The global proliferation of antibiotics like oxytetracycline (OTC) and ciprofloxacin (CIP) in aquatic environments poses considerable risks to both human health and delicate ecosystems. These persistent contaminants, often resistant to conventional wastewater treatments, contribute to the rise of antibiotic-resistant strains. A team of scientists addressed this urgent challenge by developing a novel adsorbent derived from agricultural waste, specifically cotton husk. Their creation, a nano zero-valent iron (nZVI) supporting magnetic cotton husk-derived biochar, termed Fe₂O₃@BMBC, offers a promising strategy for effective water purification.

As the world transitions towards renewable energy, the demand for safe, cost-effective, and environmentally friendly energy storage solutions has become paramount. Zinc-ion energy storage devices (ZESDs), including batteries and capacitors, have emerged as a highly promising alternative to conventional lithium-ion technologies due to the natural abundance and low toxicity of zinc. A new review published in Carbon Research provides a systematic overview of the critical role that carbon materials play in advancing these sustainable technologies. The work, authored by a team from Changsha University of Science and Technology, Sichuan University, and Xiamen University, consolidates the latest strategies for using carbon to enhance every component of ZESDs, from the cathode to the notoriously problematic zinc anode.

By introducing fairness from the beginning with ‘fuzzy’ systems that understand ambiguity and shades of correctness, the evolved AIs balanced fairness and accuracy even when tasked with coming up with solutions for complicated financial and ethical issues.

In a novel approach that bridges sustainable agriculture and climate technology, scientists have successfully used cow manure as a superior, green alternative to chemical additives for creating high-performance carbon-capture materials. A collaborative team from the Chinese Academy of Agricultural Sciences (CAAS) and China Agricultural University has demonstrated that protein-rich cow manure is more effective than conventional urea for producing nitrogen-doped biochar, a porous material designed to adsorb CO₂ from the atmosphere. This finding presents a dual solution, tackling agricultural waste management while advancing carbon capture technology.

The research, led by Yuxuan Sun, Jixiu Jia, and Zonglu Yao, focused on developing a more environmentally friendly method for enhancing biochar. The standard process often relies on synthetic, energy-intensive nitrogen sources like urea to improve biochar’s ability to trap CO₂ molecules. The team instead explored a circular-economy model, using corn straw as the base carbon material and cow manure as a biological nitrogen source. They prepared different biochar samples through hydrothermal carbonization, a process that uses heated water under pressure, followed by a potassium hydroxide activation step to create a highly porous final product.

This study rationally engineered the esterase Aes72 based on its resolved crystal structure and quantum mechanical calculations, yielding a mutant with substantially enhanced degradation activity of polyurethane. The results provide an efficient enzymatic resource and mechanistic foundation for the biological recycling of polyurethane.

The National Institutes of Health has renewed support for Artificial Intelligence for Alzheimer’s Disease, or AI4AD. The new $12.6 million award to advance the project’s next phase, AI4AD2, brings its total investment in AI4AD to $30.7 million. Led by Paul M. Thompson, PhD, associate director of the USC Mark and Mary Stevens Neuroimaging and Informatics Institute (Stevens INI) at the Keck School of Medicine of USC, the multi-institutional initiative will develop artificial intelligence (AI) tools to uncover the biological causes of Alzheimer’s and related dementias, improve predictions of disease progression, and help develop more precise treatment options. AI4AD2 unites 10 investigators and 23 co-investigators from 10 institutions in pursuit of four interconnected research goals. The consortium will analyze large-scale datasets, including whole-genome sequencing, brain imaging, cognitive testing, and other biological data, to advance the diagnosis and treatment of dementia. This work builds on the original AI4AD initiative launched in 2020, which developed AI tools to detect Alzheimer’s-related patterns in brain scans and showed how machine learning can link imaging findings to underlying genetic risk. AI4AD2 will also develop new “genomic language models,” a type of AI inspired by the same broad family of technology used in language-based artificial intelligence systems. Instead of analyzing words, these models will analyze genomic sequences to identify combinations of DNA changes associated with Alzheimer’s disease, disease progression, and key biomarkers. The project will train and evaluate these methods using data from over 58,000 participants across 57 cohorts. In practical terms, that involves teaching AI to search vast genetic datasets for patterns that traditional methods could not identify.