Human–machine interface (HMI) systems require energy harvesters that can operate efficiently under low contact forces, yet conventional tactile triboelectric nanogenerators (TENGs) suffer from low surface charge density and unstable output. Here, we propose a human skin electric field-induced air-breakdown TENG (AB-TENG) with a transistor-inspired architecture. The device employs a base terminal to collect electrons from human skin via an ionized air channel formed by air breakdown, enabling efficient conversion of the skin’s electric field through two operational modes: indirect (accumulated output) and direct (instant high output). In direct mode, the AB-TENG delivers 165 V at 2 N and 290 V at 24 N, with a peak power of 22 mW—22 times higher than conventional tactile TENGs. Practical utility is demonstrated through a self-powered infrared remote control and an ultrathin keyboard. This work establishes a new design paradigm that transforms air breakdown from a limitation into a functional mechanism, advancing skin-electricity-enhanced thin-film TENGs toward next-generation self-sustaining HMI platforms.

Media artist and KAIST professor Jinjoon Lee's doctoral thesis 'Empty Garden' officially acquired by the Ashmolean Museum, UK, for permanent collection

Korean artistic and academic achievement recognized as public cultural heritage at a museum predating the Louvre by 110 years — the 'heart of Western intellectual history'

Blending Eastern aesthetics of 'wandering' (거닐기) and 'emptiness' with data technology in the AI era — awarded Oxford's unanimous 'No Corrections' in just 2.5 years in 2020

First work by a contemporary Korean artist to enter the Ashmolean's permanent collection — officially confirmed by the museum's curator

Korean artistic and academic achievement officially recognised as intellectual cultural heritage — permanently preserved, researched, and exhibited within the Western public knowledge system

In a new study published in The Lancet Digital Health, scientists at the USC Mark and Mary Stevens Neuroimaging and Informatics Institute have discovered that the brains of people who experience severe physical impairment after a stroke may reorganize themselves in unexpected ways, showing signs of “younger” brain structure in undamaged regions as they adapt to injury. The international research effort is part of the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Stroke Recovery Working Group, which analyzed brain scans from more than 500 stroke survivors across 34 research sites in eight countries. Using deep learning models trained on tens of thousands of MRI scans, the researchers estimated the “brain age” of different regions in each hemisphere to see how stroke damage affects brain structure and recovery. The research team used an advanced form of artificial intelligence known as a graph convolutional network to predict the biological age of 18 brain regions from MRI data. When the team associated these measurements with motor performance scores, they found a striking pattern: stroke survivors with severe movement deficits, even after more than 6 months of rehabilitation, showed younger-than-expected brain age in regions opposite the lesion, particularly within the frontoparietal network, a key system involved in motor planning, attention, and coordination. By observing how patterns of brain aging and reorganization develop over time, clinicians might be able to customize interventions based on each patient’s unique neural adaptation process, ultimately improving recovery outcomes and quality of life in the near future.