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CO2回収用炭素系吸着材：メカニズム主導の材料設計とデータ駆動型最適化

Carbon‐Based Adsorbents for CO 2 Capture: Mechanism‐Guided Material Design and Data‐Driven Optimization (原題)

Jinliang Cai, Jianchao Zhang, Zhiyong Peng, Yihe Miao, Jialing Xu

Carbon Neutralization📚 査読済 / ジャーナル2026-10-22#CCUSOrigin: CN経営インパクト: コスト削減対象セクター: power
DOI: 10.1002/cnl2.70211
原典: https://doi.org/10.1002/cnl2.70211

🤖 gxceed AI 要約

日本語

本レビューはCCUS上流の中核であるCO2吸着回収に用いる炭素系吸着材を対象に、細孔制御・表面化学修飾・複合化といった材料最適化の進展を整理する。さらに機械学習による記述子構築、予測対象、モデル検証、機構解釈、合成最適化までを俯瞰し、合成条件・物性・吸着性能の非線形関係の解明を試みる。データ品質やデータセット規模、汎化性の限界も指摘し、実験とデータ駆動手法の融合による今後の材料最適化の方向性を示す。

English

This review surveys carbon-based adsorbents for CO2 capture, covering pore engineering, surface modification, and composite design, and compares representative studies. It then examines machine learning across descriptor construction, prediction targets, model validation, mechanistic interpretation, and synthesis optimization. The authors stress that data quality, dataset size, and model generalization still limit reliability, and argue for combining experimental and data-driven approaches in future material design.

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

CCUSは日本のGX推進戦略やエネルギー基本計画で重点技術とされ、特に分離回収コスト低減は産業実装の鍵となる。本レビューは材料開発の効率化手法として機械学習の活用可能性と限界を示しており、国内のCCUS研究開発や実証事業の技術ロードマップ検討に資する。

In the global GX context

Carbon capture is central to global net-zero pathways and increasingly embedded in transition finance and industrial decarbonization policy. This review's focus on data-driven material optimization speaks to the broader question of how AI can accelerate hard-tech decarbonization, though it sits upstream of disclosure and accounting frameworks like TCFD or ISSB.

👥 読者別の含意

🔬研究者:炭素系吸着材の材料設計と機械学習活用の現状・課題を俯瞰でき、研究のギャップ特定に有用。

🏢実務担当者:CCUS導入を検討する企業が、分離回収技術の成熟度と材料最適化の方向性を把握する参考になる。

🏛政策担当者:CCUSのコスト低減に向けた研究開発支援やデータ基盤整備の政策的意義を検討する材料となる。

📄 Abstract（原文）

ABSTRACT Carbon capture is the upstream cornerstone of carbon capture, utilization and storage, where dispersed CO 2 is concentrated into controlled streams. As adsorbent performance and process adaptability improve, adsorption‐based capture has attracted increasing attention. Carbon‐based materials, as industrially mature adsorbents, have secured a place in this field owing to their balanced performance in cost‐effectiveness, stability, and efficiency. However, compared with crystalline porous adsorbents, their less ordered pore environments and nonpolar surface characteristics result in relatively weak affinity toward CO 2 . To address these limitations, extensive efforts have been devoted to pore engineering, surface chemistry modification, and composite construction. Nevertheless, the complex nonlinear relationships between synthesis conditions, physicochemical properties, and adsorption performance remain difficult to elucidate through empirical optimization alone. Machine learning has introduced a new paradigm to uncover key descriptors and structure–performance relationships from complex datasets, accelerating the rational design of carbon‐based materials. Despite this progress, existing studies remain fragmented in scope and focus. Therefore, this review begins with the adsorption mechanisms and evaluation criteria of carbon‐based adsorbents, with attention to the limitations of commonly reported performance metrics. Recent progress in material optimization is then discussed according to the main functional roles of different strategies, together with comparisons across representative studies. The review further examines machine learning from descriptor construction and prediction targets to model development, validation, mechanistic interpretation, and synthesis optimization. Particular attention is given to how data‐driven methods can help analyze coupled material variables, while their reliability remains limited by data quality, dataset size, and model generalization. Overall, this review summarizes current understanding of carbon adsorbent design and discusses how experimental and data‐driven approaches can be used together to support further material optimization.

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