Deeper learning in electrocatalysis: realizing opportunities and addressing challenges

Machine learning: Next promising trend for microplastics study

Microplastics (MPs), as an emerging pollutant, pose a significant threat to humans and ecosystems. However, traditional MPs characterization methods are limited by sample requirements and characterization time. Machine Learning (ML) has emerged as a vital technology for analyzing MPs pollution due to its accuracy, broad application, and powerful feature extraction. Nevertheless, environmental scientists require threshold knowledge before using ML, restricting the ML application in MPs research. Furthermore, imbalanced development of ML in MPs research is a pressing concern. In order to achieve a wide ML application in MPs research, in this review, we comprehensively discussed the size and sources of MPs datasets in relevant literature to help environmental scientists deepen their understanding of the construction of MPs datasets. Commonly used ML algorithms are analyzed from the perspective of interpretability and the need for computer facilities. Additionally, methods for improving and evaluating ML model performance, such as dataset pre-processing, model optimization, and model assessment metrics, are discussed. According to datasets and characterization techniques, MPs identification using ML was divided into three categories in this work: spectral identification, image identification, and spectral imaging identification. Finally, other applications of ML in MPs studies, including toxicity analysis, pollutants adsorption, and microbial colonization, are comprehensively discussed, which reveals the great application potential of ML. Based on the discussion above, this review suggests an algorithm selection strategy to assist researchers in selecting the most suitable ML algorithm in different situations, improving efficiency and decreasing the costs of trial and error. We believe that this work sheds light on the application of ML in MPs study.

Analysis of the Scale of Global Human Needs and Opportunities for Sustainable Catalytic Technologies

We analyzed the enormous scale of global human needs, their carbon footprint, and how they are connected to energy availability. We established that most challenges related to resource security and sustainability can be solved by providing distributed, affordable, and clean energy. Catalyzed chemical transformations powered by renewable electricity are emerging successor technologies that have the potential to replace fossil fuels without sacrificing the wellbeing of humans. We highlighted the technical, economic, and societal advantages and drawbacks of short- to medium-term decarbonization solutions to gauge their practicability, economic feasibility, and likelihood for widespread acceptance on a global scale. We detailed catalysis solutions that enhance sustainability, along with strategies for catalyst and process development, frontiers, challenges, and limitations, and emphasized the need for planetary stewardship. Electrocatalytic processes enable the production of solar fuels and commodity chemicals that address universal issues of the water, energy and food security nexus, clothing, the building sector, heating and cooling, transportation, information and communication technology, chemicals, consumer goods and services, and healthcare, toward providing global resource security and sustainability and enhancing environmental and social justice.