Self-Encapsulation of High-Entropy Alloy Nanoparticles inside Carbonized Wood for Highly Durable Electrocatalysis.

High-entropy alloy nanoparticles (HEAs) show great potential in emerging electrocatalysis due to their combination and optimization of multiple elements. However, synthesized HEAs often exhibit a weak interface with the conductive substrate, hindering their applications in long-term catalysis and energy conversion. Herein, a highly active and durable electrocatalyst composed of quinary HEAs (PtNiCoFeCu) encapsulated inside the activated carbonized wood (ACW) is reported. The self-encapsulation of HEAs is achieved during Joule heating synthesis (2060 K, 2 s) where HEAs naturally nucleate at the defect sites. In the meantime, HEAs catalyze the deposition of mobile carbon atoms to form a protective few-layer carbon shell during the rapid quenching process, thus remarkably strengthening the interface stability between HEAs and ACW. As a result, the HEAs@ACW shows not only favorable activity with an overpotential of 7 mV at 10 mA cm-2 for hydrogen evolution but also negligible attenuation during a 500 h stability test, which is superior to most reported electrocatalysts. The design of self-encapsulated HEAs inside ACW provides a critical strategy to enhance both activity and stability, which is also applicable to many other energy conversion technologies.

Deep learning for precise diagnosis and subtype triage of drug-resistant tuberculosis on chest computed tomography.

Deep learning, transforming input data into target prediction through intricate network structures, has inspired novel exploration in automated diagnosis based on medical images. The distinct morphological characteristics of chest abnormalities between drug-resistant tuberculosis (DR-TB) and drug-sensitive tuberculosis (DS-TB) on chest computed tomography (CT) are of potential value in differential diagnosis, which is challenging in the clinic. Hence, based on 1176 chest CT volumes from the equal number of patients with tuberculosis (TB), we presented a Deep learning-based system for TB drug resistance identification and subtype classification (DeepTB), which could automatically diagnose DR-TB and classify crucial subtypes, including rifampicin-resistant tuberculosis, multidrug-resistant tuberculosis, and extensively drug-resistant tuberculosis. Moreover, chest lesions were manually annotated to endow the model with robust power to assist radiologists in image interpretation and the Circos revealed the relationship between chest abnormalities and specific types of DR-TB. Finally, DeepTB achieved an area under the curve (AUC) up to 0.930 for thoracic abnormality detection and 0.943 for DR-TB diagnosis. Notably, the system demonstrated instructive value in DR-TB subtype classification with AUCs ranging from 0.880 to 0.928. Meanwhile, class activation maps were generated to express a human-understandable visual concept. Together, showing a prominent performance, DeepTB would be impactful in clinical decision-making for DR-TB.

Magnetic Heating Amorphous NiFe Hydroxide Nanosheets Encapsulated Ni Nanoparticles@Wood Carbon to Boost Oxygen Evolution Reaction Activity.

The oxygen evolution reaction (OER) has significant effects on the water-splitting process and rechargeable metal-air batteries; however, the sluggish reaction kinetics caused by the four-electron transfer process for transition metal catalysts hinder large-scale commercialization in highly efficient electrochemical energy conversion devices. Herein, a magnetic heating-assisted enhancement design for low-cost carbonized wood with high OER activity is proposed, in which Ni nanoparticles are encapsulated in amorphous NiFe hydroxide nanosheets (a-NiFe@Ni-CW) via direct calcination and electroplating. The introduction of amorphous NiFe hydroxide nanosheets optimizes the electronic structure of a-NiFe@Ni-CW, accelerating electron transfer and reducing the energy barrier in the OER. More importantly, the Ni nanoparticles located on carbonized wood can function as magnetic heating centers under the effect of an alternating current (AC) magnetic field, further promoting the adsorption of reaction intermediates. Consequently, a-NiFe@Ni-CW demonstrated an overpotential of 268 mV at 100 mA cm-2 for the OER under an AC magnetic field, which is superior to that of most reported transition metal catalysts. Starting with sustainable and abundant wood, this work provides a reference for highly effective and low-cost electrocatalyst design with the assistance of a magnetic field.