Aluminum-ion batteries (AIBs) have become a research hotspot in the field of energy storage due to their high energy density, safety, environmental friendliness, and low cost. However, the actual capacity of AIBs is much lower than the theoretical specific capacity, and their cycling stability is poor. The exploration of energy storage mechanisms may help in the design of stable electrode materials, thereby contributing to improving performance. In this work, molybdenum disulfide (MoS₂) was selected as the host material for AIBs, and carbon nanofibers (CNFs) were used as the substrate to prepare a molybdenum disulfide/carbon nanofibers (MoS₂/CNFs) electrode, exhibiting a residual reversible capacity of 53 mAh g⁻¹ at 100 mA g⁻¹ after 260 cycles. The energy storage mechanism was understood through a combination of electrochemical characterization and first-principles calculations. The purpose of this study is to investigate the diffusion behavior of ions in different channels in the host material and its potential energy storage mechanism. The computational analysis and experimental results indicate that the electrochemical behavior of the battery is determined by the ion transport mechanism between MoS₂ layers. The insertion of ions leads to lattice distortion in the host material, significantly impacting its initial stability. CNFs, serving as a support material, not only reduce the agglomeration of MoS₂ grown on its surface, but also effectively alleviate the volume expansion caused by the host material during charging and discharging cycles.

In order to improve the utilization efficiency of corn seeds and meet the demand of single-seed seeding technology in agriculture, this study was conducted to explore the effect of freezing damage detection on the endosperm and embryo sides of single corn seeds, based on hyperspectral imaging combined with a feature fusion algorithm and a machine learning method. First, hyperspectral image data of the endosperm and embryo sides of three freezing damage categories of corn seeds were collected, and the average spectra of the endosperm part and embryo part were obtained by threshold segmentation. Then, the spectral data were preprocessed (none, SNV, and 5-3 smoothing), and the feature wavelengths were extracted using the feature wavelength extraction algorithm (SPA and 2DCOS). The modeling accuracy results based on the hyperspectral data of the endosperm and embryo sides at the full waveband and feature wavelength (including feature wavelength fusion) were compared and analyzed. In the endosperm side’s freezing damage identification, the SNV+SVM model obtained the highest accuracies of 92.9% and 90.0% with the training set and testing set, based on the full-waveband data. The SNV+SPA-2DCOS+SVM model, based on the feature wavelengths, obtained the highest accuracies of 92.9% and 91.2% with the training set and testing set, respectively. In terms of the embryo side’s freezing damage identification, the results on the embryo side were better than those on the endosperm side. The 5-3 smoothing+LDA model, based on the full-waveband data, achieved the highest accuracy results of 97.7% and 95.9% with the training and testing sets. In the meantime, the none+SPA-2DCOS+LDA model, based on the feature wavelengths, achieved the same highest accuracy results with the training and testing sets. When the fusion algorithm consisting of SPA and 2D-COS was used, the model’s performance on the endosperm side was better than that of the full-waveband analysis with only 19 feature wavelengths, while the recognition effect on embryo side could be achieved with only 15 feature wavelengths. These results provide a theoretical basis for constructing a multi-spectral detection system for the rapid and nondestructive identification of frozen corn seeds.

Rechargeable aluminum-ion batteries (AIBs), as novel energy storage systems featuring low-cost, high-energy density, and superior safety, demonstrate promising potential as a next-generation battery technology. However, the lack of high-performance cathode materials remains a critical barrier to practical implementation. In this study, highly crystalline cobalt sulfide (Co₉S₈) nanoparticles were synthesized using a one-step hydrothermal method and systematically evaluated their electrochemical performance and energy storage mechanisms in AIBs. Structural characterization revealed that while the synthesized material maintained high crystallinity, it formed agglomerates during the synthesis process that induced severe electrode polarization and limited ion diffusion kinetics. Electrochemical analysis demonstrated a reversible capacity of 48 mAh g⁻¹ after 500 cycles at a current density of 100 mA g⁻¹, indicating moderate cycling stability. DFT calculations with Bader charge analysis provided atomic-scale insights, revealing that Al³⁺ preferentially occupies Co. lattice sites through a pseudo-isomorphic substitution mechanism, exhibiting a 52.5% lower formation energy compared to S-site substitution. This work establishes critical correlations between morphological characteristics and electrochemical performance while proposing a novel cation substitution mechanism for energy storage. These findings provide fundamental insights for designing high-kinetics transition metal sulfide cathodes and advance the development of practical multivalent-ion battery systems.