Synthesis of covalent organic frameworks for photocatalytic hydrogen peroxide production guided by large language models

Domain: chemistry
Published: 2026-02-21
Editorial outcome: minor_revision

Abstract
The photosynthetic production of hydrogen peroxide (H2O2) from water and oxygen presents a sustainable alternative to the energy-intensive anthraquinone process. Covalent organic frameworks (COFs) have emerged as promising photocatalysts for H2O2 generation. However, most existing COF photocatalysts yield H2O2 at concentrations too low for practical applications, largely due to ongoing challenges in simultaneously optimizing photocatalytic activity and structural stability. Here, we introduce a large language model-driven design strategy for the targeted synthesis of high-performance COF photocatalysts. By analyzing a curated corpus of 355 peer-reviewed articles on COF-based photocatalysis with a language model-driven knowledge extraction pipeline, we extract and structure over 11,000 chemical relationships related to building block identity, linkage robustness, and H2O2 yield. Guided by this artificial intelligence-derived knowledge base, we identify 4,4′,4″-(1,3,5-triazine-2,4,6-triyl)trianiline and benzo[1,2-b:3,4-b′:5,6-b″]trithiophene-2,5,8-tricarbaldehyde as optimal building blocks and thiazole as the preferred linkage motif for constructing a robust, photocatalytic COF. The resulting Thz-COF achieves a high H2O2 concentration of 82.3 mM (~0.28 wt%) in aqueous solution (without using sacrificial agents), with a solar-to-chemical energy conversion efficiency of 1.39%.

Introduction
Hydrogen peroxide (H2O2) has a long history of environmentally friendly use in a wide variety of chemical synthesis, wastewater treatment and food sterilization1. Industrial production of industrial H2O2 has been dominated by the anthraquinone oxidation process, while this process needs considerable energy input and produces waste2. Photocatalysis technology marks an important milestone in driving chemical reactions by solar energy, which enables the generation of H2O2 that only requires water and oxygen3–5. Covalent organic frameworks (COFs) have been widely exploited as photocatalysts owing to their earth-abundant composition, extended visible light absorption, and structural/functional tunability6–8. Extensive strategies, such as the construction of donor-acceptor (D-A) structure9–11, topological design12,13 and electronic distribution14–16, have been devoted to COF-based photocatalysts to boost the H2O2 production performance, with a high solar-to-chemical conversion efficiency (SCC) exceeding 1%13,14.

Despite these efforts, the achievable H2O2 concentrations in sacrificial agent-free systems remain significantly below 0.1 wt%17. This concentration is inferior to the working solution (0.8-1.9 wt%) of the anthraquinone oxidation process2, and is drastically insufficient to compete with the commercial H2O2 (30 wt%). Consequently, this cannot meet the minimum thresholds required for real-world applications such as detoxification of aflatoxins in foods18.

Designing COF photocatalysts capable of high-concentration H2O2 generation requires the simultaneous optimization of two interdependent properties. First, the framework must incorporate photocatalytically active building blocks—or chromophores—with frontier orbital energies, charge-carrier mobilities, and O2-binding affinities that collectively promote the two-electron oxygen reduction reaction. Second, these chromophores must be linked by chemically resilient bonds that can withstand hydrolysis, photo-oxidation, and radical attack under extended light exposure. A material optimized in only one dimension is insufficient: if the linkage is unstable, the catalyst degrades; if the photophysics are suboptimal, activity stalls. In both cases, the maximum achievable H2O2 concentration is fundamentally limited.

Chemists have long tackled this multi-objective design challenge by turning to the literature—reading dozens of papers, cross-referencing structure-property data with prior knowledge, and mentally synthesizing insights before proposing a new COF. The COF literature indeed holds a vast and valuable body of knowledge: it documents H2O2 yields, linker stabilities, and key optoelectronic properties across a wide array of structures. However, this information is mostly encoded in unstructured text, dispersed across a large volume of publications. Extracting, filtering, and integrating these insights manually is not only laborious, if possible at all, and time-consuming, but also inherently opaque and prone to confirmation bias—making a purely human-driven design process increasingly impractical for ambitious goals such as achieving high-concentration H2O2 production.

Recent advances in retrieval-augmented generation (RAG) combined with large language models (LLMs) offer a powerful, scalable alternative to manual literature-driven workflows in chemistry19. Once known primarily for their fluency in natural language, LLMs have now been shown capable of identifying relevant studies, extracting structure–property relationships, and synthesizing design insights from across vast scientific corpora—tasks traditionally reserved for expert researchers20. Early demonstrations of this potential focused on agentic LLMs—autonomous systems that combine reasoning with tool use21. Notably, ChemCrow integrated GPT-4 with 18 domain-specific tools to autonomously plan multi-step syntheses, discover new chromophores, and evaluate catalysts, illustrating the promise of tool-augmented reasoning in chemical research22. Building on this concept, the Coscientist system connected GPT-4 to web search, code execution, and robotic lab control, successfully designing and executing experiments with minimal human input—providing a striking proof-of-concept that LLMs can close the loop from hypothesis to bench23.

In parallel, another major research thread has emphasized knowledge extraction and organization. Microsoft’s GraphRAG architecture introduced the use of property graphs to store LLM-extracted triples, enhancing the accuracy and traceability of scientific question answering on unstructured text24. Chemistry-focused applications soon followed. It has been demonstrated that the use of RAG facilitated the mining of quantitative reaction data from primary literature with high precision and recall, enabling automated dataset generation for machine learning models25. Similarly, a RAG-based approach was applied to literature on layered metal-oxide cathodes, constructing a searchable database that links composition, structure, and electrochemical performance in sodium-ion batteries26.

Together, these developments reflect a fundamental shift in how chemical knowledge is accessed, interpreted, and applied. LLMs are rapidly evolving from passive text-generation tools into domain-aware research assistants, capable of reading, reasoning over, and operationalizing complex scientific literature. By coupling these models with RAG and structured knowledge frameworks such as GraphRAG, it becomes possible to unify fragmented insights across hundreds of publications into a coherent, quarriable knowledge base27. In the context of this work, such capabilities are directly harnessed to identify and recommend building blocks and COF linkage for high-performance COF photocatalysts, replacing laborious manual curation with a scalable, transparent, and data-rich pipeline. As LLMs continue to improve in accuracy, interpretability, and chemical reasoning, they are poised to become essential tools in knowledge-driven materials discovery, enabling rapid ideation, prioritization, and validation of new candidates for catalysis, synthesis, and beyond.

In this work, we implemented a two-step artificial intelligence (AI) pipeline to support the rational design of COF photocatalysts for high-concentration H2O2 production. We assembled a curated collection of 355 peer-reviewed papers on COF-based photocatalysis spanning 2022 to 2024 and used DeepSeek V3 (December 2024) to extract structured chemical knowledge from the corpus28. The model segmented each article, identified relevant text passages, and distilled them into 11,238 structured triples capturing relationships between building blocks, linkages, stability traits, and photocatalytic performance. These outputs were organized into a knowledge graph stored in a set of Parquet files using a GraphRAG framework, allowing transparent and quarriable integration of the literature. We then applied natural language prompts—converted into GraphRAG queries—to retrieve and rank promising linker–linkage combinations, using a scoring scheme weighted toward chemical stability, photocatalytic efficacy, and synthetic feasibility.

This process identified 4,4′,4″-(1,3,5-triazine-2,4,6-triyl)trianiline (TAPT) and benzo[1,2-b:3,4-b′:5,6-b″]trithiophene-2,5,8-tricarbaldehyde (BTT), joined by thiazole linkages, as the top-performing combination. The resulting Thz-COF exhibited robust chemical and photostability, a long-lived charge-separated state, efficient O2 adsorption, and H2O2 desorption from the catalyst surface. These synergistic properties enabled COF to achieve a high H2O2 concentration of 82.3 mM (~0.28 wt%) in aqueous solution (without using sacrificial agents) after 72 hours of irradiation, with a SCC efficiency of 1.39%. At this concentration, the reaction liquid was directly effective for dye decolourisation (~100%), and almost 100% bactericidal efficacy against Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus, illustrating the practical impact of AI-accelerated COF discovery for both environmental remediation and biomedical applications.

Results
LLM-guided selection of building blocks and linkages
We first curated a dataset of 355 peer-reviewed articles on COF-based photocatalysis, published between 2022 and 2024 (the full publication list is provided in the GitHub repository containing the text preprocessing pipeline), encompassing diverse COF chemistries and structures, as well as function-led design objectives. Each article was processed using DeepSeek V3 (Dec 2024 snapshot), which segmented the text, retrieved contextually relevant passages, distilled their content into a knowledge graph of 2861 entity nodes and 3880 node relationship records. Such a knowledge graph captured key relationships among building block identities, linkage types, structural stability descriptions, and H2O2 production-related properties. The resulting structured data were indexed into a knowledge graph in Apache Parquet format via the GraphRAG framework (see Extended Data, Fig. 1), enabling transparent, provenance-linked knowledge integration.

Unlike traditional text-based RAG systems, GraphRAG supports semantically rich and queryable knowledge graphs, in which nodes represent molecular substructures and chemical properties, while edges encode structure-function and synthesis-performance relationships. This architecture allowed us to issue natural language queries—translated internally into search queries that traversed the literature-derived graph to retrieve optimal building block and linkage candidates, forwarding the data to a conversational LLM, and creates a natural language report. For instance, the query “Based on the knowledge graph, please recommend 10 linker candidates for synthesizing a structurally stable planar COF” initiates a search for nodes of COF linkers closely related to nodes suggesting structural stability and planarity. All GraphRAG query responses are manually fact-checked against their original documents, with full credibility, indicating that the GraphRAG architecture robustly inhibits model hallucinations in data retrieval, ensuring the authenticity of responses.

Linkers recommended by GraphRAG queries are scored by DeepSeek R1 (May 2025 snapshot), with the top 3 rated linkers being 4,4′,4″-(1,3,5-triazine-2,4,6-triyl)trianiline (TAPT), 5,10,15,20-tetra(4-aminophenyl)porphyrin (TAPP), and benzo[1,2-b:3,4-b′:5,6-b″]trithiophene-2,5,8-tricarbaldehyde (BTT) (Original LLM conversation records is provided in Supplementary Information). Therefore, we chose TAPT and BTT as linkers for the COF to be synthesized. For the linkage, we’ve extracted a comprehensive list from the knowledge graph. Based on the synthetic practicality, the imine—a more frequent appearance in knowledge graph, and the thiazole—a structural stability enhancement from the more common imine linkage29, has been selected as our COF’s linkage.

This LLM-guided process led us to identify TAPT and BTT as high-potential photochromic building blocks to be connected via thiazole linkages. These choices were guided not only by their documented photocatalytic performance but also by their chemical resilience and synthetic accessibility. The knowledge graph also facilitated exploration of additional strategies for enhancing structural stability, offering design suggestions that would be difficult to obtain through manual literature review alone. Overall, this LLM-enabled approach consolidated dispersed findings across hundreds of papers into a single, quarriable knowledge framework, offering researchers a rapid and scalable method for rational COF design (Fig. 1a, b and Supplementary Figs. 1 and 2).

Physicochemical characterization
Based on the LLM-guided, the imine and thiazole-linked COFs were synthesized via one-pot solvothermal approach, named Imi-COF and Thz-COF, respectively. The crystalline structures of the isoreticular COFs were analyzed using powder X-ray diffraction (PXRD). Both Thz-COF and Imi-COF exhibited high structural crystallinity, as indicated in their diffraction patterns shown in Fig. 2a, b. For Thz-COF, the PXRD pattern revealed dominant reflections at 4.59°, 7.97°, 9.20°, 12.61° and 25.24°, indexed to the (100), (110), (200), (120), and (001) crystallographic planes, respectively. The Imi-COF shows very similar diffraction patterns (Fig. 2b). As illustrated in Supplementary Fig. 3, a comparison of the experimental PXRD patterns with theoretical model patterns reveals that both COFs align well with their eclipsed AA stacking sequence.

The successful formation of the COFs was unequivocally confirmed by the Fourier transform infrared (FT-IR) spectroscopy and solid-state 13C nuclear magnetic resonance (13C-NMR) spectroscopy. In the FT-IR spectra (Fig. 2c and Supplementary Fig. 4), Imi-COF exhibited a strong absorption band at 1657 cm−1, characteristic of the imine (C=N) stretching vibration, consistent with its expected Schiff-base linkage30. In contrast, Thz-COF displayed distinct peaks at 1618 and 965 cm−1, assigned to the breathing of the benzothiazole ring, respectively31. The solid-state 13C-NMR spectrum additionally provides evidence of distinct chemical environments for the linkages in Thz-COF and Imi-COF. As shown in Supplementary Fig. 5, a characteristic thiazole carbon signal is observed at approximately 158 ppm for Thz-COF, while the peak around 151 ppm corresponds to the imine carbon of Imi-COF. Both COFs exhibit similar peaks around 115-133 ppm, 142 ppm, and 167 ppm, which are assigned to the carbon atoms in the BTT and TAPT units32.

Nitrogen adsorption-desorption measurements were conducted at 77.3 K to ascertain the apparent Brunauer-Emmett-Teller (BET) surface areas and pore size distributions of the COFs. Both COFs exhibited Type I isotherms, characteristic of microporous materials, with a high BET surface area calculated to be 1208 and 1320 m2g−1 (Fig. 2d). The pore size distribution analysis using nonlocal density functional theory (NLDFT) revealed similar main pore size distributions for Thz-COF and Imi-COF, which were both centered around 1.4 nm (Supplementary Fig. 6). The physicochemical stability of the COFs was initially evaluated by immersing Thz-COF and Imi-COF in acid (1 M HCl), base (1 M NaOH), and oxidizing agent (30 wt% H2O2) aqueous solutions for 48 hours. Thz-COF maintained both its structural integrity (PXRD) and chemical composition (FT-IR) in all environments (Fig. 2e, f). In contrast, Imi-COF suffered complete crystallinity loss in the base solution and showed significant degradation in its FT-IR spectrum (Supplementary Figs. 7 and 8), particularly in the imine bond region (1657 cm−1). Thermal gravimetric analysis (TGA) further demonstrated the good thermal stability of Thz-COF, which remained stable up to 500 °C in a nitrogen atmosphere, while Imi-COF showed a gradual weight decrease starting at 320 °C (Supplementary Fig. 9). This high stability of Thz-COF can be attributed to the inherent robustness of thiazole linkages compared to imine bonds, which are prone to hydrolysis under the base condition33,34.

The morphology of the COFs was elucidated through field emission scanning electron microscopy (FE-SEM) and high-resolution transmission electron microscopy (HR-TEM). The FE-SEM images revealed a nano-rod structure for Thz-COF and a nano-block structure for Imi-COF (Supplementary Fig. 10). HR-TEM analysis confirmed the crystalline nature of both COFs, with clearly resolved lattice fringes corresponding to d-spacings of 0.39 nm (Thz-COF) and 0.41 nm (Imi-COF), which align with their (001) planes (Supplementary Figs. 11, 12). The observed periodic pore arrangements and long-range structural order further validate the high crystallinity indicated by PXRD. Both COFs demonstrated hydrophilic characteristics, exhibiting small water contact angles of 39.0° for Thz-COF and 36.7° for Imi-COF (Supplementary Fig. 15), suggesting favorable interfacial compatibility with aqueous reaction systems, which is a critical advantage for photocatalytic applications involving water-based processes35.

Optical-physical properties
The optical-physical properties of COFs were investigated using the ultraviolet-visible diffuse reflection spectroscopy (UV/Vis DRS). Thz-COF showed enhanced π-conjugation due to the thiazole linkage32, resulting in a red shift and an extended optical absorption edge of approximately 750 nm compared to Imi-COF, which had an absorption edge at 700 nm (Fig. 3a). Correspondingly, the optical bandgaps for Thz-COF and Imi-COF were calculated using the Tauc plot method (Fig. 3b), revealing Thz-COF to have a narrower bandgap of 2.32 eV compared to 2.46 eV for Imi-COF. Mott-Schottky measurements at various frequencies confirmed n-type semiconductor characteristics for both materials, showing characteristic positive slopes (Supplementary Fig. 16). The conduction band (CB) potentials of Thz-COF and Imi-COF were estimated to be −0.52 V and −0.56 V (vs. NHE, pH = 7), respectively, using the Nernst equation. As illustrated in Fig. 3c, Thz-COF and Imi-COF are thermodynamically able to drive both the 2e- oxygen reduction reaction (ORR) and water oxidation reaction (WOR) for photocatalytic H2O2 production.

Photoelectrochemical measurements were conducted to gain a comprehensive understanding of the charge separation and transfer behavior of the COFs. Transient photocurrent responses under visible light illumination revealed that Thz-COF generated a photocurrent density of 0.11 mA cm−2, almost 2-fold higher than Imi-COF (Supplementary Fig. 17), indicating efficient photoexcitation in the thiazole-linked framework. The Nyquist plot from the AC electrochemical impedance spectroscopy (EIS) measurements revealed a reduced semicircle diameter for Thz-COF (Supplementary Fig. 18), further corroborating accelerated interfacial charge mobility, attributed to the thiazole unit’s electron-delocalizing properties. Density functional theory (DFT) simulations reveal that the thiazole linkage in Thz-COF enforces near-perfect coplanarity (dihedral angle ≈0°) between the TAPT and BTT unit, in stark contrast to the distorted imine-linked analog (Supplementary Fig. 19). This structural alignment optimizes orbital distribution, creating spatially separated highest occupied molecular orbital (HOMO) (localized on BTT) and lowest unoccupied molecular orbital (LUMO) (centered on thiazole-triazine) states (Supplementary Fig. 20) that enhance charge transfer efficiency—a critical advantage for photocatalytic performance29,36,37.

Charge transfer dynamics
As shown in the time-resolved photoluminescence (PL) decay spectra (Fig. 3f and Supplementary Table 2), Thz-COF exhibited a longer average fluorescence lifetime of 0.72 ns compared to Imi-COF (0.42 ns), indicating suppressed radiative recombination pathways in the thiazole-linked framework. Temperature-dependent PL spectroscopy was then performed to investigate the effect of linkages on the exciton properties at temperatures ranging from 80 to 298 K under 338 nm excitation. As illustrated in Fig. 3d, e, the PL intensity for both COFs gradually decreased with increasing temperature, indicating a thermal quenching process in the PL emission. By fitting the integrated PL intensity with temperature, the exciton binding energies (Eb) were estimated to be 43.9 and 53.5 meV for Thz-COF and Imi-COF via the model of the Arrhenius formula (I(T)=I0/(1+Aexp(-Eb/kBT)))38. The lower Eb of Thz-COF indicates a more favorable exciton dissociation process, which could inhibit electron-hole recombination and contribute to a greater number of free charge carriers involved in photocatalysis39.

Femtosecond transient absorption spectroscopy (fs-TAS) was employed to gain insights into the ultrafast carrier and excited-state dynamics of the COFs. Upon photoexciting by 339 nm pumping laser, the fs-TAS spectra of both COFs showed broad negative peaks, indicating the ground-state bleaching (GSB) resulting from the excitation of the molecular building blocks. Thz-COF exhibited pronounced GSB bands at 414 nm and 616 nm after excitation, occurring within 1 ps (Fig. 3g and Supplementary Fig. 21), while Imi-COF displayed distinct GSB peaks at 414, 500, and 616 nm, and rapidly returned to the ground state after 50 ps (Fig. 3h and Supplementary Fig. 22). This transition process is further supported by their TA kinetics probed at the specified wavelengths (Fig. 3i). Within the first 10 ps, Imi-COF showed fast bleach recovery kinetics, whereas Thz-COF exhibited long-lived characteristics. The corresponding average decay lifetimes were fitted biexponentially to be 4.07 and 1.25 ps for Thz-COF and Imi-COF at 616 nm (Supplementary Figs. 23, 24 and Supplementary Table 3). The long-lived GSB in Thz-COF results in a higher probability of electron-hole separation within the COF structure and enhances the interfacial transfer process, which crucially influences its photocatalytic performance40,41.

Photocatalytic performance of H2O2 and practical application potential
The photocatalytic H2O2 production activity of the COFs was systematically evaluated at an O2-saturated atmosphere in pure water without any sacrificial agent, using visible light (λ > 420 nm) illumination. As displayed in Fig. 4a, Thz-COF demonstrated an H2O2 production rate of 1.6 mM h−1, which is twice that of Imi-COF (0.7 mM h−1). The H2O2 concentration presented linearly over an extended reaction time of up to 5 hours (Supplementary Fig. 26). In addition, the external quantum efficiency (EQE) of Thz-COF was evaluated at different incident light wavelengths, displaying a wavelength-dependent on its UV/Vis spectrum, which suggests the photogenerated H2O2 production process (Fig. 4b). The EQE values for Thz-COF were determined to be 13.7% at 405 nm and 11.7% at 420 nm. Furthermore, the SCC efficiency of Thz-COF was measured under simulated sunlight irradiation (AM 1.5G, 100 mW cm−2). The results demonstrated that Thz-COF exhibits a notable average SCC efficiency of 1.39% (Supplementary Fig. 27), which is significantly higher than the solar-to-biomass conversion efficiency of plants (~0.1%)42 and exceeds that of most other organic photocatalysts under comparable experimental conditions (Supplementary Fig. 28 and Supplementary Table 5).

Thz-COF demonstrated a continuous increase in H2O2 concentration over a reaction period extended to 72 h, achieving a high concentration of 82.3 mM (~0.28 wt%), which is 5.5 times higher than the concentration obtained from Imi-COF (15.1 mM) (Fig. 4c). This represents the highest concentration of H2O2 produced from water and oxygen by using organic polymeric photocatalysts, compared to the reported materials, which generally produce less than 10 mM (Supplementary Fig. 28). This concentration significantly exceeds the minimum required for food detoxification (at least 0.05 wt%)18, and we evaluated its potential practical applications using the filtered photocatalytic solution. The H2O2 solution produced by Thz-COF not only showed of nearly 100% dye degradation efficiency for methylene blue, methyl orange, and rhodamine B within just 10 seconds of adding to the photocatalytic solution (Supplementary Fig. 29), but it also achieved almost 100% bactericidal efficacy against Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus at room temperature (Supplementary Fig. 30). In addition, we implemented a biphasic water/benzyl alcohol system to amplify H2O2 accumulation. After 94 hours of continuous irradiation, Thz-COF achieved a ~1.9 wt% H2O2 solution (Supplementary Fig. 31), which already exceeds the thresholds required for use as a sanitizer in the food industry or tooth bleaching43,44.

Thz-COF exhibited decent photocatalytic durability across five consecutive cycles (total reaction of 120 h). No significant decrease was observed in either photocatalytic activity (Fig. 4d) or chemical structure (Supplementary Fig. 32), attributed to the enhanced connectivity provided by the thiazole ring, as discussed previously. As shown in Supplementary Fig. 33, Thz-COF maintained its crystalline structure after 48 h of reaction. In sharp contrast, Imi-COF nearly lost its crystallinity due to its weaker interlayer interaction (−60.2 kJ mol−1), which is lower than that of Thz-COF (−79.6 kJ mol−1) (Supplementary Fig. 34). However, a decrease in crystallinity for Thz-COF was observed after 120 hours of irradiation (Supplementary Fig. 35), likely due to the mechanical stimuli reducing particle size45, visualized via atomic force microscope (AFM) (Supplementary Fig. 36).

The concentration of H2O2 exhibited asymptotic stabilization for Imi-COF after 24 h of irradiation (Fig. 4c), indicating a dynamic equilibrium between photoinduced generation and decomposition of H2O246. Supplementary Fig. 37 shows that the concentration of H2O2 in the Imi-COF solution decreased by almost half compared to that in the Thz-COF solution in a 0.5 M H2O2 solution under visible light irradiation in an N2 atmosphere for 5 hours. The decomposition of H2O2 in Imi-COF may be attributed to the adsorption of H2O2 at the imine bond (Supplementary Fig. 38), which prevents the release of photogenerated H2O2 from the catalyst surface47. DFT calculation further elucidated the correlation between the type of linkage and H2O2 adsorption behavior. Evidence indicates that the adsorption energy of H2O2 is higher for the imine linkage compared with the thiazole linkage (Fig. 5a and Supplementary Fig. 39), consistent with the results of the decomposition experiments. Collectively, these findings demonstrate that the thiazole-linked Thz-COF exhibits high performance and durability, highlighting its substantial potential for real-world environmental and biomedical applications.

Reaction pathway and intermediates study
Several control experiments were performed to analyze the reaction pathway of photocatalytic H2O2 production. For the ORR, it’s well accepted that there are two different reaction routes: the direct one-step 2e- reduction (O2 + 2H+ → H2O2) and the stepwise 2e- reduction (O2 → •O2- + 2H+ → H2O2)17. We initially use isopropanol (IPA) and p-benzoquinone (p-BQ) as the quenching agents of holes and •O2- intermediates. The photocatalytic performance of both Thz-COF and Imi-COF increased in the H2O/IPA solution, confirming the predominance of reductive pathways, while the activity significantly decreased in the presence of p-BQ, implying the crucial role of the •O2- intermediates in ORR (Fig. 5b)48. To identify the radicals generated during the photocatalytic process, the electron paramagnetic resonance (EPR) measurements were performed using 5,5-dimethyl-1-pyrroline N-oxide (DMPO) as a radical trapping agent. In contrast to the dark condition, typical signals of •O2-–DMPO were detected for both COFs after 10 minutes of irradiation (Supplementary Fig. 40)49, supporting the •O2- involved stepwise 2e- reduction process. Moreover, the generated •O2- intermediates for Thz-COF and Imi-COF were confirmed and quantified via the nitro blue tetrazolium (NBT) method50. As displayed in Fig. 5c and Supplementary Fig. 41, Thz-COF yielded •O2- at a concentration of 7.2×10−5 M, which was higher than the 5.9×10−5 M measured for Imi-COF, indicating the favorable formation of •O2- intermediates during the ORR process. In situ attenuated total reflection surface-enhanced infrared absorption spectroscopy (ATR-SEIRAS) was conducted to capture the dynamic evolution of reaction intermediates during the photocatalytic H2O2 generation reaction for Thz-COF. The ATR-SEIRAS spectra showed pronounced peaks at around 1100 and 1297 cm−1 after equilibrating with water in dark conditions, corresponding to the •O2- and endoperoxide intermediates51, which continuously increased upon light irradiation (Fig. 5d). Meanwhile, a gradually emerging vibrational band around 1381 cm−1 was observed, attributed to the formation of the HOOH* intermediates52. Furthermore, the average electron transfer number (n) were estimated to be 2.3 for Thz-COF and 1.8 for Imi-COF, determined through rotating disk electrode (RDE) experiments in the O2-saturated atmosphere (Fig. 5e and Supplementary Fig. 43). To identify the proton source for H2O2 generation, D2O was employed as the solvent. Thz-COF exhibited comparable performance in both H2O and D2O, and the absence of an H2O2 signal in the 1H-NMR spectrum confirmed that the protons originated from H2O (Supplementary Fig. 44)53. These results provide compelling evidence for the stepwise 2e- ORR pathway in photocatalytic H2O2 production, and the favorable generation of •O2- intermediates in Thz-COF.

We next utilized rotating ring-disk electrode (RRDE) measurements to investigate the water oxidation processes of the COFs. The increased disk currents were observed for both COFs when the potential exceeded 1.56 V vs. RHE (Supplementary Fig. 45), indicating that water oxidation occurs at the rotating disk electrode. No reduction currents were detected at the Pt ring electrode during the RRDE tests, and no obvious O2 could be detected during 5 h irradiation (Supplementary Fig. 46), thereby the 4e- WOR pathway for generating O2 was excluded54. In addition, in a nitrogen atmosphere, the application of potassium bromate (KBrO3) to quench electrons in the photocatalytic reaction system resulted in both COFs displaying a significant increase in H2O2 production compared to menthol, a hole quenching agent (Fig. 5f). This led to a continuous accumulation of H2O2 over 5 hours of irradiation (Supplementary Fig. 47), indicating the direct generation of H2O2 from H2O. Therefore, the WOR pathway was concluded to be the direct 2e- WOR from H2O to H2O2 for both Thz-COF and Imi-COF.

Computational insights into active sites and reaction thermodynamics
To further investigate the reactive sites of the COFs during the photocatalytic H2O2 production process, we conducted a series of calculations to gain a deep understanding between the structure and performance. The possible active sites for the ORR and WOR for both COFs were firstly revealed through Mulliken (MK) charge distribution and electrostatic surface potential (ESP) map based on the optimized fragment of Thz-COF and Imi-COF. It was found that a more negative MK charge and negative electrostatic potential (red region) were located on the nitrogen atoms in the thiazole unit and the imine linkage of both COFs, indicating their potential for the O2 reduction reaction. Conversely, a more positive MK charge and positive electrostatic potential (blue region) were observed on the sulfur and carbon atoms in the BTT unit, suggesting their potential role in oxidation reactions involving H2O (Supplementary Figs. 48, 49).

Building on these findings, we calculated the adsorption energies of O2 and H2O on the possible reactive sites, as shown in Fig. 6a, b. Initially, both COFs exhibited a similar trend in O2 adsorption, favoring the nitrogen site in the thiazole or imine linkage (site 2) over the triazine unit (site 1) (Supplementary Fig. 50), indicating a favorable interaction with O2. The nitrogen site of the thiazole linkage displayed a higher O2 adsorption energy of −11.1 kJ mol−1 compared to −7.1 kJ mol−1 for the imine linkage, suggesting that the thiazole-linked Thz-COF has a stronger interaction with O2, which benefits the ORR during photocatalytic reactions (Fig. 6c). The H2O adsorption energy was also calculated to identify the WOR site for the two COFs. Figure 6b, d show a favorable interaction between H2O and the sulfur site in the BTT unit (−13.7 kJ mol−1, site 3) for Imi-COF, while a higher H2O adsorption energy of −16.0 kJ mol−1 was estimated for the carbon site in the BTT unit (site 4) of Thz-COF (Supplementary Fig. 51), indicating enhanced H2O adsorption to facilitate WOR.

The active sites for O2 reduction of Thz-COF were further confirmed through metal-coordinated control experiments by introducing the Zn ions to occupy the ORR active site (site 2)55. The distinct signal of Zn was observed by the X-ray photoelectron spectroscopy (XPS) spectra (Supplementary Fig. 53), while the chemical state and local environment of Zn were further revealed by X-ray absorption fine structure spectroscopy (XAFS). As shown in Fig. 6e and Supplementary Fig. 54, the near-edge absorption energies of Thz-COF-Zn were slightly lower than that of ZnO, with three coordination paths identified in the R-space at 1<R<2 Å, 2<R<2.5 Å and 2.5<R<3.5 Å, respectively. Meanwhile, the successful coordination of Zn between the N of the thiazole linkage and S of the BTT unit was evidenced by fitting the EXAFS data for the first coordination shell of Thz-COF-Zn (Fig. 6f, Supplementary Figs. 55–57 and Supplementary Table 4), which was also confirmed by the wavelet transform (WT) analysis (Fig. 6g and Supplementary Fig. 58). As a result, Zn-coordinated Thz-COF-Zn exhibited a reduced ORR performance while maintaining sustainable WOR activity compared with the pristine Thz-COF (Fig. 6h). This provided direct and strong evidence that the thiazole linkage serves as the active site for ORR during the photocatalytic process.

The Gibbs free energy (ΔG) changes for the 2e- ORR and WOR were then calculated at the analyzed active sites for Thz-COF and Imi-COF. As is well known, the formation of OOH* intermediates is a key step in the stepwise 2e- ORR15. Thz-COF exhibited a lower energy barrier (ΔG=−1.93 eV) compared to Imi-COF (ΔG=−1.04 eV) (Fig. 6i), suggesting optimized binding strength of OOH* intermediates to the thiazole linkage in Thz-COF. In addition, Thz-COF showed a reduced ΔG of 1.76 eV for OH* formation (Fig. 6j), which is considered the rate-determining step (RDS) in the WOR process, indicating that the H2O2 generation in the WOR pathway is more thermodynamically favorable. These results align with the superior photocatalytic H2O2 production activity observed experimentally.

In summary, an LLM-driven design strategy was employed for the targeted synthesis of high-performance COF photocatalysts. Based on AI-derived knowledge, 4,4′,4″-(1,3,5-triazine-2,4,6-triyl)trianiline and benzo[1,2-b:3,4-b′:5,6-b″]trithiophene-2,5,8-tricarbaldehyde were identified as optimal building blocks and thiazole as the linkage to construct a robust COF photocatalyst. The resulting thiazole-linked COFs—Thz-COF was successfully synthesized, featuring a stable photochemical structure, extended π-conjugation, and long-lived charge separation state. It demonstrated notable photocatalytic performance, achieving a high H2O2 concentration of 82.3 mM (~0.28 wt%) after 72 h of irradiation and a SCC efficiency of 1.39%. The produced H2O2 solution can be directly applied for dye degradation and antibiosis, achieving nearly 100% efficiency and demonstrating its strong potential for environmental and biomedical applications. Both DFT calculation and control experiments results revealed that the thiazole linkage not only enhanced the O2 adsorption, but also served as an ORR active site, lowering the energy barrier for the formation of OOH* intermediates. In the future, by leveraging LLM embeddings to assist in task inference, the LLM can efficiently index vast amounts of literature and recommend optimal options, demonstrating flexibility in materials design when integrated with multiple functionalities56,57.

Methods
Generation of knowledge graph
We gathered 355 PDF files of research articles, which were translated into Markdown documents by the Marker software package. These documents were processed by a three-step pipeline: A pattern detection pipeline that removes artifacts from text, then an LLM agent scans each line of text, deciding whether that line is relevant to the focus of our research: COF linkers and building strategies, finally, for each document, text related to our topic is distilled into one natural language paragraph, which is within a reasonable text length range for knowledge graph generation. Utilizing the Indexing functionality of GraphRAG software, with DeepSeek V3 as a chat model and Nomic Embed Text as an embedding model, we created a knowledge graph from the processed documents.

Information retrieval via human-LLM interaction
To identify promising COF linkers with stability and functionality, a multi-step screening protocol was implemented using a GraphRAG knowledge graph integrated with LLM analysis. Two independent queries were executed on the knowledge graph to retrieve linker candidates: the first targeting structural stability potential and the second focusing on H2O2 photocatalytic activity. Each query returned 10 candidate linkers. Cross-referencing these results identified 6 linkers present in both candidate sets, suggesting their potential for simultaneous structural integrity and photocatalytic performance. These 6 candidates were subsequently evaluated using DeepSeek R1, which assigned quantitative scores for both structural stability and H2O2 photocatalytic potential. The three highest-scoring linkers were selected for further investigation based on their combined performance metrics across both criteria (see the details in Supplementary Information and Supplementary Data 2).

Synthesis procedures
Synthesis of Thz-COF. The synthesis of Thz-COF was based on the method from the previous report with some modifications34. A Pyrex tube (10 mL) was charged with TAPT (35.4 mg, 0.1 mmol), BTT (33.0 mg, 0.1 mmol), and sulfur (28.8 mg, 0.9 mmol). To this solid mixture, DCB (0.9 mL), n-BuOH (1.0 mL) and DMSO (0.1 mL) were added. After ultrasonication for two minutes and adding aqueous acetic acid (0.2 mL, 6 M), the mixture was flash-frozen in liquid nitrogen (77 K) and subjected to three freeze-pump-thaw cycles for degassing. The tube was then sealed under vacuum and heated at 120 °C for 72 h. The resulting precipitate was collected upon cooling, washed extensively with THF, and subjected to solvent exchanges with acetone. The final product was dried at 90 °C under vacuum overnight. Anal. Calcd for (C12H4N2S2)n: C, 60.00; H, 1.70; N, 11.70; S, 26.70. Found: C, 63.47; H, 2.77; N, 10.99; S, 15.49.

Synthesis of Imi-COF. Similar to the synthesis of Thz-COF, TAPT (35.4 mg, 0.1 mmol) and BTT (33.0 mg, 0.1 mmol) were used as reaction monomers, and a mixture of DCB (1.0 mL), n-BuOH (1.0 mL) and aqueous acetic acid (0.2 mL, 6 M) served as the solvent system. Anal. Calcd for (C12H6N2S)n: C, 69.90; H, 2.91; N, 13.59; S, 15.53. Found: C, 58.58; H, 2.92; N, 9.84; S, 13.61.

In situ attenuated total reflection surface-enhanced infrared absorption spectroscopy (ATR-SEIRAS)
ATR-SEIRAS experiments were conducted in a custom spectro-electrochemical cell featuring a side-mounted Si ATR crystal, which permitted electrolyte stirring via a magnetic stir bar. The working electrode was prepared by drop-casting an aqueous dispersion of the COF powder onto the reflecting plane of a Si prism. A graphite rod and an Ag/AgCl electrode (3.0 M KCl, BASi) served as the counter and reference electrodes, respectively, for all experiments. Before use, the electrolyte was purified by applying a constant negative current to an Au foil working electrode to extract potential metal impurities. Spectra were acquired using a Nicolet iS50 FTIR spectrometer (Thermo Scientific) equipped with a liquid nitrogen-cooled MCT detector, with a resolution of 4 cm−1 and at least 128 co-added scans (Supplementary Fig. 42). Electrochemical measurements were conducted using a Princeton potentiostat (Princeton Applied Research)58,59.

Photocatalytic experiments
The as-prepared COF photocatalyst was dispersed in deionized water via 10 minutes of ultrasonication. This dispersion was transferred to a 100 mL reaction flask and stirred under different atmospheres with cooling water to keep the system temperature at 25 °C. It was then illuminated by a 300 W xenon lamp (Microsolar 300, Beijing Perfectlight) equipped with a 420 nm cutoff filter (Supplementary Fig. 25). After reaction, aliquots (1.0 mL) were extracted with a syringe and filtered through a 0.2 μm Millipore filter to remove the catalyst before analysis. The concentration of generated H2O2 was quantified using a standard KI titrimetric method60.

Long-term stability tests were performed under visible light (λ > 420 nm) using 50 mg of catalyst in 20 mL of O2-saturated deionized water.

Dye degradation experiment
10 mg methylene blue (MB), methyl orange (MO) or rhodamine B (RhB) was dissolved, respectively in a 1 L volumetric flask charged with 1 M FeSO4·7H2O. Taking out 3.5 mL dye solution and putting it into vitro, and then adding 0.5 mL photocatalytic solution (long-term photocatalytic reaction for 72 h). Dye degradation rate was calculated by using UV-Vis adsorption spectra after adding for 30 s.

Antibacterial experiments
The antibacterial experiments were estimated by the plate colony counting method. Escherichia coli (E. coli), Pseudomonas aeruginosa (P. aeruginosa) and Staphylococcus aureus (S. aureus) were cultivated in LB broth for a period of 16 h within a shaking incubator. Thereafter, 1 mL of the photocatalytic solution was mixed with 1 mL of the bacteria solution for 1 h and left at 37 °C for 16 h. Following this, 1% of the sample was spread on nutrient agar plates and cultured at 37 °C for 24 h.

Theoretical-calculation details
The calculations were performed using spin-polarized density functional theory (DFT) as implemented in the Vienna Ab initio Simulation Package (VASP), employing the Projector Augmented Wave (PAW) method. The exchange–correlation interactions were treated with the Perdew–Burke–Ernzerhof (PBE) functional, incorporating Grimme’s DFT-D3 correction for van der Waals interactions. A plane-wave kinetic energy cutoff of 520 eV was used. Geometry optimizations and frequency calculations for all structures were carried out with the M06-2X functional and the 6-31G(d) basis set using Gaussian 16; frequency analyses provided Gibbs free-energy corrections at 298.15 K61. Subsequently, single-point energies were computed with the 6-311+G(d,p) basis set. Orbital distributions and electrostatic potential (ESP) maps were also obtained with Gaussian 16 (Supplementary Data 1).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript presents an innovative strategy driven by a large language model (LLM) for the design and synthesis of a\nnovel thiazole-linked COF photocatalyst (Thz-COF), aimed at achieving efficient overall photocatalytic H2O2 production.\nThe as-synthesized Thz-COF demonstrates remarkable performance, with both H₂O₂ concentration and solar-to-chemical\nenergy conversion (SCC) efficiency reaching high levels among reported values. It also exhibits significant potential in\nenvironmental remediation and antibacterial applications. This study highlights the promising integration of artificial\nintelligence and experimental science, possessing considerable academic value and application prospects. I recommend\npublication after minor revisions, as detailed below:\n1. In addition to the imine-linked COF (Imi-COF) used for comparison, were other types of highly stable linkers (such as\namide, enamine, azole, etc.) considered and evaluated? Did the LLM also suggest these options when recommending\nbuilding blocks? Please clearly state the primary rationale for ultimately selecting the thiazole linkage over other highly\nstable linkers—was it based on quantitative scoring from the LLM, or synthetic practicality? Furthermore, although the LLM\nrecommended three building units (TAPT, TAPP, BTT), why were only TAPT and BTT chosen for synthesis?\n2. FE-SEM results show that Thz-COF exhibits a nano-rod morphology, whereas Imi-COF appears as nano-block. Could\nsuch morphological differences (e.g., aspect ratio, exposed crystal facets) also significantly influence the photocatalytic\nperformance?\n3. Regarding the TA kinetics decay profiles analysis of the COF materials, please provide a detailed description of the fitting\nprocedure and supply complete, uncropped figures that include raw data points and fitted curves.\n4. The manuscript indicates that H2O2 is generated through both the oxygen reduction reaction (ORR) and water oxidation\nreaction (WOR). However, experimental verification and mechanistic analysis of the WOR pathway are relatively limited.\nPlease supplement relevant data and discussion to comprehensively elucidate the H2O2 formation pathway.。\n5. There are obvious errors in the legends or captions of some other figures (such as Figure 2d, solid-state 13C nuclear\nmagnetic resonance spectroscopy of the COFs.), and the author should carefully review the manuscript.", "reviewer_2": "This paper proposes a large language model (LLM)-driven design strategy for synthesizing high-performance COF\nphotocatalysts. Various LLM models and LLM-based information retrieval methods were combined to discover the targeted\nCOF photocatalysts. Comprehensive experimental evaluations demonstrated the effectiveness and practicality of the\nproposed strategy. However, I have several concerns as follows.\n1. Is there a module specifically designed for the target task, or did the existing model happen to work well when just\napplied?\n2. Various data-driven strategies for discovering the desired chemical compounds have been proposed in both chemical\nscience and machine learning. For example, diffusion models and reinforcement learning showed remarkable performances\nin these tasks. Please clarify the benefits of the proposed LLM-driven strategy.\nThe uploaded source code appears to be work; however, since some of the resources required for execution were not\nprovided, I was not able to actually run the source code.", "reviewer_3": "The study by Shu et.al, using a large language model (LLM) to analyze extensive literature, an AI-guided strategy was\ndeveloped for designing high-performance covalent organic frameworks (COFs) for photocatalytic H2O2 production. The\noptimally synthesized thiazole-linked Thz-COF exhibits remarkable stability and activity, achieving a record H2O2\nconcentration of 82.3 mM and a solar-to-chemical conversion efficiency of 1.39%. This work highlights the potential of LLM-\ndriven approaches in accelerating the discovery of functional materials with tailored properties. This is a very interesting\nwork with robust technical content and high-quality characterization methodologies. Therefore, I would like to recommend its\npublication after addressing/responding to the following comments:\n1. The process of converting 354 PDFs into a queryable knowledge graph involves several automated steps (PDF-to-\nMarkdown, artifact removal, text distillation by an LLM agent). An error or loss of critical context at any stage could propagate\nthrough the entire workflow, leading to a flawed final recommendation. What quality control measures were implemented at\neach step to ensure data fidelity? Specifically, how was the LLM agent's distillation of an entire article into a single\nparagraph validated to ensure it accurately and comprehensively represented the key structure-property findings of the\noriginal publication?\n2. Could the authors explain the rationale for first running two independent queries (stability and photocatalytic activity) and\nthen cross-referencing the results, rather than using a single, more complex query to find candidates with both attributes?\nWhat are the perceived advantages of this approach in mitigating model bias or improving the quality of the retrieved\ncandidates?\n3. To provide a comprehensive context for the current research, it is imperative to incorporate and critically analyze more\nrecent, cutting-edge photocatalytic H2O2 production studies based on COFs. Notable examples include, but are not limited\nto: J. Am. Chem. Soc. 2025, 147, 33, 30287–30295; Nat Commun 16, 7654 (2025); Angew. Chem. Int. Ed. 2025,\ne202511687; ACS Appl. Mater. Interfaces 2025, 17, 25828−25838\netc. A comparative analysis of these works would significantly enhance the scholarly depth and relevance of the manuscript.\n4. The Isotope experiments should be supplemented to verify the proton source.\n5. The experiment detail of some characterizations have not been listed in the section of Characterization methods, such as\nTemperature-dependent PL spectra.\n6. The author claimed that the exfoliation of Thz-COF after long time reaction, some evidence should be provided.\n7. To better understand the charge carrier dynamics, we suggest that the authors add the fitted parameters from the\nphotoluminescence decay spectra. These parameters are crucial for elucidating the efficiency of charge separation and\ntransport in the material. The test method can be referred to Angew. Chem. Int. Ed. 2023, 62, e202304173, etc.\n8. Please deeply check English language level and correct some spelling and grammatical errors. For example, In Figure\nS8, the “slovents” should be solvents."}, "author_response": "Manuscript ID: NCOMMS-25-57806 Response to Reviewers’ Comments\nGiven below are our responses (in BLUE colour) to the reviewers’ comments. The\nchanges to the manuscript and supporting information are marked in RED in the\nresponse letter, which are highlighted in YELLOW in the revised manuscript and\nsupporting information.\nReviewer #1:\nThis manuscript presents an innovative strategy driven by a large language model\n(LLM) for the design and synthesis of a novel thiazole-linked COF photocatalyst (Thz-\nCOF), aimed at achieving efficient overall photocatalytic H O production. The as-\n2 2\nsynthesized Thz-COF demonstrates remarkable performance, with both H O\n2 2\nconcentration and solar-to-chemical energy conversion (SCC) efficiency reaching high\nlevels among reported values. It also exhibits significant potential in\nenvironmental remediation and antibacterial applications. This study highlights the promising\nintegration of artificial intelligence and experimental science, possessing considerable\nacademic value and application prospects. I recommend publication after minor\nrevisions, as detailed below:\n1. In addition to the imine-linked COF (Imi-COF) used for comparison, were other\ntypes of highly stable linkers (such as amide, enamine, azole, etc.) considered and\nevaluated? Did the LLM also suggest these options when recommending building\nblocks? Please clearly state the primary rationale for ultimately selecting the thiazole\nlinkage over other highly stable linkers—was it based on quantitative scoring from the\nLLM, or synthetic practicality? Furthermore, although the LLM recommended three\nbuilding units (TAPT, TAPP, BTT), why were only TAPT and BTT chosen for synthesis?\nResponse: We thank the reviewer for these insightful questions. We will address each\npoint sequentially. For the stable linker consideration and suggestions by LLM, other\ntypes of highly stable linkers were indeed considered during our process. The\nknowledge graph was constructed using broad keywords such as “H O photocatalyst”\n2 2\nand “structural stability”, without pre-specifying any particular linkage chemistry. As\ncan be seen in the raw LLM query responses provided in the Supplementary\nInformation, the LLM’s output contained a variety of building blocks suitable for\nforming different linkages, including amide, enamine, azole, and others. While building\nblocks for imine-based (Schiff base) COFs appeared more frequently due to their\nprevalence in the existing literature, the system did recommend monomers compatible\nwith other stable covalent bonds. For the linkage selection, the LLM firstly extracted a\nseries of linkages for constructing structurally stable COFs from the knowledge graph,\nand the final selection of the thiazole linkage was primarily based on synthetic\npracticality and expert chemical knowledge (Nat. Commun. 2018, 9, 2600; J. Am. Chem.\nSoc. 2020, 142, 11131).\nThe LLM recommended three potential building blocks: 1,3,5-Tris(4-\naminophenyl)triazine (TAPT), 1,3,6,8-Tetrakis(4-aminophenyl)pyrene (TAPP), and\n2,5-Bis(2-thienyl)thiazolo[5,4-d]thiazole (BTT). To demonstrate the proof of concept,\nwe synthesize the COF using only TAPT and BTT, based on fundamental topological\naccessibility for forming the desired 2D hexagonal lattice structure and the feasibility\nof COF synthesis. We have updated the “LLM-guided selection of building blocks and\nlinkages” part to give a clearer description on linkage selection.\nResults:\n“For the linkage, we’ve extracted a comprehensive list from the knowledge graph.\nBased on the synthetic practicality, the imine—a more frequent appearance in\nknowledge graph, and the thiazole—a structural stability enhancement from the more\ncommon imine linkage,29 has been selected as our COF’s linkage.”\nPhysicochemical characterization:\nBased on the LLM-guided, the imine and thiazole-linked COFs were synthesized via\none-pot solvothermal approach, named Imi-COF and Thz-COF, respectively.\n2. FE-SEM results show that Thz-COF exhibits a nano-rod morphology, whereas Imi-\nCOF appears as nano-block. Could such morphological differences (e.g., aspect ratio,\nexposed crystal facets) also significantly influence the photocatalytic performance?\nResponse: We thank the reviewer. Based on the literature, COF morphology or particle\nsize, for example, the nanosphere, fiber, and oriented thin film, can influence the\nphotocatalytic processes as dispersibility, aggregation behavior, and light-harvesting\ncapability evolves in water (Adv. Mater., 2022, 34, 2202751; Nat. Common., 2024, 15,\n6482). However, the chemical structures of Thz-COF and Imi-COF are different, which\nmay show more influence than their morphology. Moreover, compared to the inorganic\nphotocatalysts, such as TiO (Nature 2008, 453, 638–641; Nat. Mater. 2017, 16, 461–\n2\n466; J. Am. Chem. Soc. 2025, 147, 22, 19391-19399), the COF materials show much\nsmaller crystalline domains, in which crystal facets exposure and aspect ratio may have\nless impact to photocatalytic performance.\nIn this specific case, we conclude that the obvious difference of photocatalytic\nperformance for Thz-COF and Imi-COF was strongly attributed to the charge transfer\nand reactive sites of COFs. The Thz-COF exhibited an extended π-conjugation and\nlong-lived charge separation state, and the thiazole linkage in Thz-COF could enhance\nthe O adsorption, and serve as an ORR active site, lowering the energy barrier for the\n2\nformation of OOH* intermediates. Therefore, the Thz-COF showed an impressive\nphotocatalytic performance, which is twice that of Imi-COF. Previous studies also\nrevealed that the difference of photocatalytic performance in the very similar or\nisoreticular COFs was owing to the electronic structure, linkage environment or\ntopological effects, which significantly influence the charge separation and transfer, and\nthe nature of the catalytic active site, although they displayed significant difference in\nmorphology (Nat Catal. 2024, 7, 195-206; Adv. Mater. 2025, 37, 2410247; Nat\nCommun. 2025, 16, 5658).\n3. Regarding the TA kinetics decay profiles analysis of the COF materials, please\nprovide a detailed description of the fitting procedure and supply complete, uncropped\nfigures that include raw data points and fitted curves.\nResponse: We thank the reviewer. The single-wavelength kinetic fitting was performed\non these two COFs under a specific wavelength (An Introduction to Processing, Fitting,\nand Interpreting Transient Absorption Data. Vol. 204 (1940-087X, 2024)). The raw\ndata and kinetic parameters of COFs are shown below and we also updated these in\nFigure 3i and the supporting information.\nResults:\n0\n-2\n-4\nt = 4.07 ps\nu.)-6 ave\nThz-COF at 616 nm\na.-8\nExpDec2 fitting curve\nO D\n(\n-2\n-4 t = 1.25 ps\nave\n-6 Imi-COF at 616 nm\nExpDec2 fitting curve\n-8\n0 100 200 300 400 500\nDelay time (ps)\nFigure 3. Optical-physical properties and charge transfer dynamics of the COFs i, TA\nkinetics decay profiles of the COFs.\nSupporting data:\n(a) (b)\n0.0\n0\n-0.5\n-2\n-1.0\nD\nO D -1.5 O D -4\nD\n-2.0\n-6\n-2.5\nThz-COF at 455 nm -8 Thz-COF at 616 nm\n-3.0 ExpDec2 fitting curve ExpDec2 fitting curve\n0 100 200 300 400 500 0 100 200 300 400 500\nDelay time (ps) Delay time (ps)\nFigure S23. TA kinetics and corresponding fitting curves of Thz-COF excited at (a)\n455 nm and (b) 616 nm.\n(b) (c)\n1\n0.0 Imi-COF at 414 nm 0.5\nExpDec2 fitting curve 0\n0.0\n-0.5 -1\n-0.5\n-1.0 -1.0 -2\nO\nD-1.5\nO\nD-1.5\nO\nD-3\nD D D-4\n-2.0\n-2.0 -5\n-2.5\n-2.5 -6\n-3.0 - - 3 3 . . 5 0 I E m x i p -C D O ec F 2 a fi t t t 5 in 0 g 0 c n u m rve -7 I E m x i p -C D O ec F 2 a fi t t t 6 in 1 g 6 c n u m rve\n-8\n0 100 200 300 400 500 0 100 200 300 400 500 0 100 200 300 400 500\nDelay time (ps) Delay time (ps) Delay time (ps)\nFigure S24. TA kinetics and corresponding fitting curves of Imi-COF excited at (a)\n414 nm, (b) 500 nm and (c) 616 nm.\nTable S3. Kinetic parameters of COFs at 616 nm from TAS spectra.a\nSample A τ (ps) A τ (ps) τ (ps)\n1 1 2 2 ave\nThz-COF -0.999 4.07 7.81*10-4 17.9 4.1\nImi-COF 9.97*10-4 15.5 -0.825 1.25 1.3\naThe single-wavelength kinetic fitting was performed on these two COFs under specific\nwavelength. The calculation formula is as follows: τ = (A (τ )2 + A (τ )2) / (A τ +\nave 1 1 2 2 1 1\nA τ ).3\n2 2\n4. The manuscript indicates that H O is generated through both the oxygen reduction\n2 2\nreaction (ORR) and water oxidation reaction (WOR). However, experimental\nverification and mechanistic analysis of the WOR pathway are relatively limited. Please\nsupplement relevant data and discussion to comprehensively elucidate the H O\n2 2\nformation pathway.\nResponse: We thank the reviewer for this comment. For the WOR pathway to produce\nH O , there are three reaction routes: (1) H O could oxidize directly to form H O with\n2 2 2 2 2\ntwo photogenerated holes; (2) H O could oxidize with four photogenerated holes to\n2\ngenerate O , and therefore participate in ORR reaction to generate H O ; (3) H O can\n2 2 2 2\nalso be oxidized to H O through a stepwise WOR by generate hydroxyl radical\n2 2\nintermediates with one photogenerated hole and then couple with another ·OH to\nproduce H O . However, the pathway (3) requires a significantly higher potential of\n2 2\n+2.73 V vs. NHE, at pH=0, which is thermodynamically unfavorable. We have\nperformed photocatalytic oxygen generation experiments for both COFs, the results\nshowed that no obvious O can be detected during 5 hours of irradiation. Moreover, the\n2\nrotating ring-disk electrode (RRDE) measurements also reveal that no reduction\ncurrents were detected at the Pt ring electrode during the RRDE tests (Figure S42),\nindicating COFs cannot produce O through 4e- WOR pathway. In addition, the H O\n2 2 2\ncan be detected in the presence of electron sacrificial agent (KBrO ) in a nitrogen\n3\natmosphere (Figure S44), suggesting the 2e- direct WOR pathway to generate H O for\n2 2\nboth COFs. To give a better understanding of the WOR pathway, we have revised this\npart in the main text.\nResults:\n“We next utilized rotating ring-disk electrode (RRDE) measurements to investigate the\nwater oxidation processes of the COFs. Increased disk currents were observed for both\nCOFs when the potential exceeded 1.56 V vs. RHE (Figure S42), indicating that water\noxidation occurs at the rotating disk electrode. No reduction currents were detected at\nthe Pt ring electrode during the RRDE tests, and no obvious O could be detected during\n2\n5 hours irradiation (Figure S43), thereby the 4e- WOR pathway for generating O was\n2\nexcluded.54 Additionally, in a nitrogen atmosphere, the application of potassium\nbromate (KBrO ) to quench electrons in the photocatalytic reaction system resulted in\n3\nboth COFs displaying a significant increase in H O production compared to menthol,\n2 2\na hole quenching agent (Figure 5f). This led to a continuous accumulation of H O over\n2 2\nfive hours of irradiation (Figure S44), indicating the direct generation of H O from\n2 2\nH O. Therefore, the WOR pathway was concluded to be the direct 2e- WOR from H O\n2 2\nto H O for both Thz-COF and Imi-COF.”\n2 2\nSupporting data:\n1.0\n) ol0.8\nm\nµ\n(\nn\no0.6\ncti\nu\nd\npr\no0.4\nn\ne\ng\ny0.2\nx\nO\n0.0\nThz-COF Imi-COF\nFigure S43. Photocatalytic oxygen evolution experiment of COFs for 5 hours\nirradiation (20 mg catalyst in a mixture of deionized water 1M AgNO and 0.2 g La O ,\n3 2 3\nλ > 420 nm). The performance was measured using an automatic online gas analysis\nphotocatalytic system (Labsolar-6A, Beijing Perfectlight Technology Co.).\n5. There are obvious errors in the legends or captions of some other figures (such as\nFigure 2d, solid-state 13C nuclear magnetic resonance spectroscopy of the COFs.), and\nthe author should carefully review the manuscript.\nResponse: We sincerely thank the reviewer for this valuable feedback. We have revised\nthe figure caption of Figure 2d as below. In addition, the entire manuscript, including\nthe main text and supporting information, has been carefully revised to ensure clarity\nand correctness.\nc e\nThz-COF Experimental Thz-COF as-synthesized\nPawly refined Thz-COF\nDifference\nB\nC\nr\na\na\nlc\ng\nu\ng\nl a\np\nt\no\ne\ns\nd\nition\n1618 cm-1\n965 cm-1\nSoaked in 1M NaOH\nSoaked in 1M HCl\nImi-COF\n1657 cm-1\nSoaked in 30wt% H2O2\n5 10 15 20 25 30 2500 2000 1500 1000 500 2500 2000 1500 1000 500\nImi-COF Experimental 500 Thz-COF\nPawly refined 400\nD Br if a fe g r g e n p c o e sition -1) g 300 Thz-COF as-synthesized\nR R w p= p= 4 5 .2 .8 8 1 % % Calculated 3 (c m\nu a\nntlty\na ds\nor b e\nd\n1 2\n2\n3\n4\n5\n0 0\n0\n0\n0\n0\n0 0\n0\n0\n0\n0 0 Imi-COF Intensity\n(a.u.)\nT\nT\nT\nh\nh\nh\nz\nz\nz\n-\n-\n-\nC\nC\nC\nO\nO\nO\nF\nF\nF\ns\ns\ns\no\no\no\na\na\na\nk\nk\nk\ne\ne\ne\nd\nd\nd\ni\ni\ni\nn\nn\nn\n1\n1\nM\n0w\nN\nH\nt%\na\nC\nO\nl\nH\nH\n2O2\nQ 100\n0\n5 10\n1\nt\n5\na (degr\n2\ne\n0\ne)\n25 30 0.0 0.\nR\n2\nelative\n0 .\np\n4\nressur\n0\ne\n. 6\n(P/P)\n0.8 1.0 5 10\n15\nta (degre\n20\ne)\n25 30\nPXRD patterns of a, Thz-COF and b, Imi-COF. Fourier transform infrared\nspectroscopy c, and d, Nitrogen adsorption/desorption isotherms of the COFs. e, FT-IR\nspectra and f, PXRD pattern of Thz-COF after soaked in different solvents.\nReviewer #2:\nThis paper proposes a large language model (LLM)-driven design strategy for\nsynthesizing high-performance COF photocatalysts. Various LLM models and LLM-\nbased information retrieval methods were combined to discover the targeted\nCOF photocatalysts. Comprehensive experimental evaluations demonstrated the\neffectiveness and practicality of the proposed strategy. However, I have several\nconcerns as follows.\n1. Is there a module specifically designed for the target task, or did the existing model\nhappen to work well when just applied?\nResponse: We thank the reviewer for this excellent question, which touches on a key\naspect of our methodology. While we utilized the foundational DeepSeek models\nwithout fine-tuning, our contribution lies in the development of a structured framework\naround them, composed of distinct operational modules. The system’s success is not\nincidental but is engineered through these modules:\nGraphRAG for Knowledge Grounding: We implemented a GraphRAG module to serve\nas the primary knowledge source. This component is crucial for minimizing\nhallucinations and providing the LLM with the necessary domain-specific context that\nit inherently lacks.\nPrompt Engineering for Task Guidance: We designed a sophisticated prompt\nengineering framework that functions as a control module. It provides meticulously\ncrafted instructions that guide the LLM's behavior, ensuring its reasoning aligns with\nthe task requirements and that the output conforms to the desired structure.\nIn summary, our work is not about finding a model that “happened to work”, but about\narchitecting a system where specialized modules—GraphRAG and our prompting\nframework—enable a general-purpose LLM to achieve specialized, high-quality results.\n2. Various data-driven strategies for discovering the desired chemical compounds have\nbeen proposed in both chemical science and machine learning. For example, diffusion\nmodels and reinforcement learning showed remarkable performances in these tasks.\nPlease clarify the benefits of the proposed LLM-driven strategy.\nResponse: We thank the reviewer for this insightful question. The primary benefit of\nour proposed LLM-driven strategy lies in its ability to operate effectively in data-scarce\nenvironments.\nWhile methodologies like diffusion models and reinforcement learning have\ndemonstrated remarkable performance, their efficacy is fundamentally dependent on\nthe availability of large, well-curated, and structured datasets. In the field of covalent\norganic frameworks (COFs), such structured data is significantly less abundant\ncompared to related areas like metal-organic frameworks (MOFs). This scarcity of\nstructured data presents a significant barrier to the application of conventional data-\ndriven models, limiting their capacity to efficiently explore the COF chemical space.\nOur LLM-based approach circumvents this limitation by directly leveraging the vast\ncorpus of unstructured data embedded within the scientific literature. The model excels\nat parsing and synthesizing information from individual research articles to construct\nan implicit knowledge graph. By doing so, it unlocks a wealth of information that is\ninaccessible to models reliant on pre-structured databases, thereby offering a more\npromising avenue for knowledge discovery in emerging fields like COFs.\n3. The uploaded source code appears to be work; however, since some of the resources\nrequired for execution were not provided, I was not able to actually run the source code.\nResponse: We thank the reviewer for their diligence in examining the provided source\ncode. The reviewer is correct that the primary data resources—the original PDF files of\nthe research articles—are required for execution.\nWe have updated the GitHub repository to contain the complete list of these articles.\nHowever, due to copyright and publisher licensing agreements, we are unable to\nredistribute the articles themselves. We apologize for the inconvenience.\nReviewer #3:\nThe study by Shu et.al, using a large language model (LLM) to analyze extensive\nliterature, an AI-guided strategy was developed for designing high-performance\ncovalent organic frameworks (COFs) for photocatalytic H O production. The\n2 2\noptimally synthesized thiazole-linked Thz-COF exhibits remarkable stability and\nactivity, achieving a record H O concentration of 82.3 mM and a solar-to-chemical\n2 2\nconversion efficiency of 1.39%. This work highlights the potential of LLM-driven\napproaches in accelerating the discovery of functional materials with tailored properties.\nThis is a very interesting work with robust technical content and high-quality\ncharacterization methodologies. Therefore, I would like to recommend its publication\nafter addressing/responding to the following comments:\n1. The process of converting 354 PDFs into a queryable knowledge graph involves\nseveral automated steps (PDF-to-Markdown, artifact removal, text distillation by an\nLLM agent). An error or loss of critical context at any stage could propagate through\nthe entire workflow, leading to a flawed final recommendation. What quality control\nmeasures were implemented at each step to ensure data fidelity? Specifically, how was\nthe LLM agent's distillation of an entire article into a single paragraph validated to\nensure it accurately and comprehensively represented the key structure-property\nfindings of the original publication?\nResponse: We thank the reviewer for this important question regarding data fidelity.\nWe acknowledge the potential for error propagation in our multi-step data processing\npipeline and implemented quality control measures at each stage.\nThe trade-off between data granularity and computational feasibility is a central\nchallenge. While the text distillation step is necessary to construct a knowledge graph\nof this scale without incurring prohibitive computational costs, we took care to validate\nits output.\nFor the initial PDF-to-Markdown conversion, we recognize that image-based\ninformation is a known limitation of the current workflow. For the subsequent stages,\nwe implemented the following manual checks: (1) Artifact removal: a random sample\nof 10 articles was manually inspected to ensure that only non-essential artifacts (e.g.,\nheaders, page numbers, formatting errors) were removed, leaving the core scientific\ntext intact. (2) LLM-based text distillation: To validate the accuracy of the LLM agent's\nsummary, a domain expert manually reviewed a random selection of 10 source articles\nalongside their corresponding distilled paragraphs. The validation criterion was to\nconfirm that all key structure-property findings, as presented in the original\npublication's abstract, results, and conclusion, were accurately preserved. We found a\nhigh degree of fidelity in the sampled articles, confirming the efficacy of our approach.\n2. Could the authors explain the rationale for first running two independent queries\n(stability and photocatalytic activity) and then cross-referencing the results, rather than\nusing a single, more complex query to find candidates with both attributes?\nResponse: We thank the reviewer for this insightful question about our query\nmethodology. The rationale for employing two independent queries followed by cross-\nreferencing is to intentionally decouple the task of knowledge retrieval from that of\ncomplex scientific reasoning.\nOur architecture leverages two distinct models for these stages. The initial retrieval\nfrom the knowledge graph is handled by the GraphRAG engine, which is optimized for\nhigh-fidelity, factual retrieval based on specific concepts (e.g., ‘stability’ or\n‘photocatalytic activity’). This model is designed to maximize recall and precision for\na single, well-defined attribute.\nThe subsequent cross-referencing and analysis are performed by a more advanced\nmodel with superior reasoning capabilities. By separating these processes, we ensure\nthat the initial candidate pool for each desired attribute is as comprehensive and\naccurate as possible. The final synthesis step is then based on two high-quality,\nindependently-retrieved sets of data, which we argue enhances the scientific validity\nand reliability of the final recommendations.\n3. To provide a comprehensive context for the current research, it is imperative to\nincorporate and critically analyze more recent, cutting-edge photocatalytic H O\n2 2\nproduction studies based on COFs. Notable examples include, but are not limited to: J.\nAm. Chem. Soc. 2025, 147, 33, 30287–30295; Nat Commun 16, 7654 (2025); Angew.\nChem. Int. Ed. 2025, e202511687; ACS Appl. Mater. Interfaces 2025, 17, 25828−25838\netc. A comparative analysis of these works would significantly enhance the scholarly\ndepth and relevance of the manuscript.\nResponse: We thank the reviewer for this excellent suggestion. We have now\nthoroughly studied these references and added the recently novel works in the\nmanuscript and supporting table as below.\nIntroduction:\nExtensive strategies, such as the construction of donor-acceptor (D-A) structure,9-11\ntopological design12,13 and electronic distribution,14-16 have been devoted to COF-based\nphotocatalysts to boost the H O production performance, with a high solar-to-chemical\n2 2\nconversion efficiency (SCC) exceeding 1%.13,14\nCharge transfer dynamics:\nThe long-lived GSB in Thz-COF results in a higher probability of electron-hole\nseparation within the COF structure and enhances the interfacial transfer process, which\ncrucially influences its photocatalytic performance.40,41\nReferences:\n15. Ju, Y. et al. Regulating the electronic structure of covalent organic frameworks via\nheterocyclic isomers for highly efficient photocatalytic H O generation. Nat. Commun.\n2 2\n16, 5658 (2025).\n16. Sun, Z. et al. Simultaneous high-efficiency photocatalytic production of H O and\n2 2\nsynthesis of 3-HBA using COFs broadened by π conjugate networks and\nphotogenerated charges. ACS Appl. Mater. Interfaces 17, 25828-25838 (2025).\n41. Yang, S. et al. Engineering imine carbon catalytic sites in covalent organic\nframeworks for enhanced overall H O photosynthesis. J. Am. Chem. Soc. 147, 30287-\n2 2\n30295 (2025).\nSupporting tables:\nTable S5. Summary of the photocatalytic hydrogen peroxide production performance\nof various reported organic photocatalysts.\nPhotocatalyst λ (nm) Dosage Reaction solutiona H O production Concentration EQE (%) SCC Ref.\n2 2\n(g/L) (μM/h) μM (h)b (%) \nTTT-COF ≥420 0.1 H O:EtOH (9:1) 2990 ~735000(200) 12.0@400 nm 0.32 39\nn1 2\nTTBA-TP-COF ≥400 0.05 H O:EtOH:TEA 594.6 ~1200(2) - - 42\n2\n(40:5:1)\naEtOH: ethanol; TEA: triethylamine; BA: benzyl alcohol; ATN: acetonitrile. breaction\ntime.\nSupporting References:\n39. Yang, J. et al. Homologous heteropolyaromatic covalent organic frameworks for\nenhancing photocatalytic hydrogen peroxide production and aerobic oxidation. Nat.\nCommun. 16, 7654 (2025).\n40. Yang, S. et al. Engineering imine carbon catalytic sites in covalent organic\nframeworks for enhanced overall H O photosynthesis. J. Am. Chem. Soc. 147, 30287-\n2 2\n30295 (2025).\n41. Liu, Q. et al. Fully exposed silver clusters enabling highly efficient photocatalytic\nH O production in pure water. Angew. Chem. Int. Ed. 64, e202511687 (2025).\n2 2\n42. Sun, Z. et al. Simultaneous high-efficiency photocatalytic production of H O and\n2 2\nsynthesis of 3-HBA using COFs broadened by π conjugate networks and\nphotogenerated charges. ACS Appl. Mater. Interfaces 17, 25828-25838 (2025).\n4. The Isotope experiments should be supplemented to verify the proton source.\nResponse: We thank the reviewer. As suggested, we conducted isotope experiments\nusing D O as the solvent. The photocatalytic performance of Thz-COF in D O was\n2 2\nnearly identical to that in H O, and no significant H O signal was detected in the ¹H-\n2 2 2\nNMR spectrum (Figure S41). These results indicate that H O is generated from H O\n2 2 2\nand O , consistent with previous reports (Nat. Commun. 2024, 15, 5316). We have\n2\nupdated these data and discussion in the main text as below.\nResults:\nTo identify the proton source for H O generation, D O was initially employed as the\n2 2 2\nsolvent. Thz-COF exhibited comparable performance in both H O and D O, and the\n2 2\nabsence of an H O signal in the 1H-NMR spectrum confirmed that the protons\n2 2\noriginated from H O (Figure S41).53\n2\n(a) (b)\n2.0\nM) 1.5\nm\n(\nn\no\ncti\n1.0\nd\nu\no\npr\nO 2\nH20.5\n0.0\nThz-COF in HO Thz-COF in DO\n2 2\nFigure S41. (a) Photocatalytic H O production performances for Tz-QH-COF (10 mg\n2 2\ncatalyst in 20 ml H O or D O under visible light (λ > 420 nm for 1 hour)). (b) 1H-NMR\n2 2 2\nspectroscopy of of the photocatalytic D O production of the Thz-COF in D O.\n2 2 2\n5. The experiment detail of some characterizations have not been listed in the section\nof Characterization methods, such as Temperature-dependent PL spectra.\nResponse: We have added the experiment detail of the temperature-dependent PL\nspectra in the supporting information.\nCharacterization methods:\n“Time-resolved photoluminescence (TRPL) and temperature-depended\nphotoluminescence measurements were conducted using a single photon counting\ncontroller (Fluorohub, Horiba Scientific) to collect the photoluminescence decay\nprofiles and photoluminescence spectra from 80 to 298K.”\n6. The author claimed that the exfoliation of Thz-COF after long time reaction, some\nevidence should be provided.\nResponse: We sincerely thank the reviewer for this valuable suggestion. The Atomic\nforce microscope (AFM) was introduced to reveal the particle size of Thz-COF after\nlong-term reaction. It was found that the reduction in particle size, rather than\nexfoliation after long-term reaction (Figure S34), which might be the result of\nmechanical stimuli, resulting in the decrease in crystallinity (Figure S33). We have\nincluded this data in the revised manuscript.\nResults:\n“However, a decrease in crystallinity for Thz-COF was observed after 120 hours of\nirradiation (Figure S33), likely due to the mechanical stimuli reducing particle size,45\nvisualized via atomic force microscope (AFM) (Figure S34).”\n(a) (b)\nFigure S34. Atomic force microscope images and analysis of Thz-COF (a) before\nphotocatalytic reaction and (b) after 5 cycling experiments.\nReferences:\n45. Chandra, S. et al. Chemically stable multilayered covalent organic nanosheets from\ncovalent organic frameworks via mechanical delamination. J. Am. Chem. Soc. 135,\n17853–17861 (2013).\n7. To better understand the charge carrier dynamics, we suggest that the authors add the\nfitted parameters from the photoluminescence decay spectra. These parameters are\ncrucial for elucidating the efficiency of charge separation and transport in the material.\nThe test method can be referred to Angew. Chem. Int. Ed. 2023, 62, e202304173, etc.\nResponse: We thank the reviewer for this comment. We have added the fitted\nparameters of the photoluminescence decay spectra in the supporting information as\nTable S2 as below:\nSupporting tables:\nTable S2. The fitted parameters obtained from PL decay curves of the COFs.a\nSample B τ (ns) (Rel. %) B τ (ns) (Rel.%) τ (ns)\n1 1 2 2 ave\nThz-COF 1333 0.426 (45.78) 694 0.969 (54.22) 0.72\nImi-COF 2238 0.487 (82.76) 186 0.121 (17.24) 0.42\naτ and τ represent the lifetime, respectively; B and B are the corresponding pre-\n1 2 1 2\nexponential factors of the lifetime. The calculation formula is as follows: τ = τ *Rel. %\nave 1 1\n+ τ Rel. %.\n2* 2\n8. Please deeply check English language level and correct some spelling and\ngrammatical errors. For example, In Figure S8, the “slovents” should be solvents.\nResponse: We thank the reviewer for this comment. We have carefully reviewed the\nentire manuscript, including the main text and supporting information, to identify and\ncorrect typographical and grammatical errors. The language has been polished to\nimprove clarity and readability, ensuring that the manuscript meets the high standards\nexpected for publication.\nSupporting data:\nThz-COF as-synthesized\nImi-COF as-synthesized\nThz-COF soaked in 30wt% HO\n2 2 Imi-COF soaked in 30wt% HO\nThz-COF soaked in 1M HCl 2 2\nThz-COF soaked in 1M NaOH Imi-COF soaked in 1M HCl\nImi-COF soaked in 1M NaOH\nu.) u.)\na. a.\nsit\ny\n(\nsit\ny\n(\nn n\ne e\nnt nt\nI I\n5 10 15 20 25 30 5 10 15 20 25 30\nFigure S8. PXRD patterns of the COFs after soaked in different solvents for 48 h (a)\nThz-COF and (b) Imi-COF."}, {"round": 2, "reviewer_comments": {"reviewer_1": "accepted as it is", "reviewer_4": "The authors reported an interesting strategy to utilize AI for the direct synthesis of COF photocatalysts for the production of\nhigh-concentration H2O2. They used the large language model DeepSeek to analyze more than 300 research articles on\nCOF-based photocatalysis to make recommendations for potential building blocks. The prepared COF photocatalysts\nexhibit excellent activity and stability and achieved a record high H2O2 concentration of 82.3 mM (~0.28 wt%) in aqueous\nsolution. Overall, it is an insightful research for using AI for catalyst designs. I have also reviewed carefully the comments\nfrom the previous round of peer review and I think the authors have supplied sufficient explanations and additional data to\naddress the comments. In particular, for the comments of Reviewer 2, I think the authors have clarified the AI engineering\nsteps, the advantages of the LLM model, and supplemented the uploaded source codes, which have solidified the\napplication of the AI model in this work. Overall, I think this work can be considered for Nature Communications after\naddressing a few minor comments.\n1. What is the H2O2 concentration in the industrializing producing process? This data is very important to show because this\nhelps to make the target clear.\n2. For accelerating the future discovery of photocatalysts, are there any potential methods or AI models that can be further\nconsidered to improve the design efficiency?\n3. In Figure 6a and 6c, it is not easy to distinguish sites 1 and 2 (as well as site 3 and 4). It is suggested that the authors\nimprove the figure demonstrations."}, "author_response": "Manuscript ID: NCOMMS-25-57806A Response to Reviewers’ Comments\nGiven below are our responses (in BLUE colour) to the reviewers’ comments. The\nchanges to the manuscript and supporting information are marked in RED in the\nresponse letter, which are highlighted in YELLOW in the revised manuscript and\nsupporting information.\nReviewer #4:\nThe authors reported an interesting strategy to utilize AI for the direct synthesis of COF\nphotocatalysts for the production of high-concentration H O . They used the large\n2 2\nlanguage model DeepSeek to analyze more than 300 research articles on COF-based\nphotocatalysis to make recommendations for potential building blocks. The prepared\nCOF photocatalysts exhibit excellent activity and stability and achieved a record high\nH O concentration of 82.3 mM (~0.28 wt%) in aqueous solution. Overall, it is an\n2 2\ninsightful research for using AI for catalyst designs. I have also reviewed carefully the\ncomments from the previous round of peer review and I think the authors have supplied\nsufficient explanations and additional data to address the comments. In particular, for\nthe comments of Reviewer 2, I think the authors have clarified the AI engineering steps,\nthe advantages of the LLM model, and supplemented the uploaded source codes, which\nhave solidified the application of the AI model in this work. Overall, I think this work\ncan be considered for Nature Communications after addressing a few minor comments.\n1. What is the H O concentration in the industrializing producing process? This data\n2 2\nis very important to show because this helps to make the target clear.\nResponse: We thank the reviewer for these insightful comments. In the industrial\nprocess, the H O can be initially obtained at a concentration of 0.8-1.9 wt% in the\n2 2\nworking solution through the anthraquinone method (Angew. Chem. Int. Ed. 2006, 45,\n6962–6984). We have added this important data to the manuscript as below.\nIntroduction:\n“Industrial production of industrial H O has been dominated by the anthraquinone\n2 2\noxidation process, while this process needs considerable energy input and produces\nwaste.2\nDespite these efforts, the achievable H O concentrations in sacrificial agent-free\n2 2\nsystems remain significantly below 0.1 wt%17. This concentration is inferior to the\nworking solution (0.8-1.9 wt%) of anthraquinone oxidation process,2 and is drastically\ninsufficient to compete with the commercial H O (30 wt%). Consequently, this cannot\n2 2\nto meet the minimum thresholds required for real-world applications such as\ndetoxification of aflatoxins in foods.18\n2. For accelerating the future discovery of photocatalysts, are there any potential\nmethods or AI models that can be further considered to improve the design efficiency?\nResponse: Our work has demonstrated the abilities of LLMs in chemical literature\nknowledge summarization, knowledge graph creation, and on-demand retrieval, as well\nas basic chemical reasoning and analysis; however, we have just touched the tip of the\niceberg on the applications of LLMs in photocatalysis research. Recent research by\nZheng et al. (Nat Mach Intell. 2025, 7, 437-447) reported that LLMs are capable of\ndirectly predicting chemical properties with provided training samples, surpassing\nvarious traditional property prediction methods in multiple fields, demonstrating their\npotential in precise, quantitative chemical reasoning that could be useful in\nphotocatalyst design. Another recent paper by Pruyn et al. (J. Am. Chem. Soc. 2025,\nDOI: doi.org/10.1021/jacs.5c11789) introduced a hybrid approach of combining\nstructured data (databases) and unstructured data (scientific literature) on MOFs, named\nMOF-ChemUnity, has demonstrated agentic LLM tools capabilities on building a\nhuman-readable and LLM-navigable knowledge base, which is a step forward from the\ntraditional knowledge graph approach. Such a paradigm has potential in enhancing\nLLM’s understanding in other fields, including photocatalysis. Overall, we believe that\nthese progresses will be helpful for the discovery of novel photocatalysts and further\ndevelopments are on the horizon.\n3. In Figure 6a and 6c, it is not easy to distinguish sites 1 and 2 (as well as site 3 and 4).\nIt is suggested that the authors improve the figure demonstrations.\nResponse: We thank the reviewer for this good comment. We have revised Figure 6\nand Figure 4 to give a clear demonstration as below:\nResults:\nc Site 1 Site 2\n0\n) ol\nm\n(kJ/\ngy -5\nn\ner\nO\na\nds\nor\npti\no\nn\ne\n2\n-10\nT\nIm\nh\ni\nz\n-C\n-C\nO\nO\nF\nF\n-15\nSite 3 Site4\n0\n) ol\nm\n(kJ/\ngy\n-5\ner\nor\npti\no\nn\ne\nn\n-10\nds-15\nSite 4 H O a 2 -20 T Im h i z -C -C O O F F 1 1 . . 5 8 7 0 5 0\n4.0 1.350\nZn foil Zn-N/O Thz-COF-Zn Thz-COF-Zn\nNor\nmalized\nabsorption\n(a.u.) Z T n h O z-COF-Zn\n3\n|\n(\n| ( )\nF\nk) T\n×k -4\nc\nÅ\nZn-S\nZn-Zn\nFitting\nR ( Å)\n1\n2\n2\n3\n3\n.\n.\n.\n.\n.\n5\n0\n5\n0\nZ\n/O\nn-Z\nZ\nn\nn-S\n0\n0\n0\n0 0\n1\n.\n.\n.\n.\n.\n.\n0\n2\n4\n6 9\n1\n0\n2\n5\n7 0\n2\n0\n5\n0\n5 0\n5\n0\n0\n0 0\n1.0\n9650 9700 9750 9800 0 2 4 6 -5 0 5 10 15\nh Energy (eV) R (Å) k (Å -1)\n1600 3 1\nThz-COF Thz-COF\nThz-COF Imi-COF Imi-COF\nO\n( M)\nproduction\nm\n2\n12\n8\n0\n0\n0\nFree\nenergy\n(e V)\n1\nenergy\n(e V)\n-1\nThz-COFThz-COF-Zn\n0\nReaction\nO H\nco\n*\nordinate\nH2O2 -3 O2(g) O\nR\n2*\neact\nO\nio\nO\nn\nH\nc\n*\noord\nH\nin\nO\na\nO\nte\nH* H2O2\nFigure 6. Active sites and reaction thermodynamics. Different adsorption sites of a,\nO and b, H O on Thz-COF and Imi-COF.\n2 2\nb c\n2.0 16 90\n1.0 %) T Im h i z - - C C O O F F\nH\nO\n( m M) production\n2\n2\n0\n1\n1\n.\n.\n.\n5\n0\n(a.u.)\n0\n0\n0\n0\n.\n.\n.\n.\n2\n4\n6\n8\n4\n8\n(\nxternal\nquantu m\nefficiency\nH O\nconcentration ( m M)\n2 2\n3\n6\n0\n0 ~0.28 wt%\n0.0 0.0 0 0\nThz-COF Imi-COF 300 400 500 600 700 800 0 10 20 30 40 50 60 70 80\nWavelength (nm) Time (hour)\nd e 90 This Work\nTotal reaction time: 120 hours QP-HPTP-COF TPB-COF-OH\nH O\n( m M) production\n2 21\n2\n3\n0\n0\nconcentration ( m M)\n2 2 3\n6\n0\nR\nP\nH\no T T\nS\n-\np\nP A\nI\nO H E\nF\nE\nC\n-\nD -\nB\nE P\n/\nF F\nP\nO\nO\n-\n-\nz\nT\nD\n-\n- - M\nD\nT\nH T\nF -\n-\nH\nF Q\nA\nT\n-\nf P O\nT\np\nT\nT\nP\nP\nD M\np\nB\nP\nB\nT\nA\nP\np e\nB\n-\nz\n-\nC\ny C\nd\nO\nO\na\nF\nF\nS\nB\nT\nB T P T C\nC\nT T F\nA B\nA P\nO\ny\nT\np N\nT\n-\nP\nH\nt\nD\nD\nF\nN\n0\n-\nH\n-\nT\n-\nC\n- - \nB\n- T a\nB\nC D C\nC\n-\n-\nT\n-\n-\n-\nT\nD\nM O\nC\nO A C F F\nO\nD\nl\nN\nF\nO\nF\nF\nT\nP\nF\nB\nP\n.2\nN\n-COF\nCOF-N32 COF-JLU51\nTACOF-1-COOH TPT-Cz-phCN\n0 0 TD-COF TB-COF\n1 2\nCycle n\n3\number\n4 5 0.0 0.2 0.4\nSo\n0\nla\n.6\nr to ch\n0\ne\n.\n8\nm\nical e\n1\nf\n.\n0\nf\nicienc\n1\ny\n. 2\n(%)\n1.4 1.6 1.8\nFigure 4. e, Concentration of photocatalytic produced H O and SCC of Thz-COF\n2 2\ncompared with other reported materials."}]

Decision letter