Pseudocapacitance electrode and asymmetric supercapacitor based on biomass juglone/activated carbon composites

Carbon/copper oxide electrode materials with high atomic utilization constructed by in-situ induced growth strategy of nano metal-organic frameworks

Carbon/metal composites derived from metal-organic frameworks (MOFs) have attracted widespread attention due to their excellent electronic conductivity, adjustable porosity, and outstanding stability. However, traditional synthesis methods are limited by the dense stereo geometry and large crystal grain size of MOFs, resulting in many metals active sites are buried in the carbon matrix. While the common strategy involves incorporating additional dispersed media into material, this leads to a decrease in practical metal content. In this study, nanosized copper-metal-organic frameworks (Cu-MOFs) are in-situ grown on surface of carbon spheres by pre-anchoring copper ions, and the hybrid composite of porous carbon/copper oxide with high copper atom utilization rate is prepared through activation and pyrolysis methods. This strategy effectively addresses the issue of insufficient exposure of metal sites, and the obtained composite material exhibits high effective copper atom utilization rate, large specific surface area (2052.3 m2·g-1), diverse pore structure, outstanding specific capacity (1076.5F·g-1 at 0.5 A·g-1), and excellent cycle stability. Furthermore, this highly atom-economical universal method has positive significance in application fields of catalysis, energy storage, and adsorption.

Hierarchical Biobased Macroporous/Mesoporous Carbon: Fabrication, Characterization and Electrochemical/Ion Exchange Properties

With the goal of improving the mechanical properties of porous hierarchical carbon, cellulosic fiber fabric was incorporated into the resorcinol/formaldehyde (RF) precursor resins. The composites were carbonized in an inert atmosphere, and the carbonization process was monitored by TGA/MS. The mechanical properties, evaluated by nanoindentation, show an increase in the elastic modulus due to the reinforcing effect of the carbonized fiber fabric. It was found that the adsorption of the RF resin precursor onto the fabric stabilizes its porosity (micro and mesopores) during drying while incorporating macropores. The textural properties are evaluated by N₂ adsorption isotherm, which shows a surface area (BET) of 558 m²g^-1. The electrochemical properties of the porous carbon are evaluated by cyclic voltammetry (CV), chronocoulometry (CC), and electrochemical impedance spectroscopy (EIS). Specific capacitances (in 1 M H₂SO₄) of up to 182 Fg^-1 (CV) and 160 Fg^-1 (EIS) are measured. The potential-driven ion exchange was evaluated using Probe Bean Deflection techniques. It is observed that ions (protons) are expulsed upon oxidation in acid media by the oxidation of hydroquinone moieties present on the carbon surface. In neutral media, when the potential is varied from values negative to positive of the potential of zero charge, cation release, followed by anion insertion, is found.

Composition-dependent activity of Mn-doping NiS2 nanosheets for boosting photocatalytic H2 evolution

Two-dimensional transition metal disulfides are excellent photocatalytic materials, which can be significantly improved by optimizing the composition and structure. Herein, Mn-doping NiS2 of (Ni1-xMnx)-S with various Ni/Mn molar ratios is proposed via a facile and low-cost solvothermal method. The optimal (Ni4/6Mn2/6)-S exhibits pinecone-like morphology composed of tiny nanosheets with enlarged active sites, which facilitates the separation of photoinduced electrons and holes, improves the electron transfer ability and conductivity, and enlarges the active sites compared with pure NiS2 and MnS. Also, the negative shift of the conduction band derived from Mott-Schottky plots and the empirical formula provides a high thermodynamic driving force for hydrogen catalytic reaction. (Ni4/6Mn2/6)-S performs an ultrahigh hydrogen evolution rate of 24.86 mmol g-1 h-1 under UV-visible light irradiation, which is 1.5 times higher than pure NiS2 (16.92 mmol g-1 h-1) and 2.3 times higher than pure MnS (10.69 mmol g-1 h-1). The outstanding repeatability of 86.7% retention and apparent quantum yield of 46.9% are also achieved. Therefore, this work offers a novel bimetallic sulfide of (Ni1-xMnx)-S to improve the conversion efficiency of solar energy to chemical energy for photocatalytic hydrogen production.

Hierarchical Activated Carbon-MnO2 Composite for Wide Potential Window Asymmetric Supercapacitor Devices in Organic Electrolyte

The consumption of electrical energy grows alongside the development of global industry. Generating energy storage has become the primary focus of current research, examining supercapacitors with high power density. The primary raw material used in supercapacitor electrodes is activated carbon (AC). To improve the performance of activated carbon, we used manganese dioxide (MnO₂), which has a theoretical capacitance of up to 1370 Fg^-1. The composite-based activated carbon with a different mass of 0-20% MnO₂ was successfully introduced as the positive electrode. The asymmetric cell supercapacitors based on activated carbon as the anode delivered an excellent gravimetric capacitance, energy density, and power density of 84.28 Fg^-1, 14.88 Wh.kg^-1, and 96.68 W.kg^-1, respectively, at 1 M Et₄NBF₄, maintaining 88.88% after 1000 test cycles.

Screening and functional prediction of differentially expressed genes in walnut endocarp during hardening period based on deep neural network under agricultural internet of things

The deep neural network is used to establish a neural network model to solve the problems of low accuracy and poor accuracy of traditional algorithms in screening differentially expressed genes and function prediction during the walnut endocarp hardening stage. The paper walnut is used as the research object to analyze the biological information of paper walnut. The changes of lignin deposition during endocarp hardening from 50 days to 90 days are observed by microscope. Then, the Convolutional Neural Network (CNN) and Long and Short-term Memory (LSTM) network model are adopted to construct an expression gene screening and function prediction model. Then, the transcriptome and proteome sequencing and biological information of walnut endocarp samples at 50, 57, 78, and 90 days after flowering are analyzed and taken as the training data set of the CNN + LSTM model. The experimental results demonstrate that the endocarp of paper walnut began to harden at 57 days, and the endocarp tissue on the hardened inner side also began to stain. This indicates that the endocarp hardened laterally from outside to inside. The screening and prediction results show that the CNN + LSTM model’s highest accuracy can reach 0.9264. The Accuracy, Precision, Recall, and F1-score of the CNN + LSTM model are better than the traditional machine learning algorithm. Moreover, the Receiver Operating Curve (ROC) area enclosed by the CNN + LSTM model and coordinate axis is the largest, and the Area Under Curve (AUC) value is 0.9796. The comparison of ROC and AUC proves that the CNN + LSTM model is better than the traditional algorithm for screening differentially expressed genes and function prediction in the walnut endocarp hardening stage. Using deep learning to predict expressed genes’ function accurately can reduce the breeding cost and significantly improve the yield and quality of crops. This research provides scientific guidance for the scientific breeding of paper walnut.