Researchers at Seoul National University have compiled the latest technologies in the field of fabricating stretchable synaptic transistors, a neuromorphic device capable of learning even under deformation. This review introduces materials, manufacturing processes (photopatterning, printing, lamination-and-transfer), and structures (vertical, wave-like, textile) that maintain low-voltage stable operation even under deformation. By clearly identifying remaining technical challenges, such as n-type/bipolar semiconductors, CMOS-compatible patterning, and biocompatibility, it provides a roadmap for developing scalable wearable and neuroprosthetic systems.

The study reveals that OIF promotes marine carbon sinks by adding iron to “high-nutrient, low-chlorophyll” marine regions (e.g., the Southern Ocean), which enhances the photosynthesis of surface phytoplankton and thereby increases marine carbon sinks. In contrast, AOA improves seawater alkalinity by adding alkaline materials (such as limestone) to the ocean; through carbonate chemical processes, this method drives the ocean to absorb more CO₂ from the atmosphere.

A detailed new review published in Brain Medicine outlines how electrical and magnetic stimulation of the brain is changing treatment for obsessive-compulsive disorder (OCD), especially for patients who do not respond to traditional therapy or medication. The article, “Neuromodulation techniques in obsessive-compulsive disorder: Current state of the art” by Dr. Kevin Swierkosz-Lenart, Dr. Carolina together with their colleagues at Lausanne University Hospital, in collaboration with Prof. Luc Mallet from Paris-Est Créteil University and University of Geneva brings together the latest evidence on transcranial direct current stimulation (tDCS), repetitive transcranial magnetic stimulation (rTMS), and deep brain stimulation (DBS). The authors describe how each approach interacts with dysfunctional brain networks and how personalization, imaging, and biomarker discovery could shape the next generation of psychiatric treatments.

High-entropy oxides (HEOs) have emerged as a promising class of memristive materials, characterized by entropy-stabilized crystal structures, multivalent cation coordination, and tunable defect landscapes. These intrinsic features enable forming-free resistive switching, multilevel conductance modulation, and synaptic plasticity, making HEOs attractive for neuromorphic computing. This review outlines recent progress in HEO-based memristors across materials engineering, switching mechanisms, and synaptic emulation. Particular attention is given to vacancy migration, phase transitions, and valence-state dynamics—mechanisms that underlie the switching behaviors observed in both amorphous and crystalline systems. Their relevance to neuromorphic functions such as short-term plasticity and spike-timing-dependent learning is also examined. While encouraging results have been achieved at the device level, challenges remain in conductance precision, variability control, and scalable integration. Addressing these demands a concerted effort across materials design, interface optimization, and task-aware modeling. With such integration, HEO memristors offer a compelling pathway toward energy-efficient and adaptable brain-inspired electronics.

A rapidly growing field is piezoresistive sensor for accurate respiration rate monitoring to suppress the worldwide respiratory illness. However, a large neglected issue is the sensing durability and accuracy without interference since the expiratory pressure always coupled with external humidity and temperature variations, as well as mechanical motion artifacts. Herein, a robust and biodegradable piezoresistive sensor is reported that consists of heterogeneous MXene/cellulose-gelation sensing layer and Ag-based interdigital electrode, featuring customizable cylindrical interface arrangement and compact hierarchical laminated architecture for collectively regulating the piezoresistive response and mechanical robustness, thereby realizing the long-term breath-induced pressure detection. Notably, molecular dynamics simulations reveal the frequent angle inversion and reorientation of MXene/cellulose in vacuum filtration, driven by shear forces and interfacial interactions, which facilitate the establishment of hydrogen bonds and optimize the architecture design in sensing layer. The resultant sensor delivers unprecedented collection features of superior stability for off-axis deformation (0–120°, ~ 2.8 × 10^–3 A) and sensing accuracy without crosstalk (humidity 50%–100% and temperature 30–80 °C). Besides, the sensor-embedded mask together with machine learning models is achieved to train and classify the respiration status for volunteers with different ages (average prediction accuracy ~ 90%). It is envisioned that the customizable architecture design and sensor paradigm will shed light on the advanced stability of sustainable electronics and pave the way for the commercial application in respiratory monitory.

University of Maryland computer scientists developed an AI-enhanced system that protects personal voice data from automated surveillance.