Refractory wounds cause significant harm to the health of patients and the most common treatments in clinical practice are surgical debridement and wound dressings. However, certain challenges, including surgical difficulty, lengthy recovery times, and a high recurrence rate persist. Conductive hydrogel dressings with combined monitoring and therapeutic properties have strong advantages in promoting wound healing due to the stimulation of endogenous current on wounds and are the focus of recent advancements. Therefore, this review introduces the mechanism of conductive hydrogel used for wound monitoring and healing, the materials selection of conductive hydrogel dressings used for wound monitoring, focuses on the conductive hydrogel sensor to monitor the output categories of wound status signals, proving invaluable for non-invasive, real-time evaluation of wound condition to encourage wound healing. Notably, the research of artificial intelligence (AI) model based on sensor derived data to predict the wound healing state, AI makes use of this abundant data set to forecast and optimize the trajectory of tissue regeneration and assess the stage of wound healing. Finally, refractory wounds including pressure ulcers, diabetes ulcers and articular wounds, and the corresponding wound monitoring and healing process are discussed in detail. This manuscript supports the growth of clinically linked disciplines and offers motivation to researchers working in the multidisciplinary field of conductive hydrogel dressings.

A new study published in Frontiers in Energy employs a "segmented voltage monitoring method," which integrates voltage leads into three series batteries within the SOEC stack, enabling the quantitative separation of degradation contributions from the cell body and the interconnect-cell interface, thereby providing direct experimental evidence for optimizing stack design.

Huang Feihe at Zhejiang University, Jonathan Sessler of the University of Texas at Austin, and colleagues reported a novel cation recognition mode which mimics the biological allosteric effect and achieves efficient recognition of cations by cationic compounds. This work, published in CCS Chemistry, achieves continuous recognition of anions and cations by synergizing various recognition modes while also utilizing the allosteric effect during the recognition process to explore a new cation recognition mode.

In a pioneering study that combines advanced technology with agricultural practices, researchers are exploring how capacitance-enhanced biochars can significantly increase the removal of ginsenoside Rb1 from soils. The study, titled "Increased Removal of Ginsenoside Rb1 Through the Application of Capacitance-Enhanced Biochars in Soils," is led by Prof. Bo Pan from the Yunnan Provincial Key Lab of Soil Carbon Sequestration and Pollution Control at Kunming University of Science & Technology in Kunming, Yunnan, China. This research offers valuable insights into sustainable soil management and the potential of biochars in addressing soil contamination.

By using a lead-free perovskite derivative ETP₂SbCl₅ (ETP = (C₆H₅)₃PC₂H₅), Li's group fabricate a series of recyclable LSCs, achieving the highest power conversion and optical efficiencies of ~5.56% and ~32.5%, respectively. The damaged LSCs could be mass recycled to phosphor by ethanol or mild heating treatments, which still maintains nearly initial fluorescent performance. This work presents a paradigm for the sustainable use of fluorescent materials and offers a reliable path toward low-carbon globalization.

Optical synapses have an ability to perceive and remember visual information, making them expected to provide more intelligent and efficient visual solutions for humans. As a new type of artificial visual sensory devices, photoelectric memristors can fully simulate synaptic performance and have great prospects in the development of biological vision. However, due to the urgent problems of nonlinear conductance and high-energy consumption, its further application in high-precision control scenarios and integration is hindered. In this work, we report an optoelectronic memristor with a structure of TiN/CeO₂/ZnO/ITO/Mica, which can achieve minimal energy consumption (187 pJ) at a single pulse (0.5 V, 5 ms). Under the stimulation of continuous pulses, linearity can be achieved up to 99.6%. In addition, the device has a variety of synaptic functions under the combined action of photoelectric, which can be used for advanced vision. By utilizing its typical long-term memory characteristics, we achieved image recognition and long-term memory in a 3 × 3 synaptic array and further achieved female facial feature extraction behavior with an activation rate of over 92%. Moreover, we also use the linear response characteristic of the device to design and implement the night meeting behavior of autonomous vehicles based on the hardware platform. This work highlights the potential of photoelectric memristors for advancing neuromorphic vision systems, offering a new direction for bionic eyes and visual automation technology.