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煤炭大分子结构解析与模型构建研究进展.

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Title: 煤炭大分子结构解析与模型构建研究进展.
Alternate Title: Research progress on analysis of coal macromolecular structure and model construction.
Authors: 付柯鸣1,2 4126316004@stu.xjtu.edu.cn, 杨盼曦1,2, 郝烜至1,2, 郭 伟1,2, 俞尊义1,2, 李红强1,2, 郑宇行1,2, 杨伯伦1,2, 吴志强1,2 zhiqiang-wu@mail.xjtu.edu.cn
Source: Clean Coal Technology. 2026, Vol. 32 Issue 2, p42-64. 23p.
Subject Terms: *Molecular models, *Structural analysis (Science), *Artificial intelligence, *Structural models, *Clean coal technologies, *Spectroscopic imaging, *Carbonization
Abstract (English): The precise analysis and efficient modeling of coal macromolecular structures are essential for understanding its macroscopic properties and microscopic reaction mechanisms, offering important theoretical insights for the clean and efficient use of coal. To comprehensively understand the current research status and development trends in coal macromolecular structure analysis and model construction, and to build molecular models that more closely resemble real coal systems, this study systematically discusses coal macromolecular structure from the perspectives of analytical methods, structural elucidation, and model construction. It discusses the limitations of current traditional construction methods, and looks forward to artificial intelligence (AI) -driven intelligent construction of coal macromolecular models. In the field of coal macromolecular structure research, analytical methods have evolved from early traditional methods relying on single spectroscopic approaches to modern integrated characterization systems that combine multi-scale and multi-technique strategies, enabling systematic revelation of the three-dimensional spatial structure and chemical bond distribution in coal. Based on this, studies have further clarified that coal macromolecules form a multi-scale complex system built on "basic structural units," consisting of regular and irregular components connected through covalent and non-covalent interactions, and have summarized the evolutionary patterns of coal structure with increasing coalification. In terms of model construction, the current commonly followed "experimental characterization-structural derivation - model construction" workflow faces bottlenecks such as poor cross-rank adaptability, low efficiency in large-scale model construction, and weak predictive capability for dynamic reactions, which hinder in-depth research on coal molecular structure and breakthroughs in clean and efficient coal utilization technologies. Accordingly, this paper systematically reviews the evolutionary trajectory of typical macromolecular models across different coal ranks to visually illustrate their development trends. Furthermore, it prospects the direction of AI-driven intelligent construction of coal molecular models, aiming to provide theoretical support for the intelligent and green transformation of the coal industry and to offer novel technical pathways for molecular-level precise regulation in the clean and efficient utilization of coal. [ABSTRACT FROM AUTHOR]
Abstract (Chinese): 煤炭大分子结构的精准解析与模型高效构建是理解其宏观性质与微观反应机理的关键, 对煤 炭的高效清洁利用具有重要理论指导意义。为全面了解煤炭大分子结构解析和模型构建研究现状和 发展趋势, 构建更贴近真实煤炭体系的分子模型, 从煤炭大分子结构的分析方法、结构解析以及模型构 建等方面进行了系统论述, 讨论了当前传统构建方法存在的局限性, 并对人工智能 (AI) 驱动的煤炭 大分子智能构建进行了展望。在煤炭大分子结构研究领域, 分析方法已从早期依赖单一谱图技术的传 统手段, 发展为多尺度、多技术融合的现代综合表征体系, 能够系统揭示煤的三维空间结构与化学键分 布信息。基于此, 研究进一步明确了煤炭大分子是以"基本结构单元"为基础, 由规则与不规则部分 通过共价与非共价键协同连接而成的多尺度复杂体系, 并总结了煤结构随煤化程度加深的演化规律。 在模型构建方面, 目前普遍遵循"试验表征—结构推导—模型构建"的常规构建流程, 存在跨煤阶适 配性差、大规模模型构建效率低、动态反应预测能力弱等瓶颈, 制约了煤炭分子结构研究的深化及其清 洁高效利用技术的突破。为此, 系统梳理了不同煤阶典型大分子模型的演进脉络, 以直观展现其发展 趋势。展望了以人工智能驱动的煤炭分子模型智能构建方向, 旨在为煤炭行业智能化、绿色化转型提 供理论支撑, 为煤炭清洁高效利用的分子级精准调控提供全新技术路径。 [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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PubType: Academic Journal
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  Data: 煤炭大分子结构解析与模型构建研究进展.
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  Data: Research progress on analysis of coal macromolecular structure and model construction.
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  Data: <searchLink fieldCode="AR" term="%22付柯鸣%22">付柯鸣</searchLink><relatesTo>1,2</relatesTo><i> 4126316004@stu.xjtu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22杨盼曦%22">杨盼曦</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22郝烜至%22">郝烜至</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22郭+伟%22">郭 伟</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22俞尊义%22">俞尊义</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22李红强%22">李红强</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22郑宇行%22">郑宇行</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22杨伯伦%22">杨伯伦</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22吴志强%22">吴志强</searchLink><relatesTo>1,2</relatesTo><i> zhiqiang-wu@mail.xjtu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Clean+Coal+Technology%22">Clean Coal Technology</searchLink>. 2026, Vol. 32 Issue 2, p42-64. 23p.
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– Name: Abstract
  Label: Abstract (English)
  Group: Ab
  Data: The precise analysis and efficient modeling of coal macromolecular structures are essential for understanding its macroscopic properties and microscopic reaction mechanisms, offering important theoretical insights for the clean and efficient use of coal. To comprehensively understand the current research status and development trends in coal macromolecular structure analysis and model construction, and to build molecular models that more closely resemble real coal systems, this study systematically discusses coal macromolecular structure from the perspectives of analytical methods, structural elucidation, and model construction. It discusses the limitations of current traditional construction methods, and looks forward to artificial intelligence (AI) -driven intelligent construction of coal macromolecular models. In the field of coal macromolecular structure research, analytical methods have evolved from early traditional methods relying on single spectroscopic approaches to modern integrated characterization systems that combine multi-scale and multi-technique strategies, enabling systematic revelation of the three-dimensional spatial structure and chemical bond distribution in coal. Based on this, studies have further clarified that coal macromolecules form a multi-scale complex system built on "basic structural units," consisting of regular and irregular components connected through covalent and non-covalent interactions, and have summarized the evolutionary patterns of coal structure with increasing coalification. In terms of model construction, the current commonly followed "experimental characterization-structural derivation - model construction" workflow faces bottlenecks such as poor cross-rank adaptability, low efficiency in large-scale model construction, and weak predictive capability for dynamic reactions, which hinder in-depth research on coal molecular structure and breakthroughs in clean and efficient coal utilization technologies. Accordingly, this paper systematically reviews the evolutionary trajectory of typical macromolecular models across different coal ranks to visually illustrate their development trends. Furthermore, it prospects the direction of AI-driven intelligent construction of coal molecular models, aiming to provide theoretical support for the intelligent and green transformation of the coal industry and to offer novel technical pathways for molecular-level precise regulation in the clean and efficient utilization of coal. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label: Abstract (Chinese)
  Group: Ab
  Data: 煤炭大分子结构的精准解析与模型高效构建是理解其宏观性质与微观反应机理的关键, 对煤 炭的高效清洁利用具有重要理论指导意义。为全面了解煤炭大分子结构解析和模型构建研究现状和 发展趋势, 构建更贴近真实煤炭体系的分子模型, 从煤炭大分子结构的分析方法、结构解析以及模型构 建等方面进行了系统论述, 讨论了当前传统构建方法存在的局限性, 并对人工智能 (AI) 驱动的煤炭 大分子智能构建进行了展望。在煤炭大分子结构研究领域, 分析方法已从早期依赖单一谱图技术的传 统手段, 发展为多尺度、多技术融合的现代综合表征体系, 能够系统揭示煤的三维空间结构与化学键分 布信息。基于此, 研究进一步明确了煤炭大分子是以"基本结构单元"为基础, 由规则与不规则部分 通过共价与非共价键协同连接而成的多尺度复杂体系, 并总结了煤结构随煤化程度加深的演化规律。 在模型构建方面, 目前普遍遵循"试验表征—结构推导—模型构建"的常规构建流程, 存在跨煤阶适 配性差、大规模模型构建效率低、动态反应预测能力弱等瓶颈, 制约了煤炭分子结构研究的深化及其清 洁高效利用技术的突破。为此, 系统梳理了不同煤阶典型大分子模型的演进脉络, 以直观展现其发展 趋势。展望了以人工智能驱动的煤炭分子模型智能构建方向, 旨在为煤炭行业智能化、绿色化转型提 供理论支撑, 为煤炭清洁高效利用的分子级精准调控提供全新技术路径。 [ABSTRACT FROM AUTHOR]
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      – SubjectFull: Artificial intelligence
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      – SubjectFull: Spectroscopic imaging
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              Text: 2026
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