Seoul National University's College of Engineering announced that a joint research team led by Prof. Jeonghun Kwak of the Department of Electrical and Computer Engineering and Prof. Jaehoon Lim of Sungkyunkwan University's Department of Energy Science has developed an AI-based platform that inversely designs the optimal solvent properties for arranging quantum dots uniformly and densely during the fabrication of QLEDs.

Seoul National University College of Engineering announced that a joint research team led by Professors Namkyoo Park and Sunkyu Yu of the Department of Electrical and Computer Engineering at SNU, in collaboration with Professor Xianji Piao of the School of Electrical and Computer Engineering at the University of Seoul, has developed a photonic integrated circuit that can slow light on demand.

Seoul National University College of Engineering announced that a joint research team led by Professor Tae-Woo Lee of the Department of Materials Science and Engineering at SNU and Professor Samuel D. Stranks of the University of Cambridge has developed a vapor-deposited perovskite light-emitting diode (PeLED) with world-leading efficiency. The team achieved this by discovering a new X-type perovskite emitter capable of thermodynamically stabilizing a luminescence-favorable phase during the vacuum deposition process.

Hydrogel materials possess unique physicochemical properties, including high water absorption, strong moisture retention, biocompatibility, tunable mechanical properties, environmental responsiveness, biodegradability, and a three-dimensional network structure. These characteristics endow them with significant practical value and broad application prospects in fields such as tissue engineering and biomedicine.

A research group is developing a portable rapid diagnostic system for Ebola virus disease caused by Bundibugyo virus. The project has been selected for funding by the Global Health Innovative Technology Fund.

Lithium metal is a prime contender for the next generation of rechargeable batteries. But in order to live up to that potential, scientists have had to try and find a solution to the dendrite problem. Lithium tends to grow into needle-like structures (dendrites) which reduce the battery life and create safety hazards. The answer may be like the key to most things in life: balance. Researchers from Tohoku University's Institute for Materials Research (IMR)discovered that, rather than increasing the amount of lithium salt in a battery, an optimal lithium concentration allows lithium to deposit evenly and form a stronger protective layer on the battery surface. 

A laser-based technology being developed at Adelaide University could soon help authorities detect deadly counterfeit alcohol, expose wine fraud and even identify dangerous chemicals inside sealed bottles – all without opening them.

Thermal energy systems (TES) are an essential part of industries that have evolved over time through the engagement of managers and researchers. The development of digital twin (DT) technology has enabled accurate prediction of their performance. The inherent limitations of complex thermal systems, such as noisy input data and occasional lack of measurement data or boundary conditions, have recently created opportunities to apply physics-based problem-solving alongside DT technology. This paper aims to systematically review the novel physics-informed neural network-digital twin (PINN-DT) methodology as a potential solution to these challenges, and to present a taxonomy for problem-solving. The outcome of this study provides valuable guidance in selecting PINN-DT technology in thermal energy system (TES) modeling. A review of the proposed loss functions demonstrates that their design is critical for achieving precise outcomes in this technology, effectively serving as the foundational core of PINN-DT. As a result, it is recommended that the construction of the loss function be fundamentally guided by two principal considerations: forecasting accuracy and compliance with physical principles, which serve as foundational pillars in the surrogate model design framework. A significant gap exists in applying this technology to industries that use discrete sampling for quality control. Implementing the PINN-DT framework could address this issue by determining optimal sampling intervals, thereby offering vital decision-making support. Moreover, the absence of exergy analysis in formulating the physical loss component of the loss function represents a significant research gap. Future studies should therefore incorporate the exergy concept into the design of the loss function.