青岛滨海学院

In this work, we present a mechanism-informed screening workflow that integrates COSMO-RS with thermodynamic and transport models to translate solute-HDES interactions into process-relevant mass transfer descriptors for liquid-liquid extraction. This approach enables systematic ranking of extractants and identification of feasible operating windows. Screening 83 hydrophobic deep eutectic solvents (HDESs) identified three lead systems-TBACl:AceAc, TBACl:OctAc, and TBACl:DecAc (1:1)-with infinite dilution performance indices of 8.69, 8.36, and 8.27, respectively. Speciation-aware calculations reveal strong pH dependence of the apparent distribution coefficient, which reaches extremely high values at pH 3 due to the favorable transfer of neutral succinic acid. As pH increases across the dissociation range, distribution coefficients decline sharply as deprotonation enhances solute hydrophilicity. TBACl:AceAc shows higher performance near this boundary due to balanced hydrogen-bonding capacity and moderate polarity predicted by COSMO-RS. Benchscale extractions yield removal efficiencies of 55.2%, 61.4%, and 71.1% for TBACl:AceAc, TBACl:OctAc, and TBACl:DecAc, respectively, reflecting the interplay of thermodynamic affinity, hydrophobicity, phase stability, and mass transfer behavior. Although TBACl:AceAc exhibits stronger specific interactions, its higher polarity increases aqueous miscibility and hinders phase disengagement. In contrast, TBACl:DecAc's lower polarity enhances interfacial stability and extraction efficiency. This integrated framework accelerates solvent discovery for energy-efficient recovery of platform bio-acids.

Industrial-scale hydrogen production from seawater is a paramount goal for a sustainable energy future, yet it is severely hampered by the rapid deactivation of electrocatalysts under harsh operating conditions. Here, we introduce a robust self-supporting aerogel catalyst designed to address the two intertwined challenges of activity and stability in high-current-density seawater electrolysis. Our strategy involves creating strong metal-support interactions by anchoring ultrasmall platinum nanoparticles onto a porous N-doped carbon aerogel (Pt@N/CFP). Theoretical calculations reveal that this unique Pt-N interface serves a dual critical function: it not only lowers the kinetic barrier for water dissociation but also creates an electronic shield that effectively prevents chloride ion poisoning of the Pt active sites. When implemented as the cathode in a practical anion-exchange membrane (AEM) electrolyzer, the Pt@N/CFP catalyst demonstrates exceptional performance, achieving a low cell voltage of 1.688 V at an industrial-grade current density of 1000 mA cm−2 and maintaining outstanding stability for over 300 h. This work provides guidance for creating exceptionally durable catalysts capable of withstanding extreme electrochemical environments.

This study examines the impact of contract farming on the income of smallholder oyster farmers in coastal Shandong Province, China, addressing the mixed findings in existing literature on its effectiveness in developing countries. Using survey data from 510 oyster farming households across four coastal cities, the study applies a combined coarsened exact matching (CEM) and propensity score matching (PSM) approach to mitigate selection bias. Treatment effects are estimated via weighted least squares (WLS) regression, and a mediation model explores the role of new equipment adoption and technology upgrades as pathways linking contract participation to income improvement. Results show that participation in contract farming significantly increases both total farm income and farm income per output. A dual mediation effect is confirmed: Contract farming facilitates the adoption of new equipment, which in turn drives technological upgrading, ultimately enhancing income. However, new equipment alone can temporarily reduce income due to high initial costs. Regional heterogeneity analysis reveals that income effects with high levels of regional policy support. This study contributes to the literature by providing new empirical evidence from an under-researched sector—oyster aquaculture—and by introducing a sequential mediation model that uncovers the mechanisms behind income changes.

This study addresses key challenges in image recognition using Artificial Intelligence neural networks, specifically the lack of trust and limited interpretability caused by their "black-box" decision-making mechanisms. Unlike existing research, which often focuses on a single interpretability method or model performance optimization, this study systematically investigates visualization and trust-building strategies for high-risk applications. An enhanced Convolutional Neural Network (CNN) is developed by integrating Grad-CAM and LIME methods to explore interpretability techniques in visual image recognition. Evaluated on the CIFAR-10 dataset and a subset of ImageNet, the proposed model achieves recognition accuracies of 89.8% and 96.33%, respectively, marking a 3.2% improvement over the baseline AlexNet model. A novel dual-modal interpretability framework is introduced. Grad-CAM generates class-discriminative heatmaps to visualize global attention regions, while LIME provides localized feature attribution for individual predictions. The combination of these techniques significantly enhances model transparency, achieving an interpretation consistency score of 0.82. Experimental results show that the model maintains strong classification performance, with an F1-score of 92.2%. Insertion and deletion tests further confirm the reliability of heatmap-based visualizations. To comprehensively evaluate interpretability, a multi-dimensional assessment system is implemented, combining quantitative metrics with expert human evaluation. This study provides valuable insights for the development of trustworthy AI systems, especially in high-stakes domains, such as medical diagnostics and autonomous driving.

The kinetics of alkaline hydrogen evolution reaction (HER) of platinum (Pt) is markedly slower owing to the poor proton supply rate and sluggish water dissociation. Herein, we report an efficient strategy based on polyoxometalates (POMs) hydrogels to prepare the self-supporting porous conductive carbon aerogels embedded with Pt-doped molybdenum carbide (Pt@Mo2C) active sites for promoting the alkaline HER performance. It is found that as-prepared self-supporting Pt@Mo2C carbon aerogels possesses continuous porous conductive networks, which improves the electron and mass transfer. Moreover, the heterostructure of Pt and Mo2C can significantly promote the water dissociation with the assistant of Mo-OH and Pt–H bonding, thereby improving alkaline HER activities. The optimized Pt@Mo2C/Carbon fiber paper (Pt@Mo2C/CFP) electrocatalyst has Pt content as low as 0.03 mg cm−2, which can achieve overpotential of 17 mV at 10 mA cm−2, surpassing the commercial Pt/C. Moreover, it exhibits an excellent sustained stability for over 100 h at 500 mA cm-2 in a practical alkaline water electrolyzer. The work offers new opportunities to design and synthesize superior performance electrocatalysts for the alkaline HER.