Magnetic resonance imaging often yields inconsistent results when assessing the brain’s structural characteristics in children with attention deficit/hyperactivity disorder (ADHD). To address this, scientists from Japan have used a harmonization method called traveling-subject (TS) to reduce measurement bias in brain imaging datasets from multiple sites. The TS harmonized datasets showed significant reductions in measurement bias and revealed apparent volumetric changes in specific brain regions, indicating promise for developing a more robust diagnosis for ADHD.

An ultrasound device that can precisely stimulate areas deep in the brain without surgery has been developed by researchers from UCL and the University of Oxford, opening up new possibilities for neurological research and treatment of disorders such as Parkinson’s disease and depression.

Designing high-performance electrocatalysts is one of the key challenges in the development of microbial electrochemical hydrogen production. Transition metal-based (TM-based) electrocatalysts are introduced as an astonishing alternative for future catalysts by addressing several disadvantages, like the high cost and low performance of noble metal and metal-free electrocatalysts, respectively. In this critical review, a comprehensive analysis of the major development of all families of TM-based catalysts from the beginning development of microbial electrolysis cells in the last 15 years is presented. Importantly, pivotal design parameters such as selecting efficient synthesis methods based on the type of material, main criteria during each synthesizing method, and the pros and cons of various procedures are highlighted and compared. Moreover, procedures for tuning and tailoring the structures, advanced strategies to promote active sites, and the potential for implementing novel unexplored TM-based hybrid structures suggested. Furthermore, consideration for large-scale application of TM-based catalysts for future mass production, including life cycle assessment, cost assessment, economic analysis, and recently pilot-scale studies were highlighted. Of great importance, the potential of utilizing artificial intelligence and advanced computational methods such as active learning, microkinetic modeling, and physics-informed machine learning in designing high-performance electrodes in successful practices was elucidated. Finally, a conceptual framework for future studies and remaining challenges on different aspects of TM-based electrocatalysts in microbial electrolysis cells is proposed.

Injecting carbon-rich bio-oil into the deep shafts of abandoned crude oil wells could be an economically feasible system of removing carbon dioxide from the air for long-term storage.