Confined growth of Ultrathin, nanometer-sized FeOOH/CoP heterojunction nanosheet arrays as efficient self-supported electrode for oxygen evolution reaction.

Self-supported electrodes, featuring abundant active species and rapid mass transfer, are promising for practical applications in water electrolysis. However, constructing efficient self-supported electrodes with a strong affinity between the catalytic components and the substrate is of great challenge. In this study, by combining the ideas of in-situ construction and space-confined growth, we designed a novel self-supported FeOOH/cobalt phosphide (CoP) heterojunctions grown on a carefully modified commercial Ni foam (NF) with three-dimensional (3D) hierarchically porous Ni skeleton (FeOOH/CoP/3D NF). The specific porous structure of 3D NF directs the confined growth of FeOOH/CoP catalyst into ultra-thin and small-sized nanosheet arrays with abundant edge active sites. The active FeOOH/CoP component is stably anchored on the rough pore wall of 3D NF support, leading to superior stability and improved conductivity. These structural advantages contributed to a highly facilitated oxygen evolution reaction (OER) activity and enhanced durability of the FeOOH/CoP/3D NF electrode. Herein, the FeOOH/CoP/3D NF electrode afforded a low overpotential of 234 mV at 10 mA cm-2 (41 mV smaller than FeOOH/CoP grown on unmodified Ni foam) and high stability for over 90 h, which is among the top reported OER catalysts. Our study provides an effective idea and technique for the construction of active and robust self-supported electrodes for water electrolysis.

HoRDA: Learning higher-order structure information for predicting RNA-disease associations.

CircRNA and miRNA are crucial non-coding RNAs, which are associated with biological diseases. Exploring the associations between RNAs and diseases often requires a significant time and financial investments, which has been greatly alleviated and improved with the application of deep learning methods in bioinformatics. However, existing methods often fail to achieve higher accuracy and cannot be universal between multiple RNAs. Moreover, complex RNA-disease associations hide important higher-order topology information. To address these issues, we learn higher-order structure information for predicting RNA-disease associations (HoRDA). Firstly, the correlations between RNAs and the correlations between diseases are fully explored by combining similarity and higher-order graph attention network. Then, a higher-order graph convolutional network is constructed to aggregate neighbor information, and further obtain the representations of RNAs and diseases. Meanwhile, due to the large number of complex and variable higher-order structures in biological networks, we design a higher-order negative sampling strategy to gain more desirable negative samples. Finally, the obtained embeddings of RNAs and diseases are feed into logistic regression model to acquire the probabilities of RNA-disease associations. Diverse simulation results demonstrate the superiority of the proposed method. In the end, the case study is conducted on breast neoplasms, colorectal neoplasms, and gastric neoplasms. We validate the proposed higher-order strategies through ablative and exploratory analyses and further demonstrate the practical applicability of HoRDA. HoRDA has a certain contribution in RNA-disease association prediction.

3D Co-Doping α-Ni(OH)2 Nanosheets for Ultrastable, High-Rate Ni-Zn Battery.

The α-Ni(OH)2 is regarded as one promising cathode for aqueous nickel-zinc batteries due to its high theoretical capacity of ≈480 mAh g-1 , its practical deployment however suffers from the poor stability in strong alkaline solution, intrinsic low electrical conductivity as well as the retarded ionic diffusion. Herein, a 3D (three dimensional) macroporous α-Ni(OH)2 nanosheets with Co doping is designed through a facile and easily scalable electroless plating combined with electrodeposition strategy. The unique micrometer-sized 3D pores come from Ni substrate and rich voids between Co-doping α-Ni(OH)2 nanosheets can synergistically afford facile, interconnected ionic diffusion channels, sufficient free space for accommodating its volume changes during cycling; meanwhile, the Co-doping can stabilize the structural robustness of the α-Ni(OH)2 in the alkaline electrolyte during cycling. Thus, the 3D α-Ni(OH)2 shows a high capacity of 284 mAh g-1 at 0.5 mA cm-2 with an excellent retention of 78% even at 15 mA cm-2 , and more than 2000 stable cycles at 6 mA cm-2 , as well as the robust cycling upon various flexible batteries. This work provides a simple and efficient pathway to enhance the electrochemical performance of Ni-Zn batteries through improving ionic transport kinetics and stabilizing crystal structure of cathodes.

Enhanced treatment of shale gas fracturing waste fluid through plant-microbial synergism.

Cost-efficient and environmentally friendly treatment of hydraulic fracturing effluents is of great significance for the sustainable development of shale gas exploration. We investigated the synergistic effects of plant-microbial treatment of shale gas fracturing waste fluid. The results showed that illumination wavelength and temperature are direct drivers for microbial treatment effects of CODCr and BOD5, while exhibit little effects on nitrogen compounds, TDS, EC, and SS removals as well as microbial species and composition. Plant-microbial synergism could significantly enhance the removal of pollutants compared with removal efficiency without plant enhancement. Additionally, the relative abundance and structure of microorganisms in the hydraulic fracturing effluents greatly varied with the illumination wavelength and temperature under plant-microbial synergism. 201.24 g water dropwort and 435 mg/L activated sludge with illumination of 450-495 nm (blue) at 25 °C was proved as the best treatment condition for shale gas fracturing waste fluid samples, which showed the highest removal efficiency of pollutants and the lowest algal toxicity in treated hydraulic fracturing effluents. The microbial community composition (36.73% Flavobacteriia, 25.01% Gammaproteobacteria, 18.55% Bacteroidia, 9.3% Alphaproteobacteria, 4.1% Cytophagia, and 2.83% Clostridia) was also significantly different from other treatments. The results provide a potential technical solution for improved treatment of shale gas hydraulic fracturing effluents.