With the growing emphasis on sustainable development, the demand for environmentally friendly solvents in green chemical processes and carbon dioxide capture is increasing. Ionic liquids (ILs), as promising green solvents, offer significant potential but face considerable challenges, particularly in solvent selection. To overcome the limitations of traditional screening methods, machine learning (ML) techniques have recently been applied, offering a more efficient and data-driven approach. This review provides an overview of key ML methods used in solvent screening and compares them with traditional experimental and theoretical techniques. It examines the role of descriptor selection in structure—property-based methods, such as quantitative structure-activity relationships (QSAR) and quantitative structure—property relationships (QSPR), which are critical for predicting IL properties. The review also explores the application of these methods to screen IL properties, including toxicity, viscosity, density, and CO₂ solubility. Additionally, it discusses challenges in selecting appropriate models based on data scale and task complexity, integrating physical information for model interpretability, and achieving multi-objective optimization to balance key properties in ionic liquid (IL) design. Finally, it summarizes the achievements, limitations, and prospects of ML applications in ILs research, offering insights into how these methods can advance the development of sustainable ILs.

Developing environmentalyl friendly and energy-efficient CO₂ adsorbents for post-combustion capture is a critical step toward achieving toward carbon neutrality. While aqueous amines and metal oxides have play pivotal roles in CO₂ capture, their application is limited by issues such as secondary pollution and high energy consumption. In contrast, Zn-based metal-organic frameworks (Zn-based MOFs) have emerged as a green alternative, offering low toxicity reduced regeneration temperatures, and high efficiency in both CO₂ adsorption and catalytic conversion into valuable fuels and chemicals. This mini review begins with a general introduction to MOFs in CO₂ capture and conversion, followed by an overview of early studies on Zn-based MOFs for CO₂ capture. It then summarizes recent research advancements in Zn-based MOFs for integrated CO₂ capture and conversion. Finally, it discusses key challenges and future research directions for post-combustion CO₂ capture and conversion using Zn-based MOFs.

Human cognition relies on the seamless integration of multiple senses, allowing the brain to associate, infer, and even imagine across modalities. Replicating this capability in artificial systems has long remained a challenge, particularly under strict energy constraints. This study presents a bioinspired multisensory framework that integrates vision, touch, hearing, smell, and taste within a self-powered architecture. By enabling cross-modal association and adaptive reconfiguration, the system allows one sensory input—such as touch or sound—to trigger corresponding representations in other sensory domains. Beyond conventional recognition, the framework demonstrates higher-level cognitive functions, including inference and generative pattern creation. These advances point toward a new generation of intelligent machines capable of human-like perception and cognition.

Achieving both high efficiency and high selectivity remains a central challenge in catalytic hydrogenation reactions, as rapid reactant activation often leads to overly strong binding of intermediates and unwanted over-reduction. A new photocatalytic strategy now demonstrates how this long-standing trade-off can be overcome. By harnessing nonequilibrium charge carriers generated under visible light, the system promotes efficient hydrogen dissociation while steering the reaction toward selective semihydrogenation. Active hydrogen species migrate away from their formation sites to neighboring catalytic surfaces, where product formation and desorption are energetically favored. This spatial separation of reaction steps enables near-complete conversion under ambient conditions while maintaining excellent selectivity, offering a powerful new design principle for advanced catalytic systems.

Hydrogen peroxide is a widely used chemical essential to water treatment, disinfection, and green manufacturing, yet its conventional production relies on energy-intensive and centralized processes. Recent research highlights an emerging electrochemical route that generates hydrogen peroxide directly from oxygen and water under ambient conditions. Central to this approach is the use of noble metal-free single-atom electrocatalysts, where isolated metal atoms embedded in nitrogen-doped carbon precisely steer oxygen reduction toward the desired two-electron pathway. By combining atomic-scale catalyst design with advanced reactor engineering, this strategy offers a cleaner, safer, and more flexible way to produce hydrogen peroxide on demand, opening new possibilities for decentralized and sustainable chemical manufacturing.

Breaks MOCVD’s bottleneck in high-strain quantum well epitaxy, significantly boosts 1.2 μm VECSEL performance. Atomic-scale characterization clarifies the strain compensation’s indium segregation suppression mechanism. 590 nm second harmonic output: near diffraction limit, brightness >1.65 GW·cm⁻²·sr⁻¹. Paves a new way for ultra-high-brightness yellow lasers, accelerating VECSEL’s lab-to-commercialization transition.

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.