[Overview of the Application of Machine Learning for Identification and Environmental Risk Assessment of Microplastics].

Microplastics are an emerging contaminant that can persist in the environment for extended periods， posing risks to ecological systems. Recently， microplastic pollution has emerged as a major global environmental problem. In order to ensure accurate and scientific evaluation of the ecological risks associated with microplastic pollution， it is of paramount importance to improve the simplicity and reliability of microplastic identification， systematically analyze the pollution characteristics of microplastics in various environmental media， and clarify their environmental impacts. Machine learning technology has gained widespread attention in microplastic research by learning and analyzing large volumes of data to establish result evaluation or prediction models. The use of machine learning can enhance the automation and identification efficiency of visual and spectral identification of microplastics， provide scientific support for tracing the sources of microplastic pollution， and help reveal the complex environmental effects of microplastics. This review provides a summary of the application characteristics and limitations of machine learning in the aforementioned areas by reviewing the progress made in research that employs machine learning technology in microplastic identification and environmental risk assessment. Furthermore， the findings of the review will provide suggestions and prospects for the development and application of machine learning in related areas.

Fabrication of bacterial cellulose with TiO2-ZnO nanocomposites as a multifunctional membrane for water remediation.

Superhydrophilic/underwater superoleophobic (SUS) membrane technology has attracted extensive attention for water purification. However, the fabrication of multifunctional membranes to satisfy the complex wastewater treatment is still a big challenge. In this work, bacterial cellulose (BC) based multifunctional SUS membranes were designed for water purification. Membranes were prepared by blending BC nanofibers with TiO2 nanoparticles (NPs), and further modified by the in situ growth of ZnO-NPs. The composite membranes showed oil/water (o/w) separation under a small driving pressure (0.2-0.3 bar) with a flux rate of 8232.81 ± 212 L m-2h-1 and with a high separation efficiency (>99.9%). Membranes could also separate oil-in-water emulsion with a separation flux of 1498 ± 74 L m-2h-1 and with high efficiency (99.25%). Moreover, the composite membrane exhibited photocatalytic activity under visible light with a high efficiency (>92%). The composite membranes were also investigated for antibacterial activity against Gram-positive and Gram-negative bacterial strains. This work may inspire the fabrication of next-generation multifunctional membranes for wastewater treatment, particularly oily wastewater, dyes and microbial contaminated water.

Cellulose-based special wetting materials for oil/water separation: A review.

Oil spill accidents and oily wastewater discharged by petrochemical industries have severely wasted water resources and damaged the environment. The use of special wetting materials to separate oil and water is efficient and environment-friendly. Cellulose is the most abundant renewable resource and has natural advantages in removing pollutants from oily wastewater. The application and modification of cellulose as special wetting materials have attracted considerable research attention. Therefore, we summarized cellulose-based superlipophilic/superhydrophobic and superhydrophilic/superoleophobic materials exhibiting special wetting properties for oil/water separation. The treatment mechanism, preparation technology, treatment effect, and representative projects of oil-bearing wastewater are discussed. Moreover, cellulose-based intelligent-responsive materials for application to oil/water separation and the removal of other pollutants from oily wastewater have also been summarized. The prospects and potential challenges of all the materials have been highlighted.