(t)\nFigure R21. Point defect concentration datasets of monolayer WSi N . A weighted\n2 4\naveraging method was used to evaluate the concentration of V and Vw, while\nSi2N(m)\nGaussian fitting was applied to the datasets for other types of defects.\n7. The W_Si antisite defect has the lowest formation energy in Fig. 3 and Fig. S16,\nwhile its density is quite low. Why? The formation mechanism of defects should be\ndiscussed.\nResponse: We appreciate the reviewer for the insightful comment and suggestion.\nSeveral factors may account for the discrepancy between the calculated formation\nenergy and the statistical concentration for W defects. First, the formation energies of\nSi\npoint defects are strongly influenced by chemical potential, which may differ between\nexperimental and calculation conditions. Second, our computational modeling was\nconfined to point defects within a 4 × 4 × 1 supercell (already large for the DFT\ncalculations), which corresponds to an unrealistically high defect concentration (~ 0.74\nnm-2) compared to the much lower experimental concentration (~0.01 nm-2). Third, our\ncalculations assume charge-neutral defects, but the possibility of charged defects\ncannot be excluded. Fourth, while we assume defect-induced charge distribution is\ndelocalized across the lattice, the potential impact of charge localization on formation\nenergy remains a possible factor. To rule out the possible influence of statistical scope\nand data volume, we expanded our statistical dataset from the initial 78 images\n(comprising 3700 defects) to 140 images (encompassing 6600 defects). The updated\nresults (Fig. R13) align with the statistical trends presented in our original manuscript,\nconfirming the robustness of our conclusions.\nThe van der Waals-layered material WSi N consists of seven elementally distinct\n2 4\natomic layers. The formation of point defects during its growth is highly sensitive to\nCVD atmosphere. In our original experiments, a long quartz tube served as Si source,\ncreating a Si-rich environment. Meanwhile, the supply of N was limited because a\nhigher NH flow rate would cause undesirable etching of the Cu growth substrate.\nand N vacancies is thermodynamically favored. This is because excess silicon can\nsubstitute atoms in various layers while maintaining coordination symmetry, and N\ndeficiency promotes vacancy formation across all four N layers. Experimentally, the\ndominant point defects observed include antisites (e.g., Si , Si , Si2 , and Si ) and\nN(t) N(m) W W\nvacancy complexes (e.g., V and V ), consistent with these expectations. In new\nN(m)2 N(t)2\nexperiments, we used a small quartz plate to precisely control Si supply. As shown in\nFig. R1, the total concentration of silicon substituting tungsten antisite defects (Si and\nW\nSi2 ) is significantly increased from ~0.018 nm-2 to ~0.085 nm-2 when increasing the\nW\nSi supply by reducing the distance between the quartz plate and growth substrate. This\nresult further confirms our preliminary understanding on the formation mechanism of\ndefects.\nWe have added the above discussions into the revised manuscript.\n8. How is the beam irradiation effect? The images are taken at the voltage of 300 kV,\nthis high energy would cause damage, especially for N atoms. So how can we make\nsure the defects are pristine, not caused by the beam.\nResponse: We appreciate the reviewer for the insightful comments.\nTo reveal the beam irradiation effect, we conducted in-situ electron-irradiation\nexperiments at 300 kV acceleration voltage and ~9 pA beam current. The STEM image\nacquisition was conducted under the condition of 5 μs dwell time with 2048  2048\npixels and 1.32  107 e- nm-2 electron dose, and the image preview was typically\nconducted under the condition of 1 μs dwell time with 1024  1024 pixels and 6.6×105e- nm-2 electron dose. During the observation for normal photography with only one\nbrief irradiation event, no new point defects were formed. After ~33 s STEM image\npreview, only a limited number of new nitrogen vacancies emerged (Fig. R2), along\nwith the migration of a few tungsten adatoms. Continued exposure for 102 s (~15 pA,\n1 μs dwell time with 2048  2048 pixels, 4.4  106 e- nm-2) led to occasional ejection\nof W atoms from the lattice at a few sites (Fig. R3). These results demonstrate the\nexcellent stability of monolayer WSi N under electron beam irradiation. Therefore, the\n2 4\nexperimental results reflect the intrinsic defect state in monolayer WSi N .\n2 4\n9. In Fig.5, the Si antisite defects form a superstructure, but the central Si atoms give\nvery weak intensity indicated by the yellow circles and arrows, this means the STEM\nimaging should be very high-quality and also the monolayer sample should be\nultraclean to exclude the effect of possible surface contaminations or residues.\nResponse: We appreciate the reviewer for the insightful comments.\nTo achieve high-quality imaging of the “superstructure” (corrected as 2D network\nfor Si and 1D chain for Si2 in the revised manuscript, respectively), several critical\nN(t) Mo\nexperimental conditions must be simultaneously satisfied. As shown by the comparison\nbetween experimental data and simulated results (Fig. 7d in the revised manuscript),\nthe contrast of the Si antisite defects in the 2D network is inherently weak. To ensure\nN(t)\nimage quality, we performed annealing treatment to remove surface contaminants as\nthoroughly as possible before STEM imaging. The analyzed regions were confirmed to\nbe free of contamination. To improve the image contrast and signal-to-noise ratio of Si\nsubstitutions in the 2D networks and 1D chains, we selected strictly flat zones in\nmonolayer MoSi N samples and extended the image exposure time to 10 μs dwell time\n2 4\nwith 2048 × 2048 pixels. These optimized experimental conditions laid the foundations\nfor achieving high-quality images to identify the 2D networks and 1D chains.\nWe have added the above experimental details in the revised manuscript.\n10. Fig. S10 shows the calculated absorption spectra, could the authors show the\nexperimental absorption spectrum?\nResponse: We appreciate the reviewer for the constructive suggestion.\nFollowing the reviewer’s suggestion, we measured the absorption spectra of WSi N\n2 4\nsamples with different defect concentration (Figs. R6 and R7). The sample with a higher\nconcentration of V defects exhibits a reduced bandgap of ~1.8 eV, compared to the\nN(t)2\npristine sample (~2.1 eV). This result is consistent with the computational prediction\nthat V leads to a redshift in the spectra and a decrease in the bandgap.\nN(t)\nWe have added the above results in the revised manuscript.\n11. Could the authors provide magnetic or transport properties of the monolayers, to\ncompare with the DFT prediction.\nResponse: We appreciate the reviewer for the constructive suggestion.\nFollowing the reviewer’s suggestion, we conducted superconducting quantum\ninterference device (SQUID) tests on monolayer WSi N . No detectable magnetic\n2 4\nsignals were observed in the samples (Fig. R22), even after attempting to increase the\nconcentrations of point defects such as V through Ar annealing. To understand this\nN(t)2\nresult, we performed DFT calculations to investigate whether the defect concentration\nin the WSi N sample might be too low to generate a measurable magnetic signal. Our\n2 4\ncalculations show that V defects induce spin polarization in nearby Si atoms,\nN(t)\nyielding a net magnetic moment. However, this magnetic moment is highly localized\nwithin a small region around the V , involving only a few Si atoms, and the magnitude\nN(t)\nof the net moment is independent of the supercell size (Table R1). No magnetic moment\nwas found on other atomic sites.\nUnlike conventional ferromagnets such as iron, where each Fe atom carries a\nsubstantial magnetic moment and strong exchange coupling leads to macroscopic\nferromagnetic hysteresis loops, the magnetic density (magnetic moment per volume) in\nthe WSi N system with V is extremely low (e.g., 0.50 × 10-3 μB/Å3 for the 4 × 4\n2 4 N(t)\nsupercell and 0.1455 × 10-3 μB/Å3 for the 8 × 8 supercell). This low magnetic density\nmakes the signal difficult to detect by conventional SQUID techniques. Atomic-\nsensitive techniques such as nuclear magnetic resonance spectroscopy (NMR) might be\nuseful to probe the magnetic properties of defective WSi N in the future.\n2 4\nThe electrical transport properties of a material are influenced not only by its quality\nbut also by the device fabrication process and electrical contacts. For the recently\nemerging MoSi N and WSi N materials, the device structure and fabrication\n2 4 2 4\nprocesses have not been well established. Therefore, establishing a clear correlation\nbetween defects and electrical transport properties (e.g., carrier mobility) remains a\nsignificant challenge.\nAlternatively, we used atomic-scale scanning tunneling microscopy (STM) to\ndirectly probe the impact of point defects on the local electronic structure of monolayer\nWSi N , a key prediction in our work. The surface tunneling properties of STM allowed\n2 4\nus to identify V and Si defects from topographic images (Fig. R5). Scanning\nN(t) N(t)\ntunneling spectroscopy (STS) measurements on a defect-free region (Figs. R5a, b)\nrevealed a bandgap of ~2.05 eV, consistent with our DFT prediction (2.09 eV). In\ncontrast, STS on defect sites showed significantly reduced bandgaps (Fig. R5c and R5d),\nwith values approaching the calculated gap (0.84 eV) for V defects. Further\nN(t)\nmeasurements on two additional defect sites revealed one with a small bandgap and\nanother exhibiting metallic characteristic (Fig. R5e and R5f), which we attribute to Si\nN(t)\ndefects, consistent with our prediction of their metallic characteristic. These STM/STS\nresults strongly confirm the decisive influence of point defects on the electronic\nproperties of WSi N .\n2 4\nWe have added the STM results in the revised manuscript.\nFigure R22. The hysteresis loop of monolayer WSi N under different treatment\n2 4\nconditions. The results show no detectable magnetic signals in any of the samples.\nTable R1. Magnetic moments corresponding to different point defect concentrations.\nThe calculations considered only one V defect per supercell, corresponding to V\nN(t) N(t)\nconcentration of 1/32, 1/64, and 1/128 in the 4 × 4, 4 × 8, and 8 × 8 supercells,\nrespectively.\nV concentration 1/32 1/64 1/128\nN(t)\nTotal magnetic moment (μB) 0.541 0.518 0.518\nSystem volume (excluding vacuum) (Å3) 1027.63 1779.91 3559.82\nMagnetic moment density (Total magnetic\nmoment/ System volume)\n0.500 0.291 0.146\n(×10-3 μB/Å3)\nReviewer 3:\nTong et al present a study of point defects in WSi2N4 and MoSi2N4 using scanning\ntransmission electron microscopy (STEM) and complementary density functional\ntheory (DFT). There is certainly value in such an imaging and DFT study and I\nrecoganise that the imaging of such systems may be challenging, especially if they are\nelectron beam sensitive (although the sensitivity of the materials is not discussed in the\nmanuscript except to qualitatively state without experimental evidence that WSi N is\n2 4\nmore stable than MoSi N ). The work presents several interesting observations that will\n2 4\nbe of interest to those exploiting the properties of this relatively new class of materials.\nHowever, there are several areas where I feel clarification and improvement to the\nSTEM data analysis presented is required before publication in Nature Comms could\nbe recommended.\nResponse: We appreciate the reviewer for the positive comments and constructive\nsuggestions.\nWe subjected monolayer MoSi N and WSi N to identical 200 kV STEM imaging\n2 4 2 4\nconditions to compare their stability. As shown in Fig. R23a, noticeable lattice damage\nappeared in MoSi N after ~169 s irradiation and evolved into voids by ~ 360 s, leading\n2 4\nto severe structural damage. In contrast, monolayer WSi N exhibited only slightly\n2 4\nincreased point defects after ~329 s, with minor damage appearing after ~514 s (Fig.\nR23b). This comparison confirms the better irradiation stability of monolayer WSi N\n2 4\nthan monolayer MoSi N .\n2 4\nWe have added these results in the revised manuscript.\nFigure R23. The ADF-STEM images of monolayer MoSi N (a) and WSi N (b)\n2 4 2 4\nacquired at distinct time under electron beam irradiation.\n1. The authors explain that simultaneous iDPC and ADF imaging modes are required\nto give confidence in the presence of the different defects being observed. However,\ntheir simulations are only for the ADF image mode. It is essential that the comparison\nis made with STEM image simulations of both iDPC and ADF images. The authors\ncompare line scans for the simulations of defective material to line scans from the\nsimulations of the pristine system, but they also need to directly compare line scans to\nequivalent line scans from the experimental data, to give confidence in the assignment\nof different defects and subsequent analysis. Especially, in Figure 2 the assignment of\nthe point defects of the VN(m)2, VN(t)2 and SiN2 is not convincingly shown.\nResponse: We appreciate the reviewer for the insightful comments.\nFollowing the reviewer’s suggestion, we conducted iDPC and ADF STEM image\nsimulations for all observed point defect types. The iDPC and ADF simulations are\nself-consistent for each defect assignment (Figs. R24 and R25). Moreover, the ADF\nline scans across defective areas show good agreement between experimental and\nsimulated images (Figs. R26 and R27). These consistent results validate the reliability\nof our defect assignments.\nWe have added these results in the revised manuscript.\nFigure R25. The ADF- and iDPC-STEM images and the corresponding simulation\nimages of monolayer WSi N with vacancies. Scale bars: 5Å.\n2 4\nFigure R26. The ADF- and iDPC-STEM images and the corresponding simulation\nimages of monolayer WSi N with antisite point defects. Scale bars: 5Å.\n2 4\nFigure R27. (a,b) ADF-STEM images (a) and the corresponding simulation images (b)\nof monolayer WSi N with vacancies. (c) Comparison of ADF line scan profiles of\n2 4\nexperimental and simulated images across the defect regions. The defect regions are\nmarked by red circles and arrows. Scale bars: 5 Å.\nFigure R28. (a,b) ADF- STEM images (a) and the corresponding simulation images (b)\nof monolayer WSi N with antisite point defects. (c) Comparison of ADF line scan\n2 4\nprofiles of experimental and simulated images across defect regions. The defect regions\nare marked by red circles and arrows. Scale bars: 5 Å.\n2. Also for the VW image in Figure 2 the neighboring Si columns in the experimental\ndata do not appear to be in the same position as they are in the simulation. Were the\nsimulations performed on DFT relaxed xyz structure files or simply by removing the\nvacancy atom from the lattice? Simulations should be performed on the DFT relaxed\nstructures. If the former relaxed DFT models are already used, possibly this VW\nvacancy could be a larger defect than just a single W vacancy?\nResponse: We appreciate the reviewer for the insightful comments.\nIn this study, all the point defect simulations were performed using DFT relaxed\nstructure models. A comparison of the V before and after relaxation (Fig. R29) reveals\nW\nslight structure distortions. The observed shift in the surrounding Si atomic columns is\nlikely attributed to two factors. First, the DFT calculations were performed for a charge\nneutral system, while the actual defect in the experiment may possess a specific charge\nstate, which could influence both the local atomic relaxation and the resulting image\ncontrast. Second, the surrounding Si atomic columns with dangling bonds around the\nmissing W atom were susceptible to external disturbances derived from the sample\ntransfer or annealing process.\nThe V shown in Fig. 2 is indeed a single W vacancy. The apparent reduction in\nW\ncontrast at the adjacent Si columns is attributed to a W adatom at the top site. For clarity,\nwe have re-presented the image by moving the W adatom out of the field and enhancing\nthe image contrast. As shown in the ADF- and iDPC-STEM images (Figs. R30a, b), the\nsurrounding Si and N atoms (solid circles) adjacent to the missing W atom (green\ndashed circles) are clearly resolved.\nFurthermore, it should be noted that W-containing vacancy complexes do exist. For\ninstance, Figs. R30c, d show the absence of two Si (red dashed circles) and two N(t)\natoms (yellow dashed circles) around the missing W atom (green dashed circle). Owing\nto the multiple atomic configurations of W-containing vacancy complexes and their\ninherently low concentration, all instances were included in the statistical analysis of\nV . For simplicity, the term \"tungsten vacancies (V )\" was used representatively to\nW W\npresent the statistical results.\nWe have added the above results in the revised manuscript.\nFigure R29. Atomic models of W vacancies. (a) Original model without DFT\nrelaxation. (b) DFT relaxed model with slight structural distortion.\nFigure R30. (a,b) The ADF- (a) and iDPC- (b) STEM images of monolayer WSi N\n2 4\nwith a V . The surrounding Si and N atoms adjacent to the missing W atom (green\nW\ndashed circle) are denoted by red and yellow solid circles, respectively. (c,d) The ADF-\n(c) and iDPC- (d) STEM images of monolayer WSi N with a V vacancy\n2 4 WSi2N(t)2\ncomplex. The missing Si and N atoms around the missing W atom (green dashed circle)\nare denoted by red and yellow dashed circles, respectively. Scale bars: 5Å.\n3. In Figure S2 it is difficult to assess the confidence of the defect assignment as even\nthe ADF simulations are not shown alongside the experimental data. The authors should\nalso further explain why the atoms are assumed to be substitutional point defects rather\nthan adatoms on the surface (due to knock on damage from the electron beam). I agree\nthat there is clearly a long defect line where W atoms are missing, visible in Figure\n5g,h,i. However, I do not think that the assignment of SiN(t) locations and\nagglomerations in Figure 5a-c (and SI Figure S11) is sufficiently convincingly\ndemonstrated. I also do not think that the term “superstructure” is justified given the\ndisorder and inconsistency of the assignment of defects.\nResponse: We appreciate the reviewer for the insightful comments.\nFollowing the reviewer’s suggestion, we performed ADF and iDPC image\nsimulations for the monolayer MoSi N containing point defects presented in original\n2 4\nFig. S2. The simulated images show good agreement with the experimental results (Figs.\nR31 and R32), confirming the reliability of our defect assignment. In addition, the\nadatoms on the WSi N surface are highly susceptible to displacement under electron\n2 4\nbeam irradiation during observation. In our statistical analysis, however, most atomic\nconfigurations remained stable. Combined with the low formation energy of antisite\ndefects, this supports their identification as antisite defects.\nRegarding the assignment of Si locations and agglomerations, the key evidence\nN(t)\nlies in the consistent contrast match between each individual site in these structures and\nthat of an isolated Si point defect. This confirms that the agglomeration is a collective\nN(t)\nassembly of such defects. We considered several possible Si aggregation\nN(t)\nconfigurations, as shown in Fig. R33. Based on the binding energy calculations (Fig.\nR34), we found that the Si aggregations located on one side are more stable\nN(t)\nconfigurations. Moreover, the binding energy further increases when silicon atoms\nreplace all nitrogen atoms on the same side, indicating that such a defect aggregation\nconfiguration is highly stable. These results suggest the Si aggregation tends to adopt\nN(t)\nan ordered distribution rather than a disordered arrangement.\nWe agree with the reviewer that the term \"superstructure\" is not appropriate for our\ncase. By definition, a superstructure requires the emergence of new long-range order\nwith enlarged periodicity. In our case, the aggregation configuration retains the same\nperiodicity as the pristine MoSi N lattice, with no change in long-range ordering.\n2 4\nTherefore, we have replaced the term “superstructure” with “2D network” or “1D chain”\nin the revised manuscript.\nWe have added the above results in the revised manuscript.\nFigure R31. The ADF- and iDPC-STEM images and the corresponding simulation\nimages of monolayer MoSi N with vacancies. Scale bars: 5Å.\n2 4\nFigure R32. The ADF- and iDPC-STEM images and the corresponding simulation\nimages of monolayer MoSi N with antisites. Scale bars: 5Å.\n2 4\nFigure R33. The atomic models of different Si aggregation configurations in\nN(t)\nmonolayer MoSi N . Si substitutions at the upper and lower sides are highlighted by\n2 4\nsolid circles and dashed circles, respectively.\nFigure R34. The binding energy of different Si aggregation configurations shown in\nN(t)\nFig. R33.\n4. The scale of the sets of images in Figure 2 and Figure S2 are also not presented with\nan identical scale and the scales of the experiments does not always match the\nsimulations, which does not help with the comparison and is distracting.\nResponse: We appreciate the reviewer for the insightful comments.\nWe have unified the scale in both the experimental and simulated images in the\nrevised manuscript, as exemplified in Figs. R35 and R36. This consistency ensures\ndirect comparability, facilitating the precise identification of defect characteristics.\nFigure R35. Point defects in monolayer WSi N . Scale bars: 5Å.\n2 4\nFigure R36. Point defects in monolayer MoSi N . Scale bars: 5Å.\n2 4\n5. The imaging presented here is performed at 300 kV so the defects are likely to be\noverwhelmingly electron beam induced. At such an accelerating voltage, knock-on\ndamage is likely to be dominant and N is likely to be more susceptible to knock on\ndamage than Si and Mo. The authors need to consider the knock on damage cross\nsections of the different atomic species when interpreting their defect results. They\nshould also explain why 300 kV was chosen over an accelerating voltage of 80 kV\nwhich is commonly recommended for reducing defect creation when imaging transition\nmetal dichalcogenides.\nResponse: We appreciate the reviewer for the insightful comments.\nWe proposed migration paths for W, Si, N(m), and N(t) in monolayer WSi N under\n2 4\nelectron beam irradiation (Fig. R20), and the calculated transition barrier are 18.45 eV,\n12.82 eV, 5.26 eV, and 11.04 eV, respectively. The corresponding knock-on damage\nthreshold voltages were 846 kV for W, 144 kV for Si, 33 kV for N(m) (N(m)→V ),\nN(t)\nand 66 kV for N(t). These results confirm that N atoms are indeed more susceptible to\ndamage than Si and W.\nTo study knock-on damage, we conducted in-situ electron-irradiation experiments at\n300 kV acceleration voltage and ~9 pA beam current. The STEM image acquisition\nwas conducted under the condition of 5 μs dwell time with 2048  2048 pixels and 1.32\n 107 e- nm-2 electron dose, and the image preview was typically conducted under the\ncondition of 1 μs dwell time with 1024  1024 pixels and 6.6×105 e- nm-2 electron\ndose. During the observation for normal photography with only one brief irradiation\nevent, no new point defects were formed. After ~33 s STEM image preview, only a\nlimited number of new nitrogen vacancies emerged (Fig. R2), along with the migration\nof a few tungsten adatoms. Continued exposure for 102 s (~15 pA, 1 μs dwell time with\n2048  2048 pixels, 4.4  106 e- nm-2) led to occasional ejection of W atoms from the\nlattice at a few sites (Fig. R3). These results collectively demonstrate the excellent\nstability of monolayer WSi N under STEM imaging. Moreover, to minimize\n2 4\ncumulative beam effects, in our experiments, we frequently moved the samples to\ndifferent locations. Each position was subjected to no more than 5 s preview and only\none image acquisition. Therefore, the experimental results reflect the intrinsic defect\nstates of the monolayer WSi N .\n2 4\nWe also performed 80 kV STEM imaging on monolayer WSi N (Fig. R37), as\n2 4\ncommonly used for transition metal dichalcogenides. However, the lower voltage\nresulted in a poorer signal-to-noise ratio and enhanced the visibility of surface\ncontaminants, complicating defect identification and statistical analysis. Therefore, 300\nkV acceleration voltage was selected for its optimal efficiency and accuracy in point\ndefect identification.\nWe have added the related discussions in the revised manuscript.\nFigure R37. STEM images of monolayer WSi N acquired at 80 kV. (a) ADF-STEM\n2 4\nimage. (b) Corresponding iDPC-STEM image. Both images were processed with a\nGaussian filter for clarity. The surface contaminants are highlighted by red arrows.\n6. Given the complexity and ambiguity of several of their defect assignments, the\nmanuscript lacks sufficient evidence to support the experimental evidence of defect\npopulation data in Figure 3.The authors need to show evidence of examples of labelling\nof defects over large STEM fields of view, a statistical analysis of the defect\nconcentration as a function of total electron fluence and a clear statement of the number\nof each type of defect that was identified. Where several possible defect types have\nbeen combined in the histogram or where it is not possible to detect a particular defect\ntype due to insufficient signal to noise ratio this should be noted more clearly in the\nfigure caption.\nResponse: We appreciate the reviewer for the constructive suggestions.\nFollowing the reviewer’s suggestion, we selected a large field-of-view STEM image\n(6.68 × 6.68 nm2) to illustrate the diversity of point defect structures (Fig. R38).\nHowever, to precisely identify the defect types in appropriate resolution and signal-to-\nnoise ratio (SNR), the STEM image magnification should not be too small. In our\nexperiments, we performed updated statistical analysis based on 140 STEM images\n(typical imaging area of 9.45 × 9.45 nm2) acquired at an electron beam current of ~ 9\npA, encompassing over 6600 point defects. Point defects were identified by comparing\nthe experimental and simulated image contrasts. The updated statistics (Fig. R13) align\nwith the trends in our original manuscript, confirming the reliability of our analysis.\nAdditionally, consistent distribution characteristics were observed in data acquired at\n~5 pA (Fig. R39), demonstrating that our imaging parameters are reliable and do not\naffect the intrinsic defect structures. The number of each type of defect that was\nidentified was given in Table R2.\nIn the histogram, the statistical counts for V may include both V and V\nN(m)2 SiN(m) Si\nconfigurations due to their nearly identical contrast in STEM images. Additionally,\nwhile V and V are key point defects expected to have high concentration, they\nN(t) N(m)\nremain unresolved in our STEM imaging. For reference, we have included their\ntheoretical formation energies in the histogram in the manuscript.\nWe have added the above results and information in the revised manuscript.\nFigure R38. Largefield-of-view ADF- (a) and iDPC- (b) STEM images of monolayer\nWSi N . Different types of point defects are marked by color-coded circles, with the\n2 4\ncorresponding defect types listed below the images.\n0.30\n0.25\n0.20\n0.15\n0.10\n0.05\n0.00\nFigure R39. Statistical distribution of point defect concentration acquired at ~5 pA\nbeam current.\nTable R2. The number of each type of defect acquired under different beam currents.\nOriginal dataset Expanded dataset Expanded dataset\n(9 pA) (9 pA) (5 pA)\nV 645 1304 317\nN(m)2\nV 590 1187 267\nN(t)2\nV 21 98 15\nSi2N(m)\nV 17 28 6\nW\nSi 1418 2508 632\nN(t)\nSi 659 968 175\nN(m)\nSi2 119 157 50\nW\n)2-mn(\nnoitartnecnoC\nSi 133 164 26\nW\nW 66 122 54\nSi\nW 52 100 29\nN(t)\n7. The authors should also demonstrate how they know the materials are monolayer by\ncomparing to image simulations for bilayer and thicker materials. Figure S2 shows\nmany bright atoms around the defects that are circled. These should be discussed. Are\nthese Mo or W impurity adatoms?\nResponse: We appreciate the reviewer for the insightful comments.\nAccording to the reviewer’s suggestion, we conducted ADF-STEM image\nsimulations for monolayer, bilayer and trilayer WSi N structures to differentiate\n2 4\nsamples with different thicknesses. The atomic models and corresponding ADF\nsimulations are shown in Fig. R40a and Fig. R40b, respectively. The simulation results\nclearly show that the monolayer WSi N exhibits six-membered rings with alternating\n2 4\nbright and dark contrasts, while the centers of these six-membered rings remain\nfeatureless (Fig. R40b). In contrast, the bilayer and trilayer WSi N configurations\n2 4\ndisplay six-membered ring structures with distinct central features, the characteristics\nof which depend on the number of stacking layers (Fig. R40b). Therefore, ADF-STEM\nimaging allows us to directly determine whether the WSi N is monolayer.\n2 4\nIn the original Fig. S2, the bright atoms, sitting on top of the Mo sites in MoSi N\n2 4\nlattice around the defects, are Mo adatoms. These adsorbed Mo atoms likely originated\nduring the TEM sample fabrication process, which involved transfer from metal\nsubstrate to a TEM grid and subsequent thermal annealing for contaminant removal. To\navoid misunderstanding, we have replaced the relevant pictures in the revised\nmanuscript.\nWe have added the above results in the revised manuscript.\nFigure R40. Atomic models (a) and the corresponding simulated ADF-STEM images\n(b) of monolayer, bilayer, and trilayer WSi N .\n2 4\n8. The atomic models of the point defects should be shown more clearly and not only\nas tiny covering up part of the experimental images. If the authors feel there is not space\nin the main text figures, they can be shown clearly and from several different\ncrystallographic direction angles in the SI. I also so not understand why some of the\nADF panels (in Figure 5a-c and examples in SI) are presented on a red colour look up\ntable. I did not find it helped to follow the assignment of defect types in these images.\nResponse: We appreciate the reviewer for the constructive suggestions.\nFollowing the reviewer’s suggestion, we have provided the atomic models of all\npoint defects (Fig. R41) in the revised manuscript, with the defective sites explicitly\nmarked by red dashed circles. Additionally, the false coloring in the original Figs. 5 a-\nc has been removed.\nFigure R41. Atomic models of point defects in monolayer WSi N .\n2 4\nResponse to the reviewers’ comments\nWe sincerely appreciate the reviewers’ insightful comments and constructive\nsuggestions, which have helped us significantly improve the quality and clarity of our\nwork. Below, we provide a point-by-point response to each reviewer’s concerns. All\nrevisions in the manuscript are highlighted in blue for ease of review.\nReviewer #1 (Remarks to the Author):\nThis manuscript presents a systematic study of point defects in monolayer WSi₂N₄ and\nMoSi₂N₄. The atomistic identification of ten defect types and the discovery of exotic\nlow-dimensional defect aggregates are exceptional contributions. However, the study\nremains at the level of observation and prediction. The significance and impact are\ncurrently limited by a lack of deeper mechanistic analysis (e.g., defect formation\nkinetics) and a direct connection to experimentally validated device-level properties.\nResponse: We thank the reviewer for the insightful comment and constructive\nsuggestion. In response, we have incorporated a detailed mechanistic analysis of defect\nformation kinetics and expanded the discussion to explicitly connect atomic-scale\ndefect analysis to device-level performance, as detailed in the point-to-point response\nbelow. These additions directly address the reviewer’s point and elevate the study from\nobservation to a more predictive and impactful analysis.\nQ1: The DFT calculations presented in the manuscript compellingly predict the\nemergence of localized magnetic moments at specific defect sites, such as VN(t),\nVN(m)², and WSi. This is a fascinating and potentially significant finding. While the\nauthors rightly note that the averaged magnetic signal was below the detection limit of\nconventional SQUID magnetometry, the logical next step for confirming this prediction\nwould be to employ techniques that probe magnetism at the atomic scale.\nResponse: We sincerely thank the reviewer for highlighting the significance of our DFT\npredictions regarding localized magnetic moments at defect sites (e.g. V , V ,\nN(t) N(m)2\nW ). We fully agree that direct experimental confirmation at the atomic scale would be\nSi\nhighly valuable.\nIn response to the reviewer’s suggestion, we have made substantial efforts to pursue\nspin-polarized scanning tunneling microscopy (SP-STM) measurements to probe\natomic-scale magnetism. Given that the SP-STM measurement is much more\nchallenging than conventional STM, our endeavor required considerable amount of\ntime and resources. Unfortunately, we have not yet obtained definitive SP-STM data,\nand completing such experiments within the available timeframe appears unlikely.\nConsidering these technical challenges and time constraints, we lean towards\nreporting the DFT predictions as a theoretical finding in the current work. We believe\nthis result will motivate future experimental investigations - using advanced techniques\nsuch as 4D-STEM or more accessible SP-STM methodologies – to directly verify the\nlocal magnetism in WSi N .\n2 4\nQ2: While the in-situ irradiation experiments convincingly demonstrate that the\nelectron beam does not create new defects under imaging conditions, a more\nquantitative discussion is needed to rule out that it may alter the apparent defect\npopulations (might systematically biasing the observed statistics.).\nResponse: We appreciate the reviewer for the constructive suggestion.\nRegarding the statistical analysis of point defects, in addition to the original dataset\nacquired at a STEM imaging current of ~9 pA, we collected expanded datasets at ~5\npA and ~20 pA, as shown in Figs. R1 and Table R1. The proportional distributions of\npoint defects remain consistent across these different beam currents. Minor variations\nin concentration likely arise from sample-to-sample differences and variations in the\nvolume of data analyzed. These results confirm that the statistical results of defect\nconcentrations are robust under the imaging conditions used here.\nWe have added the relative date and discussions in the revised manuscript.\nFigure R1. Statistical distribution of point defect concentrations acquired at different\nbeam currents.\nTable R1. The number of each type of defect acquired under different beam currents.\nOriginal Expanded Expanded\ndataset (9 pA) dataset (5 pA) dataset (20 pA)\nV 1304 317 785\nN(m)2\nV 1187 267 583\nN(t)2\nV 98 15 26\nSi2N(m)\nV 28 6 11\nW\nSi 2508 632 1116\nN(t)\nSi 968 175 260\nN(m)\nSi2 157 50 77\nW\nSi 164 26 28\nW\nW 122 54 42\nSi\nW 100 29 28\nN(t)\nQ3: The discovery of 1D Si₂Mo chains and 2D SiN(t) networks is a remarkable\nobservation. While the binding energy calculations convincingly establish their\nthermodynamic stability post-formation, the manuscript is silent on their kinetic\nformation pathway. We suggest the authors include a discussion on the possible\natomistic mechanisms. Were these extended structures formed directly during the CVD\ngrowth, or do they result from the post-growth migration and assembly of isolated point\ndefects?\nResponse: We thank the reviewer for raising this important point regarding the kinetic\nformation pathway of the observed 1D Si2 chains and 2D Si networks. Our\nMo N(t)\nanalysis supports a formation pathway that occurs directly during CVD growth, rather\nthan through post-growth migration and assembly of isolated point defects, as detailed\nbelow.\n1) In‑situ formation during growth: The high‑temperature CVD environment provides\nsufficient thermal energy and atomic mobility for defects to nucleate and arrange into\nextended configurations during deposition. The simultaneous supply of all constituent\nelements (Mo, Si, N) enables the direct incorporation of such multi‑atom defect\ncomplexes into the growing lattice.\n2) Spatial distribution and morphology: The observed 1D chains and 2D networks are\nfound embedded seamlessly within otherwise pristine crystalline regions, without\nevidence of disruptive post‑growth rearrangement. Their well‑defined orientations\nalign with the crystal lattice, consistent with growth‑governed alignment rather than\nrandom, diffusion‑limited aggregation after growth.\n3) Thermodynamic driving force: Our binding‑energy calculations showed that\naggregated defect configurations are energetically favorable compared to isolated point\ndefects. At the kinetically active growth front, once a critical nucleus forms, continued\naddition of atoms likely proceeds in a way that minimizes localized energy, promoting\nthe extension of chains or networks along preferential crystallographic directions.\n4) Kinetically limited post‑growth mobility: Our new first-principles calculations\nwithin a 4×4×1 supercell reveal that the direct migration of individual Si and Si2\nN(t) Mo\ndefects to adjacent lattice sites in the absence of vacancies requires overcoming\nsignificantly high energy barriers (10.41 eV for Si and 33.75 eV for Si2 , see\nN(t) Mo\nFigs. R2a,c). This indicates that forming extended structures via post growth defect\nmigration is kinetically unlikely. Even when nitrogen or molybdenum vacancies are\npresent nearby—which reduces the migration barrier—the required energy remains\nsubstantial (> 4 eV, see Figs. R2b,d), effectively suppressing long-range defect\ndiffusion.\nIn summary, the combination of high energy barriers and the structural coherence of\nthe observed assemblies indicate that large-scale defect reorganization after growth is\nkinetically unfavorable. While our current study does not include in‑situ imaging of the\ngrowth front, the collective evidence points to a growth‑mediated, rather than\npost‑growth diffusion‑mediated, formation pathway. We have added a brief discussion\non this point in the revised manuscript to clarify the likely kinetic origin of these\nintriguing low‑dimensional defect assemblies.\nFigure R2. Migration pathways and energy barriers for individual Si and Si2 in\nN(t) Mo\nmonolayer MoSi N . (a,c) Defect migration in the absence of vacancy.\n2 4\n(b,d) Defect\nmigration in the presence of a nearby N or Mo vacancy, respectively. The intermediate\nconfiguration with lowered energy in (d) corresponds to a state where two Si atoms\nsubstitute the two vacant Mo sites.\nQ4: Given the high theoretical thermal conductivity of pristine WSi₂N₄ and the\nestablished role of point defects as potent phonon scatterers, the observed high\nconcentrations of defects (e.g., VN(m)², SiN(t)) are likely to significantly suppress\nlattice thermal transport. To broaden the impact of this work into the realms of thermal\nmanagement and thermoelectrics, we suggest the authors include an estimate or a brief\ndiscussion on this effect.\nResponse: We appreciate the reviewer for the constructive suggestion.\nWe agree that point defects can significantly suppress lattice thermal conductivity\nthrough phonon scattering, as demonstrated in various materials. For instance, a 0.5%\nconcentration of Mo (S) vacancy defects reduce thermal conductivity by about 60%\n(35%) in monolayer MoS [J. Phys. Chem. C, 119, 16358-16365 (2015)]. Similarly,\n2\ndivacancy defects (Se vacancies and Sn vacancies) in SnSe disrupt translation\nsymmetry and markedly reduce lattice thermal conductivity [Nat. Commun. 15, 4231\n(2024)]. Given the prevalence of point defects in monolayer WSi N —such as V\n2 4 N(m)2\nand Si —we expect a similar substantial reduction in its lattice thermal conductivity.\nN(t)\nSuch reduction is highly desirable for thermoelectrics, as lowering lattice thermal\nconductivity directly enhances the thermoelectric figure of merit ZT. This effect has\nbeen demonstrated in systems such as heavily Hf-doped p-type FeNbSb half-Heusler\ncompound [Nat. Commun. 6, 8144 (2015)] and Cu-doped n-type PbSe [Nat. Commun.\n16, 656 (2025)]. When coupled with defect-tuned electronic properties—for instance,\nthe metallic states introduced by Si defects, which may improve electrical\nN(t)\nconductivity—the concurrent optimization of electrical and thermal transport could\nposition defective WSi N as a promising candidate for thermoelectric applications. By\n2 4\ncontrast, pristine WSi N , with its inherently high thermal conductivity, remains well-\n2 4\nsuited for transistor applications where efficient heat dissipation is essential.\nWe have added these discussions into the revised manuscript at page 23.\nQ5: This work provides a superb atomic-scale catalog of defects and their electronic\nsignatures. To maximize its impact, the manuscript would benefit from a forward-\nlooking discussion on the potential device-level implications. For instance, how might\nthe prevalent defects influence key metrics in a prototypical WSi₂N₄ field-effect\ntransistor (e.g., contact resistance, leakage current)?\nResponse: We appreciate the reviewer for the constructive suggestion.\nConnecting our atomic-scale defect analysis to device-level performance is indeed a\ncritical next step. In monolayer WSi N , prevalent point defects can substantially tune\n2 4\nfield-effect transistor (FET) characteristics through distinct, and often competing,\nmechanisms involving channel transport and contact interfaces. For example, defects\nsuch as V introduce in-gap states that act as strong scattering and trapping centers,\nN(t)\nseverely degrading channel mobility. Our first-principles calculations showed that hole\nmobility decreases from 411 cm2 V-1 s-1 in the pristine structure to only 2 cm2 V-1 s-1 in\nthe presence of such defects. These in-gap states can also promote trap-assisted\ntunneling, potentially increasing the off-state leakage current and reducing the\nswitching ratio.\nConversely, the Si antisite defect substantially enhances hole mobility to 740 cm2\nN(t)\nV-1 s-1, suggesting its utility for channel optimization. In addition, certain defects (e.g.,\nV , Si , Si , and Si ) can induce an insulator-to-metal transition. When\nN(t)2 N(t) N(m) W\nstrategically engineered at the metal-semiconductor interfaces, such metallic phases\nmay lower the Schottky barrier height, reduce contact resistance, and enhance on-state\ncurrent density. Similarly, specific defects like the Si antisite create a shallow acceptor\nW\nstate near the valence band maximum, enabling effective p-type doping for threshold\nvoltage tuning.\nThus, optimizing FET performance—balancing high mobility, low leakage, and low\ncontact resistance—will require precise spatial defect engineering: suppressing\nchannel-degrading defects, leveraging mobility-enhancing defects in the active region,\nand potentially incorporating beneficial defects at contact interfaces. Our atomic-scale\ncatalog of defects and their properties establish an essential foundation for guiding such\ntargeted material synthesis and device design strategies.\nWe have added the above discussions in the revised manuscript at page 22 to show\nthe potential of defects for device-level implications.\nQ6: For practical applications in electronics or spintronics, it is crucial to understand\nhow these defects behave under ambient exposure (moisture/oxygen), electrical bias,\nor thermal cycling. Do the charge states of electrically active defects (e.g., SiN(t))\nremain stable under gate voltage? Could the observed N vacancies act as degradation\nsites during device operation? A discussion on these aspects would bridge the gap\nbetween fundamental defect characterization and realistic device reliability\nconsiderations.\nResponse: We appreciate the reviewer for the constructive suggestion.\nWe experimentally evaluated defect stability under three distinct conditions: (1)\nambient exposure, (2) electrical bias, and (3) thermal cycling.\n(1) Ambient exposure: To assess environmental stability, we compared a freshly\nprepared monolayer WSi N sample (Fig. R3) with a sample stored under ambient\n2 4\nconditions for two months (Fig. R4). No significant changes in either the types or\nconcentrations of point defects were observed, confirming their high stability against\nambient degradation.\nFigure R3. STEM images of freshly prepared WSi N monolayers.\n2 4\nFigure R4. STEM images of WSi N monolayers after ambient exposure for two\n2 4\nmonths.\n(2) Electrical bias: Biasing-STEM experiments were performed to probe defect\nbehavior under an applied electric voltage. A constant bias of 1 V was applied to a\nmonolayer WSi N transferred onto a biasing chip. STEM images acquired shortly after\n2 4\nbiasing (Figs. R5a,b) and after 1 hour of continuous bias (Figs. R5c,d) showed no\ndetectable change in defect types or concentrations, indicating robust structural stability\nunder electrical bias.\nFigure R5. STEM images of monolayer WSi N under electrical bias. (a,b) Images\n2 4\nacquired shortly after applying a 1 V bias. (c,d) Images acquired after maintaining 1 V\nbias for 1 hour.\n(3) Thermal cycling: The thermal stability of defects was examined via heating-\nSTEM. The sample was transferred onto a heating chip and subjected to thermal cycling\naccording to the temperature–time profile in Fig. R6. The procedure consisted of: (1)\nheating to 500 °C for 1 h, (2) cooling to room temperature for 0.5 h, and (3) reheating\nto 500 °C for 1 h. STEM images acquired at 500 °C after each 1 h hold (Fig. R7)\nrevealed no measurable variation in defect types or concentrations, demonstrating\nexcellent thermal stability of the defects under the conditions examined.\nFigure R6. Thermal cycling profile for monolayer WSi N .\n2 4\nFigure R7. STEM images of monolayer WSi N after thermal cycling. (a,b) STEM\n2 4\nimages acquired at the first 500 ℃ heating stage. (c,d) STEM images acquired at the\nsecond 500 ℃ heating stage.\nThe equilibrium charge state of a point defect is thermodynamically determined by\nthe position of the Fermi level (E ) relative to the defect’s characteristic charge\nF\ntransition levels within the band gap. This relationship underpins the kinetics of carrier\ntrapping and de-trapping at defect sites. In a field-effect transistor, the applied gate\nvoltage modulates the surface potential and thus shifts E within the channel. If this\nF\nshift causes E to cross a defect’s charge transition level, it can thermodynamically\nF\nfavor a change in the defect’s occupation, leading to charge exchange with the bands.\nThe kinetics and practical impact of this process depend on whether the defect\nintroduces shallow or deep electronic levels. Normally, under moderate gate voltage\nmodulation, the charge states of shallow dopants are highly sensitive to Fermi level\nshifts, while the charge states of deep-level defects may remain largely unchanged due\nto their large ionization energies, even when gate voltage induces dramatic Fermi level\nshifts, as reported previously [e.g., U. Mishra & J. Singh, Semiconductor Device\nPhysics and Design, Springer, (2008); Dieter K. Schroder, Semiconductor Material and\nDevice Characterization (3rd ed.), Wiley-IEEE Press, (2005)].\nTaken together, these experiments establish that point defects in monolayer WSi N\n2 4\nremain stable under ambient, electrical, and thermal stresses, supporting their potential\nfor electronic applications.\nWe have added the above results to page 14 in the revised manuscript.\nReviewer #2 (Remarks to the Author):\nAll the concerns raised have been well addressed, and the text and figures are now\norganized much better, before the manuscript to be published, only a few minor points\nneed to clarify:\n1. In Fig. 6cd, the STS of the defects 2-4 look almost the same line shape, there are\nremarkably two electronic states at 1eV, close to the conduction band edge. The authors\nassign this defect as V_N(t), and the STM image also shows this defect is abundant,\nhowever this defect is not common according to the statistical results in Fig. 4 c. The\nassignment of Si_N(t) is reasonable, as it has high concentration in STEM image\nanalysis and its STS shows it is metallic.\nResponse: We thank the reviewer for the positive feedback and for agreeing that the\nmanuscript is now suitable for publication. We are pleased that all major concerns have\nbeen addressed. The few remaining minor points are clarified below. Regarding the\nSTS features and defect assignment, the discrepancy between the easy identification of\nV by STM and its limited representation in STEM images can be explained by the\nN(t)\ndistinct imaging mechanisms and sensitivities of the two techniques.\nSTM is a surface-sensitive technique, and thus predominantly detects the defects on\nor near the surface. This explains the frequent identification of V by STM.\nN(t)\nIn contrast,\nHAADF-STEM imaging is less sensitive to light elements such as nitrogen,\nmaking single N vacancies (V ) challenging to resolve directly. Although\nN(t)\niDPC-STEM can identify double vacancies (V ), isolated V defects remain\nN(t)2 N(t)\ndifficult to distinguish due to their low Z-contrast and local strain effects.\nFurthermore, our DFT calculations indicated that the formation energy of a single\nV (3.89 eV) is only about half that of V (8.31 eV), implying that V should\nN(t) N(t)2 N(t)\noccur at significantly higher concentrations than V —consistent with its frequent\nN(t)2\nappearance in STM. Therefore, assigning the abundant STS-active defects in Fig. 6c to\nV is well supported by both electronic-structure predictions and formation energetics,\nN(t)\neven though V is underrepresented in the STEM-based statistics due to the imaging\nN(t)\nlimitations noted above.\nWe have added a sentence to explain the discrepancy at page 20-21 in the revised\nmanuscript.\n2. The defect 4 (purple arrow) and defect 2 (blue arrow) give different contrast in the\nSTM image in Fig. 6c, but they correspond to the same defect, please explain why or\nremove some of the arrows to avoid misunderstanding, since the defect assignment in\nSTM is quite difficult and heavily depends on the experience.\nResponse: We appreciate the reviewer for the constructive suggestion.\nSuch local contrast variations typically arise from substrate inhomogeneity in STM\nimaging, a common phenomenon for epitaxial vdW monolayer. To avoid potential\nmisunderstanding, we have removed the purple arrows in Fig. 6c and the gray arrows\nin Fig. 6e, as shown below (Fig. R8).\nWe have modified Figure 6 accordingly in the revised manuscript.\nFigure R8. The modified Figure 6 in the revised manuscript.\nReviewer #3 (Remarks to the Author):\nThe authors have added additional simulations, analysed a larger data volume and\nadded STM data which has improved their manuscript. I now recommend it for\npublication. It is unfortunate that the authors do not plan to include the image data\nuploaded to a free repository for the field to benefit from and I recommend they\nconsider doing this.\nResponse: We thank the reviewer for the positive assessment of our revisions and for\nthe recommendation for publication. We also appreciate the reviewer’s suggestion\nregarding the public deposition of the image data. We will certainly consider this option\nto maximize the utility of our work for the research community. Following the\nreviewer’s suggestion, we have uploaded the image datasets to the free figshare\nrepository.\nThe author investigated point defects in monolayer MoSi2N4 and WSi2N4 using\nSTEM and first-principles calculations, with a particular focus on WSi2N4. The\nfindings revealed 11 distinct types of point defects in WSi2N4, which lead to\nsignificant reductions in the bandgap, including complete gap closure that facilitates\ninsulator-to-metal transitions. However, several critical issues need to be addressed\nbefore further consideration:\n1. Will the concentration and types of defects be influenced by CVD parameters,\nsuch as precursor ratios? The author should conduct additional experiments to\nexplore the effects of CVD synthesis parameters, as the current concentration\ncalculations lack rigor.\n2. Layered materials with defects can be sensitive to air and prone to oxidation\nduring STEM analysis. The author should comment on the stability of the\nmaterial. It is suggested that additional in-situ dynamic analyses be performed\nto elucidate the defect formation process, which would enhance the\nunderstanding of defect concentration distribution.\n3. The author provides only a qualitative description of defect morphology through\nSTEM. This work lacks significance in the field due to the absence of defect\nengineering strategies aimed at enhancing material properties, such as device\nperformance. Establishing a clear connection between defects and device\nperformance - such as carrier mobility and catalytic activity - would\nsignificantly elevate the impact of this research beyond fundamental\ncharacterization.\nMinor Issues\n1. In the introduction, the author states that WSi2N4 was studied due to its higher\ntolerance to electron beam irradiation compared to MoSi2N4. The advantages of\nWSi2N4 over other layered materials should be further elaborated, especially\ngiven the excellent properties of MoSi2N4, such as high thermal conductivity\nand unique electrical characteristics.\n2. The author synthesized monolayer WSi2N4 and MoSi2N4 via CVD, with an\noptical image of WSi2N4 provided in Figure 1(a). However, to accurately\nconfirm the material’s composition and structure, additional characterization\ntechniques, such as X-ray photoelectron spectroscopy (XPS), should be\nemployed. The Supporting Information would also benefit from expanded\ncharacterization data for MoSi2N4 (e.g., XPS) to ensure a thorough comparison\nbetween the two materials.\n3. A detailed discussion is needed to clarify whether the observed disorder arises\nfrom intrinsic defects (e.g., vacancies or grain boundaries), strain-induced\ndistortions, or artifacts from the synthesis process.\n4. Figure 5(d) presents an observed superstructure, but its atomic configuration\nremains ambiguous. To facilitate a comprehensive understanding, the authors\nshould provide a clear atomic model of the superstructure, ideally accompanied\nby simulated STEM images for direct comparison with experimental data.\n5. Additional explanations are needed for the following points.\nThe study primarily focuses on defect types and concentrations, lacking a\ndynamic analysis to clarify the defect formation process. This oversight limits\nreaders' understanding of changes in defect concentration. For instance, the\nreason why the calculated formation energy for WSi defects is the lowest in the\nchart, yet the statistical concentration is the lowest, needs to be explicitly\naddressed. The current explanations lack rigor.\nThe authors demonstrate that defect types and concentrations impact bandgap\nreduction, which subsequently affects electrical properties. However, the\nexperimentally measured defect concentrations do not fully align with\ncalculated results, suggesting that defect formation is uncontrollable and may\nbe influenced by synthesis parameters. Thus, while DFT calculations indicate\nthat the bandgap can be adjusted, controlling this adjustment via defects remains\na challenge. The uncontrolled nature of defect concentration underscores the\nimportance of understanding the growth kinetics model in the defect\nformation process.\n6. The calculations for defect concentration distribution lack rigor. The statement,\n“However, since V and V have similar image contrasts with V (Fig.\nSi SiN(m) N(m)2\nS4), the concentration statistics for V might include V and V leading\nN(m)2 Si SiN(m),\nto an overestimation” needs clarification."}, {"round": 2, "reviewer_comments": {"reviewer_1": "This manuscript presents a systematic study of point defects in monolayer WSi₂N₄ and MoSi₂N₄. The atomistic identification of ten defect types and the discovery of exotic low-dimensional defect aggregates are exceptional contributions. However, the study remains at the level of observation and prediction. The significance and impact are currently limited by a lack of deeper mechanistic analysis (e.g., defect formation kinetics) and a direct connection to experimentally validated device-level properties.\nQ1: The DFT calculations presented in the manuscript compellingly predict the emergence of localized magnetic moments at specific defect sites, such as V N(t), V N(m)², and W Si. This is a fascinating and potentially significant finding. While the authors rightly note that the averaged magnetic signal was below the detection limit of conventional SQUID magnetometry, the logical next step for confirming this prediction would be to employ techniques that probe magnetism at the atomic scale.\nQ2: While the in-situ irradiation experiments convincingly demonstrate that the electron beam does not create new defects under imaging conditions, a more quantitative discussion is needed to rule out that it may alter the apparent defect populations (might systematically biasing the observed statistics.).\nQ3: The discovery of 1D Si₂Mo chains and 2D SiN(t) networks is a remarkable observation. While the binding energy calculations convincingly establish their thermodynamic stability post-formation, the manuscript is silent on their kinetic formation pathway. We suggest the authors include a discussion on the possible atomistic mechanisms. Were these extended structures formed directly during the CVD growth, or do they result from the post-growth migration and assembly of isolated point defects?\nQ4: Given the high theoretical thermal conductivity of pristine WSi₂N₄ and the established role of point defects as potent phonon scatterers, the observed high concentrations of defects (e.g., VN(m)², SiN(t)) are likely to significantly suppress lattice thermal transport. To broaden the impact of this work into the realms of thermal management and thermoelectrics, we suggest the authors include an estimate or a brief discussion on this effect.\nQ5: This work provides a superb atomic-scale catalog of defects and their electronic signatures. To maximize its impact, the manuscript would benefit from a forward-looking discussion on the potential device-level implications. For instance, how might the prevalent defects influence key metrics in a prototypical WSi₂N₄ field-effect transistor (e.g., contact resistance, leakage current)?\nQ6: For practical applications in electronics or spintronics, it is crucial to understand how these defects behave under ambient exposure (moisture/oxygen), electrical bias, or thermal cycling. Do the charge states of electrically active defects (e.g., SiN(t)) remain stable under gate voltage? Could the observed N vacancies act as degradation sites during device operation? A discussion on these aspects would bridge the gap between fundamental defect characterization and realistic device reliability considerations.", "reviewer_2": "All the concerns raised have been well addressed, and the text and figures are now organized much better, before the manuscript to be published, only a few minor points need to clarify:\n1. In Fig.6cd, the STS of the defects 2-4 look almost the same line shape, there are remarkably two electronic states at 1eV, close to the conduction band edge. the author assign this defect as V_N(t), and the STM image also shows this defect is abundant, however this defect is not common according to the statistical results in Fig.4 c. the assignment of Si_N(t) is reasonable, as it has high concentration in STEM image analysis and its STS shows it is metallic.\n2. The defect 4 (purple arrow) and defect 2 (blue arrow) give different contrast in the STM image in Fig. 6c, but they correspond to the same defect, please explain why or remove some of the arrows to avoid misunderstanding, since the defect assignment in STM is quite difficult and heavily depends on the experience.", "reviewer_3": "The authors have added additional simulations, analysed a larger data volume and added STM data which has improved their manuscript. I now recommend it for publication. It is unfortunate that the authors do not plan to include the image data uploaded to a free repository for the field to benefit from and I recommend they consider doing this."}, "author_response": "Response to the reviewers’ comments\nWe sincerely appreciate the reviewers’ insightful comments and constructive\nsuggestions, which have helped us significantly improve the quality and clarity of our\nwork. Below, we provide a point-by-point response to each reviewer’s concerns. All\nrevisions in the manuscript are highlighted in blue for ease of review.\nReviewer #1 (Remarks to the Author):\nThis manuscript presents a systematic study of point defects in monolayer WSi₂N₄ and\nMoSi₂N₄. The atomistic identification of ten defect types and the discovery of exotic\nlow-dimensional defect aggregates are exceptional contributions. However, the study\nremains at the level of observation and prediction. The significance and impact are\ncurrently limited by a lack of deeper mechanistic analysis (e.g., defect formation\nkinetics) and a direct connection to experimentally validated device-level properties.\nResponse: We thank the reviewer for the insightful comment and constructive\nsuggestion. In response, we have incorporated a detailed mechanistic analysis of defect\nformation kinetics and expanded the discussion to explicitly connect atomic-scale\ndefect analysis to device-level performance, as detailed in the point-to-point response\nbelow. These additions directly address the reviewer’s point and elevate the study from\nobservation to a more predictive and impactful analysis.\nQ1: The DFT calculations presented in the manuscript compellingly predict the\nemergence of localized magnetic moments at specific defect sites, such as VN(t),\nVN(m)², and WSi. This is a fascinating and potentially significant finding. While the\nauthors rightly note that the averaged magnetic signal was below the detection limit of\nconventional SQUID magnetometry, the logical next step for confirming this prediction\nwould be to employ techniques that probe magnetism at the atomic scale.\nResponse: We sincerely thank the reviewer for highlighting the significance of our DFT\npredictions regarding localized magnetic moments at defect sites (e.g. V , V ,\nN(t) N(m)2\nW ). We fully agree that direct experimental confirmation at the atomic scale would be\nSi\nhighly valuable.\nIn response to the reviewer’s suggestion, we have made substantial efforts to pursue\nspin-polarized scanning tunneling microscopy (SP-STM) measurements to probe\natomic-scale magnetism. Given that the SP-STM measurement is much more\nchallenging than conventional STM, our endeavor required considerable amount of\ntime and resources. Unfortunately, we have not yet obtained definitive SP-STM data,\nand completing such experiments within the available timeframe appears unlikely.\nConsidering these technical challenges and time constraints, we lean towards\nreporting the DFT predictions as a theoretical finding in the current work. We believe\nthis result will motivate future experimental investigations - using advanced techniques\nsuch as 4D-STEM or more accessible SP-STM methodologies – to directly verify the\nlocal magnetism in WSi N .\n2 4\nQ2: While the in-situ irradiation experiments convincingly demonstrate that the\nelectron beam does not create new defects under imaging conditions, a more\nquantitative discussion is needed to rule out that it may alter the apparent defect\npopulations (might systematically biasing the observed statistics.).\nResponse: We appreciate the reviewer for the constructive suggestion.\nRegarding the statistical analysis of point defects, in addition to the original dataset\nacquired at a STEM imaging current of ~9 pA, we collected expanded datasets at ~5\npA and ~20 pA, as shown in Figs. R1 and Table R1. The proportional distributions of\npoint defects remain consistent across these different beam currents. Minor variations\nin concentration likely arise from sample-to-sample differences and variations in the\nvolume of data analyzed. These results confirm that the statistical results of defect\nconcentrations are robust under the imaging conditions used here.\nWe have added the relative date and discussions in the revised manuscript.\nFigure R1. Statistical distribution of point defect concentrations acquired at different\nbeam currents.\nTable R1. The number of each type of defect acquired under different beam currents.\nOriginal Expanded Expanded\ndataset (9 pA) dataset (5 pA) dataset (20 pA)\nV 1304 317 785\nN(m)2\nV 1187 267 583\nN(t)2\nV 98 15 26\nSi2N(m)\nV 28 6 11\nW\nSi 2508 632 1116\nN(t)\nSi 968 175 260\nN(m)\nSi2 157 50 77\nW\nSi 164 26 28\nW\nW 122 54 42\nSi\nW 100 29 28\nN(t)\nQ3: The discovery of 1D Si₂Mo chains and 2D SiN(t) networks is a remarkable\nobservation. While the binding energy calculations convincingly establish their\nthermodynamic stability post-formation, the manuscript is silent on their kinetic\nformation pathway. We suggest the authors include a discussion on the possible\natomistic mechanisms. Were these extended structures formed directly during the CVD\ngrowth, or do they result from the post-growth migration and assembly of isolated point\ndefects?\nResponse: We thank the reviewer for raising this important point regarding the kinetic\nformation pathway of the observed 1D Si2 chains and 2D Si networks. Our\nMo N(t)\nanalysis supports a formation pathway that occurs directly during CVD growth, rather\nthan through post-growth migration and assembly of isolated point defects, as detailed\nbelow.\n1) In‑situ formation during growth: The high‑temperature CVD environment provides\nsufficient thermal energy and atomic mobility for defects to nucleate and arrange into\nextended configurations during deposition. The simultaneous supply of all constituent\nelements (Mo, Si, N) enables the direct incorporation of such multi‑atom defect\ncomplexes into the growing lattice.\n2) Spatial distribution and morphology: The observed 1D chains and 2D networks are\nfound embedded seamlessly within otherwise pristine crystalline regions, without\nevidence of disruptive post‑growth rearrangement. Their well‑defined orientations\nalign with the crystal lattice, consistent with growth‑governed alignment rather than\nrandom, diffusion‑limited aggregation after growth.\n3) Thermodynamic driving force: Our binding‑energy calculations showed that\naggregated defect configurations are energetically favorable compared to isolated point\ndefects. At the kinetically active growth front, once a critical nucleus forms, continued\naddition of atoms likely proceeds in a way that minimizes localized energy, promoting\nthe extension of chains or networks along preferential crystallographic directions.\n4) Kinetically limited post‑growth mobility: Our new first-principles calculations\nwithin a 4×4×1 supercell reveal that the direct migration of individual Si and Si2\nN(t) Mo\ndefects to adjacent lattice sites in the absence of vacancies requires overcoming\nsignificantly high energy barriers (10.41 eV for Si and 33.75 eV for Si2 , see\nN(t) Mo\nFigs. R2a,c). This indicates that forming extended structures via post growth defect\nmigration is kinetically unlikely. Even when nitrogen or molybdenum vacancies are\npresent nearby—which reduces the migration barrier—the required energy remains\nsubstantial (> 4 eV, see Figs. R2b,d), effectively suppressing long-range defect\ndiffusion.\nIn summary, the combination of high energy barriers and the structural coherence of\nthe observed assemblies indicate that large-scale defect reorganization after growth is\nkinetically unfavorable. While our current study does not include in‑situ imaging of the\ngrowth front, the collective evidence points to a growth‑mediated, rather than\npost‑growth diffusion‑mediated, formation pathway. We have added a brief discussion\non this point in the revised manuscript to clarify the likely kinetic origin of these\nintriguing low‑dimensional defect assemblies.\nFigure R2. Migration pathways and energy barriers for individual Si and Si2 in\nN(t) Mo\nmonolayer MoSi N . (a,c) Defect migration in the absence of vacancy.\n2 4\n(b,d) Defect\nmigration in the presence of a nearby N or Mo vacancy, respectively. The intermediate\nconfiguration with lowered energy in (d) corresponds to a state where two Si atoms\nsubstitute the two vacant Mo sites.\nQ4: Given the high theoretical thermal conductivity of pristine WSi₂N₄ and the\nestablished role of point defects as potent phonon scatterers, the observed high\nconcentrations of defects (e.g., VN(m)², SiN(t)) are likely to significantly suppress\nlattice thermal transport. To broaden the impact of this work into the realms of thermal\nmanagement and thermoelectrics, we suggest the authors include an estimate or a brief\ndiscussion on this effect.\nResponse: We appreciate the reviewer for the constructive suggestion.\nWe agree that point defects can significantly suppress lattice thermal conductivity\nthrough phonon scattering, as demonstrated in various materials. For instance, a 0.5%\nconcentration of Mo (S) vacancy defects reduce thermal conductivity by about 60%\n(35%) in monolayer MoS [J. Phys. Chem. C, 119, 16358-16365 (2015)]. Similarly,\n2\ndivacancy defects (Se vacancies and Sn vacancies) in SnSe disrupt translation\nsymmetry and markedly reduce lattice thermal conductivity [Nat. Commun. 15, 4231\n(2024)]. Given the prevalence of point defects in monolayer WSi N —such as V\n2 4 N(m)2\nand Si —we expect a similar substantial reduction in its lattice thermal conductivity.\nN(t)\nSuch reduction is highly desirable for thermoelectrics, as lowering lattice thermal\nconductivity directly enhances the thermoelectric figure of merit ZT. This effect has\nbeen demonstrated in systems such as heavily Hf-doped p-type FeNbSb half-Heusler\ncompound [Nat. Commun. 6, 8144 (2015)] and Cu-doped n-type PbSe [Nat. Commun.\n16, 656 (2025)]. When coupled with defect-tuned electronic properties—for instance,\nthe metallic states introduced by Si defects, which may improve electrical\nN(t)\nconductivity—the concurrent optimization of electrical and thermal transport could\nposition defective WSi N as a promising candidate for thermoelectric applications. By\n2 4\ncontrast, pristine WSi N , with its inherently high thermal conductivity, remains well-\n2 4\nsuited for transistor applications where efficient heat dissipation is essential.\nWe have added these discussions into the revised manuscript at page 23.\nQ5: This work provides a superb atomic-scale catalog of defects and their electronic\nsignatures. To maximize its impact, the manuscript would benefit from a forward-\nlooking discussion on the potential device-level implications. For instance, how might\nthe prevalent defects influence key metrics in a prototypical WSi₂N₄ field-effect\ntransistor (e.g., contact resistance, leakage current)?\nResponse: We appreciate the reviewer for the constructive suggestion.\nConnecting our atomic-scale defect analysis to device-level performance is indeed a\ncritical next step. In monolayer WSi N , prevalent point defects can substantially tune\n2 4\nfield-effect transistor (FET) characteristics through distinct, and often competing,\nmechanisms involving channel transport and contact interfaces. For example, defects\nsuch as V introduce in-gap states that act as strong scattering and trapping centers,\nN(t)\nseverely degrading channel mobility. Our first-principles calculations showed that hole\nmobility decreases from 411 cm2 V-1 s-1 in the pristine structure to only 2 cm2 V-1 s-1 in\nthe presence of such defects. These in-gap states can also promote trap-assisted\ntunneling, potentially increasing the off-state leakage current and reducing the\nswitching ratio.\nConversely, the Si antisite defect substantially enhances hole mobility to 740 cm2\nN(t)\nV-1 s-1, suggesting its utility for channel optimization. In addition, certain defects (e.g.,\nV , Si , Si , and Si ) can induce an insulator-to-metal transition. When\nN(t)2 N(t) N(m) W\nstrategically engineered at the metal-semiconductor interfaces, such metallic phases\nmay lower the Schottky barrier height, reduce contact resistance, and enhance on-state\ncurrent density. Similarly, specific defects like the Si antisite create a shallow acceptor\nW\nstate near the valence band maximum, enabling effective p-type doping for threshold\nvoltage tuning.\nThus, optimizing FET performance—balancing high mobility, low leakage, and low\ncontact resistance—will require precise spatial defect engineering: suppressing\nchannel-degrading defects, leveraging mobility-enhancing defects in the active region,\nand potentially incorporating beneficial defects at contact interfaces. Our atomic-scale\ncatalog of defects and their properties establish an essential foundation for guiding such\ntargeted material synthesis and device design strategies.\nWe have added the above discussions in the revised manuscript at page 22 to show\nthe potential of defects for device-level implications.\nQ6: For practical applications in electronics or spintronics, it is crucial to understand\nhow these defects behave under ambient exposure (moisture/oxygen), electrical bias,\nor thermal cycling. Do the charge states of electrically active defects (e.g., SiN(t))\nremain stable under gate voltage? Could the observed N vacancies act as degradation\nsites during device operation? A discussion on these aspects would bridge the gap\nbetween fundamental defect characterization and realistic device reliability\nconsiderations.\nResponse: We appreciate the reviewer for the constructive suggestion.\nWe experimentally evaluated defect stability under three distinct conditions: (1)\nambient exposure, (2) electrical bias, and (3) thermal cycling.\n(1) Ambient exposure: To assess environmental stability, we compared a freshly\nprepared monolayer WSi N sample (Fig. R3) with a sample stored under ambient\n2 4\nconditions for two months (Fig. R4). No significant changes in either the types or\nconcentrations of point defects were observed, confirming their high stability against\nambient degradation.\nFigure R3. STEM images of freshly prepared WSi N monolayers.\n2 4\nFigure R4. STEM images of WSi N monolayers after ambient exposure for two\n2 4\nmonths.\n(2) Electrical bias: Biasing-STEM experiments were performed to probe defect\nbehavior under an applied electric voltage. A constant bias of 1 V was applied to a\nmonolayer WSi N transferred onto a biasing chip. STEM images acquired shortly after\n2 4\nbiasing (Figs. R5a,b) and after 1 hour of continuous bias (Figs. R5c,d) showed no\ndetectable change in defect types or concentrations, indicating robust structural stability\nunder electrical bias.\nFigure R5. STEM images of monolayer WSi N under electrical bias. (a,b) Images\n2 4\nacquired shortly after applying a 1 V bias. (c,d) Images acquired after maintaining 1 V\nbias for 1 hour.\n(3) Thermal cycling: The thermal stability of defects was examined via heating-\nSTEM. The sample was transferred onto a heating chip and subjected to thermal cycling\naccording to the temperature–time profile in Fig. R6. The procedure consisted of: (1)\nheating to 500 °C for 1 h, (2) cooling to room temperature for 0.5 h, and (3) reheating\nto 500 °C for 1 h. STEM images acquired at 500 °C after each 1 h hold (Fig. R7)\nrevealed no measurable variation in defect types or concentrations, demonstrating\nexcellent thermal stability of the defects under the conditions examined.\nFigure R6. Thermal cycling profile for monolayer WSi N .\n2 4\nFigure R7. STEM images of monolayer WSi N after thermal cycling. (a,b) STEM\n2 4\nimages acquired at the first 500 ℃ heating stage. (c,d) STEM images acquired at the\nsecond 500 ℃ heating stage.\nThe equilibrium charge state of a point defect is thermodynamically determined by\nthe position of the Fermi level (E ) relative to the defect’s characteristic charge\nF\ntransition levels within the band gap. This relationship underpins the kinetics of carrier\ntrapping and de-trapping at defect sites. In a field-effect transistor, the applied gate\nvoltage modulates the surface potential and thus shifts E within the channel. If this\nF\nshift causes E to cross a defect’s charge transition level, it can thermodynamically\nF\nfavor a change in the defect’s occupation, leading to charge exchange with the bands.\nThe kinetics and practical impact of this process depend on whether the defect\nintroduces shallow or deep electronic levels. Normally, under moderate gate voltage\nmodulation, the charge states of shallow dopants are highly sensitive to Fermi level\nshifts, while the charge states of deep-level defects may remain largely unchanged due\nto their large ionization energies, even when gate voltage induces dramatic Fermi level\nshifts, as reported previously [e.g., U. Mishra & J. Singh, Semiconductor Device\nPhysics and Design, Springer, (2008); Dieter K. Schroder, Semiconductor Material and\nDevice Characterization (3rd ed.), Wiley-IEEE Press, (2005)].\nTaken together, these experiments establish that point defects in monolayer WSi N\n2 4\nremain stable under ambient, electrical, and thermal stresses, supporting their potential\nfor electronic applications.\nWe have added the above results to page 14 in the revised manuscript.\nReviewer #2 (Remarks to the Author):\nAll the concerns raised have been well addressed, and the text and figures are now\norganized much better, before the manuscript to be published, only a few minor points\nneed to clarify:\n1. In Fig. 6cd, the STS of the defects 2-4 look almost the same line shape, there are\nremarkably two electronic states at 1eV, close to the conduction band edge. The authors\nassign this defect as V_N(t), and the STM image also shows this defect is abundant,\nhowever this defect is not common according to the statistical results in Fig. 4 c. The\nassignment of Si_N(t) is reasonable, as it has high concentration in STEM image\nanalysis and its STS shows it is metallic.\nResponse: We thank the reviewer for the positive feedback and for agreeing that the\nmanuscript is now suitable for publication. We are pleased that all major concerns have\nbeen addressed. The few remaining minor points are clarified below. Regarding the\nSTS features and defect assignment, the discrepancy between the easy identification of\nV by STM and its limited representation in STEM images can be explained by the\nN(t)\ndistinct imaging mechanisms and sensitivities of the two techniques.\nSTM is a surface-sensitive technique, and thus predominantly detects the defects on\nor near the surface. This explains the frequent identification of V by STM.\nN(t)\nIn contrast,\nHAADF-STEM imaging is less sensitive to light elements such as nitrogen,\nmaking single N vacancies (V ) challenging to resolve directly. Although\nN(t)\niDPC-STEM can identify double vacancies (V ), isolated V defects remain\nN(t)2 N(t)\ndifficult to distinguish due to their low Z-contrast and local strain effects.\nFurthermore, our DFT calculations indicated that the formation energy of a single\nV (3.89 eV) is only about half that of V (8.31 eV), implying that V should\nN(t) N(t)2 N(t)\noccur at significantly higher concentrations than V —consistent with its frequent\nN(t)2\nappearance in STM. Therefore, assigning the abundant STS-active defects in Fig. 6c to\nV is well supported by both electronic-structure predictions and formation energetics,\nN(t)\neven though V is underrepresented in the STEM-based statistics due to the imaging\nN(t)\nlimitations noted above.\nWe have added a sentence to explain the discrepancy at page 20-21 in the revised\nmanuscript.\n2. The defect 4 (purple arrow) and defect 2 (blue arrow) give different contrast in the\nSTM image in Fig. 6c, but they correspond to the same defect, please explain why or\nremove some of the arrows to avoid misunderstanding, since the defect assignment in\nSTM is quite difficult and heavily depends on the experience.\nResponse: We appreciate the reviewer for the constructive suggestion.\nSuch local contrast variations typically arise from substrate inhomogeneity in STM\nimaging, a common phenomenon for epitaxial vdW monolayer. To avoid potential\nmisunderstanding, we have removed the purple arrows in Fig. 6c and the gray arrows\nin Fig. 6e, as shown below (Fig. R8).\nWe have modified Figure 6 accordingly in the revised manuscript.\nFigure R8. The modified Figure 6 in the revised manuscript.\nReviewer #3 (Remarks to the Author):\nThe authors have added additional simulations, analysed a larger data volume and\nadded STM data which has improved their manuscript. I now recommend it for\npublication. It is unfortunate that the authors do not plan to include the image data\nuploaded to a free repository for the field to benefit from and I recommend they\nconsider doing this.\nResponse: We thank the reviewer for the positive assessment of our revisions and for\nthe recommendation for publication. We also appreciate the reviewer’s suggestion\nregarding the public deposition of the image data. We will certainly consider this option\nto maximize the utility of our work for the research community. Following the\nreviewer’s suggestion, we have uploaded the image datasets to the free figshare\nrepository."}]

Decision letter