Convection enhanced phase change composite fibers for advanced thermal management

Domain: neuroscience
Published: 2026-04-09
Editorial outcome: major_revision

Abstract
High heat fluxes challenge electronic thermal management, as phase change materials (PCMs) suffer from low heat transfer efficiency. In this work, we present a convection enhanced strategy using molten phase transport in phase change composites (PCCs). We fabricate porous fibers featuring interconnected core channels over 90% volume and a dense, conductive shell via nonsolvent induced phase separation (NIPS). Experimental and numerical analyses reveal that specific channel structures activate internal convection, contributing over 100% more than intrinsic thermal conduction. Optimized PCC fibers achieve a thermal conductivity of 1.05 W·m-1·K-1 with only 3 wt% additives, which increases to 2.48 W·m-1·K-1 upon melting, outperforming fibers with substantially higher intrinsic conductivity. Device-level demonstrations confirm the thermal buffering potential of our PCC fibers. Overall, this work highlights the vital role of thermal convection, which is often overlooked, in the design and evaluation of PCCs.

Introduction
The relentless drive towards miniaturization and increased power densities in modern electronics, fueled by advancements in 5G communication1, 2, artificial intelligence3, 4, and high-integration computing modules5, 6, has precipitated an escalating thermal management challenge that critically impacts device performance, operational stability, and overall longevity7, 8. While active cooling solutions such as fans and liquid cooling systems offer high heat dissipation capacities, their associated bulk, complexity, and power requirements limit their applicability in the burgeoning field of lightweight, energy-efficient smart electronics that demand passive or intermittent thermal buffering solutions9. Phase change materials (PCMs), by their substantial latent heat absorption during solid-liquid transitions, have emerged as highly promising candidates for thermal management to prevent sudden hotspots and regulate heat fluxes10, 11, 12, 13. Nevertheless, the practical use of PCMs is limited by their inherently low thermal conductivities of typically 0.1-0.5 W·m-1·K-1, a value that falls considerably short when fabricating thermal management materials14, 15, 16, 17.

The convection of fluid provides an effective way to transfer heat, which also promises great potential to solve the low thermal conductivity problem in phase change composites (PCCs). This approach has been validated by the success of advanced two-phase systems, such as heat pipes and vapor chambers18, 19. In these systems, the fluid convection driven by temperature differences transfers heat so effectively that their equivalent thermal conductivity reaches ultrahigh values of 5,000-100,000 W·m-1·K-1 through efficient convective heat transfer mechanisms in vapor-liquid phase change20, 21, 22. However, current designs of PCCs in thermal management usually focus on developing thermally conductive leak-proof matrices with paraffin-based PCMs23, 24, 25, 26. These approaches employ a matrix for heat transfer. Yet, they also confine the main heat transfer within the matrix because these matrices, usually in branched or networked geometries, increase flow resistance and significantly suppress the potential convective heat transfer in the molten fluidic phase of PCMs27, 28, 29. As a result, although porosity is widely presented in PCCs, it is typically treated as a storage volume rather than being systematically engineered as an active convective heat transfer unit30. An alternative approach that integrates thermally conductive pathways with thermal convection contributions is thus crucial to overcoming the bottleneck imposed by conventional filler-matrix designs.

Herein, we propose a convection-enhanced strategy for PCCs that improves heat transfer performance by activating molten-phase fluid transport. By combining the coaxial wet spinning method with the non-solvent-induced phase separation (NIPS)31 technique, we fabricate core-shell fibers with continuous internal fluid channels. The robust shell layer ensures mechanical integrity and leakage resistance, while facilitating efficient axial heat transfer, which enables the PCC fibers to achieve a thermal conductivity of 1.05 W m-1 K-1 with only 3 wt% conductive additives. Meanwhile, the highly porous fiber core (with a volume fraction of 90%) provides ample space for molten PCM flow and convection. Through precise control of the NIPS process, the internal channel diameter can be programmably tuned from 500 to 5000 nm, allowing for a systematic investigation of structure-dependent heat transfer behavior. Through a combination of experimental investigations and multiphysics simulations, we further demonstrated that specific channel configurations can markedly enhance thermal convection of the molten PCM, yielding an effective thermal conductivity enhancement exceeding 100%, with values increasing from 1.05 W m-1 K-1 to 2.48 W m-1 K-1. This finding reveals a previously underappreciated, yet pivotal, mechanism for enhancing the thermal performance of PCCs. Furthermore, both simulations and experiments show that this convection-enhanced mechanism is particularly effective for horizontally placed, low thermal conductivity, fiber-based PCC thermal management materials, which closely reflect practical application scenarios.

Finally, we demonstrated the advantages of using these fibers as effective thermal buffers for central processing unit (CPU), further underscoring their significant potential in advanced thermal management. Our work introduces a design paradigm for PCCs, emphasizing structural engineering of convection as an effective route to fully unlock the latent potential of PCCs for next-generation thermal regulation technologies.

Results
A major challenge for PCM composites is to achieve the balance of high PCM loading, sufficient mechanical strength32, enhanced thermal transport33, and reliable leakage resistance34. To address this multifaceted problem, we propose a hierarchical core-shell fiber structure with multiple functional domains (Fig 1a): (i) a continuous and interconnected pore network within the core that ensures uniform thermal contact and efficient heat uptake by the encapsulated PCM; (ii) a high internal void fraction that provides sufficient volume for PCM accommodation; and (iii) a mechanically reinforced aramid nanofiber/boron nitride nanosheets (ANF/BNNS) outer shell that offers structural stability, directional thermal pathways, and an effective barrier against liquid leakage.

Our initial focus was on the efficacy of the macro channels as a PCM host. Compared with conventional strategies for creating porous scaffolds, such as supercritical drying35 and freeze-drying36, our one-step coaxial wet spinning process (Fig 1b) enables direct formation of this hierarchical structure without post-processing. As illustrated in Fig 1c, once contact with the coagulation bath, the solvent in the core gel rapidly diffuses outward, triggering NIPS37, and generating radially expanding finger-like macropores31. In contrast, the ANF-rich shell rapidly solidifies due to its dense hydrogen-bonded network38, 39, producing a compact and mechanically robust sheath in which BNNSs exhibit orientational alignment and contribute to structural and interfacial stability40, 41.

Cross-sectional scanning electron microscopy (SEM) images (Fig 1d) verify the well-defined core-shell morphology, showing a uniform shell thickness and finger-like pore structure within the core. Notably, the core with the thermal conduction paths points from the periphery towards the center (Fig S1), suggesting promoted and structured heat transfer compared to the simple addition of thermally conductive fillers42. Computed Tomography (CT) in Fig 1e further revealed that these macropores form a continuous and tortuous channel network rather than isolated voids. Such interconnected pore pathways are essential for complete infiltration and subsequent mobility of molten PCM throughout the fiber. Brunauer-Emmett-Teller (BET) analysis (Fig S2) indicates a large internal surface area of 15.7 m2·g-1, suggesting large solid-liquid interfacial contact between PCM and the conductive skeleton, which is beneficial for their heat exchange43.

Mercury intrusion porosimetry (Fig 1f) shows that the pore diameter ranges from 500 nm to 10 µm, far exceeding the hydrodynamic radius of the Polyethylene glycol (PEG) molecule (PEG 2000, hydrodynamic radius 12.5 nm), indicating that molecular-scale confinement is negligible44. The experimentally measured porosity of 89% closely matches the theoretical estimate of 92%, confirming that the fiber possesses an almost ideally interconnected pore system45, capable of accommodating liquid PCM infiltration, retention, and migration46. Differential Scanning Calorimetry (DSC) analysis (Fig S3) and thermogravimetric analysis (TGA, Fig S4) indicate that the fibers exhibit a latent heat of ~118 J g-1, corresponding to a PEG loading of ~90 wt%. Collectively, these results confirm that the hierarchical core-shell structure offers high porosity, abundant interfacial area, structural stability, and excellent encapsulation capability, making it an effective host platform for phase-change materials.

We then emphasized the role of the ANF/BNNS composite shell in reinforcing and stabilizing the porous fiber. The stress-strain curves in Fig 2a show that the shell-free porous fiber (PF-5) exhibits a low tensile strength of only 0.92 MPa, whereas the porous fiber with the addition of the shell (PFS-5) increases the strength to 5.55 MPa. The fracture-surface SEM images (inset of Fig 2a) further illustrate this difference: PCF-5 shows a clean and flat cross-section characteristic of brittle fracture47, while PFS-5 undergoes substantial deformation before failure, indicating that the shell effectively dissipates mechanical energy and delays crack propagation48. The flexural properties were also investigated and shown in Fig 2b. PF-5 breaks readily upon bending, whereas PFS withstands bending and twisting without structural failure. Together, these results demonstrate that the core-shell structure significantly enhances both the toughness and flexibility of the fiber, ensuring reliable mechanical properties as a PCM host.

The shell also plays a crucial role in enhancing heat transfer. Transient electrothermal (TET) measurements49 in Fig 2c reveal that the thermal conductivity of PF-5 is only 0.01 W·m-1·K-1, whereas that of PFS-5 increases to 1.16 W·m-1·K-1 owing to the additional ANF/BNNS shell. PCM infiltration further improves thermal conductivity by increasing density and filling internal voids50, raising the conductivity to 0.46 W·m-1·K-1 for PIF-5 and to 1.22 W·m-1·K-1 for PIFS-5 (detailed data in Tables S3-S4). These results highlight that the shell contributes dominantly to the overall conduction pathway. Dynamic thermal performance was evaluated via thermal test (setup in Fig S5, thermal images in Fig 2d). At an equal distance from the heat source, the temperature of PIFS-5 reaches 65 °C after 200 s, whereas PIF-5 reaches only 61 °C (Fig 2e), indicating that the shell facilitates faster heat redistribution along the fiber.

The poor shape stability of liquefied PCM inevitably causes leakage51. Long-term thermal cycling (Fig 2f and 2g) illustrated the shell's crucial role in effectively encapsulating the PCM. After 100 heating-cooling cycles, PIF-5 loses 22.0% of its mass and 21.7% of its latent heat, and SEM images reveal voids left by leaked PCM (Fig 2f inset). In contrast, PIFS-5 retains 99.1% of its mass and 98.7% of its latent heat, and cross-sectional SEM confirms the absence of detectable PCM loss. These findings demonstrate that the core-shell structure effectively addresses the shortcomings of the porous host, resulting in substantial improvements in mechanical strength, thermal conductivity, and long-term thermal stability.

In PCC systems, the configuration of the channel structure plays a crucial role in the melting profile and heat transfer distribution28, 52. To build a structurally programmable platform, we systematically varied the BNNS content in the core dope to tailor the porous skeleton of the PIFS fibers. As shown in Fig 3a, owing to the strong structuring ability of 2D BNNS, we could regulate the fiber core’s structure by adjusting the BNNS content in the core spinning solution. The 2D BNNS shows a narrow lateral size distribution (Fig S6), and Energy Dispersive X-ray Spectroscopy (EDS) mapping confirms that they are homogeneously dispersed in the polymer matrix without forming large agglomerates (Fig S7-S10 and Tab. S5). This allows changes in morphology to be attributed to the BNNS loading rather than filler variability. Coaxial spinning produces an identical ~33 μm ANF/BNNS shell for all samples, and CT analysis shows that this shell occupies ~13% of the total fiber volume (Fig S11 and Tab. S6). This structural uniformity ensures that the observed differences among PFS-1 to PFS-7 arise only from variations in the core pore structure.

Cross-sectional SEM images of PFS-1 to PFS-7 (Fig 3b-i) reveal an evolution in core morphology with increasing BNNS content53. At low BNNS loading, the core contains sparse, highly branched finger-like macropores separated by thin walls, characteristic of NIPS-dominated demixing54. As the BNNS content increases, these fingers gradually become narrower and more closely spaced, while the skeleton thickens and forms a denser framework. At the highest BNNS loading (70 wt%), the core is transformed into a compact network55 with smaller pores. SEM cross-sections of the extreme cases PFS-0 and PFS-9 (Fig S12) further show that insufficient BNNS leads to hollow collapse due to a lack of mechanical support. In contrast, excessive BNNS causes binder-deficient collapse, thus defining a practical working window of 10-70 wt% BNNS for stable channel formation. CT and schematic models (Fig S13) confirm that the voids observed in the SEM are surface manifestations of a tortuous, interconnected channel network that permeates throughout the fiber core, rather than isolated cavities. This connectivity is a common structural feature across the entire PFS system and is essential for sufficient PCM access. Fourier Transform Infrared Spectroscopy (FTIR) spectra (Fig S14) show no new absorption bands upon mixing BNNS with PVB56, indicating that the evolution in pore structure is governed by physical structure modulation rather than chemical reactions57.

To mechanistically understand how BNNS loading influences the formation of the porous skeleton, we examined the phase separation dynamics of the core dope through rheology, optical images, and numerical modeling. Rheological curves (Fig S15) show that increasing BNNS content substantially elevates the viscosity and slows the relaxation of the polymer-rich phase58, implying a reduced effective diffusivity and a shift toward a viscosity-dominated demixing regime. Consistent with this trend, optical microscopy of the coagulated core dope (Fig S16) reveals that the characteristic NIPS morphology becomes progressively refined as BNNS loading increases.

Guided by these experimental results, we conducted a NIPS model based on a Cahn-Hilliard framework (details in Note S1). In the simulations, increasing BNNS content is represented by a higher viscosity of the polymer-rich phase and a reduced mobility of both the polymer and solvent. The resulting morphologies reproduce the experimentally observed transition from coarse, widely spaced finger-like pores to compact, finely segmented channel networks, establishing a direct mechanistic link between BNNS loading and the evolution of the porous skeleton (Fig 3j).

Despite the differences in pore structure, quantitative porosimetry shows that all PFS samples maintain a high porosity of ~90% (Fig S21). Mercury intrusion porosimetry reveals the decrease in average pore diameter from 5309 nm for PFS-1 to 502 nm for PFS-7. Even in PFS-7, the pores remain far larger than the hydrodynamic radius of PEG2000 (~12.5 nm), so confinement effects are negligible. This ensures that PCM can fully infiltrate the accessible pore volume. This is also confirmed by SEM images of PIFS cross sections (Fig S22) and TGA analysis (Fig S23), which show that all compositions achieve a similar PCM loading of ~90 wt% with no obvious unfilled voids. Additional mechanical tests and thermal-cycling experiments (Supplementary Fig S24-S27) demonstrate that the increased loading of BNNS not only strengthens as the pore network becomes denser but also retains its morphology, latent heat, and mass with greater than 98% retention after 100 cycles. After removing the PCM by water washing, the macrochannel framework remains intact (Fig S28), confirming the structural robustness of the porous skeleton. Overall, core BNNS loading provides control over the channel scale without compromising the overall thermal stability, which also provides a clear structural basis for the thermal behavior analysis that follows.

We then investigated how these structural differences influence heat transfer. Fig 4a reveals that the intrinsic thermal conductivity increases with core BNNS content59: for PFS samples, thermal conductivity rises from 0.85 to 1.49 W·m-1·K-1, and after PCM infusion, from 0.89 to 1.55 W·m-1·K-1. Density and latent heat remain nearly constant across all compositions (Fig 4b), ensuring that variations in thermal behavior arise from structural factors rather than changes in PCM loading.

However, dynamic IR thermal testing reveals a contrary trend. The thermal image of the PIFSs during the heating and cooling stages is given in Fig 4c. More quantitatively, Fig 4d shows the temperature-time curves of point A. During the initial heating stage, all the fibers exhibited similar temperature distributions due to the same thermal conductive shell. However, when the temperature rose into the phase change range, PIFS-3 demonstrated accelerated temperature rising despite its lower thermal conductivity, whereas PIFS-7 with higher thermal conductivity was slower than it. This mismatch does not occur in fibers without PCM infusion (Fig S29) or at temperatures below the melting point (Fig S30), indicating that the phenomenon originates from molten-phase transport rather than solid conduction. Analysis of the temperature-time profiles (Fig 4e) reveals two characteristic features: a low-temperature peak associated with the expansion of the PCM during melting, which enhances thermal contact with the solid framework60, and a higher-temperature peak attributed to convection within the molten PCM61. These signatures show that the divergence in temperature evolution arises from convection activated during the melting process.

To assess the influence of pore structure on molten-phase transport, we conducted water flow experiments on the corresponding PIF samples (Fig 4f and S31). PIF-1, which contains the largest and most open channels, exhibits the highest and fastest water flow, indicating the lowest hydraulic resistance. In contrast, PIF-7, with the smallest and most tortuous pores, shows the most significant resistance to flow. These opposing trends highlight a fundamental competition in the system: solid-phase conduction increases with BNNS loading, whereas molten-phase mobility decreases. Considering both effects, Fig S32 provides a schematic illustration of this competition between enhanced conduction and suppressed liquid mobility. At the balance point between these two tendencies, PIFS-3 offers sufficient solid conduction while still maintaining appreciable molten-phase mobility, resulting in the most favorable overall thermal performance.

To further quantify the convection contribution, we conducted TET measurements (Fig S33, data in Table S7) to record the thermal conductivity of the fibers at different temperatures. As shown in Fig 4h, the effective thermal conductivity remains nearly constant below the melting point but increases significantly above it, fitting the trends observed in the infrared (IR) thermal test. We attribute this enhancement to thermal convection within the molten PCM. Notably, PIF-1 and PIF-3 exhibit an increase exceeding 100% (Fig 4g), far beyond what can be accounted for by thermal conduction alone, thus account for the convective contribution. To isolate the role of channel structure from compositional effects, we also examined a control sample, PIFS-3R, which has the same content as PIFS-3 but lacks the engineered directional macrochannels. Unlike PIFS-3, PIFS-3R shows only a minimal increase in effective thermal conductivity above the melting point. This contrast confirms that the convection-assisted thermal enhancement arises from the engineered pore alignment rather than from material composition alone.

To understand the influence of channel structure on convective heat transfer, numerical simulations were performed with four geometries (Fig 5a) based on the characteristic features of the PIFS samples. All models share the same material composition and solid-phase thermal conductivity, with a fixed porosity of 90%. Variations among the geometries were introduced solely by adjusting the thickness and spacing of the internal framework to redistribute the fluid flow. Under these constraints, any differences in heat-transfer behavior originate exclusively from changes in internal convection rather than from variations in solid conduction. As the skeleton becomes progressively thicker and more densely packed from Geom-1 to Geom-4, the flow channels narrow and the resistance to fluid motion increases markedly (Fig S34). This trend is quantitatively reflected in the hydraulic diameter and Darcy permeability (Fig 5b), both of which decrease by orders of magnitude. Accordingly, Geom-1 represents a highly permeable structure, whereas Geom-4 corresponds to a strongly confined network with substantial internal flow resistance.

To visualize how these structural differences affect fluid motion, the development of buoyancy-driven convection during heating was analyzed. While heating up, the volume-average fluid velocity increases as natural-convection circulation establishes, reaches a maximum at ~2.5 s, and subsequently decays as the system approaches a quasi-steady state (Fig 5c). At 2.5 s, when convection is most intense, the velocity fields exhibit clear distinctions among the four geometries (Fig 5d). Geom-1 supports large-scale circulation loops spanning most of the pore volume, enabling efficient transport of hot fluid from the heated boundary toward the cold side (Fig S35). In contrast, as the skeleton becomes denser, recirculation is progressively restricted, and in Geom-4 the fluid remains nearly stagnant. To quantify these stagnant regions, “dead zones” in Fig 5e were defined as grid cells in which the local velocity was below 50% of the global average velocity. In such regions, heat transfer is primarily dominated by conduction, with convection providing some enhancements. At the characteristic time of maximum convection, the dead-zone fraction reaches 17.7% and 70.1% for Geom-1 and Geom-2, respectively. It then increases sharply to 98% for Geom-3 and remains nearly 100% throughout the entire process for Geom-4. This indicates that the denser structural converts the pore space into a less convective region.

These flow-field differences directly affect thermal performance. Fig 5f presents the effective thermal conductivity keff calculated from the total heat flux and temperature drop, together with a conduction-only reference case in which fluid motion is fully suppressed. All four geometries exhibit convective enhancement relative to pure conduction, but the magnitude of enhancement is strongly structure dependent. Geom-1 achieves the most pronounced increase in keff from 1.75 W·m-1·K-1 to 3.51 W·m-1·K-1, over 100 % enhancement. Geom-4, with highly confined channels and extensive dead zones, yields only a modest 10% enhancement. Notably, the evolution time windows of keff closely follow that of the fluid velocity, further confirming that the enhancement originates from fluid convection. Consistent trends are also observed in the thermal images and cold-side temperature-time curves (Fig 5g,h).

Considering the practical deployment of fiber-based thermal management components, we investigated how heat flux and gravity orientation influence buoyancy-driven convection within the porous network. Taking Geom-1 as a representative case, three representative cases were examined: gravity perpendicular to the imposed heat flux direction, gravity aligned with the heat flux, and gravity opposing the heat flux. Thermal images (Fig 5i) visualize how gravity orientation reorganizes the internal heat transport. When gravity is perpendicular to the heat flux, the temperature field reveals an extended recirculation pathway spanning the porous domain (Fig S36), enabling efficient lateral heat redistribution62, 63, 64. In contrast, when gravity is aligned with or opposes the heat flux, buoyancy-driven motion becomes localized, leading to stronger thermal stratification65, 66 and reduced utilization of the pore volume. These differences are also reflected quantitatively in the evolution of the cold-side temperature and the effective thermal conductivity (Fig 5j,k), both of which reach their highest values in the perpendicular case. We further conducted the temperature-dependent TET measurements of PIFS-1 under different orientations. It shows that convection enhancements of over 123% for the perpendicular direction, 103% for the gravity-aligned case, and only 47% for the opposing one (Fig 5l). These results indicate that horizontal placement of the structured fiber on device surfaces is more favorable for heat dissipation, which is also consistent with engineering intuition drawn from heat pipes and vapor chambers.

Beyond gravity orientation, parametric analyses further show that convective enhancement is most pronounced in systems with low intrinsic thermal conductivity of both the skeleton and the phase-change material (Fig S37). In such cases, the system tends to form a larger temperature gradient, and buoyancy-driven convection becomes the dominant pathway for thermal enhancement67. Together, these findings show the importance of considering heat convection when designing PCC for practical thermal management applications68.

In compact electronic devices, heat dissipation is often constrained by limited space, weak ambient convection, and the lack of room for bulky heat sinks69. Under such conditions, stabilizing device temperature and suppressing thermal fluctuations can be more critical than maximizing thermal conductivity alone70, 71. To evaluate the practical performance of thermal buffering in this case, a mat fabricated by PIFS-3 fibers was applied directly onto the CPU of a development board (Orange Pi) for device-level testing. The CPU was subjected to a sustained maximum-load stress test for 1200 s, while surface temperatures were monitored by infrared imaging. Internal CPU temperature and operating frequency were recorded from system logs, and additional thermocouples tracked the temperatures of component surfaces. The performance of our thermal buffer was compared with that of a 5 W·m-1·K-1 heat spreader, and with a case without any additional thermal management. Infrared images in Fig 6a reveal that, in the absence of thermal management, heat generated by the CPU progressively accumulates within the confined space and spreads across the entire printed circuit board (PCB), reaching temperatures of over 90 °C. Both the heat spreader and the thermal buffer effectively reduce the overall PCB temperature. Analysis of the CPU core temperature evolution in Fig 6b shows that the heat spreader maintains a lower CPU temperature during the initial stage of operation. However, the thermal buffer leads to a slow temperature rise and results in a lower CPU temperature after 600 s of full-load operation. As shown in Fig 6c, excessive CPU temperature triggers frequency throttling under high computational load. While both the heat spreader and the thermal buffer enable sustained operation compared to the no-add-on case, the CPU attached with the thermal buffer exhibits noticeably fewer throttling events after approximately 15 min, indicating improved long-term thermal stability.

This behavior is notable, given that the intrinsic thermal conductivity of the PIFS fibers forming the thermal buffer is below 2 W·m-1·K-1, which is significantly lower than that of the heat spreader. Temperature measurements collected by thermocouples placed on the CPU, WiFi, and memory module (Fig 6d-g) further show that the thermal-buffered case shows the lowest surface temperatures across all monitored components. This effect arises from the latent heat absorption of the phase-change material, which temporarily stores thermal energy and suppresses peak temperature propagation72. In contrast, under air convection-limited conditions, a highly conductive heat spreader can redistribute heat from the CPU to adjacent components, leading to unintended temperature increases elsewhere on the PCB. Numerical simulations were conducted further to examine this behavior (Fig 6h and S38 and Note. S2). The simulated temperature distributions are consistent with the experimental observations, confirming that thermal buffering more effectively limits temperature rise across the PCB, CPU and surrounding components in confined environments. Beyond electronic cooling, our PIFS fibers can also provide additional thermal-management functionalities. As shown in Fig S39 and Note. S3, our thermal storage fabrics can be rapidly “charged” under quick heating up, yet release heat slowly during cooling, thereby extending the thermal comfort window with improved temperature stability.

Methods
Materials
Raw hexagonal boron nitride (h-BN) with an average size of 5 μm and a purity of 99.5 % was procured from Eno High-Tech Material Development. Kevlar was acquired from IVA Carbon Fiber Materials Co. Chemicals including dimethyl sulfoxide (DMSO, purity ≥99.9%), potassium hydroxide (KOH, purity ≥90.0%), isopropyl alcohol (IPA, purity ≥99.5%), absolute ethanol (EA, purity ≥99.7%), dimethylacetamide (DMAc, purity ≥99.0%), and polyethylene glycol (PEG, chemically pure) MW2000 were sourced from Titan Scientific Co. Polyvinyl butyral (PVB, chemically pure) MOWITAL® SB 70 HH was obtained from Kuraray Co., Japan. The Millipore Milli-Q® Reference Ultrapure Water Purification System supplied deionized water with a resistance of 18.2 megaohms.

Preparation of boron nitride nanosheets
The synthesis of boron nitride nanosheets (BNNS) followed the protocols outlined in our previous studies73. A ball milling jar was typically loaded with 20 g of pristine boron nitride powder (PT110, 5 μm) and 50 g of Dimethylacetamide (DMAc). The mixture was filled with 300 g of large milling beads (10 mm) and 100 g of small milling beads (2 mm). The jars were locked and transferred to a planetary ball mill. The milling process started at 600 rpm for 24 hours. After milling, a white slurry was obtained, diluted to 10mg/ml, and initially centrifuged at 100 ×g for 30 minutes to remove incompletely exfoliated large flakes. The sediment was collected and dispersed to achieve a 10 wt% BNNS@DMAc dispersion.

Preparation of ANF dispersion
The dispersion process of ANF entails deprotonating PPTA fibers in alkaline, as introduced in previous studies74, 75. Briefly, 10 g of short-cut K69 Kevlar fibers were mixed with 10 g of KOH and then added to 480 g of DMSO. The mixture was sealed in a bottle and then shaken reciprocally at 50 °C, 150 rpm for 72 h in a shaker to obtain a transparent dark red viscous liquid, indicating that the deprotonation reaction has reached its endpoint76.

Preparation of PVB@DMAc solution
At room temperature, 10 g of PVB and 90 g of DMAc were magnetically stirred in a sealed container for 30 min. This process resulted in forming a transparent solution of 10 wt% PVB@DMAc.

Preparation of spinning solution
The obtained 10 wt% BNNS@DMAc dispersion and 2 wt% ANF@DMSO were mixed and diluted with DMSO for a spinning solution with a solids content of 1 wt% as the shell spinning solution. In this case, BNNS content in the shell is set at 70wt%. The core spinning solution has 10 wt% BNNS@DMAc dispersion and 10 wt% PVB/DMAc solution at different proportions. The content of BNNS varied at weight percentages of 0, 10, 30, 50, 70, and 90. All the mixing is carried out using a high-speed shear mixer to process the spinning solution at 10,000 rpm for 30 minutes. The precise value is determined by the TGA results depicted in Fig S8.

Preparation of fiber and PCC fiber
Fiber fabrication: Coaxial wet spinning was conducted with a 20 mL glass microsyringe on a syringe pump (LSP01-1A) fitted with a coaxial needle (core: 25 G, inner diameter 0.50 mm; shell: 18 G, inner diameter 0.86 mm). The core and shell feed rates were each 0.8 mL min-1. Spinning was performed at 24 °C and 25% RH into a rotating deionized-water coagulation bath (linear surface speed 30 cm s-1). As-spun filaments were rinsed overnight in deionized water to promote core fibrillation, collected on a drum, and dried at 60 °C for 120 min.

Shell-free porous fibers were prepared by uniaxial wet spinning of a single PVB/BNNS dope containing 50 wt% BNNS (inner channel only), followed by the same coagulation, water-soak, and drying steps. This control is denoted PF-5.

In all PFS-x samples, both shell and core contained BNNS. The shell dope was ANF/BNNS with a fixed BNNS content of 70 wt%. The core dope was PVB/BNNS with BNNS at 10, 30, 50, or 70 wt%, yielding PFS-1, PFS-3, PFS-5, and PFS-7, respectively.

PCC Fiber fabrication: The vacuum infusion method was used to fill the PCM. The completely dried fibers and PEG were placed in a long trough container in a vacuum drying oven at 55 °C. They were preheated for 2 hours until the PEG melted to a liquid state. The fibers were immersed in the liquid PEG and kept under vacuum for 24 hours. The fibers were removed from the liquid PEG after impregnation, placed between two filter papers, and kept in the oven at 55 °C for 2 hours to remove the PEG residue from the surface.

PF-5 after PEG infusion is denoted PIF-5. PFS-x after PEG infusion is denoted PIFS-x, where x matches the BNNS mass fraction in the PVB/BNNS core dope of the corresponding PFS-x.

Noted, PIFS-3R was prepared as a reference sample with the same composition as PIFS-3. Specifically, the core phase of PIFS-3 was first removed by washing with IPA. Subsequently, a molten mixture of PEG, PVB, and BNNS was refilled into the fiber core at the same composition ratio as that used in PIFS-3. In contrast to PIFS-3, the refilled core in PIFS-3R represents a randomly distributed microstructure without any deliberate orientation or structural design. As a result, PIFS-3R serves as a composition-matched control sample to isolate the effect of core structural organization on thermal transport performance.

Cyclic thermal tests were conducted to evaluate the thermal stability of the PIF and PIFS fibers. The PIF and PIFS fibers were sandwiched between two pieces of filter paper and loaded with a 50 g weight. The assembly was then placed on a heating pad, as shown in Fig S40. The heating pad's heating program was set to hold at 60 °C for 30 minutes and then cool naturally for 30 minutes, constituting one cycle.

Fabrication of PCM mats and PCM fabrics was accomplished through the assembly of PIFS. For PCM mats, PIFS bundles were uniformly laid flat within a mold to ensure surface smoothness. A 5 wt% fluorocarbon resin was applied as a binder, and the assembly was compressed under a constant load of 50 N overnight. Upon removal, excess material was trimmed to obtain the final mat. The fabrication process for PCM fabrics followed a similar approach to that used for anisotropic fiber mats77. PIFSs were continuously collected on a cylindrical roller, and a 0.5 wt% PVB adhesive was applied dropwise to form a fiber mat with an oriented layout.

Numerical Study
The numerical study was performed in COMSOL Multiphysics. For simulations focusing on structure-dependent convection enhancement, the simulations employed a core-shell rectangular domain with a length of 1000 μm as a two-dimensional axial cross-sectional model of the cylindrical fiber. The shell thickness was fixed at 30 μm, and the inner core diameter was set to 300 μm.To maintain a constant porosity of 0.9, different geometric models were constructed by systematically adjusting the number, length, and width of the skeleton structures within the core. These variations correspond to the distinct geometries investigated in this case. Meshing was performed using physics-controlled meshing with the element size set to extra fine. Specifically, the Geom-1 model consisted of 170 vertex elements, 5,198 boundary elements, and 213,486 domain elements, whereas the Geom-4 model contained 410 vertex elements, 43,293 boundary elements, and 433,669 domain elements (Fig S41).

For the multiphysics heat transfer simulations, all models were initialized at a uniform temperature of 25 °C. A constant temperature boundary condition of 70 °C was applied to drive heat transfer. Based on prior measurements of the compacted PPVB framework (Fig S42), the thermal conductivity of the solid framework was uniformly set to 2.00 W m-1 K-1 for all samples, while the outer shell was set to 10.00 W m-1 K-1. The PCM filling was assigned a thermal conductivity of 0.26 W m-1 K-1, an initial density of 1225 kg m⁻³, and a dynamic viscosity of 0.3 kg m-1 s-1. These properties were assumed to vary linearly once the temperature exceeded the phase transition temperature of 53 °C. The phase-change temperature interval was set to 3 °C. Gravitational effects were incorporated using the Boussinesq approximation. Transient behavior was analyzed using a time-dependent study with a convergence criterion of 1E-6. A time step of 0.001 s was employed, and the heat-transfer process was simulated over a total duration of 20 s to capture transient thermal dynamics. Note that Fig S43 presents the FTIR spectra of the fibers before and after vacuum impregnation with PCM. Without new absorption peaks, the spectra suggest that no chemical interactions occurred between the PCM and the fiber host. Based on this, no adsorption between the fluid and the wall is considered.

Additional simulation cases and corresponding parameters presented in this work are described in detail in the Supporting Information.

Characterization
The crystal structures of the samples were determined by X-ray diffraction (XRD) technique using a Bruker D8 ADVANCE X-ray diffractometer. Thermal gravimetric analysis (TGA) was conducted with a TA Q600 thermal analyzer to determine the BNNS concentration in each sample. A NICOLET iS50 FT-IR instrument provides information on the sample's chemical bonding. Field emission scanning electron microscopy (FESEM) images were obtained using the Quanta 400 FEG to characterize the microscopic morphology of the sample. X-ray computed tomography CT was performed using an X-Mech2000 system (Hangzhou Zhonglian Zhijian Technology Co., Ltd.). Continuous scanning mode was employed with an exposure time of 1 s per projection, yielding a total of 1800 projection images. The projection data were reconstructed using an algorithm with artifact correction, followed by three-dimensional rendering and analysis using Avizo software. The optical micrographs were captured using an optical microscope (Leica DM750M). Tensile stress-strain curves were recorded utilizing a tensile testing machine (SANS UTM5000). In the thermal demonstration, the NEC AVIO R500 infrared camera records the temperature distribution and changes in the sample. The sample's thermal management capabilities were evaluated using an Orange Pi development board (Orange Pi 3 LTS ). The samples' densities were measured using a Mettler analytical balance (MS204TS) with a drainage densitometer. The TET method was used to calculate the sample's thermal diffusion coefficient.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The paper submitted by Minsu Liu et al reported a work “Convection Enhanced Phase Change Composite Fibers for\nAdvanced Thermal Management”. This work reported a strategy to overcome this limitation by harnessing convective flow in\nmolten PCMs to enhance heat transfer in phase change composites (PCCs). The optimized PCC fiber achieves an apparent\nthermal conductivity of 1.05 W·m⁻¹·K⁻¹ with only 4 wt% conductive additives, which outperforming higher-conductivity\ncomposites lacking convective enhancement. This work underscores the importance of thermal convection in PCCs and\nintroduces a new design strategy that integrates internal channels to maximize heat transfer efficiency in thermal energy\nstorage systems.\nThe paper can be published in the journal after major revisions.\n1.The explanation for why \"specific channel structures (especially PFS-3) exhibit optimal performance\" is not deep enough\nand lacks quantification. It does not clearly elucidate how channel parameters (size, spacing, connectivity) affect convective\nheat transfer efficiency.\n2.My major concern is whether you can further demonstrate through experimental design why the channel structure of PIFS-\n3 (a specific channel network formed by a 10% BNNS concentration) can most effectively promote natural convection or\nminimize flow resistance. Is it because the channel size is more conducive to initiating convection? Or is it because better\nchannel connectivity reduces \"dead zones\" and makes the channel distribution more uniform?\n3.Can the formation reasons of different fiber structures (such as PIFS-1, PIFS-2, PIFS-3) be explained through simulation\ncalculations, such as velocity distribution within the channel, and further demonstrate the correlation mechanism between\ntheir heat transfer performance and structure?\n4.I believe it is necessary to further investigate the relationship between pressure drop and flow rate, or indirectly infer the\nflow pattern/viscosity effect by conducting detailed measurements under different directions/gravity conditions, in order to\nfurther explain the mechanism behind the formation of optimal fiber structures.\n5.The article lacks a comparison with data from traditional structures. It is advisable to prepare PCC samples with the same\nBNNS content but different fiber structures, and compare their thermal conductivity with that of PFS fibers.", "reviewer_2": "Manuscript number: NCOMMS-25-53359\nTitle: Convection Enhanced Phase Change Composite Fibers for Advanced Thermal Management\nComments:\nThe authors develop core-shell fibers with thermally conductive reinforcement. The fiber is thermally reinforced by BNNSs\nand filled with phase change materials, resulting in improved thermal conductivity. The design achieves a relatively high\nenergy storage density (118 J g-1) and enhanced heat diffusion (1.05 W m-1 K-1) by promoting synergistic conduction and\nlocal convection of molten PCM. In addition, the unique shell layer gives the fiber good mechanical properties. However, the\nmechanism of thermal conductivity enhancement is not well described. In fact, I think the thermal conductivity enhancement\nmainly originates from the addition of BNNSs rather than convective reinforcement. Further, the authors focused mainly on\nthe axial thermal conductivity of the fibers and neglected whether the radial thermal conductivity is consistent with the\napplication scenario of the fibers. Overall, the results and discussion in the manuscript have poor originality and significance\nof content. Thus, the manuscript does not fully comply with the standards of the Journal. I think it is not recommended for\npublication.\nMajor comments:\n1.The innovation of the manuscript is insufficient. Many literatures have already been published on phase change fibers.\nThe convective heat transfer enhancement mentioned in the article has not been deeply analyzed, and the essential\nrelationship between the designed structure and the enhanced convection has not been explained in detail. Furthermore,\nthe simulation model used is rather idealized and does not effectively highlight the advantages of the material’s structural\ndesign.\n2.The authors claimed that a phase change fiber with a thermally conductive reinforced shell layer is prepared, but the\npreparation process of the fiber is vaguely described, and the authors are requested to elaborate in detail how PF and PFS\nfibers are prepared respectively, and point out how the phase change material is injected.\n3.Although the manuscript describes the enhancement of convective heat transfer by molten PCM within the porous\nstructure, it lacks experimental or theoretical analyses that quantitatively distinguish the respective contributions of\nconvection and conduction. Also, this manuscript provides insufficient analysis of the phase change materials.\n4.The difference between PFS and PF is whether there is an external encapsulation layer or not, but there is no clear\ndistinction between the boundary layer in the SEM characterization, please provides the clear SEM images and add the\nvolume ratio relationship between the boundary layer and the internal encapsulation layer.\n5.The cloud diagram data in Fig2d shows that the heat transfer performance of PFS fiber is obviously better than that of PIF\nfiber, but PIF is better than PFS in the subsequent Fig2e temperature curve, and the data of the two diagrams do not\ncorrespond to each other, so the authors are requested to check the facts and explain the reasons.\n6.The authors suggested that the volume fraction of BNNSs affects the fiber morphology. Although the manuscript\nsystematically investigates the effects of varying BNNS content on fiber structure and thermal properties, the relationship\nbetween sample labels (such as PFS-5) and the actual BNNS loading is not immediately clear and requires readers to\ncross-reference multiple sections. Additionally, the manuscript does not directly provide a one-to-one correspondence\nbetween BNNS content and key performance parameters. It is recommended that authors should indicate the BNNS wt%\ndirectly on relevant figures to improve the clarity and comparability of the data. Please give the particle size distribution of\nBNNSs and further ESD characterization in Fig3 to show the specific distribution of each component in order to understand\nthe mechanism of its influence on the fiber structure.\n7.According to Fig4d, it can be found that the cloud diagram of the fiber shows a conical shape, which should be caused by\nthe leakage of PCM, while there is no leakage problem of PIFS fiber in the previous paper. The authors are requested to add\nwhat is the content of PIFS fibers chosen in the previous paper and explain the reason for the difference.\n8.The method of using infrared camera to record the CPU temperature in Fig6 leads to distortion of the data, which is due to\nthe phase change fibers blocking the transfer of thermal radiation, please add the real-time measurement of temperature\nthermocouples and compare the data with the thermocouple data.\n9.This work creates discrete, non-continuous macro-pores within the fiber core, which enhances local PCM mobility and\ninternal convection. However, the absence of fully interconnected or continuous channels may restrict the long-range flow of\nmolten PCM, potentially limiting the maximum achievable convective strength. While the authors systematically analyze the\neffects of these non-continuous channels, the manuscript does not provide a direct comparison between continuous and\nnon-continuous channel structures in terms of heat transfer performance. Further studies examining the impact of channel\nconnectivity on convective heat transfer would offer valuable guidance for future optimization.\n10.Although demonstrations were conducted for both CPU thermal management and\ntextile applications, the discussion of each scenario remains superficial, and the data presented is rather limited. It is recommended to select one application scenario and provide an in-depth analysis of it.\n11.The figure annotations in the manuscript are unclear, and some experimental data, such as thermal conductivity and\ntensile strength, appear multiple times in both the main text and the figures. It is recommended to standardize the data\npresentation and clarify any discrepancies."}, "author_response": "Response to Referees Letter\nDear EditorandReviewers:\nThank you for your valuable comments on our manuscript. We have read your\ncomments carefully and revised our manuscript accordingly.All comments have been\naddressed below in Blue. Revisions are shown in our revised manuscript, and\ncorresponding modifications are highlighted in Red.\nResponsetoEditor：\nWesincerely thank you for providing us with theopportunity to revise our manuscript.\nIn this major revision, we have made significant improvements to both the\nexperimental design and the manuscript content, addressing theconcerns raised bythe\nreviewers. We believe these revisions have strengthened the scientific quality and\nclarity ofour work.\nBelow,wesummaries themajor revisions and enhancements wehave made:\n1.QuantificationofConvection-StructureCorrelation\nIn response to the reviewers' concerns regarding the quantification of the\nstructure-performance relationship, we have introduced several significant updates to\nthemanuscript:\nWaterFlowTests:We added water flow tests to evaluate the hydraulic resistance and\nmobility of different channel structures. These tests quantify the influence of\nstructures on fluid flow. We demonstrate that fibers with larger channel (PFS-1)\nexhibit higher flow capacity, while denser structures with smaller channels (PFS-7)\nlimit flow. These findings are incorporated into the revised manuscript, providing a\nmore comprehensive understanding of how channel geometry impacts thermal\nperformance.\nTemperature-Dependent TET Measurements: We added temperature-dependent\nTETmeasurements to illustrate the contribution of convection to thermal conductivity.\nThe updated data show that convection plays an unignorable role in improving\nthermal conductivity when thePCMis aboveits meltingpoint.\nConvection Enhancement Ratios: To quantify the contribution of convection, we\nintroduce convection enhancement ratios for each fiber. We found that fibers with\nlarger channels (PIFS-1) show a more significant increase in effective thermal\nconductivity compared to thosewithdenser,smaller channels (PIFS-7).\n2.Updated Numerical Simulations forStructure-ConvectiveEnhancement\nIn response to reviewers' concerns, we refined our numerical simulations to\ndemonstrate further how the structural characteristics of the fibers contribute to their\nthermal performance.\nHydraulic Diameter and Darcy Permeability: We introduced hydraulic diameter\nand Darcy permeability into our numerical models to better quantify the effects of\npore size and channel spacing on flow resistance. The results show that larger\nhydraulic diameters and higher Darcy permeability correspond to lower flow\nresistance, afinding also supported byourwater flowresults.\nMultiphysics Simulations: We reconstructed a more realistic physical model for the\nsimulations, matching SEM and CT imaging results to capture the fiber geometry and\ninternal channel structure accurately. This ensures that the simulations closely align\nwithour experimental results.\nImpact on Thermal Performance: We examined how channel structure affects\nthermal performance by analysing dead zone proportions, fluid velocity, thermal\nconductivity, and cold-end temperature. Smaller channels resulted in higher dead\nzones, slower fluid velocities, and less efficient convection, leading to a greater\nreliance on conduction. Larger channels enhanced fluid flow, increased convection,\nand improved thermal conductivity,leading tomore efficient heat transfer.\nThese updates to the simulations provide a deeper understanding of the\nstructure-convection relationship and reinforce our experimental data, which show\nhowspecific channel structures enhance thermal performance.\n3.StrengthenedDescription ofFabrication andStructureFormation\nWe further clarified and expanded the description of the fabrication process and\nstructural design, with thefollowing key additions:\nBNNS size statistics and EDS mapping: To strengthen the evidence regarding filler\nquality and dispersion, we added TEM and AFM characterisations of the BNNS to\nidentify its lateral size and thickness distributions, confirming morphological\nconsistency for dispersion. We also included EDS of representative PFS samples to\nverify the spatially uniform distribution of BNNS within the porous framework\n(presented as qualitative evidence), which supports our mechanistic discussion of how\nBNNS loading influences pore formation and theresulting transport behavior.\nDeeper analysis of the NIPS process: To explain the origin of the distinct pore\nmorphologies, we conducted additional simulations of the NIPS process using the\nCahn-Hilliard-Darcy framework and attributed the structural transitions to viscosity\nchanges in the core dope related to BNNS loading. Accordingly, the revised\nmanuscript now includes rheological curves of the core spinning solutions, the\ncorresponding NIPS simulation results, and optical microscopy images of phase\nseparation. These data exhibit consistent trends and collectively substantiate the\nproposed mechanism forstructure formation.\nMicro-CT for 3D structural reconstruction and shell-consistency verification: We\nintroduced CT to reconstruct the internal structure, enabling direct assessment of the\ninner network distribution beyond what can be inferred from formal 2D SEM alone.\nBy combining CT-based statistics with calibrated SEM measurements, we further\ndetermined the shell thickness and shell volume fraction, thereby demonstrating the\nconsistency of thedense outershell across samples.\n4.Device-LevelValidation\nWe strengthened the logic link from our structure-related convection enhancement to\npractical device integration, and refined the application narrative, with the following\nkey updates:\nGravity/heat-flux orientation: As natural convection in molten PCM is\nbuoyancy-driven, its effectiveness depends on the relative orientation of gravity and\nheat flow. We therefore added orientation-dependent analyses (simulation and\nexperiment) to demonstrate how changing the direction of gravity/heat flux influences\nthe internal flow field and convective contribution, and to identify the favourable\nplacement fordevice-level use.\nMore rigorous electronics-level demonstration and validation: We expanded the\nelectronics thermal-buffer demo by adding real-time temperature measurements (e.g.,\nthermocouple and CPU internal logs) and complementary simulations, enabling a\nmore reliable evaluation of peak-temperature suppression, thermal stability, and\nsystem-level impacts under space-constrained, weak convection conditions relevant to\ncompact electronics.\n5. Re-conduction of Infrared thermal test and Resolution of the Previously\nAnomalousData\nTo strengthen the reliability of our IR thermography data and address the concerns\nraised by reviewer 2, we re-examined the original data, identified the sources of the\nanomalous observations, and re-conducted all the IR experiments under controlled\nconditions.Thespecific updates are as follows:\nIdentification and explanation of the anomalous IR results: By rechecking the\noriginal data-processing workflow, we determined that the apparent inconsistencies\noriginated from experimental processing artefacts. Specifically, (i) an incorrect\ntemperature scale was applied during image processing, which led to misleading\nvisual comparisons between samples; and (ii) automated processing introduced\ntimestamp/serial-number conflicts, resulting in unintended image-timemismatches.\nClarification ofthe conical/tail-end heating appearance in IRimages:Weclarified\nthat the conical or tail-end heating feature noted by reviewer 2 was attributable to\nheater geometry rather than PCM leakage. This was verified by repeating the\nexperiments using a rectangular heater to remove geometric influence. In addition, we\nconsidered micro-scale IR measurement artefacts (e.g., optical blurring and lateral\nthermal spreading). We therefore ensured that all temperatures were extracted from\nstrictlyconsistent, predefined locations alongthefiber axistoimprovecomparability.\nRe-conduction of all IR thermal tests: To ensure data integrity, we repeated all IR\nthermal tests under strictly controlled and consistent conditions and replaced the\ncorresponding figures inthe revised manuscript with new data.\n6.Clarity in Data Presentation andSample Labelling\nWe have made several adjustments to improve the clarity and readability of the\nmanuscript:\nFigure Annotations: We have updated all figure captions to provide more precise\nexplanations and eliminate ambiguities, especially concerning the thermal\nconductivity dataand channel configurations.\nStandardisation of Data Presentation: We revised the presentation of data, ensuring\nthat sample labels corresponding to core BNNS content are clearly indicated in the\nfigures.\n7.ClarificationofEnhanced Novelty\nWe have enhanced the discussion of the novel aspects of our work, especially in\nrelation toconvection and conduction inphase change compositefibers.\nNovelty and Innovation: We have stated that the novelty of this work lies in the\nstructural design that emphasises the role of convection-enhanced heat transfer, which\nhas been largely overlooked in previous PCC research. By engineering the internal\nchannels and using the fluid dynamics of the molten PCM, we have introduced a new\nmechanism for enhancing PCC heat transfer performance. This distinguishes our\nwork from traditional methods that rely onsimplefiller-based enhancements.\nWe believe these substantial revisions have significantly strengthened the scientific\nqualityof themanuscript.\nSummary ofRevisions:\nMain text:\nEmphasised thework's significance in theAbstract, Introduction, and Conclusion.\nResults& Discussion:\nFig.1e:Added CTresults to visualisechannel connectivity.\nFig.2d-e: Updated IR thermal test results.\nFig. 3j: Added phase separation simulation results to explain porous structure\nformation.\nFig.4c-e: Updated IR thermal test results.\nFig. 4f-h: Added water flow tests and temperature-dependent TET results to explain\noptimalperformance.\nFig. 5: Updated with new multiphysics simulations to verify structure-convection\ncorrelation.\nFig. 6: Updated with new device-level tests to verify application potential in thermal\nmanagement.\nText:Revised relevant descriptions throughout themanuscript.\nExperimentalSection:\nDetailed sample preparation methods and nomenclature.\nUpdated numerical simulationmethodology.\nAdded CTto characterisation methods.\nSupportingInformation:\nFig.S5: Updated thermal test setup.\nFig.S6:Added BNNS sizeanalysis.\nFig.S7-S10:Added EDSanalysis for PFS samples.\nFig.S11:Added shell thickness and volumefraction results.\nFig.S13:Added CTimages and reconstructed channel distribution for PFS.\nFig.S15:Added rheological curves for different BNNS loadings.\nFig.S16:Added optical micrographs ofphase separation.\nFig.S25: Updated IR results for PFS samples.\nFig.S26: Updated IR results for PIFS (<53°C).\nFig.S27:Added water flowexperiment setup.\nFig.S28:Added schematicillustration ofPIFS-3 showing the best performance\nFig.S29:Added temperature-dependentTETtest setup.\nFig.S30:Added forced convection analysis forfour geometries.\nFig.S31:Added velocity field data during heating.\nFig. S32: Added velocity fields for Geom-1 under different gravity/heat flow\ndirections.\nFig.S33:Added heatmaps of convection enhancement vs. skeleton/PCMconductivity.\nFig.S34:Added PCB and component temperature profiles in device simulation.\nFig.S35 & NoteS3:Relocated textileapplication results and descriptions here.\nFig.S37, S40 &Note S2: Updated mesh details and simulationparameters.\nNoteS1:Added NIPS simulationdetails.\nTableS3:Added calculated elemental compositionof PFSsamples\nTableS3:Added theshell fractionof PFSsamples.\nTable S3: Added detailed thermal test results under different temperatures and heat\nflux/Gdirections.\nPoint-to-pointresponsetoReviewers:\nReviewer #1 (Remarks to the Author): The paper submitted by Minsu Liu et al\nreported a work “Convection Enhanced Phase Change Composite Fibers for\nAdvanced Thermal Management”. This work reported a strategy to overcome this\nlimitation by harnessing convective flow in molten PCMs to enhance heat transfer in\nphase change composites (PCCs). The optimized PCC fiber achieves an apparent\nthermal conductivity of 1.05 W·m-1·K-1 with only 4 wt% conductive additives, which\noutperforming higher-conductivity composites lacking convective enhancement. This\nwork underscores theimportance ofthermal convection in PCCs andintroduces anew\ndesign strategy that integrates internal channels to maximize heat transfer efficiency\nin thermal energy storage systems. The paper can be published in the journal after\nmajorrevisions.\nWe sincerely thank the reviewer for the constructive comments and insightful\nsuggestions. Below,weprovide apoint-by-point response toeach comment.\n1. The explanation for why \"specific channel structures (especially PFS-3) exhibit\noptimal performance\" is not deep enough and lacks quantification. It does not clearly\nelucidate how channel parameters (size, spacing, connectivity) affect convective heat\ntransferefficiency.\nR1-1: We thank the reviewer for the request to deepen the quantitative understanding\nof the structure-performance relationship. We have systematically incorporated\nadditional experimental and numerical evidence to evaluate thermal\nconduction/convection and quantify their respective contributions across different\nchannel structures.\nFig.R1:(a)Thermalimagesandtemperature-timeandcurvesoffibersbeforeinfusedwithPCM\nheatingupbelow53 ℃;Thermaltransportandflowcharacterizationincluding(b)thermal\nconductivityand(c)convectionenhancementratioofPIFSsamplesatdifferenttemperatures;(d)\nsetupofthewaterflowtestandthecorrespondingexperimentalresults;and(e)cross-sectionalCT\nimagesandreconstructedsectionsofPFSsamplesusedtoillustratethepore-channeldistribution.\nNote:OwingtothesmalldimensionsofPIFSfibers,theinternalporechannelscannotbeclearly\nresolvedbyCT;therefore,PFSsampleswereusedtovisualisetheporedistribution.TheFigures\naboveareincludedwithintherevisedmanuscriptasFig.S26,Fig.4g,h,Fig.S27andFig.S13\nrespectively.\nFor thermal conduction, we conducted infrared thermal testing below the PCM\nmelting point (Fig. R1a) as well as the previous conductivity measurements (Table\nS1). In this conduction-dominated regime, performance follows the BNNS loading\ntrend (PIFS-7 > PIFS-5 > PIFS-3 > PIFS-1). However, during phase transition, TET\nmeasurements (Fig. R1b) reveal that the effective thermal conductivity follows a\ndifferent order: PIFS-3 > PIFS-7 > PIFS-5 > PIFS-1. To isolate the convective\ncontribution, we calculated the convection enhancement ratio (Fig. R1c). The results\nshow that while PIFS-1 has the lowest absolute conductivity, it exhibits the highest\nrelative convective enhancement, confirming that structural confinement in\nhigh-density channel networks, such as PIFS-7, strongly suppresses internal melt\nflow.\nAs direct insitu observation of micron-scale convection is technically challenging, we\nconducted water flow experiments as a manifestation of hydraulic resistance (Fig.\nR1d). The flow capability follows the order PIFS-1 > PIFS-3 > PIFS-5 > PIFS-7,\ncorrelating well with the observed convection enhancement trends. Furthermore, 3D\nCT reconstructions (Fig. R1e) confirm that all samples possess interconnected\nnetworks. This identifies channel size distribution, tortuosity, and hydraulic resistance\nas theprimary determinants of convective efficiency.\nIn the revised manuscript, we added sub-melting IR heating tests,\ntemperature-dependent TET test with quantified convection enhancement, water flow\ntests and CT characterization; The results show that heat transfer during phase\ntransition is primarily governed by structure-controlled internal convection. (Detailed\nshown in Revised Manuscript, Page 13,Line 12to Page 14,Line 5.)\nFig.R2.Comparisonoffourgeometricmodels,including(a)schematicillustrations,(b)hydraulic\ndiameterandDarcydiffusivity,(c)globalaveragefluidvelocity,(d)temperaturefielddistributions,\n(e)temperatureprofilesatthecoldend,and(f)globaleffectivethermalconductivitycurves.The\nFiguresaboveareincludedwithintherevisedmanuscriptinFig.4.\nTo provide a qualitative measure of these effects, we introduced hydraulic diameter\nand Darcy permeability into our simulations (Fig. R2a-b). The results show that as\nthe channel spacing decreases, the Darcy permeability drops by several orders of\nmagnitude, significantly increasing the flow resistance. We then quantified convective\nintensity using the volume-averaged velocity of the molten phase (Fig. R2c).\nStructures with larger Darcy permeability and higher hydraulic diameter maintain\nsuperior velocity fields and developed circulation. This is also evident in the faster\ncold-sidethermal response and enhanced effective thermal conductivity (Fig. R2d-f).\nIn the revised manuscript, we added simulations incorporating hydraulic diameter and\nDarcy permeability and quantified melt convection via volume-averaged velocity;\nthe results show that reduced channel spacing sharply lowers permeability, increases flow\nresistance, and suppresses molten-phase convection, thereby limiting effective\nthermal conductivity enhancement.(Detailed shown in Revised Manuscript, Page 15,\nLine 14to Page 16,Line 18.)\n2. My major concern is whether you can further demonstrate through experimental\ndesign why the channel structure of PIFS-3 (a specific channel network formed by a\n10% BNNS concentration) can most effectively promote natural convection or\nminimizeflow resistance. Is it because thechannel sizeis more conducive to initiating\nconvection? Or is it because better channel connectivity reduces \"dead zones\" and\nmakes thechannel distribution moreuniform?\nR1-2: We appreciate the reviewer’s request on the mechanism of the optimal\nperformance observed in PIFS-3. To elucidate this, we have added a schematic\nillustration (Fig. R3) to the revised manuscript, which shows the competing\nmechanisms ofthermal conduction and convective enhancement.\nFig.R3.SchematicillustrationexplainingthesuperioroverallperformanceofPIFS-3,wherethe\nconductionindexisnormalisedbasedonthetrendofthermalconductivity,theoverall\nperformanceisnormalisedfromthetemperatureprofilesobtainedbyinfraredthermal\nmeasurements,andthePCMmobilityisnormalisedaccordingtotheresultsofthewaterflow\ntests.TheFigureaboveisincludedwithintherevisedmanuscriptasFig.S28.\nOur analysis clarifies that the superior performance of PIFS-3 does not come from\nmaximising either convection or conduction alone, but rather from the trade-off\nbetween thesetwo modes.\nFor conduction-dominant regime: As detailed in R1-1, solid-state heat transfer\nincreases with BNNS loading (PIFS-7 > PIFS-5 > PIFS-3 > PIFS-1). For the\nconvection-dominant regime, conversely, fluid-phase transport capacity, governed by\nhydraulic diameter and permeability, follows an inverse trend (PIFS-1 > PIFS-3 >\nPIFS-5 > PIFS-7).\nTherefore, this structural balance enables PIFS-3 to effectively leverage both\nconductive pathways and convective heat flow,resulting in thehighest overall thermal\nperformance under dynamic phase-change conditions. (Detailed shown in Revised\nManuscript Page 15,Line 14to Page 16,Line 18.)\n3. Can the formation reasons of different fiber structures (such as PIFS-1, PIFS-2,\nPIFS-3) be explained through simulation calculations, such as velocity distribution\nwithin the channel, and further demonstrate the correlation mechanism between their\nheat transfer performance and structure?\nR1-3: We thank the reviewer for the question regarding the mechanism of structure\nformation.\nFig.R4.EffectofBNNScontentontheNIPSprocess,including(a)rheologicalcurvesofspinning\ndopeswithdifferentBNNScontents,(b)simulatedresultsoftheNIPSprocess,and(c)optical\nmicroscopyimagesoftheNIPSstructuresobtainedatdifferentBNNSconcentrations.TheFigures\naboveareincludedwithintherevisedmanuscriptasFig.S15,Fig.3kandFig.S16respectively.\nTo clarify the origin of different porous structure, we performed additional NIPS\nsimulations based on the Cahn-Hilliard-Darcy framework to examine the effect of\nBNNS loading on phase separation. Rheological measurements confirm that\nincreasing BNNS content significantly increases the viscosity of the core solution\n(Fig. R4a). In the simulations, this viscosity increase suppresses hydrodynamically\ndriven fingering instabilities, resulting in a transition from sparse, finger-like\nmacrovoids to denser and more uniform porous networks (Fig. R4b). The simulated\nmorphologies agree well with SEM and in situ optical microscopy observations (Fig.\nR4c).\nIn the revised manuscript, we added Cahn-Hilliard-Darcy NIPS simulations and\nrheology-supported morphology validation; the results show that increasing BNNS\nloading raises solution viscosity, suppresses fingering instabilities, and yields denser,\nmore uniform porous networks. (Detailed shown in Revised Manuscript, Page 10,\nLine 17to Line 32andSupplementary Information NoteS1).\nRegarding the structure-thermal performance relationship, we emphasise that\nstructure formation and heat transfer should correspond to different physical stages.\nStructure formation defines the geometric constraints for subsequent molten-phase\nflow, but itdoes not define the structural framework within which heat transfer occurs.\nAccordingly, we analysed heat transfer using an independent multiphysics model in\nwhich composition and porosity were fixed, and only the channel geometry was\nvaried, allowing theeffects of theseparameters to beevaluated in isolation.\n4. I believe it is necessary to further investigate the relationship between pressure\ndrop and flow rate, or indirectly infer the flow pattern/viscosity effect by conducting\ndetailed measurements under different directions/gravity conditions, in order to\nfurther explain themechanism behind theformation of optimalfiber structures.\nR1-4: We thank the reviewer for the comments on the convection mechanism under\ndifferent operating conditions.\nFig.R5.Forced-convectionmodelusedtoevaluatetheinfluenceofthefourgeometriesonfluid\ntransportandtocalculatetheDarcydiffusivity,including(a)thepressuredropasafunctionof\ninletvelocity(5E-5,1E-4,2E-4,4E-4,8E-4,and1.6E-3ms-1)andthevelocityfielddistributions\nataninletvelocityof5E-5ms-1for(b)Geom-1,(c)Geom-2,(d)Geom-3,and(e)Geom-4.Asthe\nskeletondensityandfeaturesizeincrease,theflowbecomesprogressivelyconfinedtonarrower\ngaps,resultinginsubstantiallyhigherflowresistance.TheFiguresaboveareincludedwithinthe\nrevisedmanuscriptasFig.S30.\nFirst, we should clarify that under the natural convection considered in this work,\nbuoyancy induced by temperature gradients drives fluid motion, and the pressure field\nprimarily serves as a constraint to satisfy continuity. In that case, a unified inlet-outlet\npressure-drop boundary condition cannot be prescribed. To address this limitation, we\nadd forced-convection analysis. Under controlled inlet velocities, pressure-velocity\nrelationships and velocity fields were used to evaluate the flow resistance associated\nwith different channel geometries quantitatively. As shown in Fig. R5a, wider\nchannels exhibit lower pressure drops, whereas narrower or more densely packed\nskeletons lead to higher pressure drops.Velocity contours further show that increasing\nskeleton density progressively localises flow and reduces overall flow accessibility,\nindicating suppressed convective potential (Fig. R5b). To enable quantitative\ncomparison, we extracted the Darcy permeability from forced-convection simulations,\nwhich serves as an indicator of structural flow resistance, as lower permeability\ncorresponds to higherresistance and weaker convection (Fig. R2b).\nFig.R6.ViscosityandtemperaturefielddistributionsillustratedusingGeom-1asanexample,\nincluding(a)theviscosityfield(Pa·s)and(b)thetemperaturefield(°C),whichexhibitnearly\nidenticalspatialdistributions.\nRegarding the flow-pattern and viscosity effects, viscosity variation with temperature\nwas incorporated into the non-isothermal multiphysics simulations using a linear\napproximation,\n(  ) =     [1−  (  −     )]\nWhere is the viscosity at the reference temperature , and is the temperature\nμref Tref α\nsensitivity coefficient. As viscosity variations within the phase-transition temperature\nwindoware spatially coupled tothe temperature field (Fig. R6).\nIn the revised manuscript, we primarily add velocity fields and derived metrics, such\nas average velocity and dead zone fraction, as clearer descriptors of flow behaviour.\n(Detailed shownin Revised Manuscript, Page 15,Line 28to Page 16,Line 18.)\nFig.R7.Effectofheat-fluxandgravityorientationsillustratedusingGeom-1asanexample,\nincluding(a)temperaturefielddistributionsat2.5sunderdifferentrelativeorientationsofheat\nfluxandgravity,(b)velocityfielddistributionsatdifferenttimepointsforthecorresponding\norientations,(c)temperatureprofilesatthecoldend,(d)globaleffectivethermalconductivity,\nand(e)experimentallymeasuredthermalconductivityofPIFS-1underdifferentheat-fluxandgravity\norientations.TheFiguresaboveareincludedwithintherevisedmanuscriptasFig.4andFig.S32\nrespectively.\nWe agree that the relative orientation of gravity and heat flux is critical for natural\nconvection. Accordingly, Geom-1 was selected as a representative case to examine\nthermal behavior under different heating directions. Three configurations were\nconsidered: gravity perpendicular to the applied heat flux, parallel to it, and opposite\nto it. Thermal images (Fig. R7a) reveal that when gravity is perpendicular to the heat\nflux, buoyancy-driven recirculation more effectively spans the porous domain,\npromoting lateral heat redistribution (Fig. R7b). In contrast, when gravity is aligned\nwith or opposite to the heat flux, convection becomes localised, and thermal\nstratification intensifies. These trends are quantitatively reflected in the cold-side\ntemperature and effective thermal conductivity (Fig. R7c,d), both of which show the\nhighest in the perpendicular configuration. Orientation-dependent TET measurements\nfurther confirm this behaviour, convection enhancement ratios show 123%\n(perpendicular), 103% (aligned), and 47% (opposed) (Fig. R7e), indicating that\nhorizontally placing structured fibers on device surfaces is more favourable for heat\ntransfer. (Detailed shown in Revised Manuscript, Page 16, Line 19 to Page 17, Line\n6.)\nIn summary, forced-convection analysis was used to characterise the intrinsic flow\nresistance of different structures quantitatively. At the same time, natural-convection\nsimulations and orientation-dependent experiments confirm their roles in\nbuoyancy-driven circulation, cold-side thermal response, and enhancement of\neffectivethermal conductivity.\n5. The article lacks a comparison with data from traditional structures. It is advisable\nto prepare PCC samples with the same BNNS content but different fiber structures,\nand compare their thermal conductivity with that ofPFS fibers.\nR1-5:Wethank thereviewer for thesuggestionregarding control experiments.\nFig.R8.StructuralandthermalcharacterisationofPIFS-3R,including(a)across-sectionalSEM\nimage(scalebar100 μm),(b)thermalconductivity,and(c)convectionenhancementratioat\ndifferenttemperatures.Note:PIFS-3RwaspreparedbyuniformlydispersingmoltenPCMwith\nBNNSandPVB,followedbyvacuuminfiltrationintotheANF/BNNShollowshell;theinternal\nstructureisthereforeregardedasarandomlydistributed,non-engineeredstructure.\nTo clearly distinguish between composition effects and structural effects, we prepared\nacontrol sample, denoted as PIFS-3R.This sample has the same BNNS content, PCM,\nand PVB content as PIFS-3, but features a non-engineered porous structure. (Fig.\nR8a). Thermal measurements show that, although PIFS-3R exhibits a solid-state\nthermal conductivity comparable to that of PIFS-3 below the phase transition\ntemperature, its effective thermal conductivity increases only marginally within the\nphase transition regime (Fig. R8b, c). These control results demonstrate that, when\nmaterial composition and PCM content are held constant, engineered channel\nstructures are crucial for activating convection in molten PCM and achieving\nsignificant heat transfer enhancement. (Detailed shown in Revised Manuscript, Page\n13,Line 31to Page 14,Line 5.)\nReviewer #2 (Remarks to the Author): Manuscript number: NCOMMS-25-53359\nTitle: Convection Enhanced Phase Change Composite Fibers for Advanced Thermal\nManagement Comments: The authors develop core-shell fibers with thermally\nconductive reinforcement. The fiber is thermally reinforced by BNNSs and filled with\nphase change materials, resulting in improved thermal conductivity. The design\nachieves a relatively high energy storage density (118 J g-1) and enhanced heat\ndiffusion (1.05Wm-1 K-1) by promoting synergistic conduction and local convection\nof molten PCM. In addition, the unique shell layer gives the fiber good mechanical\nproperties. However, the mechanism of thermal conductivity enhancement is not well\ndescribed. In fact, I think the thermal conductivity enhancement mainly originates\nfrom the addition of BNNSs rather than convective reinforcement. Further, the\nauthors focused mainly on the axial thermal conductivity of the fibers and neglected\nwhether the radial thermal conductivity is consistent with the application scenario of\nthe fibers. Overall, the results and discussion in the manuscript have poor originality\nand significance of content. Thus, the manuscript does not fully comply with the\nstandards of the Journal. I think it is not recommended for publication. Major\ncomments:\nWe thank the reviewer for their careful evaluation of our manuscript and for the\ncritical questions regarding the mechanism, originality, and relevance to the\napplication of the proposed design. These comments were crucial for enhancing both\ntheclarity and significance ofthiswork.\nTo clarify the mechanism, we compared PIFS-3 with a control sample (PIFS-3R) as\nmentioned in R1-5. While their solid-state conductivities are identical, only PIFS-3\nexhibits a sharp thermal enhancement in the molten regime, whereas PIFS-3R shows\nno lower enhancement (Fig. R8). This proves that the performance gain originates\nfrom structure-driven convection rather than simply theaddition ofconductive fillers.\nFig.R9.HeatmapillustratingtheinfluenceofthethermalconductivitiesofthePCMandthe\nskeletononconvectionenhancement,showingthattheconvectionenhancementismost\npronouncedwhenboththeskeletonandPCMexhibitrelativelylowthermalconductivities.The\nFigureaboveisincludedwithintherevisedmanuscriptasFig.S33.\nBeyond this comparison, we further take thermal conductive filler loading as a\ntunable parameter in the conductive framework and performed a cross-analysis of\nconvection enhancement under different frameworks and PCM thermal conductivities\n(Fig. R9). The results show that the convection enhancement effect is most\npronounced in systems with relatively low intrinsic thermal conductivity, which is\nhighly relevant for practical PCC thermal management applications where material\nconductivity is often limited. (Detailed shown in Revised Manuscript, Page 17, Line\n7-13.)\nFig.R10.Heat-transfermodesproposedinourpreviouswork,including(a)anisotropicthermal\ntransportinfiber-basedmaterialsand(b)multidirectionalheattransferinassembledstructures.\nRegarding directions relevant to fiber-based applications, this issue has been\ndiscussed in our previous studies1(Fig. R10). Fiber structures can be regarded as a\ncombined system of a thermal interface material and a heat spreader, in which axial\nheat transport promotes heat spreading along the fiber direction. In contrast,\ncircumferential heat transport promotes heat transfer from the heat source along the\nthickness direction. In this work, the ANF/BNNS shell is designed to provide this\nanisotropic heat transfer behaviour. This anisotropy leads to non-uniform temperature\ndistributions within the fiber cross-section, which are physically relevant to\nbuoyancy-driven density variations and internal circulation in molten PCM. We\nfurther investigated the thermal behaviour of the fibers under different\nheat-flux/gravity orientations, as discussed in R1-4. We observed that, similar to heat\npipes and vapour chambers, placing the fibers horizontally results in more favourable\nheat transfer performance. This also highlights the practical relevance of the proposed\nfiber design.\nThe originality of this work lies in establishing a structure-driven\nconduction-convection paradigm that transforms the internal structure from a passive\ncarrier into an active regulator of molten-phase dynamics. By quantifying this\ntransition, we move beyond conventional filler-based strategies to provide a new\nsolution for PCC thermal enhancement. A detailed discussion of this distinction is\nprovided inourlater response R2-1.\n1. The innovation of the manuscript is insufficient. Many literatures have already\nbeen published on phase change fibers. The convective heat transfer enhancement\nmentioned in the article has not been deeply analyzed, and the essential relationship\nbetween thedesigned structure and theenhanced convection has not been explained in\ndetail. Furthermore, the simulation model used is rather idealized and does not\neffectively highlight theadvantages ofthematerial’s structural design.\nR2-1: We thank the reviewer for the comments on originality, mechanistic depth, and\nmodelling.\nRegarding originality, we emphasise that the key contribution of this work is not\nmerely the synthesis of a new PCC fiber, but the establishment of a structure-driven\nconduction-convection synergistic framework for PCC. Conventional research has\nfocused almost exclusively on enhancing static conduction through increased filler\nloading, which often occurs at the expense of energy density. Our work identifies\nmolten-phase convection as a critical, yet previously overlooked, degree of freedom\nin fiber-based systems. We demonstrate, both experimentally and theoretically, that\nthe internal channel structure serves as an active regulator of fluid dynamics rather\nthan just a passive carrier. Importantly, we show that convection can contribute\ncomparably to, oreven exceed, intrinsicconduction whilerequiring minimal additives.\nThis shifts the design focus of PCCs from material composition alone to\nstructure-enabled transport efficiency, providing a practical guideline for engineering\nnext-generation thermal storage materials where structural control of molten-phase\ntransport serves as a primary performance lever.\nRegarding the concern that convective heat transfer enhancement has not been\nsufficiently analysed, we note that this issue has been systematically and\nquantitatively addressed in our response R1-1. Briefly, the key conclusion is that\nconvective enhancement in our system is structure-dependent rather than\ncomposition-dependent and is governed by channel geometry through its effect on\nflowresistance and molten-phasemobility.\nRegarding the concern that the simulation model is idealised, we have revised the\nmodelling framework to better reflect the real structures by reconstructing\nrepresentative geometries based on CT and SEM characterisation (Fig. R1e). To\nenable quantitative comparison among different structures, we introduced hydraulic\ndiameter and Darcy permeability to characterise structural flow resistance. In addition,\nmetrics such as the dead-zone fraction and global volume-averaged fluid velocity\nwere employed to quantify convective behaviour in the simulations. Importantly,\ntemperature-dependent TET measurements exhibit trends consistent with the\nsimulation results, indicating that the revised model captures the essential\nstructure-convection relationship observed experimentally.\n2. The authors claimed that a phase change fiber with a thermally conductive\nreinforced shell layer is prepared, but the preparation process of the fiber is vaguely\ndescribed, and the authors are requested to elaborate in detail how PF and PFS fibers\nare prepared respectively and point outhow thephase changematerial is injected.\nR2-2: We thank the reviewer for pointing out the need to clarify the fabrication\nprocess.\nIn the revised manuscript, we have revised the Experimental section to distinguish the\npreparation of PF and PFS fibers clearly and to describe the PCM loading procedure\nexplicitly. (Detailed shown in Revised Manuscript, Page 22, Line 22-29, Page23,\nLine7-17.)\n3.Although the manuscript describes the enhancement of convective heat transfer by\nmolten PCM within the porous structure, it lacks experimental or theoretical analyses\nthat quantitatively distinguish the respective contributions of convection and\nconduction. Additionally, this manuscript provides an insufficient analysis of phase\nchange materials.\nR2-3:Wethank thereviewer for raising this critical point.\nThe quantitative distinction between conduction and convection has been\nsystematically addressed in ourresponse to R1-1 and above.\nRegarding the analysis of the phase change materials, the original manuscript already\nprovided fundamental characterisation of the PCCs in the Supporting Information,\nincluding DSC, TGA, and thermal cycling tests, to establish the basic thermal\nproperties, stability,and reversibility ofthePCM.\nIn the revised manuscript, we further added temperature-dependent thermal\nmeasurements during the phase transition, together with corresponding simulations, to\ndirectly correlate the melting behaviour of the phase change material with the\nobserved thermal performance. (Detailed shown in Revised Manuscript, Page 13,\nLine 24to Page 17,Line 13)\n4. The difference between PFS and PF is whether there is an external encapsulation layer or not, but there is no clear\ndistinction between the boundary layer in the SEM characterization, please provides the clear SEM images and add the\nvolume ratio relationship between theboundary layer and theinternal encapsulation layer.\nR2-4: We thank the reviewer for pointing out the need for a more apparent structural\ndistinctionbetween PFand PFSfibers.\nFig.R11:SEMandCTcharacterizationofPFSsamples,includingSEMimagesof(a)PFS-1,\n(b)PFS-3,(c)PFS-5,and(d)PFS-7,wheretheshellthicknessishighlightedinyellow(scalebar:100\nμm);radialCTcross-sectionalimagesof(e)PFS-1,(f)PFS-3,(g)PFS-5,and(h)PFS-7,inwhich\ntheredregionin(h)correspondstotheholderandwasexcludedfrommodelingandcalculations;\nand(i-l)binarizedmaskimagesoftheshellregionobtainedaftermodelreconstruction(scalebar:\n100 μm);(m)Theshellfractionofeachsamplewascalculatedfrom100selectedcross-sectional\nmasksandremainsatapproximately13%,ensuringconsistentshellcontributionsinsubsequent\nanalyses.TheFiguresaboveareincludedwithintherevisedmanuscriptasFig.S11.\nIn the original manuscript, SEM images of the outer shell region were already\nprovided. In the revised manuscript, we have added explicit thickness information\nbased on these SEM images to further clarify the shell layer.Toprovide a quantitative\ndescription, we combined calibrated SEM measurements with statistical analysis from\nCT to estimate the volume fraction of the shell layer in PIFS fibers. The results show\nthat the dense outer shell occupies approximately 13% of the total fiber volume(Fig.\nR11). This consistency forms the basis for our subsequent analysis of\nstructure-property relationships inthemanuscript.\nIn the revised manuscript, we added calibrated SEM and CT statistical quantification\nof the shell layer; the results show that the dense outer shell accounts for ~13% of the\ntotalfiber volume(Detailed shown inRevised Manuscript, Page 9,Line 19-23)\n5.The cloud diagram data in Fig2d shows that the heat transfer performance of PFS\nfiber is obviously better than that of PIF fiber, but PIF is better than PFS in the\nsubsequent Fig. 2e temperature curve, and the data of the two diagrams do not\ncorrespond to each other, so the authors are requested to check the facts and explain\nthereasons.\nR-5:Wethank the reviewer for carefully examining the figures and bringing this issue\ntoour attention.\nFig.R12:Clarificationonthermalimagesdiscrepancies.(a)Originaldatasetscontainingerrors,\nspecificallythePIFthermalimageswithanincorrectscalebar(incorrectlysetto25to80 °C\ninsteadoftheintended25to100 °C.(b)Correctedthermalimageswiththeproperscalebarrange,\n25to100 °C.Theredboxindicatesaspatialmisalignmentresultingfromtheoverlapof\ntimestampsandserialnumbersduringautomatedimageprocessing.Wehavere-conductedall\ninfraredthermalteststoensuredataconsistency.Theseresultsareintendedforvalidationpurposes\nandarenotintegratedintotherevisedmanuscript.\nUpon rechecking the original data, we found that the apparent inconsistency between\nFig. 2d and Fig. 2e originated from an incorrect scale bar used during image\nprocessing. In addition, we identified other inconsistencies introduced during\nautomated batch processing, where conflicts between time stamps and file numbering\nled to unintended image mismatches (Fig. R12).\nTo fully resolve this issue, we repeated all infrared thermal measurements and\nreprocessed the data manually to ensure strict consistency between experimental\nconditions, time sequences, and figure labelling. All corresponding figures have been\nupdated accordingly in the revised manuscript. (Fig. 2d,e, Fig. 4c-e, Fig. S5, Fig.\nS25-S26.)\n6.The authors suggested that the volume fraction of BNNSs affects the fiber\nmorphology. Although the manuscript systematically investigates the effects of\nvarying BNNS content on fiber structure and thermal properties, the relationship\nbetween sample labels (such as PFS-5) and the actual BNNS loading is not\nimmediately clear and requires readers to cross-reference multiple sections.\nAdditionally, the manuscript does not directly provide a one-to-one correspondence\nbetween BNNS content and key performance parameters. It is recommended that\nauthors should indicate the BNNS wt% directly on relevant Fig.s to improve the\nclarity and comparability of the data. Please give the particle size distribution of\nBNNSs and further ESD characterization in Fig3 to show the specific distribution of\neach component in order to understand the mechanism of its influence on the fiber\nstructure.\nR2-6: We thank the reviewer for the suggestion regarding sample labelling and\ncompositionalclarity.\nIn the revised manuscript, we have standardised the sample nomenclature to reflect\nthe BNNS content in the fiber core. Specifically, samples previously denoted as PF\nand PIFS are now renamed as PF-5 and PIFS-5, respectively, to ensure that all sample\nnames consistently indicate differences in core BNNS loading. Since the shell\ncomposition remains identical across all samples, the core BNNS content is used as\nthedefining parameter for sampleidentification.\nAdditionally, we have explicitly annotated the core BNNS content alongside sample\nnames to enhance clarity and comparability. (Detailed shown in Revised Manuscript,\nFig.4a,b)\nRegarding the influence of BNNS on fiber structure, this relationship has been\nsystematically discussed in R1-3. Briefly, increasing BNNS content raises the\nviscosity of the core solution during NIPS, which suppresses hydrodynamically\ndriven phase separation andleads to denser,more uniform porous structures.\nTo further characterise the BNNS used in this study, we have added TEM and AFM\nimages to show the lateral size distribution and thickness uniformity of the BNNS.\n(Fig. S6 in the revised Supporting Information) In addition, EDS was performed to\ndemonstrate the homogeneous distribution of BNNS within the polymer matrix.\nNoted that we were informed that EDS should not be used for quantitative\ncompositional analysis in our case, because the atomic numbers of B, C, and N are\nclose and their X-ray signals strongly overlap, which limits the quantitative reliability.\nAccordingly, the EDS results are presented as qualitative evidence only and are\nincluded in theFig. S7-S10 in therevised SupportingInformation.\n7.According to Fig4d, it can be found that the cloud diagram of the fiber shows a\nconical shape, which should be caused by the leakage of PCM, while there is no\nleakage problem of PIFS fiber in the previous paper. The authors are requested to add\nwhat is the content of PIFS fibers chosen in the previous paper and explain the reason\nforthe difference.\nR2-7: We thank the reviewer for the careful observation regarding the conical thermal\ndistribution and the apparent heating region that clearly exceeds the fiber width near\nthetail end.\nWehave carefully rechecked the infrared images and confirmed that this phenomenon\nis caused bytheheater background, rather than leakage of thephase change material.\nFigureR13.Optimizationofheatmapconsistencythroughheatsourcegeometricadjustment.(a)\nInfraredthermalimageofthegraphenefiber.(b)Infraredthermalimageunderacircularheat\nsource,wherethetailoverflowregionrepresentsthesourceedges.(c)Therevisedconfiguration\nusingarectangularheatsource,ensuringimprovededgeconsistency.\nFirst, we note that a conical or tapered thermal distribution at the fiber end is a\ncommon phenomenon in fiber thermal measurements. Similar features have also been\nreported in previous studies ongraphene fibers2(Fig. R13a).\nRegarding the pronounced heating region exceeding the fiber diameter near the tail\nend, we determined that it originates from the geometry of the circular heater used in\nthe original infrared measurements. As stated in the original supporting information\nFig. S4, a circular heating element was employed, whose coverage partially exceeded\nthe fiber width, leading to the prominent region observed in the extracted thermal\nimages (Fig. R13b). To eliminate this ambiguity, we repeated all infrared thermal\nmeasurements using a rectangular heater. Under these conditions, no abnormal\ntail-end heating feature was observed (Fig. R13c), confirming that the previous\nobservation was caused by the heater geometry rather than PCM leakage. All\ncorresponding infrared data have been updated accordingly.Additionally, as stated in\nthe original manuscript, all samples underwent thermal cycling tests. Both mass\nchange and enthalpy retention measurements show negligible variation after repeated\ncycles, indicating that the dense ANF/BNNS shell provides effective encapsulation\nand excellent leakage resistance.\nFurthermore, we acknowledge that IR imaging of micro-scale fibers may be\ninherently susceptible to optical blurring (the point-spread function effect) and lateral\nheat diffusion3, 4. These phenomena can induce a \" thermal broadening\" effect,\ncreating the illusion of heat spreading beyond the physical boundaries of the fiber.\nSuch effects are intrinsic to high-resolution IR thermography at small length scales\nand may persist under specific experimental conditions. To ensure data fidelity, we\nconducted all comparative measurements within the same batch under identical\nenvironmental parameters. Additionally, to mitigate the impact of boundary artefacts,\nall temperature data points were consistently extracted from fixed positions along the\nfiber’s central axis, ensuring a rigorous and reproducible quantification of the intrinsic\nthermal response.\n8. The method of using infrared camera to record the CPU temperature in Fig6 leads\nto distortion of the data, which is due to the phase change fibers blocking the transfer\nof thermal radiation, please add the real-time measurement of temperature\nthermocouples and compare thedata with thethermocoupledata.\nR2-8: We thank the reviewer for pointing out the potential limitations of using\ninfrared thermography to record CPUtemperature.\nFollowing the reviewer’s suggestion, we have added real-time temperature\nmeasurements using thermocouples directly attached to the CPU and other relevant\nmodules. In addition, considering that thermal management materials may partially\nblock thermal radiation, we have conducted further numerical simulations to illustrate\nthe temperature distribution of the PCB in device-level thermal management\napplications. (Detailed shown in Revised Manuscript, Page 18, Line 3 to Page 19,\nLine 13.)\n9. This work creates discrete, non-continuous macro-pores within the fiber core,\nwhich enhances local PCM mobility and internal convection. However,the absence of\nfully interconnected or continuous channels may restrict the long-range flow of\nmolten PCM, potentially limiting the maximum achievable convective strength.\nWhile the authors systematically analyze the effects of these non-continuous channels,\nthe manuscript does not provide a direct comparison between continuous and\nnon-continuous channel structures in terms of heat transfer performance. Further\nstudies examining the impact of channel connectivity on convective heat transfer\nwould offervaluable guidance forfuture optimization.\nR2-9: We thank the reviewer for their comments regarding channel connectivity and\nitspotential impact onconvective intensity.\nThisis acritical pointfor understanding theheat transfer enhancement mechanism.To\naddress this concern, we have incorporated multi-dimensional evidence into the\nrevised manuscript to clarify the interconnected nature of the channels and our\nanalytical approach toconvection.\nFirst, CT reconstructions provide direct visual evidence that the internal channels\nwithin the PIFS fibers form a spatially interconnected network. From a mechanistic\nperspective, these channels originate from the phase separation induced by\nsolvent-nonsolvent exchange during the NIPS process. Inherently, these are\ncontinuous solvent migration paths rather than isolated or closed cells. This structural\ncontinuity is further validated by our water flow experiments, where samples\nexhibited distinct fluid transport capacities under identical conditions, confirming that\nthe channels are functionally open and accessible for fluid flow. (Fig. R1d, e)\nFurthermore, hypothetically isolated regions would remain uninfected by the PCM.\nSuch regions would effectively act as low-thermal-conductivity inclusions encased by\nthe polymer matrix, contributing neither to melt-phase flow nor to effective thermal\ntransport.\nSo, we introduced the concept of the \"dead-zone fraction\" to quantitatively describe\nhow structural constraints limit convective intensity. In this context, a \"dead zone\" is\ndefined as a region where the local flow velocity is less than 50% of the average\nvelocity. In these zones, the movement of the molten phase is impeded, and the\ncontribution to convective heat transfer is significantly restricted. These regions are\nclearly identifiable in the velocity contour plots, providing a direct reflection of how\nchannel scale, tortuosity, and hydraulic resistance suppress convection (Fig. S31). By\nemploying this framework, we can differentiate between \"convection-effective\nregions\" and \"convection-restricted regions\" while maintaining the premise of overall\nconnectivity. This enables a more rigorous and nuanced analysis of convective\nenhancement across various structural geometries. (Detailed shown in Revised\nManuscript, Page 10, Line 9-13, Page 10 Line 26to Line 32,Page 15,Line 37 to Page\n16,Line 7.)\n10.Although demonstrations were conducted for both CPU thermal management and\ntextile applications, the discussion of each scenario remains superficial, and the data\npresented is rather limited. It is recommended to select one application scenario and\nprovidean in-depth analysis ofit.\nR2-10: We sincerely appreciate the reviewer’s constructive suggestion to enhance the\npractical demonstration of thermal management.\nFollowing this, we have revised the manuscript with a more rigorous device-level\nthermal management application for electronics. The thermal storage fabric-related\ncontent has been movedto theSupporting Information (Fig. S35and Note S3).\nThespecific updates are as follows: three parts:\nFor the application scenario, we evaluated the PIFS-3 thermal buffer on a\ndevelopment board under space-constrained and weak-convection conditions. The\ngoal was to demonstrate that in compact electronics, thermal buffering and\npeak-temperaturesuppression can be morecritical than thermal conductivity alone.\nFor in-depth analysis, we monitored the internal CPU core temperature and operating\nfrequency (Fig. 6b-c). We found that the thermal buffer significantly reduces\nfrequency throttling events during long-term full-load operation (1200 s), ensuring\ncomputational stability compared to heat spreaders. We then quantified the\ntemperature evolution of adjacent components (WiFi and memory modules). The\nresults (Fig. 6d-g) reveal that while high-conductivity spreaders may also \"heat up\"\nneighbouring parts, our thermal buffer effectively localises and absorbs heat via latent\nheat capacity.\nFinally, simulations were incorporated to validate our experimental results and\nprovideadditional perspectives.\nWe believe these additions provide a much more robust justification for the practical\nutility of PIFS fibers. (Detailed shown in Revised Manuscript, Page 18, Line 3 to\nPage 19,Line 13.)\n11. The Fig. annotations in the manuscript are unclear, and some experimental data,\nsuch as thermal conductivity and tensile strength, appear multiple times in both the\nmain text and the Fig.s. It is recommended to standardize the data presentation and\nclarify any discrepancies.\nR2-11: We have thoroughly revised the manuscript to standardize the data\npresentation.\nReferences:\n1. DuY,etal.AnisotropicHeatTransferinaFibrousMembranewithHierarchicallyAssembled\n2DMaterials.ACSAppliedMaterials&Interfaces16,65366-65377(2024).\n2. Xin G, et al. Highly thermally conductive and mechanically strong graphene fibers. Science\n349,1083-1087(2015).\n3. Alam M, King S, Haque M. Characterization of very low thermal conductivity thin films.\nJournalofThermalAnalysisandCalorimetry115,1541-1550(2014).\n4. Minkina W, Dudzik S. Infrared thermography: errors and uncertainties. John Wiley & Sons\n(2009)."}, {"round": 2, "reviewer_comments": {"reviewer_2": "The authors have revised and improved the manuscript according to the suggestions.", "reviewer_3": "This paper has sufficiently addressed the comments by previous reviewers. The work itself is interesting and novel, and the\nexperiments conducted from micro-scale to real-scale have demonstrated the importance of convection inside porous\nstructure and the performance of fiber material. I recommend the acceptance of this paper."}, "author_response": ""}]

Decision letter