Flexible supercapacitors can be ideal flexible power sources for wearable electronics due to their ultra-high power density and high cycle life. In daily applications, wearable devices will inevitably cause damage or short circuit during bending, stretching, and compression. Therefore, it is necessary to develop proper energy storage devices to meet the requirements of various wearable electronic devices. Herein, Poly(vinyl alcohol) linked various content of phytic acid (PVA-PAx) hydrogels are synthesized with high transparency and high toughness by a one-step freeze-thaw method. The effects of different raw material ratios and agents on the ionic conductivity and mechanical properties of the hydrogel electrolyte are investigated. The PVA-PA_21% with 2 M H₂SO₄ solution (PVA-PA_21%-2 M H₂SO₄) shows a high ionic conductivity of 62.75 mS cm⁻¹. Based on this, flexible supercapacitors fabricated with PVA-PA_21%-2 M H₂SO₄ hydrogel present a high specific capacitance at 1 A g⁻¹ after bending at 90° (64.8 F g⁻¹) and for 30 times (67.3 F g⁻¹), respectively. Moreover, the device shows energy densities of 13.5 Wh kg⁻¹ and 14.0 Wh kg⁻¹ at a power density of 300 W kg⁻¹ after bending at 90° and for 30 times during 10,000 cycles. It provides inspiration for the design and development of electrolytes for related energy electrochemical devices.

In order to alleviate the energy crisis and propel a low-carbon economy, hydrogen (H₂) plays an important role as a renewable cleaning resource. To break the hydrogen evolution reaction (HER) bottleneck, we need high-efficiency electrocatalysts. Based on the synergistic effect between bimetallic oxides, hierarchical mesoporous CoNiO₂ nanosheets can be fabricated. Combining physical representations with electrochemical measurements, the resultant CoNiO₂ catalysts present the hierarchical microflowers morphology assembled by mesoporous nanosheets. The ultrathin two-dimensional nanosheets and porous surface characteristics provide the vast channels for electrolyte injection, thus endowing CoNiO₂ the outstanding HER performance. The excellent performance with a fewer onset potential of 94 mV, a smaller overpotential at 10 mA cm⁻², a lower Tafel slope of 109 mV dec⁻¹ and better stability after 1000 cycles makes CoNiO₂ better than that of metallic Co and metallic Ni.

Natural graphite (NG) is abundant and has a high capacity for lithium-ion storage, but its narrow interlayer spacing and poor cyclic stability limit its use in high-performance lithium-ion batteries (LIBs). To address this, a N/S co-doped micro-expanded graphite composite (BFAC@MEG) was prepared by coating micro-expanded graphite (MEG) with N/S-containing amorphous carbon derived from biochemical fulvic acid (BFAC). This enhanced the electrochemical kinetics of lithium ions, improving charge transfer rates and reducing diffusion resistance. GITT results showed a higher Li⁺ diffusion coefficient than MEG and spherical graphite (SG). BFAC@MEG exhibited excellent rate performance, robust storage capacity and remarkable cycling stability. It had a specific capacity of 333 mAh g⁻¹ at 1 C, 205 mAh g⁻¹ at 3 C, and retained 81.57% capacity after 500 cycles. Even at 5 C, BFAC@MEG exhibits a high reversible capacity of 98 mAh g⁻¹ after 200 cycles. After cycling, SEM and XPS analyses revealed a low expansion rate of 15.96% cross-sectional expansion after 300 cycles at 3 C and a stable solid electrolyte interphase (SEI) film rich in LiF and Li₂CO₃.

The distributed photovoltaic (PV) power stations within the entire county exist spatiotemporal correlation. Merely considering temporal correlation makes it challenging to meet the day-ahead scheduling demands. This paper proposes a distributed PV county-level day-ahead power prediction method based on grey relational analysis and the Transformer-Graph Convolutional Attention Network (Transformer-GCAN) model. Firstly, the grey relational degree is used to measure the relevance between each distributed PV stations, and the connection relationship of the station graph is determined based on the analysis results. Secondly, the Transformer network is utilized to extract the temporal features of each PV sequence in the graph. Based on the Graph Convolutional Network (GCN) model, a Graph Attention Mechanism (GAT) is introduced to dynamically extract spatial features between each photovoltaic station in the graph. Finally, the integration of spatiotemporal features is achieved through a fully connected neural network, enabling day-ahead power prediction at the county level. Case analysis results demonstrate that compared with the Transformer-GCN model, the Root Mean Square Error (RMSE) of the power prediction model proposed in this paper is reduced by 11.90%, 15.72% and 19.61% respectively in sunny days, cloudy days and rainy days.