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Directly harvesting solar energy for battery charging represents an ultimate solution toward low-cost, green, efficient and sustainable electrochemical energy storage. Here, we design a sunlight promotion strategy into rechargeable zinc–air battery with significantly reduced charging potential below the theoretical cell voltage of zinc–air batteries. The sunlight-promoted zinc–air battery using BiVO4 or α-Fe2O3 air photoelectrode achieves a record-low charge potential of ~1.20 and ~1.43 V, respectively, under illumination, which is lowered by ~0.5–0.8 V compared to the typical charge voltage of ~2 V in conventional zinc–air battery. The band structure and photoelectrochemical stability of photoelectrodes are found to be key factors determining the charging performance of sunlight-promoted zinc–air batteries. The introduction of photoelectrode as an air electrode opens a facile way for developing integrated single-unit zinc–air batteries that can efficiently use solar energy to overcome the high charging overpotential of conventional zinc–air batteries.

Tremendous effort have recently been made in optimizing the air catalysts of flexible zinc–air batteries (ZABs). Unfortunately, the bottleneck factors in electrolytes that largely limit the working life and energy efficiency of ZABs have long been relatively neglected. Herein, an alkaline gel polymer electrolyte (GPE) is fabricated through multiple crosslinking reactions among poly(vinyl alcohol) (PVA), poly(acrylic acid), and graphene oxide followed by intense uptake of an alkali and the KI reaction modifier. The prepared GPE exhibits essentially improved properties compared to traditional PVA gel electrolyte in terms of mechanical strength, ionic conductivity, and water retention capability. In addition, the introduced reaction modifier I− in the GPE changes the path of the conventional oxygen evolution reaction, leading to a more thermodynamically favorable path. The optimized GPE enables flexible ZABs exhibiting an exceptionally low charge potential of 1.69 V, a long cycling time of 200 h, a high energy efficiency of 73%, and rugged reliability under different extreme working conditions. Moreover, the successful integration of ZABs in a variety of real wearable electronic devices demonstrates their excellent practicability as flexible power sources.

ObjectiveTo characterise the oral microbiome, gut microbiome and serum lipid profiles in patients with active COVID-19 and recovered patients; evaluate the potential of the microbiome as a non-invasive biomarker for COVID-19; and explore correlations between the microbiome and lipid profile.DesignWe collected and sequenced 392 tongue-coating samples, 172 faecal samples and 155 serum samples from Central China and East China. We characterised microbiome and lipid molecules, constructed microbial classifiers in discovery cohort and verified their diagnostic potential in 74 confirmed patients (CPs) from East China and 37 suspected patients (SPs) with IgG positivity.ResultsOral and faecal microbial diversity was significantly decreased in CPs versus healthy controls (HCs). Compared with HCs, butyric acid-producing bacteria were decreased and lipopolysaccharide-producing bacteria were increased in CPs in oral cavity. The classifiers based on 8 optimal oral microbial markers (7 faecal microbial markers) achieved good diagnostic efficiency in different cohorts. Importantly, diagnostic efficacy reached 87.24% in the cross-regional cohort. Moreover, the classifiers successfully diagnosed SPs with IgG antibody positivity as CPs, and diagnostic efficacy reached 92.11% (98.01% of faecal microbiome). Compared with CPs, 47 lipid molecules, including sphingomyelin (SM)(d40:4), SM(d38:5) and monoglyceride(33:5), were depleted, and 122 lipid molecules, including phosphatidylcholine(36:4p), phosphatidylethanolamine (PE)(16:0p/20:5) and diglyceride(20:1/18:2), were enriched in confirmed patients recovery.ConclusionThis study is the first to characterise the oral microbiome in COVID-19, and oral microbiomes and lipid alterations in recovered patients, to explore their correlations and to report the successful establishment and validation of a diagnostic model for COVID-19.

Graph Neural Networks (GNNs) are powerful tools in representation learning for graphs. However, recent studies show that GNNs are vulnerable to carefully-crafted perturbations, called adversarial attacks. Adversarial attacks can easily fool GNNs in making predictions for downstream tasks. The vulnerability to adversarial attacks has raised increasing concerns for applying GNNs in safety-critical applications. Therefore, developing robust algorithms to defend adversarial attacks is of great significance. A natural idea to defend adversarial attacks is to clean the perturbed graph. It is evident that real-world graphs share some intrinsic properties. For example, many real-world graphs are low-rank and sparse, and the features of two adjacent nodes tend to be similar. In fact, we find that adversarial attacks are likely to violate these graph properties. Therefore, in this paper, we explore these properties to defend adversarial attacks on graphs. In particular, we propose a general framework Pro-GNN, which can jointly learn a structural graph and a robust graph neural network model from the perturbed graph guided by these properties. Extensive experiments on real-world graphs demonstrate that the proposed framework achieves significantly better performance compared with the state-of-the-art defense methods, even when the graph is heavily perturbed. We release the implementation of Pro-GNN to our DeepRobust repository for adversarial attacks and defenses 1 . The specific experimental settings to reproduce our results can be found in https://github.com/ChandlerBang/Pro-GNN.

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This review highlights the critical challenges and the corresponding strategies for different metal electrodes in metal–air batteries.