Researchers have developed a low-cost, semi-automated, AI-driven method that uses remote cameras to survey bumblebees and potentially other insects.

Researchers at Kaunas University of Technology (KTU), Lithuania, report that waste-based geopolymer mortars with selected fine aggregates – particularly corundum and ceramic waste – can retain high compressive strength after exposure to 800°C. The study shows strengths up to 53 MPa with limited microcracking, outperforming conventional Portland-cement concrete under extreme heat. Made entirely from industrial and demolition waste, these geopolymers offer a lower-carbon alternative to cement, whose production generates up to 8% of global CO₂ emissions. The material is suited for fire-resistant applications such as tunnels, furnaces, and industrial infrastructure.

Researchers from Sun Yat-sen University and UNSW Sydney have developed a dual-side electrosynthesis system that simultaneously converts plastic waste and biowaste into Succinic Acid, a key building block for biodegradable plastics, pharmaceuticals, and specialty chemicals. By directing both the anodic and cathodic reactions toward the same target product, the system achieves an unprecedented succinic acid yield of 169.1%, surpassing the conventional single-electrode limit. The process combines the electrooxidation of polybutylene terephthalate (PBT)-derived 1,4-butanediol and the electroreduction of biomass-derived maleic acid, demonstrating a profitable and scalable pathway for circular chemical manufacturing.

Seoul National University (SNU) College of Engineering announced that a research team led by Professor Jeong Woo Han of the Department of Materials Science and Engineering has developed a new nanostructured catalyst with up to 14.4 times higher greenhouse gas decomposition performance compared to conventional commercial catalysts.

The capabilities of large AI systems are constantly improving, but they consume a great deal of energy during training and operation. The human brain, by contrast, is extremely energy-efficient: it requires only around 20 watts. Researchers at Graz University of Technology, in collaboration with international partners, have developed a novel, brain-inspired AI model that can plan flexibly and solve complex problems. In doing so, it consumes significantly less energy than multi-layer neural networks or large language models.

Nanoporous material-based separation technology is vital in many applications due to its ability to precisely distinguish between and separate nearly identical chemical or biochemical molecules.

Previous research by Professor Susumu Kitagawa, from Kyoto University’s Institute for Integrated Cell-Material Sciences (WPI-iCeMS), and colleagues, published in Nature, had applied this technology to the separation of two very similar types of water molecules—regular water (H₂O) and heavy water (D₂O), which have similar properties overall but slightly different masses. But the underlying mechanics of that separation weren’t well understood.

Now, a study led by Professor Shinji Saito from the Institute for Molecular Science in Japan and published in Nature Communications in July has provided the theoretical explanation for that phenomenon, using molecules of H₂O and D₂O to study nanopore behavior in a metal-organic framework.

A research team from Saitama University and Taisei Corporation has experimentally clarified how deformation-accommodating tunnel support systems behave under isotropic stress conditions that simulate squeezing ground. Using 1/20-scale laboratory tunnel support models, the team showed that yielding elements can absorb large tunnel deformations and that their installation position influences the deformation pattern of the entire support system.

Researchers from Kochi University of Technology and Nagoya University have developed a self-adaptive copper–cobalt catalyst that efficiently converts nitrate contaminants into ammonia under ambient conditions. By combining advanced operando and quasi-operando spectroscopic analyses with density functional theory calculations, the team discovered that the catalyst dynamically reconstructs during electrochemical nitrate reduction to form hydroxyl-rich Cu–OH active sites. Amorphous cobalt oxide supplies reactive hydrogen via hydrogen spillover, enabling highly efficient ammonia synthesis. The catalyst achieved an ammonia production rate of 54.68 mg h⁻¹ mgcat⁻¹ with a Faradaic efficiency of 95.4% at −0.2 V versus the reversible hydrogen electrode and maintained stable performance for more than 60 hours in a flow cell. The work establishes adaptive catalyst reconstruction as a new design principle for sustainable electrocatalysis.