Inverse gas chromatography (IGC) has emerged as a highly sensitive, adaptable, and effective technology for material analysis. Through employing thermochemical approaches, IGC provides crucial insight into physicochemical information of materials such as dispersive surface free energy, Gibbs surface energy components and Guttamann Lewis acid-base parameters. In this comprehensive review, we delve into the historical background, instrumentation, and diverse applications of IGC. Researchers and practitioners will find valuable information on the selection and description of numerous models used in IGC experiments. The applications of IGC span various domains, including polymers, medicines, minerals, surfactants, and nanomaterials. Furthermore, IGC facilitates the measurement of important parameters such as sorption enthalpy and entropy, surface energy components (dispersive and specific), co/adhesion work, glass transition temperature, surface heterogeneity, miscibility, solubility parameters, and specific surface area. These insights contribute to a deeper understanding of material behavior and aid in the design and optimization of advanced materials. Moreover, the integration of computer vision and image processing techniques with IGC has enhanced our understanding of materials intricate surface texture, roughness, and related properties. This convergence of IGC with computer vision and artificial intelligence (AI) presents exciting opportunities for future exploration of chemical materials, opening new avenues for research and discovery. This paper not only provides a comprehensive overview of IGC, its techniques, and applications but also highlights the synergistic potential of combining IGC with AI and computer vision. The informative content and insights presented here will benefit researchers, scientists, and professionals in the field of advanced materials, enabling them to leverage IGC and AI for innovative materials discovery and development.

中文翻译：

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探索先进材料：利用反气相色谱法和人工智能视觉智能的协同作用

反气相色谱 (IGC) 已成为一种高度灵敏、适应性强且有效的材料分析技术。通过采用热化学方法，IGC 提供了对材料物理化学信息的重要洞察，例如色散表面自由能、吉布斯表面能分量和古塔曼路易斯酸碱参数。在这篇全面的回顾中，我们深入研究了 IGC 的历史背景、仪器和多样化应用。研究人员和从业者将找到有关 IGC 实验中使用的众多模型的选择和描述的宝贵信息。 IGC 的应用涵盖聚合物、药物、矿物、表面活性剂和纳米材料等各个领域。此外，IGC 有助于测量重要参数，如吸附焓和熵、表面能分量（色散和比表面积）、共/粘附功、玻璃化转变温度、表面异质性、混溶性、溶解度参数和比表面积。这些见解有助于更深入地了解材料行为，并有助于先进材料的设计和优化。此外，计算机视觉和图像处理技术与 IGC 的集成增强了我们对材料复杂表面纹理、粗糙度和相关特性的理解。 IGC 与计算机视觉和人工智能 (AI) 的融合为未来化学材料的探索提供了令人兴奋的机会，为研究和发现开辟了新的途径。本文不仅全面概述了 IGC、其技术和应用，还强调了 IGC 与人工智能和计算机视觉相结合的协同潜力。这里提供的信息丰富的内容和见解将使先进材料领域的研究人员、科学家和专业人士受益，使他们能够利用 IGC 和人工智能进行创新材料的发现和开发。