Justin R. Chimka Data-verified
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Biography and Research Information
OverviewAI-generated summary
Justin R. Chimka's research focuses on analytical chemistry, with a particular emphasis on developing and applying chemometric models for the quantification of substances in complex matrices, such as drinking water. His work addresses challenges in accurately measuring analytes, especially when natural organic matter or other interfering compounds are present. Chimka has investigated methods for nitrite quantification using second derivative chemometric models, which help mitigate interferences under specific water distribution system conditions. He also explores techniques for quantifying anions like chloronitramide in tap water using ion chromatography with different detection methods.
Beyond analytical method development, Chimka's research interests extend to statistical modeling, including budget-constrained model selection for both multiple linear regression and logistic regression. This work contributes to the efficient and effective application of statistical techniques in scientific research. He is a faculty member at the University of Arkansas at Fayetteville and leads a research group. Chimka has a publication record of 71 articles, with an h-index of 9 and 1,442 citations. He has also served as a Co-Principal Investigator on an NSF grant totaling $7,000,000 for the E-RISE Rll: Arkansas Smart Transportation Research Incubator through Data Engineering and Science.
Metrics
- h-index: 9
- Publications: 73
- Citations: 1,447
Selected Publications
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PFAS quantitation with diffusive gradients in thin-film passive samplers: Capturing time-weighted average concentrations around maximum contaminant levels to facilitate compliance (2026)
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Chloronitramide Anion Quantitation in Tap Waters by Ion Chromatography with Electrical Conductivity and Ultraviolet Absorbance Detection (2026)
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Budget Constrained Model Selection for Logistic Regression (2025)
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Nitrite Quantification by Second Derivative Chemometric Models Mitigates Natural Organic Matter Interferences under Chloraminated Drinking Water Distribution System Conditions (2022)
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Budget constrained model selection for multiple linear regression (2021)
Federal Grants 1 $7,000,000 total
E-RISE Rll: Arkansas Smart Transportation Research Incubator through Data Engineering and Science
Collaboration Network
Top Collaborators
- Budget constrained model selection for multiple linear regression
- Budget Constrained Model Selection for Logistic Regression
- Budget constrained model selection for multiple linear regression
- Budget Constrained Model Selection for Logistic Regression
- Nitrite Quantification by Second Derivative Chemometric Models Mitigates Natural Organic Matter Interferences under Chloraminated Drinking Water Distribution System Conditions
- Chloronitramide Anion Quantitation in Tap Waters by Ion Chromatography with Electrical Conductivity and Ultraviolet Absorbance Detection
- Nitrite Quantification by Second Derivative Chemometric Models Mitigates Natural Organic Matter Interferences under Chloraminated Drinking Water Distribution System Conditions
- Nitrite Quantification by Second Derivative Chemometric Models Mitigates Natural Organic Matter Interferences under Chloraminated Drinking Water Distribution System Conditions
- Nitrite Quantification by Second Derivative Chemometric Models Mitigates Natural Organic Matter Interferences under Chloraminated Drinking Water Distribution System Conditions
- Nitrite Quantification by Second Derivative Chemometric Models Mitigates Natural Organic Matter Interferences under Chloraminated Drinking Water Distribution System Conditions
- Chloronitramide Anion Quantitation in Tap Waters by Ion Chromatography with Electrical Conductivity and Ultraviolet Absorbance Detection
- Chloronitramide Anion Quantitation in Tap Waters by Ion Chromatography with Electrical Conductivity and Ultraviolet Absorbance Detection
- Chloronitramide Anion Quantitation in Tap Waters by Ion Chromatography with Electrical Conductivity and Ultraviolet Absorbance Detection
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