9 citations as recorded by crossref.

1. Application of machine learning approaches in the analysis of mass absorption cross-section of black carbon aerosols: Aerosol composition dependencies and sensitivity analyses A. May & H. Li https://doi.org/10.1080/02786826.2022.2114312
2. Machine learning approaches for monitoring environmental metal pollutants: Recent advances in source apportionment, detection, quantification, and risk assessment F. Nkinahamira et al. https://doi.org/10.1016/j.trac.2024.117980
3. Deep Learning Models for Data-Driven Laser Induced Breakdown Spectroscopy (LIBS) Analysis of Interstitial Oxygen Impurities in Czochralski-Si Crystals S. Davari & D. Mukherjee https://doi.org/10.1177/00037028221085640
4. On-line compositional measurements of AuAg aerosol nanoparticles generated by spark ablation using optical emission spectroscopy M. Snellman et al. https://doi.org/10.1016/j.jaerosci.2022.106041
5. Post-Wildfire Indoor Pollution in WUI Areas following the 2025 Los Angeles Fires. Part I. Establishing Baseline Contaminant Levels Prior to Home Reoccupation E. Goftari et al. https://doi.org/10.1021/acsestair.5c00281
6. Multielement simultaneous quantitative analysis of trace elements in stainless steel via full spectrum laser-induced breakdown spectroscopy Q. Ma et al. https://doi.org/10.1016/j.talanta.2024.125745
7. Improving quantitative analysis of spark-induced breakdown spectroscopy: Multivariate calibration of metal particles using machine learning H. Li et al. https://doi.org/10.1016/j.jaerosci.2021.105874
8. The performance of an inexpensive spark-induced breakdown spectroscopy instrument for near real-time analysis of toxic metal particles H. Li et al. https://doi.org/10.1016/j.atmosenv.2021.118666
9. A Review on the Development of Airborne Particulate Metals Detection Technologies E. Goftari & H. Li https://doi.org/10.1021/acsestair.5c00134