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Potentially temperature-resistant inorganic perovskite/silicon tandem solar cells (TSCs) are promising devices for boosting efficiency past the single-junction silicon limit. However, undesirable non-radiative recombination generally leads to a significant voltage deficit. Here, we introduce an effective strategy using nickel iodide, an inorganic halide salt, to passivate iodine vacancies and suppress non-radiative recombination. NiI2-treated CsPbI3-xBrx inorganic perovskite solar cells with a 1.80 ​eV bandgap exhibited an efficiency of 19.53% and a voltage of 1.36 ​V, corresponding to a voltage deficit of 0.44 ​V. Importantly, the treated device demonstrated excellent operational stability, maintaining 95.7% of its initial efficiency after maximum power point tracking for 300 ​h under continuous illumination in a N2 atmosphere. By combining this inorganic perovskite top cell with a narrower bandgap silicon bottom cell, we for the first time achieved monolithic inorganic perovskite/silicon TSCs, which exhibited an efficiency of 22.95% with an open-circuit voltage of 2.04 ​V. This work provides a promising strategy for using inorganic passivation materials to achieve efficient and stable solar cells.

Abstract Wide bandgap perovskite solar cells (WB‐PSCs) sustained serious deficit of open‐circuit voltage (VOC) and instability in the development process, which limited the full play of their advantages, especially in tandem solar cells. Surface passivation engineering of alkyl amine halide salts has been used extensively to improve the performance of PSCs. Here, the photovoltaic performance of FA0.83Cs0.17 Pb(I0.8Br0.2)3 WB‐PSCs based on methyl bromide (MABr) and bromide guanidine (GABr) passivation was systematically investigated. It was proved that both MABr and GABr significantly passivate the defects, resulting in lower trap density. Eventually, the efficiency arrives at 18.6% based on MABr‐treated perovskite film, and an improved VOC up to 1.235 V was obtained with a VOC‐deficit of only 415 mV, which is one of the highest value for the PSCs with 1.65 eV bandgap. It is noted that the devices treated with MABr exhibited enhanced moisture stability than that of the reference and GABr treated. X‐Ray Diffraction (XRD) revealed that GABr is likely to cause severer phase transition to δ‐phase nonperovskite. This paper provides an important insight to surface passivation strategies, both performance and stability in devices need to be considered simultaneously.

Automated characterization of spatial data is a kind of critical geographical intelligence. As an emerging technique for characterization, spatial Representation Learning (SRL) uses deep neural networks (DNNs) to learn non-linear embedded features of spatial data for characterization. However, SRL extracts features by internal layers of DNNs, and thus suffers from lacking semantic labels. Texts of spatial entities, on the other hand, provide semantic understanding of latent feature labels, but is insensible to deep SRL models. How can we teach a SRL model to discover appropriate topic labels in texts and pair learned features with the labels? This paper formulates a new problem: feature-topic pairing, and proposes a novel Particle Swarm Optimization (PSO) based deep learning framework. Specifically, we formulate the feature-topic pairing problem into an automated alignment task between 1) a latent embedding feature space and 2) a textual semantic topic space. We decompose the alignment of the two spaces into: 1) point-wise alignment, denoting the correlation between a topic distribution and an embedding vector; 2) pair-wise alignment, denoting the consistency between a feature-feature similarity matrix and a topic-topic similarity matrix. We design a PSO based solver to simultaneously select an optimal set of topics and learn corresponding features based on the selected topics. We develop a closed loop algorithm to iterate between 1) minimizing losses of representation reconstruction and feature-topic alignment and 2) searching the best topics. Finally, we present extensive experiments to demonstrate the enhanced performance of our method.

The pervasiveness of mobile and sensing technologies today has facilitated the creation of Big Crowdsourced Geotagged Data (BCGD) from individual users in real time and at different locations in the city. Such ubiquitous user-generated data allow us to infer various patterns of human behavior, which helps us understand the interactions between humans and cities. In this article, we aim to analyze BCGD, including mobile consumption check-ins, urban geography data, and human mobility data, to learn a model that can unveil the impact of urban geography and human mobility on the vibrancy of residential communities. Vibrant communities are defined as places that show diverse and frequent consumer activities. To effectively identify such vibrant communities, we propose a supervised data mining system to learn and mimic the unique spatial configuration patterns and social interaction patterns of vibrant communities using urban geography and human mobility data. Specifically, to prepare the benchmark vibrancy scores of communities for training, we first propose a fused scoring method by fusing the frequency and the diversity of consumer activities using mobile check-in data. Besides, we define and extract the features of spatial configuration and social interaction for each community by mining urban geography and human mobility data. In addition, we strategically combine a pairwise ranking objective with a sparsity regularization to learn a predictor of community vibrancy. And we develop an effective solution for the optimization problem. Finally, our experiment is instantiated on BCGD including real estate, point of interests, taxi and bus GPS trajectories, and mobile check-ins in Beijing. The experimental results demonstrate the competitive performances of both the extracted features and the proposed model. Our results suggest that a structurally diverse community usually shows higher social interaction and better business performance, and incompatible land uses may decrease the vibrancy of a community. Our studies demonstrate the potential of how to best make use of BCGD to create local economic matrices and sustain urban vibrancy in a fast, cheap, and meaningful way.

Cesium lead iodide inorganic perovskite solar cells have great potential but the phase instability hinders their development. Here Wang et al. show a controlled drying process to make phase stable and highly efficient solar cells with power conversion efficiency of 15.7%.

Solution-processable halide perovskites have high luminous efficiency and excellent chemical tunability, making them ideal candidates for light-emitting diodes. Here Yang et al. achieve high external quantum efficiency of 14% in the devices by fine-tuning the phase and passivating the surface defects.

Hybrid organic-inorganic perovskites are garnering attention for light emitting diode (LED) applications. Employing a thin hydrophilic insulating polymer, Zhanget al. report LEDs exhibiting a brightness of 91,000 cd m−2and external quantum efficiency of 10.4% using a mixed-cation perovskite.

The original version of this Article omitted an acknowledgement to the source of Fig. 1a. The following has been added to the end of the caption to Fig. 1: ‘Figure 1a adapted from ref. 13 (copyright 2016 Macmillan Publishers)’. This has been corrected in the PDF and HTML versions of the Article.

Gold nanoparticles (Au NPs) possess many advantages such as facile synthesis, controllable size and shape, good biocompatibility, and unique optical properties. Au NPs have been widely used in biomedical fields, such as hyperthermia, biocatalysis, imaging, and drug delivery. The broad application range may result in hazards to the environment and human health. Therefore, it is important to predict safety and evaluate therapeutic efficiency of Au NPs. It is necessary to establish proper approaches for the study of toxicity and biomedical effects. In this review, we first focus on the recent progress in biological effects of Au NPs at the molecular and cellular levels, and then introduce key techniques to study the interaction between Au NPs and proteins. Knowledge of the biomedical effects of Au NPs is significant for the rational design of functional nanomaterials and will help predict their safety and potential applications.

Myostatin (MSTN) has been shown to be a negative regulator of skeletal muscle development and growth. MSTN dysfunction therefore offers a strategy for promoting animal growth performance in livestock production. In this study, we investigated the possibility of using RNAi-based technology to generate transgenic sheep with a double-muscle phenotype. A shRNA expression cassette targeting sheep MSTN was used to generate stable shRNA-expressing fibroblast clones. Transgenic sheep were further produced by somatic cell nuclear transfer (SCNT) technology. Five lambs developed to term and three live lambs were obtained. Integration of shRNA expression cassette in three live lambs was confirmed by PCR. RNase protection assay showed that the shRNAs targeting MSTN were expressed in muscle tissues of three transgenic sheep. MSTN expression was significantly inhibited in muscle tissues of transgenic sheep when compared with control sheep. Moreover, transgenic sheep showed a tendency to faster increase in body weight than control sheep. Histological analysis showed that myofiber diameter of transgenic sheep M17 were bigger than that of control sheep. Our findings demonstrate a promising approach to promoting muscle growth in livestock production.