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Nanoporous metals have unique potentials for energy applications with a high surface area despite the percolating structure. Yet, a highly corrosive environment is required for the synthesis of porous metals with conventional dealloying methods, limiting the large-scale fabrication of porous structures for reactive metals. In this study, we synthesize a highly reactive Mg nanoporous system through a facile organic solution-based approach without any harsh etching. The synthesized nanoporous Mg also demonstrates enhanced hydrogen sorption kinetics and reveals unique kinetic features compared to Mg nanoparticles. The well-crystallized Mg nanoporous structure exhibits crystalline facet-dependent hydrogen sorption characteristics, featuring gradually improved hydrogen storage capacity up to 6 wt.% upon cycling. Also, continuum kinetics models coupled to atomistic simulations reveal that the compressive stress developed during the hydrogenation of nanoporous Mg enhances the sorption kinetics, as opposed to the sluggish kinetics under tensile stress in core-shell nanoparticles. It is expected that the synthetic strategy conceived in this study can be further implemented to prepare different kinds of reactive porous metals in a facile and scalable way for the development of large-scale and distributed hydrogen storage systems for the emerging low-carbon hydrogen economy. A highly reactive Mg nanoporous system is prepared via a facile organic solution-based method for advanced solid-state hydrogen storage. It reveals that Mg crystalline facets and stress states lead to improved hydrogen storage kinetics [ABSTRACT FROM AUTHOR]

Pressure ulcers (PUs) are a prevalent skin disease affecting patients with impaired mobility and in high-risk groups. These ulcers increase patients' suffering, medical expenses, and burden on medical staff. This study introduces a clinical decision support system and verifies it for predicting real-time PU occurrences within the intensive care unit (ICU) by using MIMIC-IV and in-house ICU data. We develop various machine learning (ML) and deep learning (DL) models for predicting PU occurrences in real time using the MIMIC-IV and validate using the MIMIC-IV and Kangwon National University Hospital (KNUH) dataset. To address the challenge of missing values in time series, we propose a novel recurrent neural network model, GRU-D++. This model outperformed other experimental models by achieving the area under the receiver operating characteristic curve (AUROC) of 0.945 for the on-time prediction and AUROC of 0.912 for 48h in-advance prediction. Furthermore, in the external validation with the KNUH dataset, the fine-tuned GRU-D++ model demonstrated superior performances, achieving an AUROC of 0.898 for on-time prediction and an AUROC of 0.897 for 48h in-advance prediction. The proposed GRU-D++, designed to consider temporal information and missing values, stands out for its predictive accuracy. Our findings suggest that this model can significantly alleviate the workload of medical staff and prevent the worsening of patient conditions by enabling timely interventions for PUs in the ICU. [ABSTRACT FROM AUTHOR]

Pressure-composition-temperature (PCT) curves for hydrogen absorption and desorption of AB2-type hydrogen storage alloys at arbitrary temperatures are predicted by three machine learning models such as random forest, K-nearest neighbor and deep neural network (DNN). Two data generation methods are adopted to increase the number of data points. A new form of the PCT curve functions is suggested to fit experimental data, which greatly helps improve the prediction accuracy. A van't Hoff type equation is used to generate unmeasured temperature data, which improves the model performance on the PCT behavior at various temperatures. The results indicate that a DNN is the best model for predicting the PCT behavior with a high average correlation value R2 = 0.93070. [ABSTRACT FROM AUTHOR]