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ANN 1

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PL quantum yield 1

QD aggregates 1

cesium lead halide perovskite 1

damage prediction 1

experimental modal analysis 1

quantum dots 1

time-resolved photoluminescence 1

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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

Frontiers of Chemical Science and Engineering 2021, Volume 15, Issue 1, Pages 187-197 doi: 10.1007/s11705-020-1931-z

Abstract: 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.

Keywords： 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

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 8, Pages 976-989 doi: 10.1007/s11709-022-0840-2

Abstract: Element Method (FEM) and Artificial Neural Network (ANN) combined with Butterfly Optimization Algorithm (BOAThis paper improves ANN training after introducing BOA as a hybrid model (BOA-ANN).

Keywords： damage prediction ANN BOA FEM experimental modal analysis