As intrinsically carbon-free molecules, ammonia and hydrogen are considered as fuels for internal combustion engines, mainly for long-distance or off-road applications. These alternative fuels have different combustion characteristics, reactivity, and exhaust gas compositions compared to conventional fuels, raising questions about the suitability of lubricants in engines operating with them. The impact of ammonia, hydrogen, and their blends on lubricants in internal combustion engines is a relatively new topic, with few reference studies available. However, degradation processes of lubricants have been studied in the context of hydrocarbon fuels, and in compressors using ammonia as a refrigerant, for example. This work presents a review of the literature on engine oil degradation phenomena in relation to ammonia and hydrogen combustion characteristics. In particular, it highlights the current state of knowledge regarding compatibility with unburnt gases, elevated nitrogen oxide levels, and water. Additionally, it summarizes the latest insights into the contribution of lubricants to pollutant emissions.

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.

To investigate the role of KIF13B in SICD, a research team led by Prof. Xunde Xian from Peking University conducted a series of experiments using mouse models of sepsis and cultured cardiomyocytes.

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.

When robots perform complex tasks, the pose accuracy of the end-effector is critical. However, errors from individual joints tend to accumulate along the kinematic chain, making it challenging to guarantee high pose precision at the end-effector. To address this issue, this study proposes a virtual-constraints-based end-effector pose compensator (VEPC). The method treats the actual angles of specific joints as known inputs and automatically adjusts the remaining joint angles in real time, effectively eliminating the pose errors of the end-effector caused by the joints. Experimental results demonstrate that the method can reduce the maximum end-effector position error by over 75%. Moreover, the method requires no additional sensors, offering low cost and high compatibility.