Recent advances in biomass deconstruction, microbial conversion, artificial intelligence, and carbon capture for sustainable bioenergy
持続可能なバイオエネルギーのためのバイオマス解重合、微生物変換、人工知能、炭素回収の最近の進歩 (AI 翻訳)
Vishwajit Kumar, Shikha Mishra, Pratham Joshi, Jyotsna Misra, Prakash Peralam Yegneswaran, Bhavanari Mallikarjun, Syed Shams Yazdani, Piyush Behari Lal
🤖 gxceed AI 要約
日本語
本総説は、微生物バイオ燃料生産を変換中心のシステムレベル枠組みで分析。前処理、酵素加水分解、微生物工学、AI・オミクス統合による予測最適化を評価し、統合的「リグノセルロースから燃料への経路」を提案する。
English
This review critically analyzes microbial biofuel production through a conversion-centric systems-level framework, evaluating pretreatment, enzymatic hydrolysis, microbial engineering, and the emerging role of AI and multi-omics integration for predictive optimization, proposing a unified 'lignocellulose-to-fuel' pathway.
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
日本でもバイオマス発電・液体燃料導入が進む中、本稿はAI活用による変換効率向上やコスト低減の可能性を示す。カーボンニュートラル実現に向けた技術選択肢として重要。
In the global GX context
In the global context of bioenergy and carbon capture, this review offers a systems-level framework integrating conversion technologies with AI, relevant for scaling sustainable fuels under net-zero targets.
👥 読者別の含意
🔬研究者:Researchers in bioenergy and AI can gain insights into integrated conversion pathways and predictive optimization approaches.
🏢実務担当者:Practitioners in biofuel production can learn about techno-economic perspectives and AI-driven process improvements.
🏛政策担当者:Policymakers can note the potential of advanced bioenergy systems for decarbonizing hard-to-abate sectors.
📄 Abstract（原文）
Abstract The transition from fossil fuels to low-carbon bio-based energy systems is increasingly constrained by the efficiency, scalability, and integration of conversion technologies. Addressing this challenge, this review critically analyzes microbial biofuel production through a conversion-centric and systems-level framework, emphasizing how feedstock diversity, pretreatment chemistry, enzymatic deconstruction, and microbial metabolism collectively govern overall process performance. This review evaluates how pretreatment strategies modulate biomass recalcitrance, hydrolysate chemical ecologies, inhibitor profiles, and redox balance, thereby imposing fundamental constraints on enzymatic efficiency, microbial conversion yields, and emissions outcomes. Advances in enzymatic hydrolysis are assessed in terms of bond-specific catalysis, enzyme synergy, and persistent bottlenecks arising from substrate heterogeneity, lignin enzyme interactions, and non-productive binding, while microbial engineering strategies from robust monocultures to synthetic consortia and cell-free systems are examined through techno-economic and metabolic flux perspectives. It further highlights the emerging role of artificial intelligence and multi-omics integration in enabling predictive optimization of pretreatment severity, enzyme cocktails, and metabolic routing, moving beyond empirical process tuning. This article establishes a unified framework for integrated “microbial lignocellulose-to-fuel” pathways, demonstrating how coordinated advances in conversion technologies are essential for achieving scalable, economically viable, and environmentally sustainable bioenergy systems. Graphical abstract
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.6084/m9.figshare.c.8532516first seen 2026-06-14 04:49:43 · last seen 2026-06-16 04:52:09
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