RESUMEN
Seawater electrolysis is a promising but challenging strategy to generate carbon-neutral hydrogen. A grand challenge for hydrogen evolution reaction (HER) from alkaline seawater electrolysis is the development of efficient and stable electrocatalysts to overcome the limitation of sluggish kinetics. Here, a 3D nanorod hybrid catalyst is reported, which comprises heterostructure MoO2@NiMoO4 supported Ru nanoparticles (Ru/ MoO2@NiMoO4) with a size of ≈5 nm. Benefitting from the effect of strongly coupled interaction, Ru/MoO2@NiMoO4 catalyst exhibits a remarkable alkaline seawater hydrogen evolution performance, featured by a low overpotential of 184 mV at a current density of 1.0 A cm-2, superior to commercial Pt/C (338 mV). Experimental observations demonstrate that the heterostructure MoO2@NiMoO4 as an electron-accepting support makes the electron transfer from the Ru nanoparticles to MoO2, and thereby implements the electron redistribution of Ru site. Mechanistic analysis elucidates that the electron redistribution of active Ru site enhances the ability of hydrogen desorption, thereby promoting alkaline seawater HER kinetics and finally leading to a satisfactory catalysis performance at ampere-level current density of alkaline seawater electrolysis.
RESUMEN
Background: The ageing population in China has led to a significant increase in the number of older persons with disabilities. These individuals face substantial challenges in accessing adequate activities of daily living (ADL) assistance. Unmet ADL needs among this population can result in severe health consequences and strain an already burdened care system. This study aims to identify the factors influencing unmet ADL needs of the oldest old (those aged 80 and above) with disabilities using six machine learning methods. Methods: Drawing from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) 2017-2018 data, we employed six machine learning methods to predict unmet ADL needs among the oldest old with disabilities. The predictive effects of various factors on unmet ADL needs were explored using Shapley Additive exPlanations (SHAP). Results: The Random Forest model showed the highest prediction accuracy among the six machine learning methods tested. SHAP analysis based on the Random Forest model revealed that factors such as household registration, disability class, economic rank, self-rated health, caregiver willingness, perceived control, economic satisfaction, pension, educational attainment, financial support given to children, living arrangement, number of children, and primary caregiver played significant roles in the unmet ADL needs of the oldest old with disabilities. Conclusion: Our study highlights the importance of socioeconomic factors (e.g., household registration and economic rank), health status (e.g., disability class and self-rated health), and caregiving relationship factors (e.g., caregiver willingness and perceived control) in reducing unmet ADL needs among the oldest old with disabilities in China. Government interventions aimed at bridging the urban-rural divide, targeting groups with deteriorating health status, and enhancing caregiver skills are essential for ensuring the well-being of this vulnerable population. These findings can inform policy decisions and interventions to better address the unmet ADL needs among the oldest old with disabilities.
