Researchers have demonstrated that inherent alkali and alkaline earth metals (AAEMs) play the dominant catalytic role in the CO₂ gasification of biochar derived from the energy crop Arundo donax. Even though acid washing created a more disordered carbon structure – which typically enhances reactivity – the removal of AAEMs significantly reduced gasification reactivity. Kinetic analysis showed that the average activation energy increased from 164.30 kJ·mol⁻¹ to 210.85 kJ·mol⁻¹ after AAEM removal. Temperature‑programmed desorption confirmed that AAEMs promote the formation of carbon‑oxygen surface complexes, acting as catalytic active centers that sustain a continuous reaction cycle. These findings highlight that AAEMs, rather than carbon structural order, are the key factor governing gasification efficiency.

Researchers have shown that adding acid zeolites (H‑mordenite and H‑ZSM5) during municipal sewage sludge pyrolysis at 500 °C can reduce hydrogen sulfide (H₂S) concentration in the pyrolysis gas by up to 46 %, while increasing gas yield by up to 55 % and bio‑oil yield by up to 24 %. The work demonstrates that zeolite choice and silica‑to‑alumina ratio (SAR) determine whether H₂S reduction occurs via dilution in higher gas yields or direct suppression of H₂S formation. This in‑situ catalytic approach could simplify downstream gas cleaning for energy recovery or biological fermentation.

Researchers have demonstrated that defect engineering and post‑synthetic copper metalation are two effective and complementary strategies for tailoring ammonia adsorption in the robust metal–organic framework UiO‑67. By varying the acidity and amount of modulator acids, defect density can be tuned nearly 10‑fold (from 5.4 % to 50.1 %), which directly controls the characteristic stepwise features of the adsorption isotherms. Introducing copper via bipyridyl linkers enhances uptake by over 50 % in the optimal sample. These approaches enable application‑specific design of NH₃ adsorbents for storage, separation, and sensing.

Researchers have developed a soft measurement method based on a convolutional long short‑term memory (ConvLSTM) network that predicts product yield levels directly from real‑time image sequences of a microchannel reactor during gas‑liquid sulfonation. To overcome limited experimental data, a frame‑sampling spatio‑temporal augmentation strategy expands the training set. On the experimental data set, the augmented ConvLSTM model achieved an average accuracy of 97.44 %, outperforming the model without augmentation by 19.66 % and a conventional convolutional neural network by 9.94 %. This work provides a robust, non‑invasive tool for monitoring and optimizing complex micro‑chemical processes.

Researchers have developed a novel approach that integrates complete threedimensional molecular structures with traditional quantitative structureproperty relationship (QSPR) methods using deep learning. By combining molecular descriptors with chargedensity fields from density functional theory, a convolutional neural networkenhanced artificial neural network model significantly improves the prediction of critical temperature and critical pressure for 1359 organic compounds. The model achieves high accuracy (for T_c: R²=0.888, MAPE = 5.03 %; for p_c: R²=0.919, MAPE = 6.37 %), outperforming both conventional QSPR and the widely used JOBACK group contribution method.

Researchers have developed a novel quaternary zirconia-based electrolyte by partially replacing expensive scandia with low-cost ytterbia. The optimized composition, (Yb₂O₃)₀.₀₆(Sc₂O₃)₀.₀₄(CeO₂)₀.₀₁(ZrO₂)₀.₈₉, exhibits a pure cubic phase structure, high ionic conductivity (0.088 S·cm⁻¹ at 800 °C and 0.0020 S·cm⁻¹ at 500 °C), and enhanced thermal compatibility with electrodes. A single fuel cell using this electrolyte achieved a peak power density of 0.65 W·cm⁻² at 800 °C and operated stably for 100 hours.

Structured light is a new frontier in optics because it can be programmed across multiple degrees of freedom—amplitude, phase, spatial patterns, frequency, and polarization. Yet in practice, generating and controlling such light still often relies on bulky, alignment-sensitive optical setups. Researchers led by Prof. Hongtao Lin at Zhejiang University (ZJU), China, have introduced a unified inverse-design method to bring vectorial structured light onto a chip, using topological valley photonic crystals. Their tiny couplers show ultra-low loss and broad bandwidth at telecom wavelengths, offering a practical route to compact photonic chips for communications, sensing, and quantum technologies.

POSTECH Professor Seung-Ki Min’s Research Team Compares Future Extreme Fire Weather Under ‘Net-Zero’ vs. ‘Net-Negative’ Emission Scenarios.