Papers

The toxicity of polystyrene micro- and nano-plastics on rare minnow (Gobiocypris rarus) varies with the particle size and concentration

Scientists exposed rare minnow fish to polystyrene microplastics and nanoplastics at different sizes and concentrations and found that both caused growth inhibition, tissue damage, and disrupted gut bacteria. Interestingly, larger microplastics at high concentrations were the most disruptive to gut microbiome communities, while nanoplastics caused more oxidative stress. The study shows that the health effects of plastic particles depend on both their size and amount, and that gut health is a key target of microplastic toxicity.

Performance and Mechanism of Sulfathiazole Adsorption by Magnetic Biochar: Promoting Effect of Co-existing Polystyrene and Simultaneous Removal

Researchers synthesized a magnetic biochar and tested its ability to remove the antibiotic sulfathiazole from water containing polystyrene microplastics, finding that the biochar achieved efficient removal of both contaminants simultaneously, with the microplastics actually promoting antibiotic adsorption.

Performance and Mechanism of Sulfathiazole Adsorption by Magnetic Biochar: Promoting Effect of Co-existing Polystyrene and Simultaneous Removal

Factors Influencing Urban Residents’ Intention of Garbage Sorting in China: An Extended TPB by Integrating Expectancy Theory and Norm Activation Model

Researchers extended the Theory of Planned Behavior with expectancy theory and norm activation to identify factors driving urban residents' intention to sort garbage in China, finding that subjective norms, perceived behavioral control, and personal moral norms were the strongest predictors.

Reducing uncertainty and confronting ignorance about the potential impacts of microplastic on animals: A critical review

An efficient and precise (micro)plastic identification method: feature infrared spectra extraction based on EIS-VIP-CARS and ANN modeling

Researchers developed a novel microplastic identification method combining equal interval sampling, variable importance in projection scoring, and competitive adaptive reweighted sampling to extract key infrared spectral features for artificial neural network classification. The approach reduces computational load and improves accuracy compared to standard CARS-based methods by preserving transmittance peaks during feature selection.