Naval Surface Warfare Center, Crane Division, 300 Highway 361, Crane, IN, 47522, USA.
Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, 26 W. Martin Luther King Dr., Cincinnati, OH, 45268, USA.
Chemosphere. 2022 Jan;287(Pt 1):131845. doi: 10.1016/j.chemosphere.2021.131845. Epub 2021 Aug 19.
"Green" pyrotechnics seek to remove known environmental pollutants and health hazards from their formulations. This chemical engineering approach often focuses on maintaining performance effects upon replacement of objectionable ingredients, yet neglects the chemical products formed by the exothermic reaction. In this work, milligram quantities of a lab-scale pyrotechnic red smoke composition were functioned within a thermal probe for product identification by pyrolysis-gas chromatography-mass spectrometry. Thermally decomposed ingredients and new side product derivatives were identified at lower relative abundances to the intact organic dye (as the engineered sublimation product). Side products included chlorination of the organic dye donated by the chlorate oxidizer. Machine learning quantitative structure-activity relationship models computed impacts to health and environmental hazards. High to very high toxicities were predicted for inhalation, mutagenicity, developmental, and endocrine disruption for common military pyrotechnic dyes and their analogous chlorinated side products. These results underscore the need to revise objectives of "green" pyrotechnic engineering.
“绿色”烟火药剂旨在从其配方中去除已知的环境污染物和健康危害。这种化学工程方法通常侧重于在替换有问题的成分时保持性能效果，但忽略了放热反应形成的化学产物。在这项工作中，毫克级的实验室规模烟火红色烟雾成分在热探针中进行了功能测试，通过热解-气相色谱-质谱法进行产物鉴定。热分解成分和新的副产物衍生物的相对丰度低于完整的有机染料（作为工程升华产物）。副产物包括有机染料的氯化，该有机染料由氯酸氧化剂提供。机器学习定量结构-活性关系模型计算了对健康和环境危害的影响。常见军用烟火染料及其类似的氯化副产物的吸入毒性、致突变性、发育毒性和内分泌干扰被预测为高到极高毒性。这些结果强调了需要修改“绿色”烟火工程的目标。
