Researchers have reviewed recent progress in ammonia synthesis via thermal catalysis, electrocatalysis, photocatalysis, and plasma catalysis under mild conditions, highlighting novel pathways that use nitrogen oxides (NOₓ) as the nitrogen source. The two‑step method (N₂ oxidation followed by NOₓ reduction) shows promise for green ammonia production using air and water with renewable electricity. However, challenges remain in conversion rates, energy efficiency, and detection accuracy. The review also emphasizes that rigorous ¹⁵N₂ isotope verification is essential to avoid false positives, calling for unified experimental standards to advance the field toward industrial application.

Researchers have systematically compared the combustion characteristics and oxidation kinetics of tar-rich coal and tar-inclusive coal using thermogravimetry and differential scanning calorimetry at multiple heating rates. The results show that tar-rich coal exhibits lower characteristic temperatures (by 5 to 17 °C), enabling it to reach ignition more easily. During the pyrolysis stage, the average activation energy of tar-rich coal exceeds that of tar-inclusive coal by more than 22.2 kJ·mol⁻¹, while in the combustion stage it is 5 kJ·mol⁻¹ lower. These differences arise from the hydrogen-rich molecular structure of tar-rich coal, which has important implications for both its fire risk and its in-situ thermal exploitation.

Researchers have developed AdapGNN, a novel model-agnostic framework that addresses the oversmoothing problem in graph neural networks (GNNs) for molecular property prediction while significantly improving model explainability. By integrating original atom features during message passing and introducing a weight projection module, AdapGNN preserves node uniqueness and highlights chemically important atoms. To enable quantitative evaluation of explainability, the team also created MolExplain, a new benchmark dataset with ground-truth atom importance scores. Experimental results show that AdapGNN outperforms baseline models (AFP and GCN) on 10 out of 12 tasks, with ROC-AUC for atom importance reaching up to 99.5 %.

Electric vehicles (EVs) are crucial for sustainable transport, but their environmental benefit depends heavily on when they charge. Charging during periods when the grid relies on fossil fuels can significantly increase their carbon footprint. New research from Multimedia University reveals that "carbon-aware" charging strategies can unlock substantial emission reductions simply by optimizing charging times.

The study, led by Md Sabbir Hossen and Gobbi Ramasamy from Multimedia University, quantifies the real-world carbon emission reduction potential of smart EV charging. Researchers reconstructed hourly residential charging data and combined it with time-varying grid carbon intensity from the Malaysian Peninsula power system. They then evaluated a reinforcement learning-based carbon-aware smart charging strategy, comparing its performance against both uncontrolled charging and an intelligent but carbon-unaware charging approach, all while delivering identical total energy.

The Power of Carbon Awareness

The findings are compelling: while intelligent charging without carbon awareness failed to reduce total carbon emissions, the carbon-aware strategy achieved a statistically significant reduction of approximately 1.62%. This crucial difference comes from proactively shifting charging demand away from periods when grid electricity is most carbon-intensive. Specifically, evening peak hours were found to be approximately 18% more carbon-intensive than off-peak periods, making targeted load shifting highly effective.

research team led by Professor Moon Choi of the Graduate School of Science and Technology Policy quantitatively analyzed “digital ageism” embedded in generative artificial intelligence.

older adults are outwardly described as “wise and caring,” expressions of agency and competence decrease with age.

biases in AI could, through repeated exposure, deepen the social marginalization and digital exclusion of older adults.