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.

Surface change of microplastics in aquatic environment and the removal by froth flotation assisted with cationic and anionic surfactants.

Microplastics (MPs) are increasingly released into the environment due to the widespread usage and improper management of plastics. Considerable research efforts have been devoted to the remediation of MPs. Froth flotation has been demonstrated as an effective method to remove MPs in water and sediment. However, there is a lack of knowledge on the regulation of the hydrophobicity/hydrophilicity of MPs surfaces. We found that exposure to the natural environment resulted in the increased hydrophilicity of MPs. The flotation efficiencies of polyvinyl chloride (PVC), polypropylene (PP), polystyrene (PS), and polyethylene glycol terephthalate (PET) MPs decreased to zero after six months of natural incubation in rivers. According to various characterizations, the hydrophilization mechanism is mainly correlated with surface oxidation and the deposition of clay minerals. Inspired by surface wettability conversion, we applied surfactants (collectors) to enhance MPs hydrophobicity and flotation efficiency. Anionic sodium oleate (NaOL) and cationic dodecyl trimethyl ammonium chloride (DTAC) were used to regulate surface hydrophobicity. The effects of collector concentration, pH, conditioning time, and metal ions on MPs flotation were thoroughly elucidated. Characterizations and adsorption experiments were performed to describe the heterogeneous adsorption of surfactants on MPs surfaces. The interaction between surfactants and MPs was explained through density functional theory (DFT) simulations. The dispersion energy between hydrophobic hydrocarbon chains attracts collectors on the MPs surface, and the collector molecules wrap and laminate to MPs surfaces. Flotation using NaOL exhibited a higher removal efficiency, and NaOL was environmentally friendly. Subsequently, we investigated the activation of Ca2+, Fe3+, and Al3+ to further improve the collecting efficiency of NaOL. Under the optimized conditions, MPs in natural rivers could be removed by froth flotation. This study shows the great promise of froth flotation for the application of MPs removal.

Insight into the microplastics release from disposable face mask: Simulated environment and removal strategy.

The fight against the COVID-19 epidemic significantly raises the global demand for personal protective equipment, especially disposable face masks (DFMs). The discarded DFMs may become a potential source of microplastics (MPs), which has attracted much attention. In this work, we identified the detailed source of MPs released from DFMs with laser direct infrared spectroscopy. Polypropylene (PP) and polyurethane (PU) accounted for 24.5% and 57.1% of released MPs, respectively. The melt-blown fabric was a dominant MPs source, however, previous studies underestimated the contribution of mask rope. The captured polyethylene terephthalate (PET), polyamide (PA), polyethylene (PE), and polystyrene (PS) in airborne only shared 18.4% of released MPs. To deepen the understanding of MPs release from medical mask into the aquatic environment, we investigated the effects of environmental factors on MPs release. Based on regression analysis, the effects of temperature, incubation time, and wearing time significantly affect the release of MPs. Besides, acidity, alkalinity, sodium chloride, and humic acid also contributed to the MPs release through corroding, swelling, or repulsion of fibers. Based on the exposure of medical mask to simulated environments, the number of released MPs followed the order: seawater > simulated gut-fluid > freshwater > pure water. Considering the risk of MPs released from DFMs to the environment, we innovatively established a novel flotation removal system combined with cocoamidopropyl betaine, achieving 86% removal efficiency of MPs in water. This work shed the light on the MPs release from DFMs and proposed a removal strategy for the control of MPs pollution.

How Does Career Calling Influence Preservice Teachers' Learning Engagement? A Multiple Mediating Roles of Occupational Self-Efficacy and Vocational Outcome Expectation.

This study used social cognitive career theory (SCCT) to explore the relationships between career calling, occupational self-efficacy, vocational outcome expectation, and learning engagement among preservice teachers at a normal university in China. Data from 1,029 preservice teachers were analyzed using Structural Equation Modeling. The results revealed that career calling was found to be significantly and positively affected on learning engagement; occupational self-efficacy and vocational outcome expectation were identified as key mediators of this relationship. These findings advance our knowledge of how best to promote the learning engagement of preservice teachers and may inform the future design of teacher development programs.