College of Engineering (COE)
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Note: The Department of Biological Systems Engineering is listed within the College of Agriculture and Life Sciences (CALS).
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Browsing College of Engineering (COE) by Subject "03 Chemical Sciences"
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- How mobility restrictions policy and atmospheric conditions impacted air quality in the State of Sao Paulo during the COVID-19 outbreakRudke, A. P.; Martins, J. A.; de Almeida, D. S.; Martins, L. D.; Beal, A.; Hallak, R.; Freitas, E. D.; Andrade, M. F.; Foroutan, Hosein; Baek, B. H.; Albuquerque, T. T. de A. (Academic Press-Elsevier, 2021-07-01)Mobility restrictions are among actions to prevent the spread of the COVID-19 pandemic and have been pointed as reasons for improving air quality, especially in large cities. However, it is crucial to assess the impact of atmospheric conditions on air quality and air pollutant dispersion in the face of the potential variability of all sources. In this study, the impact of mobility restrictions on the air quality was analyzed for the most populous Brazilian State, São Paulo, severely impacted by COVID-19. Ground-based air quality data (PM10, PM2.5, CO, SO2, NOx, NO2, NO, and O3) were used from 50 automatic air quality monitoring stations to evaluate the changes in concentrations before (January 01 - March 25) and during the partial quarantine (March 16 - June 30). Rainfall, fires, and daily cell phone mobility data were also used as supplementary information to the analyses. The Mann-Whitney U test was used to assess the heterogeneity of the air quality data during and before mobility restrictions. In general, the results demonstrated no substantial improvements in air quality for most of the pollutants when comparing before and during restrictions periods. Besides, when the analyzed period of 2020 is compared with the year 2019, there is no significant air quality improvement in the São Paulo State. However, special attention should be given to the Metropolitan Area of São Paulo (MASP), due to the vast population residing in this area and exposed to air pollution. The region reached an average decrease of 29% in CO, 28% in NOx, 40% in NO, 19% in SO2, 15% in PM2.5, and 8% in PM10 concentrations during the mobility restrictions period compared to the same period in 2019. The only pollutant that showed an increase in concentration was ozone, with a 20% increase compared to 2019 during the mobility restrictions period. Before the mobility restrictions period, the region reached an average decrease of 30% in CO, 39% in NOx, 63% in NO, 12% in SO2, 23% in PM2.5, 18% in PM10, and 16% in O3 concentrations when compared to the same period in 2019. On the other hand, Cubatão, a highly industrialized area, showed statistically significant increases above 20% for most monitored pollutants in both periods of 2020 compared to 2019. This study reinforces that the main driving force of pollutant concentration variability is the dynamics of the atmosphere at its various time scales. An abnormal rainy season, with above average rainfall before the restrictions and below average after it, generated a scenario in which the probable significant reductions in emissions did not substantially affect the concentration of pollutants.
- Interpretable Machine Learning of Chemical Bonding at Solid SurfacesOmidvar, Noushin; Pillai, Hemanth Somarajan; Wang, Shih-Han; Mou, Tianyou; Wang, Siwen; Athawale, Andy; Achenie, Luke E. K.; Xin, Hongliang (American Chemical Society, 2021-11-25)Understanding the nature of chemical bonding and its variation in strength across physically tunable factors is important for the development of novel catalytic materials. One way to speed up this process is to employ machine learning (ML) algorithms with online data repositories curated from high-throughput experiments or quantum-chemical simulations. Despite the reasonable predictive performance of ML models for predicting reactivity properties of solid surfaces, the ever-growing complexity of modern algorithms, e.g., deep learning, makes them black boxes with little to no explanation. In this Perspective, we discuss recent advances of interpretable ML for opening up these black boxes from the standpoints of feature engineering, algorithm development, and post hoc analysis. We underline the pivotal role of interpretability as the foundation of next-generation ML algorithms and emerging AI platforms for driving discoveries across scientific disciplines.
- Transparent and Sprayable Surface Coatings that Kill Drug-Resistant Bacteria within Minutes and Inactivate SARS-CoV-2 VirusBehzadinasab, Saeed; Williams, Myra D.; Hosseini, Mohsen; Poon, Leo L. M.; Chin, Alex W. H.; Falkinham, Joseph O. III; Ducker, William A. (American Chemical Society, 2021-11-24)Antimicrobial coatings are one method to reduce the spread of microbial diseases. Transparent coatings preserve the visual properties of surfaces and are strictly necessary for applications such as antimicrobial cell phone screens. This work describes transparent coatings that inactivate microbes within minutes. The coatings are based on a polydopamine (PDA) adhesive, which has the useful property that the monomer can be sprayed, and then the monomer polymerizes in a conformal film at room temperature. Two coatings are described (1) a coating where PDA is deposited first and then a thin layer of copper is grown on the PDA by electroless deposition (PDA/Cu) and (2) a coating where a suspension of Cu2O particles in a PDA solution is deposited in a single step (PDA/Cu2O). In the second coating, PDA menisci bind Cu2O particles to the solid surface. Both coatings are transparent and are highly efficient in inactivating microbes. PDA/Cu kills >99.99% of Pseudomonas aeruginosa and 99.18% of methicillin-resistant Staphylococcus aureus (MRSA) in only 10 min and inactivates 99.98% of SARS-CoV-2 virus in 1 h. PDA/Cu2O kills 99.94% of P. aeruginosa and 96.82% of MRSA within 10 min and inactivates 99.88% of SARS-CoV-2 in 1 h.