One-Pot Monomer-Level Fabrication of Anion Exchange Membranes for High-Performance Water Electrolysis.

Anion exchange membrane (AEM) water electrolysis (AEMWE) is considered an economical technology for producing green hydrogen energy. However, conventional AEMs comprising polymer backbones with anisotropically aligned cationic pendant groups exhibit unsatisfactory AEMWE performance and durability, limiting their practical implementation. Herein, a facile method for fabricating a durable high-performance AEM via a one-pot monomer-level Menshutkin (m-Men) reaction in a porous mechanical support is proposed.

Optimized Ion-Transport Properties of Diallylammonium-Cyclopolymerized Anion-Exchange Membranes for High-Performance Water Electrolysis.

Anion-exchange membranes (AEMs) are the key components of AEM-based water electrolysis (AEMWE) for green hydrogen production. Unfortunately, many AEMs have unsatisfactory ion conductivity, and the factors governing their ion transport remain unclear. To address these limitations, herein, a new pyrrolidinium-containing diallylammonium-cyclopolymerized (PDT) AEM is proposed.

Photocatalytic degradation of rhodamine B using zinc oxide/silver nanowire nanocomposite films under ultraviolet irradiation.

Water pollution from industrial and household waste presents significant environmental challenges, particularly owing to the widespread use and toxicity of organic dyes such as rhodamine B (RhB). This study investigates the photocatalytic degradation of RhB using composite films composed of zinc oxide (ZnO) and silver nanowires (AgNWs) under ultraviolet (UV) irradiation. ZnO is well known for its strong photocatalytic activity because of its high charge-carrier mobility and ability to generate reactive oxygen species (ROS).

Multi-microjoule broadband terahertz generation in the organic crystal BNA pumped by ytterbium laser pulses compressed via a gas-filled hollow-core fiber.

We investigate the enhanced terahertz generation in the organic crystal BNA when pumped by compressed high-energy ytterbium laser pulses. By compressing the pump pulses from 170 fs down to 43 fs using an argon-filled hollow-core fiber and chirped mirrors, the terahertz conversion efficiency is increased by 2.4 times, leading to the generation of multi-microjoule terahertz pulses with a frequency spectrum almost twice as wide, extending up to 19 THz.

Interfacially Assembled Anion Exchange Membranes for Water Electrolysis.

High-performance and durable anion exchange membranes (AEMs) are critical for realizing economical green hydrogen production through alkaline water electrolysis (AWE) or AEM water electrosysis (AEMWE). However, existing AEMs require sophisticated fabrication protocols and exhibit unsatisfactory electrochemical performance and long-term durability. Here we report an AEM fabricated via a one-pot, in situ interfacial Menshutkin reaction, which assembles a highly cross-linked polymer containing high-density quaternary ammoniums and nanovoids inside a reinforcing porous support.

Chip collection of hepatocellular carcinoma based on O heterogeneity from patient tissue.

Hepatocellular carcinoma frequently recurs after surgery, necessitating personalized clinical approaches based on tumor avatar models. However, location-dependent oxygen concentrations resulting from the dual hepatic vascular supply drive the inherent heterogeneity of the tumor microenvironment, which presents challenges in developing an avatar model. In this study, tissue samples from 12 patients with hepatocellular carcinoma are cultured directly on a chip and separated based on preference of oxygen concentration.

Approximating Intermediate Feature Maps of Self-Supervised Convolution Neural Network to Learn Hard Positive Representations in Chest Radiography.

Recent advances in contrastive learning have significantly improved the performance of deep learning models. In contrastive learning of medical images, dealing with positive representation is sometimes difficult because some strong augmentation techniques can disrupt contrastive learning owing to the subtle differences between other standardized CXRs compared to augmented positive pairs; therefore, additional efforts are required. In this study, we propose intermediate feature approximation (IFA) loss, which improves the performance of contrastive convolutional neural networks by focusing more on positive representations of CXRs without additional augmentations.

CheSS: Chest X-Ray Pre-trained Model via Self-supervised Contrastive Learning.

Training deep learning models on medical images heavily depends on experts' expensive and laborious manual labels. In addition, these images, labels, and even models themselves are not widely publicly accessible and suffer from various kinds of bias and imbalances. In this paper, chest X-ray pre-trained model via self-supervised contrastive learning (CheSS) was proposed to learn models with various representations in chest radiographs (CXRs).

Stain normalization using score-based diffusion model through stain separation and overlapped moving window patch strategies.

Hematoxylin and eosin (H&E) staining is the gold standard modality for diagnosis in medicine. However, the dosage ratio of hematoxylin to eosin in H&E staining has not been standardized yet. Additionally, H&E stains fade out at various speeds.

An Open Medical Platform to Share Source Code and Various Pre-Trained Weights for Models to Use in Deep Learning Research.

Deep learning-based applications have great potential to enhance the quality of medical services. The power of deep learning depends on open databases and innovation. Radiologists can act as important mediators between deep learning and medicine by simultaneously playing pioneering and gatekeeping roles.