Density functional theory(DFT)calculations were employed to investigate the adsorption behavior of NH_(3),AsH_(3),PH_(3),CO_(2),and CH_(4)molecules on both pristine and mono-vacancy phosphorene sheets.The pristine pho...Density functional theory(DFT)calculations were employed to investigate the adsorption behavior of NH_(3),AsH_(3),PH_(3),CO_(2),and CH_(4)molecules on both pristine and mono-vacancy phosphorene sheets.The pristine phosphorene surface showsweak physisorption with all the gasmolecules,inducing onlyminor changes in its structural and electronic properties.However,the introduction ofmono-vacancies significantly enhances the interaction strength with NH_(3),PH_(3),CO_(2),and CH_(4).These variations are attributed to substantial charge redistribution and orbital hybridization in the presence of defects.The defective phosphorene sheet also exhibits enhanced adsorption energies,along with favorable sensitivity and recovery characteristics,highlighting its potential as a promising gas sensor for NH_(3),AsH_(3),PH_(3),CO_(2),and CH_(4)at ambient conditions.展开更多
Machine learning(ML)is recognized as a potent tool for the inverse design of environmental functional material,particularly for complex entities like biochar-based catalysts(BCs).Thus,the tailored BCs can have a disti...Machine learning(ML)is recognized as a potent tool for the inverse design of environmental functional material,particularly for complex entities like biochar-based catalysts(BCs).Thus,the tailored BCs can have a distinct ability to trigger the nonradical pathway in advance oxidation processes(AOPs),promising a stable,rapid and selective degradation of persistent contaminants.However,due to the inherent“black box”nature and limitations of input features,results and conclusions derived from ML may not always be intuitively understood or comprehensively validated.To tackle this challenge,we linked the front-point interpretable analysis approaches with back-point density functional theory(DFT)calculations to form a chained learning strategy for deeper sight into the intrinsic activation mechanism of BCs in AOPs.At the front point,we conducted an easy-to-interpret meta-analysis to validate two strategies for enhancing nonradical pathways by increasing oxygen content and specific surface area(SSA),and prepared oxidized biochar(OBC500)and SSA-increased biochar(SBC900)by controlling pyrolysis conditions and modification methods.Subsequently,experimental results showed that OBC500 and SBC900 had distinct dominant degradation pathways for 1O2 generation and electron transfer,respectively.Finally,at the end point,DFT calculations revealed their active sites and degradation mechanisms.This chained learning strategy elucidates fundamental principles for BC inverse design and showcases the exceptional capacity to integrate computational techniques to accelerate catalyst inverse design.展开更多
Efficient surface passivation is critical for achieving high-performance perovskite solar cells(PSCs),yet the discovery of optimal passivators remains a time-consuming,trial-and-error process.Here,we report a synergis...Efficient surface passivation is critical for achieving high-performance perovskite solar cells(PSCs),yet the discovery of optimal passivators remains a time-consuming,trial-and-error process.Here,we report a synergistic machine learning(ML)and density functional theory(DFT)approach that enables predictive and rapid identification of effective passivation materials.By training an XGBoost model(91.3%accuracy)with DFT-derived molecular descriptors and activity calculations,we identify 2-(4-aminophenyl)-3H-benzimidazol-5-amine(APBIA)as a promising passivator.Experimental validation demonstrates that APBIA effectively removes surface impurities and passivates defects within perovskite films,leading to a significant increase in power conversion efficiency(PCE)from 22.48%to 25.55%(certified as 25.02%).This ML-DFT framework provides a generalizable pathway for accelerating the development of advanced functional materials for photovoltaic applications.展开更多
调制解调法是一种常用的微弱信号检测方法,高精度、低复杂度的解调方法的实现对于调制解调法的应用具有重要的意义.传统坐标旋转数字计算(coordinate rotation digital computer,CORDIC)算法具有占用资源多,需要缩放因子补偿等问题.因...调制解调法是一种常用的微弱信号检测方法,高精度、低复杂度的解调方法的实现对于调制解调法的应用具有重要的意义.传统坐标旋转数字计算(coordinate rotation digital computer,CORDIC)算法具有占用资源多,需要缩放因子补偿等问题.因此设计并实现了一种基于改进CORDIC算法的离散傅里叶变换(discrete Fourier transform,DFT)解调方法用于微弱信号的检测.首先改进了传统的CORDIC算法用于正余弦函数值的计算,该方法不仅免除了缩放因子,而且不需要进行旋转角度的判断,降低了算法的资源占用;然后基于该CORDIC算法设计了DFT解调算法,避免了乘法器与大量查找表的使用.最终仿真结果表明,设计的DFT解调方法在整周期采样的情况下能够实现对调制信号的高精度解调,并且具备良好的抗噪声性能,能够满足微弱信号检测的要求.展开更多
随着分布式能源的快速发展,准确预测分布式能源的出力成为了配电网可靠性评估的重要组成部分,为提高配电网可靠性评估准确性,本文提出了一种融合VMD-QRCNN-BiLSTM预测与DFT-MP-DBN建模的主动配电网可靠性评估方法。首先通过变分模态分...随着分布式能源的快速发展,准确预测分布式能源的出力成为了配电网可靠性评估的重要组成部分,为提高配电网可靠性评估准确性,本文提出了一种融合VMD-QRCNN-BiLSTM预测与DFT-MP-DBN建模的主动配电网可靠性评估方法。首先通过变分模态分解将原始风光荷时间序列分解为固有模态分量,并采用分位数回归卷积神经网络对风光出力以及负荷进行特征提取;而后使用双向长短期记忆相结合建模各变量的时间序列特征,并生成预测值;其次预测值作为动态故障树的输入,并采用连续时间马尔可夫链,并获取状态转移率矩阵;最后采用动态贝叶斯网络刻画状态的时序依赖,并加入观测或控制变量。以IEEE RBTS Bus 2系统为例,实验结果表明,所提方法的SAIFI、SAIDI、AENS和ASAI指标分别为0.231次/户/年、3.496小时/户/年、17.465 kWh/年和99.943%,显著优于传统方法,验证了其在提高配电网可靠性评估精度和效率方面的有效性。展开更多
基金financial support to conduct this research from the Science and Engineering Research Board(SERB)through a state university research excellence(SURE)grant(SUR/2022/004935).
文摘Density functional theory(DFT)calculations were employed to investigate the adsorption behavior of NH_(3),AsH_(3),PH_(3),CO_(2),and CH_(4)molecules on both pristine and mono-vacancy phosphorene sheets.The pristine phosphorene surface showsweak physisorption with all the gasmolecules,inducing onlyminor changes in its structural and electronic properties.However,the introduction ofmono-vacancies significantly enhances the interaction strength with NH_(3),PH_(3),CO_(2),and CH_(4).These variations are attributed to substantial charge redistribution and orbital hybridization in the presence of defects.The defective phosphorene sheet also exhibits enhanced adsorption energies,along with favorable sensitivity and recovery characteristics,highlighting its potential as a promising gas sensor for NH_(3),AsH_(3),PH_(3),CO_(2),and CH_(4)at ambient conditions.
基金supported by Project of National and Local Joint Engineering Research Center for Biomass Energy Development and Utilization(Harbin Institute of Technology,No.2021A004).
文摘Machine learning(ML)is recognized as a potent tool for the inverse design of environmental functional material,particularly for complex entities like biochar-based catalysts(BCs).Thus,the tailored BCs can have a distinct ability to trigger the nonradical pathway in advance oxidation processes(AOPs),promising a stable,rapid and selective degradation of persistent contaminants.However,due to the inherent“black box”nature and limitations of input features,results and conclusions derived from ML may not always be intuitively understood or comprehensively validated.To tackle this challenge,we linked the front-point interpretable analysis approaches with back-point density functional theory(DFT)calculations to form a chained learning strategy for deeper sight into the intrinsic activation mechanism of BCs in AOPs.At the front point,we conducted an easy-to-interpret meta-analysis to validate two strategies for enhancing nonradical pathways by increasing oxygen content and specific surface area(SSA),and prepared oxidized biochar(OBC500)and SSA-increased biochar(SBC900)by controlling pyrolysis conditions and modification methods.Subsequently,experimental results showed that OBC500 and SBC900 had distinct dominant degradation pathways for 1O2 generation and electron transfer,respectively.Finally,at the end point,DFT calculations revealed their active sites and degradation mechanisms.This chained learning strategy elucidates fundamental principles for BC inverse design and showcases the exceptional capacity to integrate computational techniques to accelerate catalyst inverse design.
基金supported by the National Key Research and Development Program of China (Grant No. 2024YFB4205101)the National Natural Science Foundation of China (No. 62274098 and No. 62074084)+2 种基金the Natural Science Foundation of Tianjin (No.22JCYBJC01300, No. 23JCYBJC01620 and No. 21JCYBJC00270)the Overseas Expertise Introduction Project for Discipline Innovation of Higher Edu cation of China (Grant No. B16027)the Fundamental Research Funds for the Central Universities,Nankai University (No. 63241568)
文摘Efficient surface passivation is critical for achieving high-performance perovskite solar cells(PSCs),yet the discovery of optimal passivators remains a time-consuming,trial-and-error process.Here,we report a synergistic machine learning(ML)and density functional theory(DFT)approach that enables predictive and rapid identification of effective passivation materials.By training an XGBoost model(91.3%accuracy)with DFT-derived molecular descriptors and activity calculations,we identify 2-(4-aminophenyl)-3H-benzimidazol-5-amine(APBIA)as a promising passivator.Experimental validation demonstrates that APBIA effectively removes surface impurities and passivates defects within perovskite films,leading to a significant increase in power conversion efficiency(PCE)from 22.48%to 25.55%(certified as 25.02%).This ML-DFT framework provides a generalizable pathway for accelerating the development of advanced functional materials for photovoltaic applications.
文摘随着分布式能源的快速发展,准确预测分布式能源的出力成为了配电网可靠性评估的重要组成部分,为提高配电网可靠性评估准确性,本文提出了一种融合VMD-QRCNN-BiLSTM预测与DFT-MP-DBN建模的主动配电网可靠性评估方法。首先通过变分模态分解将原始风光荷时间序列分解为固有模态分量,并采用分位数回归卷积神经网络对风光出力以及负荷进行特征提取;而后使用双向长短期记忆相结合建模各变量的时间序列特征,并生成预测值;其次预测值作为动态故障树的输入,并采用连续时间马尔可夫链,并获取状态转移率矩阵;最后采用动态贝叶斯网络刻画状态的时序依赖,并加入观测或控制变量。以IEEE RBTS Bus 2系统为例,实验结果表明,所提方法的SAIFI、SAIDI、AENS和ASAI指标分别为0.231次/户/年、3.496小时/户/年、17.465 kWh/年和99.943%,显著优于传统方法,验证了其在提高配电网可靠性评估精度和效率方面的有效性。
