Professor Yong-Hoon Kim's research team in the School of Electrical Engineering identifies the quantum mechanical limits of transistor miniaturization and contact resistance in next-generation semiconductor chips.

World's first computational design platform capable of predicting atomic-level scaling limits of transistors prior to actual fabrication.

Okayama University of Science and Murakami City, Niigata Prefecture, have launched a joint research project to establish the world’s first fully closed-cycle land-based aquaculture system for chum salmon (Oncorhynchus keta) using the university’s proprietary “Third Water” technology. The project aims to address the dramatic decline in salmon returns caused by climate change and changing marine ecosystems. More than 2,000 juvenile salmon are currently being reared in a 35-ton closed recirculation aquaculture system at the university’s Next-Generation Aquaculture Center. Researchers expect the fish to reach market size within one to two years. If successful, the project could provide a sustainable alternative to ocean-dependent salmon production and contribute to the long-term resilience of salmon fisheries in Japan and worldwide.

Tokyo, Japan – Researchers from Tokyo Metropolitan University have created a new paradigm for identifying online phishing campaigns. Their new system, PhishLumos, is triggered when links show signs of concealing information, and looks for clues in the “infrastructure” of the website to uncover the whole campaign of which the site is only a tiny part. Real-world testing showed detection which was 8 days faster than an expert, with 190,000 URLs detected over 6 months.

A new study shows that thiol-modified biochar can maintain long-term mercury immobilization in contaminated soils, even when repeated drying and rewetting mimic heatwave-accelerated weathering.

A titanium dioxide-loaded biochar material reduced arsenic mobilization and methane emissions in flooded paddy soil, offering a promising approach to address food safety and climate challenges at the same time.

A new study shows that deep learning can predict how fast biochar materials break down antibiotic contaminants, offering a faster path toward cleaner water and smarter environmental remediation.