高放废弃物在地质处置库中会长期衰变释热,引起缓冲回填材料性能降低。向膨润土主料中添加石英砂,能够显著提高导热性能,改善缓冲回填材料的热劣化。选用高庙子钠基膨润土(GMZ001)为主料,添加不同比例石英砂,压制成不同含水率、不同干...高放废弃物在地质处置库中会长期衰变释热,引起缓冲回填材料性能降低。向膨润土主料中添加石英砂,能够显著提高导热性能,改善缓冲回填材料的热劣化。选用高庙子钠基膨润土(GMZ001)为主料,添加不同比例石英砂,压制成不同含水率、不同干密度试样。利用Hot Disk TPS2500s热常数分析仪测量,分析掺砂率、含水率和干密度对导热性能的影响,探讨混合型缓冲回填材料导热系数的理论预测问题。试验数据表明,随着干密度增大,导热系数显著增大,且含水率越高增幅越明显;干密度相同时,随掺砂率的增大试样导热系数增大;同一掺砂率条件下,导热系数随着含水率增大显著增大;热扩散系数与导热系数变化趋势基本一致。引入经典热阻理论,改进膨润土导热系数计算公式,对膨润土–砂混合物导热系数进行预测;将预测值与实测值进行比较,讨论预测理论的适用性。展开更多
With the development of modern society,the requirement for energy has become increasingly important on a global scale.Therefore,the exploration of novel materials for renewable energy technologies is urgently needed.T...With the development of modern society,the requirement for energy has become increasingly important on a global scale.Therefore,the exploration of novel materials for renewable energy technologies is urgently needed.Traditional methods are difficult to meet the requirements for materials science due to long experimental period and high cost.Nowadays,machine learning(ML)is rising as a new research paradigm to revolutionize materials discovery.In this review,we briefly introduce the basic procedure of ML and common algorithms in materials science,and particularly focus on latest progress in applying ML to property prediction and materials development for energyrelated fields,including catalysis,batteries,solar cells,and gas capture.Moreover,contributions of ML to experiments are involved as well.We highly expect that this review could lead the way forward in the future development of ML in materials science.展开更多
文摘高放废弃物在地质处置库中会长期衰变释热,引起缓冲回填材料性能降低。向膨润土主料中添加石英砂,能够显著提高导热性能,改善缓冲回填材料的热劣化。选用高庙子钠基膨润土(GMZ001)为主料,添加不同比例石英砂,压制成不同含水率、不同干密度试样。利用Hot Disk TPS2500s热常数分析仪测量,分析掺砂率、含水率和干密度对导热性能的影响,探讨混合型缓冲回填材料导热系数的理论预测问题。试验数据表明,随着干密度增大,导热系数显著增大,且含水率越高增幅越明显;干密度相同时,随掺砂率的增大试样导热系数增大;同一掺砂率条件下,导热系数随着含水率增大显著增大;热扩散系数与导热系数变化趋势基本一致。引入经典热阻理论,改进膨润土导热系数计算公式,对膨润土–砂混合物导热系数进行预测;将预测值与实测值进行比较,讨论预测理论的适用性。
基金National Natural Science Foundation of China,Grant/Award Number:21933006China Postdoctral Science Foundation,Grant/Award Number:2019M660055+1 种基金This work was supported by NSFC(21933006)China Postdoctral Science Foundation(2019M660055)in China.
文摘With the development of modern society,the requirement for energy has become increasingly important on a global scale.Therefore,the exploration of novel materials for renewable energy technologies is urgently needed.Traditional methods are difficult to meet the requirements for materials science due to long experimental period and high cost.Nowadays,machine learning(ML)is rising as a new research paradigm to revolutionize materials discovery.In this review,we briefly introduce the basic procedure of ML and common algorithms in materials science,and particularly focus on latest progress in applying ML to property prediction and materials development for energyrelated fields,including catalysis,batteries,solar cells,and gas capture.Moreover,contributions of ML to experiments are involved as well.We highly expect that this review could lead the way forward in the future development of ML in materials science.
