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Long-lasting photoluminescence quantum yield of cesium lead halide perovskite-type quantum dots

Yonghyun Kim, Huiwen Liu, Yi Liu, Boa Jin, Hao Zhang, Wenjing Tian, Chan Im

《化学科学与工程前沿（英文）》 2021年第15卷第1期页码 187-197 doi: 10.1007/s11705-020-1931-z

摘要： Cesium lead halide perovskite (CsPbX , X= Cl, Br, I) quantum dots (QDs) and their partly Mn -substituted QDs (CsPb Mn X ) attract considerable attention owing to their unique photoluminescence (PL) efficiencies. The two types of QDs, having different PL decay dynamics, needed to be further investigated in a form of aggregates to understand their solid-state-induced exciton dynamics in conjunction with their behaviors upon degradation to achieve practical applications of those promising QDs. However, thus far, these QDs have not been sufficiently investigated to obtain deep insights related to the long-term stability of their PL properties as aggregated solid-states. Therefore, in this study, we comparatively examined CsPbX - and CsPb Mn X -type QDs stocked for>50 d under dark ambient conditions by using excitation wavelength-dependent PL quantum yield and time-resolved PL spectroscopy. These investigations were performed with powder samples in addition to solutions to determine the influence of the inter-QD interaction of the aged QD aggregates on their radiative decays. It turns out that the Mn -substituted QDs exhibited long-lasting PL quantum efficiencies, while the unsubstituted CsPbX -type QDs exhibited a drastic reduction of their PL efficiencies. And the obtained PL traces were clearly sensitive to the sample status. This is discussed with the possible interaction depending on the size and distance of the QD aggregates.

关键词： quantum dots cesium lead halide perovskite time-resolved photoluminescence PL quantum yield QD aggregates

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Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial neural network

Abdelwahhab KHATIR; Roberto CAPOZUCCA; Samir KHATIR; Erica MAGAGNINI

《结构与土木工程前沿（英文）》 2022年第16卷第8期页码 976-989 doi: 10.1007/s11709-022-0840-2

摘要： Vibration-based damage detection methods have become widely used because of their advantages over traditional methods. This paper presents a new approach to identify the crack depth in steel beam structures based on vibration analysis using the Finite Element Method (FEM) and Artificial Neural Network (ANN) combined with Butterfly Optimization Algorithm (BOA). ANN is quite successful in such identification issues, but it has some limitations, such as reduction of error after system training is complete, which means the output does not provide optimal results. This paper improves ANN training after introducing BOA as a hybrid model (BOA-ANN). Natural frequencies are used as input parameters and crack depth as output. The data are collected from improved FEM using simulation tools (ABAQUS) based on different crack depths and locations as the first stage. Next, data are collected from experimental analysis of cracked beams based on different crack depths and locations to test the reliability of the presented technique. The proposed approach, compared to other methods, can predict crack depth with improved accuracy.

关键词： damage prediction ANN BOA FEM experimental modal analysis