Digital twins for automated material handling systems (AMHSs) of semiconductor and display fabrication industries suffer from parameter uncertainty and discrepancy. This leads to inaccurate predictions, ultimately affecting performance. To address this, researchers have developed a new Bayesian calibration framework that simultaneously accounts for both parameter uncertainty and discrepancy, improving the prediction accuracy of digital twin models. This innovative framework holds great potential for enhancing digital twin applicability across diverse industries.

Hydrogen fluoride (HF) is the simplest and cheapest fluorinating reagent available, but its volatility and toxicity limit its practical use. Now, researchers from Shibaura Institute of Technology, Japan, introduce a new approach for the generation of HF, which is both safe and scalable. Using the cation exchange method between Potassium fluoride (KF) and Amberlyst 15DRY, they generated HF and converted it to amine-3HF complexes, thereby expanding the range of accessible complexes for fluorination reactions.

Air filters are our main defense against airborne pollutants, but conventional designs rely on weak adhesive forces and often fail to trap and retain fine particles. Now, researchers from Korea have developed a bioinspired oil-coated filter that dramatically improves particle capture and energy efficiency. Most notably, the filter’s lifespan is extended by two to three times compared to conventional designs. It also enables zero-energy filtration using natural airflow, paving the way to sustainable air filtration.

A team of researchers has published a comprehensive review in National Science Review, offering a systematic overview of the development of superconducting quantum computing. The article summarizes recent advances in chip fabrication, gate control, and experimental breakthroughs, while highlighting emerging platforms such as bosonic encodings and fluxonium. By addressing key challenges—especially scalability—and proposing solutions, the review outlines a technical roadmap toward practical, fault-tolerant quantum computing.

Summary

CORNETO is a new computational tool that helps researchers combine different types of biological data with prior biological knowledge to map how molecules like genes and proteins interact inside cells.

By analysing different samples together at once, CORNETO shows which biological processes are common and which are unique across cell types and conditions. 

Researchers have used CORNETO to reveal shared and cell-specific pathways in disease research, e.g. to identify signalling pathways associated with chemotherapy resistance in ovarian cancer patients.