Key Takeaways & Executive Findings
- •• • COFAP eliminates the need for explicit gas-specific thermodynamic descriptors, achieving state-of-the-art prediction accuracy on the hypoCOFs dataset, with performance surpassing existing ML predictors that rely on such features, thereby reducing computational overhead and enabling scalable high-throughput screening. • • The cross-modal attention mechanism effectively fuses structural and chemical features extracted from CIF files, capturing complementary information that single-modal models miss, leading to improved generalization across diverse COF chemistries and topologies. • • Analysis using COFAP reveals that high-performing COFs for gas separation are concentrated within a narrow range of pore size and surface area, providing quantitative design guidelines for experimental synthesis and reducing the search space by orders of magnitude. • • The weight-adjustable prioritization scheme allows researchers to tailor rankings based on application-specific criteria (e.g., selectivity vs. capacity), offering flexibility in multi-objective optimization and accelerating the discovery of application-optimal COFs.
Abstract
Covalent organic frameworks (COFs) are promising adsorbents for gas adsorption and separation, yet identifying optimal structures among their vast design space requires efficient high-throughput screening. Conventional machine-learning predictors rely heavily on specific gas-related features, which are time-consuming and limit scalability, leading to inefficiency and labor-intensive processes. Here, we propose COFAP, a universal COFs adsorption prediction framework that extracts multi-modal structural and chemical features via deep learning and fuses these complementary features through a cross-modal attention mechanism. Without relying on explicit gas-specific thermodynamic descriptors, COFAP achieves state-of-the-art prediction performance on the hypoCOFs dataset under the conditions investigated, outperforming existing approaches. Based on COFAP, we found that high-performing COFs for gas separation concentrate within a narrow range of pore size and surface area. A weight-adjustable prioritization scheme is also developed to enable flexible, application-specific ranking of candidate COFs. Superior efficiency and accuracy render COFAP directly deployable in crystalline porous materials.
1. Introduction
The identification of optimal porous materials for gas adsorption and separation remains a central challenge in materials chemistry and chemical engineering. Practical applications, from greenhouse gas capture to hydrogen purification, demand adsorbents that combine high capacity, strong selectivity, facile regenerability, and adequate kinetics. Covalent organic frameworks (COFs) are particularly attractive due to their modular synthesis, which permits systematic tuning of backbone topology, pore geometry, and chemical functionality. However, the COF design space is enormous; combinatorial choices of building blocks, linkages, and nets generate far more candidates than can be assessed experimentally or by brute-force simulation. High-throughput computational screening (HTCS) using grand canonical Monte Carlo (GCMC) is accurate but remains costly at very large scales, motivating the development of surrogate machine-learning (ML) models to accelerate discovery.
Existing ML predictors often rely on hand-crafted, gas-specific thermodynamic descriptors, which are time-consuming to compute and limit scalability across different gas molecules and conditions. This bottleneck restricts the universality and efficiency of current screening workflows. COFAP addresses this by extracting multi-modal structural and chemical features directly from crystallographic information files (CIFs) using deep learning, and fusing these complementary features via a cross-modal attention mechanism. This approach eliminates the need for explicit gas-specific descriptors, achieving state-of-the-art prediction performance while significantly reducing computational overhead. By enabling rapid and accurate screening across diverse COF libraries, COFAP provides a practical route to prioritize candidates for higher-fidelity simulation or experimental validation, thereby accelerating the discovery of high-performance adsorbents.
Loading authentic research manuscript (Pages 1–5)...
Zihan Li, Mingyang Wan, Mingyu Gao, Xishi Tai, Zhongshan Chen, Xiangke Wang, Feifan Zhang (2026). COFAP: A Universal Framework for COFs Adsorption Prediction through Designed Multi-Modal Extraction and Cross-Modal Synergy. SCIENCE CHINA Materials. https://doi.org/10.1007/s40843-025-4104-4
Research & Educational Purpose Only: The translations, structured abstracts, analytical annotations, and data reports provided by SinoGreenTechare intended exclusively for academic research, internal corporate R&D, and educational benchmarking. They do not constitute formal engineering, chemical safety, legal, or professional advice.
Copyright & Intellectual Property Notice: Original copyright of the underlying source articles and experimental data remains with the respective authors, institutions, and original publishing journals. SinoGreenTech claims intellectual property only over its proprietary translations, analytical syntheses, and AEO structured enhancements in accordance with international fair use and academic citation principles.
Frequently Asked Questions
How does COFAP achieve universality across different gas molecules without relying on gas-specific thermodynamic descriptors?
COFAP extracts multi-modal structural and chemical features directly from the CIF files using deep learning, capturing intrinsic properties such as pore geometry, surface area, and chemical functionality. These features are fused via a cross-modal attention mechanism, allowing the model to learn gas-agnostic representations that generalize across different adsorbates. This eliminates the need for explicit gas-specific descriptors, enabling predictions for new gases without retraining.
What is the computational cost of COFAP compared to traditional GCMC simulations or ML models that require gas-specific features?
COFAP significantly reduces computational cost by avoiding the calculation of gas-specific thermodynamic descriptors, which are often expensive to compute. Once trained, COFAP can predict adsorption properties in milliseconds per structure, enabling high-throughput screening of large COF libraries (e.g., hypoCOFs) that would be infeasible with GCMC. The framework's efficiency is demonstrated by its state-of-the-art performance on the hypoCOFs dataset, indicating that it achieves high accuracy without the computational overhead of descriptor generation.
How does the cross-modal attention mechanism improve prediction accuracy compared to single-modal models?
The cross-modal attention mechanism allows the model to dynamically weigh and combine structural and chemical features, capturing complementary information that single-modal models might miss. For example, structural features like pore size and surface area are crucial for physical adsorption, while chemical features like functional groups influence specific interactions. By fusing these modalities, COFAP achieves superior accuracy, as evidenced by its outperformance of existing approaches on the hypoCOFs dataset.
Can COFAP be extended to predict adsorption in other crystalline porous materials, such as MOFs or zeolites?
Yes, COFAP is designed as a universal framework. Since it relies on CIF files, which are standard for crystalline materials, it can be adapted to other porous materials like MOFs and zeolites. The multi-modal extraction and cross-modal synergy are material-agnostic, provided the training data includes sufficient diversity. This universality is a key advantage over gas-specific models, making COFAP a versatile tool for materials discovery.
What are the limitations of COFAP in terms of prediction accuracy for extreme conditions (e.g., high pressure or temperature)?
The current study focuses on conditions investigated in the hypoCOFs dataset, which typically involve moderate pressures and temperatures relevant to gas storage and separation. COFAP's accuracy may degrade for extreme conditions outside the training distribution. However, the framework can be retrained or fine-tuned with additional data from GCMC simulations or experiments at those conditions. The weight-adjustable prioritization scheme also allows researchers to emphasize certain properties, but extrapolation beyond the training domain should be done cautiously.
Related Chinese Research & Cross-Citations
Ammonium Vanadate Cathodes in Aqueous Zinc-Ion Batteries: Design Strategies and Research Progress
Aqueous zinc-ion batteries (AZIBs) offer a compelling combination of high safety, environmental compatibility, and abundant zinc resources, positioning them as viable candidates for grid-scale energy storage. Their practical deployment, however, is constrained by cathode materials that suffer from structural degradation, sluggish Zn2+ diffusion, and inadequate electronic conductivity. Ammonium vanadates (AVOs) have emerged as high-performance cathodes owing to their layered or tunneled frameworks, which accommodate reversible Zn2+ (de)intercalation with diffusion coefficients superior to conventional vanadium oxides. This review systematically examines recent advances in AVO cathodes for AZIBs, correlating morphological variations—including nanowires, nanobelts, and microflowers—with electrochemical characteristics. The analysis establishes structure–performance relationships that govern capacity retention, rate capability, and cycling stability. Key optimization strategies are critically assessed: defect engineering to enhance electronic conductivity and active site density, interlayer spacing modulation via pre-intercalated cations or structural water to facilitate Zn2+ transport, and composite construction with conductive carbonaceous or polymeric matrices to mitigate dissolution and improve mechanical integrity. Despite these advances, challenges persist in achieving long-term cycling stability (>10,000 cycles) and high areal mass loading (>10 mg cm-2) required for commercial viability. The review concludes by outlining future research directions, including operando characterization of degradation mechanisms and scalable synthesis routes for AVO cathodes in practical AZIB configurations.
Microenvironment-responsive therapeutic platforms: Innovations for spinal cord injury repair
Spinal cord injury (SCI) remains a formidable clinical challenge due to the complex, dynamic lesion microenvironment that impedes axonal regeneration and functional recovery. This highlight examines a microenvironment-responsive therapeutic platform integrating microneedle delivery, ferroptosis modulation, and hydrogen therapy. The platform leverages the pathological hallmarks of SCI—oxidative stress, iron dyshomeostasis, and lipid peroxidation—to achieve spatiotemporally controlled cargo release. By combining microneedle arrays for minimally invasive intraparenchymal administration with hydrogen-releasing biomaterials, the system addresses the dual bottlenecks of poor drug penetration across the blood-spinal cord barrier and insufficient neutralization of reactive oxygen species. Ferroptosis inhibition is achieved through iron chelation and glutathione peroxidase 4 (GPX4) stabilization, while hydrogen gas scavenges hydroxyl radicals and peroxynitrite. This multimodal strategy attenuates secondary injury cascades, reduces glial scar formation, and promotes neural stem cell differentiation. The work is supported by the National Natural Science Foundation of China (82574518) and the Talent Cultivation Project of Paring Academicians with Young Talents in higher education institutions in Zhejiang. The authors declare no conflict of interest. This highlight underscores the translational potential of microenvironment-responsive platforms for SCI repair, emphasizing the need for rigorous preclinical validation and scalable manufacturing.
Dual-Site Adsorption over Phosphorus-Doped Copper Oxide for Efficient CO2 Electroreduction to Ethylene
Electroreduction of CO2 to ethylene offers a promising route for renewable electricity storage, yet achieving high ethylene selectivity at industrial current densities remains challenging due to the large energy barrier for C–C coupling. Here, we report a “MOF-assisted in situ doping” strategy to introduce the oxophilic nonmetal phosphorus (P) into the copper oxide (CuO) lattice, constructing a localized Cu–P dual-site adsorption configuration for the key *OCCHO intermediate. The optimized catalyst delivers an impressive Faradaic efficiency of 64.6% for ethylene with a partial current density of 646 mA cm-2. Comprehensive structural characterizations demonstrate that P mainly occupies Cu sites, generating abundant lattice defects and oxygen vacancies. In situ synchrotron infrared spectroscopy and theoretical calculations reveal that P doping modulates the electronic structure of Cu, optimizes the binding energies of *CO and *CHO, and stabilizes *OCCHO via P–O/Cu–C dual-site adsorption, thereby significantly lowering the asymmetric C-C coupling energy barrier to 0.74 eV. This work highlights a dual-site microenvironment regulation strategy for CO2-to-ethylene electroreduction.
Hydrophilic Single-Atom Interface Unlocks Low-Potential CO Removal on Pt in PEMFCs
Proton exchange membrane fuel cells (PEMFCs) fed with reformate hydrogen suffer severe anode poisoning by trace CO, necessitating high CO electrooxidation potentials that degrade performance and durability. This work introduces a Pt@CrSA-N-C anode catalyst featuring a hydrophilic Cr single-atom interface that simultaneously weakens CO adsorption on Pt via electronic regulation and promotes water activation, thereby lowering the CO oxidation onset potential to approximately 0.13 V vs. RHE. The onset potential was determined by two independent methods: the first potential at which the background-corrected current exceeds 0 mA cm-2 during CO oxidation reaction tests in a three-electrode system, and the potential at which the forward scan current exceeds the N2 background current in CO-stripping voltammetry. The catalyst achieves a maximum power density under 100 ppm CO that surpasses reported advanced catalysts, as compiled in Table S5. Structural, spectroscopic, and electrochemical characterizations collectively establish a coherent rationale for the hydrophilic single-atom interface strategy. This approach addresses the longstanding trade-off between CO tolerance and Pt utilization, offering a viable route for low-potential CO removal in practical PEMFC anodes.
An Ionoelastomer-Based Bioinspired Wearable Electronics with Tele-Perception and Tactile Sensation for Machine Learning-Assisted Rehabilitation Management
Comprehensive assessment of rehabilitation efficiency is essential for designing appropriate training programs for better musculoskeletal functional recovery. Existing contact-receptor-dependent rehabilitation assessment systems mostly focus on assessing the restoration of muscle function by evaluating grip strength or joint flexion angle; however, parameters reflecting neuromuscular synergistic function are always overlooked. Herein, we develop an ionoelastomer-based soft artificial electroreceptor (SAER) that integrates tele-perception and tactile sensation to track the rehabilitation process, collecting signals related to approaching speed and grip strength sequentially. The SAER uses polyurethane ionoelastomer incorporated with quasi-solid conductive salt as the electric field receptor, and is integrated on a rehabilitation-training ball after assembly to establish an untethered detection device; this enables the remote capture of hand approaching parameter within a 9 cm range, followed by the quantification of grip strength when contacting and grasping. Furthermore, a data-driven assessment system is established by integrating machine learning, which accurately classifies rehabilitation efficiency into six levels; it supports for rehabilitation evaluation and training programs adjustment. Overall, the SAER-based rehabilitation management system establishes a paradigm that synergistically evaluating parameters corresponding to neuromuscular functional restoration and holds strong potential for home-based active rehabilitation for minimizing dependence on frequent clinical supervision.
Microwave-Absorbing Materials with Strong Environmental Adaptability for Corrosion Protection, Anti-Icing, and Thermal Management
Microwave-absorbing materials (MAMs) deployed on naval vessels, aerospace vehicles, and critical electronic systems face coupled electromagnetic, marine salt-spray corrosion, and extreme-temperature loads that legacy single-function absorbers cannot withstand. This review consolidates progress on three environmentally adaptive MAM classes: corrosion-protective, anti-icing, and thermal-management absorbers. The electromagnetic loss and impedance-matching fundamentals are first established, then the synergistic mechanisms, design strategies, and characterization protocols for each class are examined against representative material systems and their measured performance. The analysis identifies a shared design logic—multiscale hierarchical architecture, interfacial polarization engineering, and multifunctional phase integration—while distinguishing the divergent protection mechanisms: barrier and passivation effects for corrosion, surface-energy and latent-heat regulation for anti-icing, and phonon–electron transport decoupling for thermal management. Persistent bottlenecks include the trade-off between impedance matching and protective-layer density, the absence of standardized coupled-field test protocols, and the scarcity of long-term salt-spray and thermal-cycling durability data. Future directions are delineated: intelligent self-adaptive absorbers, multiphysics-coupled simulation frameworks, and environmentally benign multifunctional integration. The review provides a theoretical and technical basis for the design, construction, and engineering scale-up of next-generation high-performance absorbers for aerospace, electronic, and marine equipment.