Small engine maintenance and repair is a topic many agricultural educators are expected to teach. Yet, it remains one of the least emphasized subjects at the post-secondary level. Researchers with the University of Arkansas Division of Agriculture and the Dale Bumpers College of Agricultural, Food and Life Sciences at the University of Arkansas set out to examine instructional strategies to enhance learning quality in small-engine coursework with short-form video self-reflection.

Combining approaches to better discover and cultivate microbial taxa could generate novel microbes for industrial and agricultural applications.

Soil science is undergoing a profound transformation, evolving from its traditional focus on agriculture to a multidimensional discipline addressing some of the most urgent challenges of our time. 

Researchers from Worcester Polytechnic Institute have developed a new strategy to transform low-value battery waste into a next-generation cathode material with higher energy density and strong long-term performance. This offers a promising new pathway for more sustainable and economically viable battery recycling. 

Researchers at Sultan Qaboos University identified the best water conditions for improving the survival of scalloped spiny lobster (Panulirus homarus) larvae, a major challenge in lobster aquaculture. The study found that larvae survived longest — up to 15 days — in cooler water (24°C) with moderate salinity (33 ppt), while warmer and saltier conditions caused rapid mortality. The findings provide practical guidance for hatcheries and could help support sustainable lobster farming while reducing pressure on wild stocks.

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.