Researchers have developed a plasma-catalytic system using Ag/ZnO catalyst which significantly enhances the reverse water-gas shift reaction. The plasma catalytic system achieved a remarkable CO₂ conversion rate of 76.5%, a high CO selectivity of 96.8%, a CO yield of 74.1%, and an energy efficiency as high as 0.19 mmol kJ⁻¹, surpassing the plasma alone system and ZnO catalytic systems.

Researchers have developed an ultrathin metalated polymer brush (Mt-PB) coating via in situ reduction of transition metal ions within surface-grafted polymer brushes. The resulting coating exhibits high optical transmittance (~86%) and superior antibacterial efficiency. Furthermore, it demonstrates excellent durability, maintaining stable transparency and providing high-resolution images after over one month of underwater exposure.

FDA researchers present case studies using soft sensors to predict critical quality attributes and monitor crystallinity and polymorphism changes during solid oral dosage manufacturing. The study applies models to wet granulation, fluidized bed drying, milling, and tablet press operations, demonstrating how soft sensors can support science- and risk-based control strategies.

 The co-reaction of methanol with C5–C16 n-alkanes was investigated over microsphere catalysts with varying surface acidity and ZSM-5 as the active components. They found that as the carbon number of alkanes increases, the formation of C1–C4 alkanes decreases while the production of C2–C4 alkenes increases on the catalyst with weak outer surface acidity. The study also reveals how the kinetic diameter of alkanes relative to the catalyst pore size dictates the cracking location, with n-hexadecane undergoing primary cracking on microsphere catalysts’ external acid sites, while long-chain n-alkanes can adjust molecular configurations at elevated temperatures to enter pure ZSM-5 zeolite pores for internal cracking. These findings provide crucial guidance for designing industrial catalysts tailored to specific feedstocks.

The researchers explore the optimization and integrated application of artificial intelligence (AI) in the manufacturing of polymer composite materials. By analyzing the diversity of composite molding processes and the demand for multifunctional integration, and combining the potential of AI in areas such as process parameter optimization, real-time control, and quality prediction, the study presents practical cases and discusses future development trends, offering ideas and methods for economically viable engineering applications.

After high-profile water crises like the one in Flint, Michigan, some Americans distrust the safety of tap water, choosing to purchase drinking water from freestanding water vending machines or kiosks. Yet this more expensive water may contain different pollutants than local tap water, according to a study in ACS’ Environmental Science & Technology. Researchers report that water sampled from 20 kiosks in six states sometimes contained lead at levels above public health recommendations.

Ultra-low-latency data processing is central to high-frequency trading, where nanosecond-level delays define competitive advantage. To meet this demand, scientists have developed a scalable photonic neuromorphic architecture that enables next-generation trading systems operating at the speed of light. By overcoming long-standing scalability limits of photonic neurons, this approach makes large-scale, real-world data processing practical, delivering picosecond-level latency and high energy efficiency beyond the fundamental limits of state-of-the-art electronic processors.