A recent review published in National Science Review delves into the significance and potential of synchronous electrolytes for aqueous zinc-halogen batteries. The review examines challenges such as zinc corrosion and halogen instability, while proposing advanced strategies like gradient hydrogels and biphasic electrolytes to simultaneously optimize both sides. These insights pave the way for practical applications in grid-scale energy storage.

Background: Children with autism spectrum disorder (ASD) had lower vitamin D3 levels than neurotypical (NT) children, as well as deficits in language, social, and fine motor abilities. Nanotechnology has appeared as a suitable answer to absorption and bioavailability problems related to vitamin D3. The aim was to investigate the influence of vitamin D3-loaded nanoemulsion supplementation on adaptive behavior and language performance in children with ASD compared to the influence of the marketed product of vitamin D3.

Methods: Supplementation of ASD children with an oral vitamin D3-loaded nanoemulsion was performed in Group I while the marketed product of the oral vitamin D3 was used in Group II for 6 months.  Evaluation of their abilities and measuring the plasma levels of two types of vitamin D3 were performed using ultra-performance liquid chromatography before and after supplementation.

Results: Supplementation in Group I (N=40) has led to an elevation of levels of 25 (OH) and 1, 25 (OH)2 forms of vitamin D3 (p< 0.0001), to behavioral improvement in the form of a reduction in ASD severity, and to a rise in the social IQ and total language age of ASD children (p=0.0002, 0.04, 0.0009, respectively).  On the other hand, Group II (N=40) did not show adaptive behavioral improvements.

Conclusion: The vitamin D3-loaded nanoemulsion provided better vitamin D3 bioavailability and a true influence on severity, adaptive behavior, fine motor abilities, and language performance, reflecting the desired benefits of the rise of vitamin D3 levels in the blood.

Neuromorphic computing has the potential to overcome limitations of traditional silicon technology in machine learning tasks. Recent advancements in large crossbar arrays and silicon-based asynchronous spiking neural networks have led to promising neuromorphic systems. However, developing compact parallel computing technology for integrating artificial neural networks into traditional hardware remains a challenge. Organic computational materials offer affordable, biocompatible neuromorphic devices with exceptional adjustability and energy-efficient switching. Here, the review investigates the advancements made in the development of organic neuromorphic devices. This review explores resistive switching mechanisms such as interface-regulated filament growth, molecular-electronic dynamics, nanowire-confined filament growth, and vacancy-assisted ion migration, while proposing methodologies to enhance state retention and conductance adjustment. The survey examines the challenges faced in implementing low-power neuromorphic computing, e.g., reducing device size and improving switching time. The review analyses the potential of these materials in adjustable, flexible, and low-power consumption applications, viz. biohybrid spiking circuits interacting with biological systems, systems that respond to specific events, robotics, intelligent agents, neuromorphic computing, neuromorphic bioelectronics, neuroscience, and other applications, and prospects of this technology.

Silicon suboxide (SiO_x, 0 < x < 2) is an appealing anode material to replace traditional graphite owing to its much higher theoretical specific capacity enabling higher-energy-density lithium batteries. Nevertheless, the huge volume change and rapid capacity decay of SiO_x electrodes during cycling pose huge challenges to their large-scale practical applications. To eliminate this bottleneck, a dragonfly wing microstructure-inspired polymer electrolyte (denoted as PPM-PE) is developed based on in-situ polymerization of bicyclic phosphate ester- and urethane motif-containing monomer and methyl methacrylate in traditional liquid electrolyte. PPM-PE delivers excellent mechanical properties, highly correlated with the formation of a micro-phase separation structure similar with dragonfly wings. By virtue of superior mechanical properties and the in-situ solidified preparation method, PPM-PE can form a 3D polymer network buffer against stress within the electrode particles gap, enabling much suppressed electrode volume expansion and more stabilized solid electrolyte interface along with evidently decreased electrolyte decomposition. Resultantly, PPM-PE shows significant improvements in both cycling and rate performance in button and soft package batteries with SiO_x-based electrodes, compared with the liquid electrolyte counterpart. Such a dragonfly wing microstructure-inspired design philosophy of in-situ solidified polymer electrolytes helps facilitate the practical implementation of high-energy lithium batteries with SiO_x-based anodes.

Researchers from Sun Yat-sen University and Guangxi University have developed a machine learning-enhanced synchronization method for quantum key distribution, enabling secure communication over 200 km without the need for external clock synchronization or calibration. Combined with a simplified reference-frame-independent QKD protocol and a streamlined detector setup, the result is a compact, low-cost, and robust quantum communication system.

Powered by differentiable imaging, Uncertainty - Aware Fourier Ptychography (UA - FP) revolutionizes computational imaging. It models uncertainties without complex calibration, enabling high - quality reconstructions despite system flaws, and opens doors to diverse applications.

Recently, a research team led by Prof. XIE Pinhua from the Hefei lnstitutes of Physical Science of the Chinese Academy of Sciences, has developed a novel prediction model for surface ozone concentration in the North China Plain (NCP) and Yangtze River Delta (YRD) regions. The model is based on a sequential convolutional long short-term memory network framework (CNN-LSTM) framework that integrates spatiotemporal meteorological features, addressing key limitations in existing forecasting methods.

A research team from the Institute of Solid State Physics, the Hefei Institutes of Physical Science of the Chinese Academy of Sciences, has developed a new sensing platform that significantly enhances the precision and speed of hazardous chemical detection in complex real-world environments.

Recently, a research team led by Professor HUANG Xingjiu at the Institute of Solid-State Physics, the Hefei Institutes of Physical Science of the Chinese Academy of Sciences, has proposed a novel material design strategy to improve the performance of all-solid-state ion-selective electrodes (ISEs). 

A research team led by Prof. LIU Qingsong from the Hefei Institutes of Physical Science of the Chinese Academy of Science has developed IHMT-15130, a selective and irreversible inhibitor of Bone marrow kinase in chromosome X (BMX) kinase, which showed robust efficacy in preclinical models of cardiac hypertrophy.