人工知能が導くエンジニアリングナノバイオ炭：炭素管理、気候スマート農業、新興汚染物質修復に向けて：批判的ナラティブレビュー

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:AI×バイオ炭研究の証拠ギャップと外部妥当性の限界を整理し、今後の研究設計に資する。

🏢実務担当者:バイオ炭由来の炭素除去クレジットやサプライチェーン脱炭素を検討する際、LCAと現場検証の必要性を再認識できる。

🏛政策担当者:炭素除去の認証・MRV制度設計において、吸着能や炭素含有量のみに依存しない評価枠組みの重要性を示す。

Biochar sits at the intersection of biomass valorisation, carbon management, soil restoration and contaminant control, while nano-engineering and artificial intelligence (AI) are increasingly proposed as means of tailoring its properties and accelerating design. Yet the phrase “AI-driven engineered nano-biochar” risks implying a level of technological integration and field validation that the evidence has not yet achieved. This critical narrative review evaluates that intersection by separating three evidence layers: established knowledge on bulk biochar, rapidly expanding evidence on engineered and nanoscale biochar, and emerging applications of machine learning to biochar production and performance prediction. Literature published from 1 January 2000 to 19 July 2026 was appraised for methodological quality, scale, mechanistic relevance, environmental realism and claim-to-evidence alignment. The evidence is strongest for the persistence of a fraction of pyrolytic carbon, context-dependent improvements in soil properties and crop performance, and enhanced adsorption after selected physical or chemical modifications. Machine-learning studies can predict biochar yield, composition and adsorption outcomes with high within-dataset accuracy, but their external validity is constrained by heterogeneous literature-derived datasets, sparse reporting of negative results, inconsistent material characterisation and limited prospective validation. Nanostructuring can increase accessible surface area, reactive sites and dispersibility, but the same attributes raise unresolved questions about particle transport, ecotoxicity, recovery, ageing and carbon permanence. Field evidence for nano-biochar remains comparatively scarce, although recent rice and salinity studies demonstrate agricultural promise under defined conditions. For emerging contaminants, most evidence remains batch-scale and equilibrium-centred, with limited treatment of realistic mixtures, dissolved organic matter, continuous flow, regeneration and spent-sorbent management. Carbon neutrality therefore cannot be inferred from adsorption capacity, crop response or carbon content alone; it requires life-cycle accounting, durable carbon measurement, energy and reagent inventories, counterfactual biomass fate, and monitoring of downstream risks. The most defensible near-term pathway is not autonomous AI discovery, but uncertainty-aware, multi-objective decision support coupled to standardised experiments, field trials and life-cycle assessment.

• semanticscholar https://doi.org/10.9734/ajaar/2026/v26i9760first seen 2026-09-30 05:42:45 · last seen 2026-10-02 05:40:16