Comprehensive review of nitrogen‐doped porous carbon materials for <scp> CO <sub>2</sub> </scp> capture: synthesis techniques, computational modelling, machine learning and future perspectives
窒素ドープ多孔質炭素材料によるCO2回収の包括的レビュー：合成技術、計算モデリング、機械学習、将来展望 (AI 翻訳)
Shajaratuldur Ismail, Farihahusnah Hussin, Zong Yang Kong, Mohamed Kheireddine Aroua
🤖 gxceed AI 要約
日本語
本レビューは、CO2回収用窒素ドープ多孔質炭素材料の合成法（熱分解、活性化、テンプレート法等）と機械学習（ML）による性能予測を包括的に解説。密度汎関数理論による原子レベル解析とMLモデル（細孔容積、表面積、窒素種を入力）の統合により、材料設計の加速可能性を示す。データ不足や実験検証の課題も指摘し、実用化に向けた展望を提示。
English
This review comprehensively covers synthesis methods (pyrolysis, activation, template-assisted, etc.) for nitrogen-doped porous carbons for CO2 capture, and integrates machine learning (ML) models to predict adsorption capacity from key features (micropore volume, BET surface area, nitrogen speciation). It highlights the potential of combining density functional theory and ML to accelerate materials design, while addressing challenges like data scarcity and limited experimental validation.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本のCCUS戦略（鉄鋼・電力分野）では低コストCO2吸着材が重要。本レビューは窒素ドープ炭素の合成とML活用を網羅し、研究開発の方向性を示す有用な資料。
In the global GX context
Global CCUS deployment requires cost-effective adsorbents. This review integrates ML models to predict CO2 capture performance, offering a pathway to accelerate materials discovery for carbon capture applications.
👥 読者別の含意
🔬研究者:Comprehensive overview of synthesis and ML approaches for nitrogen-doped carbons for CO2 capture, useful for materials science researchers.
🏛政策担当者:Highlights the potential of ML-accelerated materials design for carbon capture, supporting policy on CCUS R&D funding.
📄 Abstract（原文）
Abstract Nitrogen‐doped porous carbons have emerged as promising and cost‐effective adsorbents for carbon dioxide (CO 2 ) capture owing to their tuneable pore structures, varied nitrogen functionalities, and compatibility with sustainable precursors. This review provides a comprehensive overview of precursor types and synthesis strategies, including pyrolysis, chemical activation, template‐assisted, hydrothermal, plasma, and microwave‐assisted methods, and examines the effects of these approaches on the structural and surface properties of the materials. The role of nitrogen doping in enhancing CO 2 adsorption capacity, selectivity, and interaction mechanisms is critically assessed. Emphasis is placed on identifying active sites and understanding the contributions of pyridinic, pyrrolic, and graphitic nitrogen under various conditions. The discussion includes stability, regeneration behaviour, and comparisons with conventional adsorbents to assess their practical applicability. The review further integrates computational and data‐driven approaches. Density functional theory studies offer atomistic insights into potential energy of CO 2 surface interactions, while machine learning (ML) models facilitate the mapping of structure performance relationships and the prediction of adsorption capacity based by analysing key input such as micropore volume, Brunauer–Emmett–Teller surface area, and nitrogen speciation. Key challenges are also addressed, including lack of data, inconsistent datasets from the literature, and limited experimental validation of ML predictions. Despite significant progress, challenges remain in scaling synthesis methods, improving long‐term stability under realistic conditions, and optimising nitrogen functionalities. The integration of experimental, theoretical, and ML approaches offers strong potential to accelerate the design and development of nitrogen‐doped porous carbons for practical CO 2 capture applications. © 2026 Society of Chemical Industry (SCI).
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1002/jctb.70213first seen 2026-06-25 04:49:41
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