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Updated: May 12, 2026

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化学污染物的生成AI授权选策略:对Per和Polyfluoroalkyl物质的案例

Yuwei Liu1, Haobo Wang1, Huaijun Xie1

  • 1Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China.

Environmental science & technology
|November 12, 2025
PubMed
概括

使用化学语言模型的生成人工智能识别了数百万种新的per-和多基基物质 (PFAS),显著扩大了已知的化学空间. 这种方法提高了环境样本中的污染物检测.

关键词:
化学语言模型模型机器学习是机器学习.分子生成分子的产生.通过和多基基物质.污染物查 污染物查 污染物查

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Bioplastics derived from microbial processes present a sustainable alternative to conventional petroleum-based plastics. Among these, polyhydroxyalkanoates (PHAs), particularly polyhydroxybutyrates (PHBs), have emerged as prominent candidates due to their biodegradability and biocompatibility. These polymers are synthesized by a variety of bacteria, such as Cupriavidus necator and Pseudomonas putida, which naturally accumulate PHAs as intracellular carbon and energy reserves, especially under...
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Microbial Bioremediation of Plastics

Polyethylene terephthalate (PET) is a synthetic polymer widely utilized in the packaging industry, particularly for bottles and containers. Due to its chemical stability and durability, PET accumulates in the environment, contributing significantly to plastic pollution. It comprises repeating units of terephthalic acid and ethylene glycol, resulting in a semi-crystalline structure that is resistant to natural degradation processes.A notable breakthrough in plastic biodegradation came with the...