As game‐changers in the photovoltaic community, perovskite solar cells are making unprecedented progress while still facing grand challenges such as improving lifetime without impairing efficiency. Herein, two structurally alike polyaromatic molecules based on naphthalene‐1,8‐dicarboximide (NMI) and perylene‐3,4‐dicarboximide (PMI) with different molecular dipoles are applied to tackle this issue. Contrasting the electronically pull–pull cyanide‐substituted PMI (9CN‐PMI) with only Lewis‐base groups, the push–pull 4‐hydroxybiphenyl‐substituted NMI (4OH‐NMI) with both protonic and Lewis‐base groups can provide better chemical passivation for both shallow‐ and deep‐level defects. Moreover, combined theoretical and experimental studies show that the 4OH‐NMI can bind more firmly with perovskite and the polyaromatic backbones create benign midgap states in the excited perovskite to suppress the damage by superoxide anions (energetic passivation). The polar and protonic nature of 4OH‐NMI facilitates band alignment and regulates the viscosity of the precursor solution for thicker perovskite films with better morphology. Consequently, the 4OH‐NMI‐passivated perovskite films exhibit reduced grain boundaries and nearly three‐times lower defect density, boosting the device efficiency to 23.7%. A more effective design of the passivator for perovskites with multi‐passivation mechanisms is provided in this study.
With the advance of sensor technologies, the Multivariate Time Series classification (MTSC) problem, perhaps one of the most essential problems in the time series data mining domain, has continuously received a significant amount of attention in recent decades. Traditional time series classification approaches based on Bag-of-Patterns or Time Series Shapelet have difficulty dealing with the huge amounts of feature candidates generated in high-dimensional multivariate data but have promising performance even when the training set is small. In contrast, deep learning based methods can learn low-dimensional features efficiently but suffer from a shortage of labelled data. In this paper, we propose a novel MTSC model with an attentional prototype network to take the strengths of both traditional and deep learning based approaches. Specifically, we design a random group permutation method combined with multi-layer convolutional networks to learn the low-dimensional features from multivariate time series data. To handle the issue of limited training labels, we propose a novel attentional prototype network to train the feature representation based on their distance to class prototypes with inadequate data labels. In addition, we extend our model into its semi-supervised setting by utilizing the unlabeled data. Extensive experiments on 18 datasets in a public UEA Multivariate time series archive with eight state-of-the-art baseline methods exhibit the effectiveness of the proposed model.
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