Elevating Charge Transport Layer for Stable Perovskite Light-Emitting Diodes.

Ion migration is a major factor affecting the long term stability of perovskite light-emitting diodes (LEDs), which limits their commercialization potential. The accumulation of excess halide ions at the grain boundaries of perovskite films is a primary cause of ion migration in these devices. Here, it is demonstrated that the channels of ion migrations can be effectively impeded by elevating the hole transport layer between the perovskite grain boundaries, resulting in highly stable perovskite LEDs. The unique structure is achieved by reducing the wettability of the perovskites, which prevents infiltration of the upper hole-transporting layer into the spaces of perovskite grain boundaries. Consequently, nanosized gaps are formed between the excess halide ions and the hole transport layer, effectively suppressing ion migration. With this structure, perovskite LEDs with operational half-lifetimes of 256 and 1774 h under current densities of 100 and 20 mA cm-2 respectively are achieved. These lifetimes surpass those of organic LEDs at high brightness. It is further found that this approach can be extended to various perovskite LEDs, showing great promise for promoting perovskite LEDs toward commercial applications.

Deep Learning for Additive Screening in Perovskite Light-Emitting Diodes.

Additive engineering with organic molecules is of critical importance for achieving high-performance perovskite optoelectronic devices. However, experimentally finding suitable additives is costly and time consuming, while conventional machine learning (ML) is difficult to predict accurately due to the limited experimental data available in this relatively new field. Here, we demonstrate a deep learning method that can predict the effectiveness of additives in perovskite light-emitting diodes (PeLEDs) with a high accuracy up to 96 % by using a small dataset of 132 molecules. This model can maximize the information of the molecules and significantly mitigate the duplicated problem that usually happened with previous models in ML for molecular screening. Very high efficiency PeLEDs with a peak external quantum efficiency up to 22.7 % can be achieved by using the predicated additive. Our work opens a new avenue for further boosting the performance of perovskite optoelectronic devices.

Orientation Regulation of One-Dimensional CsCu2I3 Perovskites for Visible-Blind Ultraviolet Photodetectors.

Ternary copper halides with a formula of CsCu2X3 (X = Cl, Br, I) have been considered as prospective materials for ultraviolet (UV) photodetectors, due to their suitable band gaps, environmental stability, eco-friendliness, and low cost. However, the crystal orientation of one-dimensional (1D) CsCu2X3 perovskites significantly affects the exciton/carrier transport in the films and thus the photodetector performance. Here, we tune the crystal orientation and exciton/charge transport of 1D CsCu2I3 perovskite films by using antisolvents during the film formation process. Compared to the randomly oriented film treated by ethyl acetate, the CsCu2I3 film using toluene as antisolvent exhibits preferential (221)-oriented growth, which induces enhanced vertical exciton diffusion/charge transport and suppressed nonradiative recombination. On the basis of this strategy, we demonstrate a self-powered, stable, and visible-blind UV photodetector with significantly enhanced response speed and detectivity. Our work clarifies that tuning the crystal orientation of 1D CsCu2X3 perovskites is the key to achieve efficient exciton diffusion/charge transport and thus high-performance lead-free perovskite optoelectronic devices.

Deep Learning and Its Applications in Biomedicine.

Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning applications, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. Finally, we offer our perspectives for the future directions in the field of deep learning.

Evidence for the role of transposons in the recruitment of cis-regulatory motifs during the evolution of C4 photosynthesis.

BACKGROUND: C4 photosynthesis evolved from C3 photosynthesis and has higher light, water, and nitrogen use efficiencies. Several C4 photosynthesis genes show cell-specific expression patterns, which are required for these high resource-use efficiencies. However, the mechanisms underlying the evolution of cis-regulatory elements that control these cell-specific expression patterns remain elusive. RESULTS: In the present study, we tested the hypothesis that the cis-regulatory motifs related to C4 photosynthesis genes were recruited from non-photosynthetic genes and further examined potential mechanisms facilitating this recruitment. We examined 65 predicted bundle sheath cell-specific motifs, 17 experimentally validated cell-specific cis-regulatory elements, and 1,034 motifs derived from gene regulatory networks. Approximately 7, 5, and 1,000 of these three categories of motifs, respectively, were apparently recruited during the evolution of C4 photosynthesis. In addition, we checked 1) the distance between the acceptors and the donors of potentially recruited motifs in a chromosome, and 2) whether the potentially recruited motifs reside within the overlapping region of transposable elements and the promoter of donor genes. The results showed that 7, 4, and 658 of the potentially recruited motifs might have moved via the transposable elements. Furthermore, the potentially recruited motifs showed higher binding affinity to transcription factors compared to randomly generated sequences of the same length as the motifs. CONCLUSIONS: This study provides molecular evidence supporting the hypothesis that transposon-driven recruitment of pre-existing cis-regulatory elements from non-photosynthetic genes into photosynthetic genes plays an important role during C4 evolution. The findings of the present study coincide with the observed repetitive emergence of C4 during evolution.