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Artificial intelligence interventions in 2D MXenes-based photocatalytic applications
Coordination Chemistry Reviews ( IF 20.3 ) Pub Date : 2025-01-24 , DOI: 10.1016/j.ccr.2025.216460
Durga Madhab Mahapatra, Ashish Kumar, Rajesh Kumar, Navneet Kumar Gupta, Baranitharan Ethiraj, Lakhveer Singh
Coordination Chemistry Reviews ( IF 20.3 ) Pub Date : 2025-01-24 , DOI: 10.1016/j.ccr.2025.216460
Durga Madhab Mahapatra, Ashish Kumar, Rajesh Kumar, Navneet Kumar Gupta, Baranitharan Ethiraj, Lakhveer Singh
Artificial Intelligence powered application have become the norms in day-to-day life. This has a tremendous role for material investigations catering diverse applications. Present day advanced materials as various MAX phases transformed into MXenes have immense applications for environmental use. MXenes have shown great potential in photocatalysis application targetingCO2 reduction, H2 O2 production, wastewater and dye treatment and nitrogen fixation. For an AI based implementation and model development, the basics of photon capture and charge transfer characteristics of photocatalytic materials, right from biological systems to organic/inorganic solar cells are crucial. This had been very thoroughly worked by compelling computational model and theories. The AI-ML based approaches have been instrumental in identification, screening, scrutiny of advanced materials, especially MXenes for varied applications via the supervised, unsupervised and reinforcement learning techniques. These exercises have provided the models that can be potentially more equipped for parallelly performing the classification and regression with a higher prediction accuracy. Use of advanced deep learning techniques have aided in establishing relation between structure-feature-properties and applications for MXenes based materials. Finally, a Criteria based AI aided Decision Support System is also discussed that prioritises environmentally sound and green MAX phase precursors for the development of photocatalytic materials. This will aid in developing technically feasible, economically viable and environmentally sustainable approaches for MXenes commercialization targeting environmentally friendly photocatalytic applications, thereby achieving sustainability development goals.
中文翻译:
人工智能干预基于 2D MXenes 的光催化应用
人工智能驱动的应用程序已成为日常生活的常态。这对于满足各种应用的材料研究具有巨大作用。当今的先进材料,如各种 MAX 相转化为 MXenes,在环境应用方面具有巨大的应用。MXenes 在光催化应用中显示出巨大的潜力,靶向 CO2 还原、H2O2 生产、废水和染料处理以及氮固定。对于基于 AI 的实现和模型开发,从生物系统到有机/无机太阳能电池,光子捕获和光催化材料的电荷转移特性的基础知识至关重要。这已经通过令人信服的计算模型和理论进行了非常彻底的工作。基于 AI-ML 的方法有助于识别、筛选和审查先进材料,尤其是通过监督、无监督和强化学习技术用于各种应用的 MXenes。这些练习提供了可能更有能力以更高的预测精度并行执行分类和回归的模型。使用先进的深度学习技术有助于建立基于 MXenes 的材料的结构-特征-特性和应用之间的关系。最后,还讨论了基于标准的人工智能辅助决策支持系统,该系统优先考虑环境无害和绿色 MAX 相前驱体用于光催化材料的开发。这将有助于开发技术上可行、经济上可行且环境可持续的 MXenes 商业化方法,以环境友好型光催化应用为目标,从而实现可持续发展目标。
更新日期:2025-01-24
中文翻译:
人工智能干预基于 2D MXenes 的光催化应用
人工智能驱动的应用程序已成为日常生活的常态。这对于满足各种应用的材料研究具有巨大作用。当今的先进材料,如各种 MAX 相转化为 MXenes,在环境应用方面具有巨大的应用。MXenes 在光催化应用中显示出巨大的潜力,靶向 CO2 还原、H2O2 生产、废水和染料处理以及氮固定。对于基于 AI 的实现和模型开发,从生物系统到有机/无机太阳能电池,光子捕获和光催化材料的电荷转移特性的基础知识至关重要。这已经通过令人信服的计算模型和理论进行了非常彻底的工作。基于 AI-ML 的方法有助于识别、筛选和审查先进材料,尤其是通过监督、无监督和强化学习技术用于各种应用的 MXenes。这些练习提供了可能更有能力以更高的预测精度并行执行分类和回归的模型。使用先进的深度学习技术有助于建立基于 MXenes 的材料的结构-特征-特性和应用之间的关系。最后,还讨论了基于标准的人工智能辅助决策支持系统,该系统优先考虑环境无害和绿色 MAX 相前驱体用于光催化材料的开发。这将有助于开发技术上可行、经济上可行且环境可持续的 MXenes 商业化方法,以环境友好型光催化应用为目标,从而实现可持续发展目标。