There is a growing demand for high-performance THz shielding and absorbing materials to prevent electromagnetic interference (EMI) or pollution. Inspired by staggered cellular structures, a bioinspired strategy was proposed to fabricate multilayer-MXene (m-Ti₃C₂T_x)/cellulose nanofibrils (CNFs) aerogel frameworks with staggered stacking architectures via direct ink writing (DIW) 3D printing technology for enhanced THz shielding and absorption performance. The framework exhibits an excellent maximum reflection loss (RL) of 54.01 dB in the 0.5-3.0 THz range (100% qualified bandwidth) and a high absorption of 99.40%. It realizes a high green shielding index (g_s), the range of g_s > 9 that meets the standard for excellent green EMI shielding up to 2.5 THz. Meanwhile, it demonstrates high shielding effectiveness (SE) exceeding 40 dB across a broad gigahertz (GHz) frequency range from 3.9 to 18 GHz, particularly reaching an excellent 101.84 dB in the Ku band. This bionic strategy of staggered structures achieved via 3D printing technology demonstrates significant potential for fabricating high-design-freedom shielding components with excellent THz shielding and absorption properties.

By imaging the superfluid density and superconducting transition temperature in an infinite-layer superconductor, researchers revealed a strong correlation between the two quantities. This correlation is quantitatively similar to that reported in cuprate superconductors, hinting at a close connection between the superconductivity of infinite-layer nickelates and that of cuprates.

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.