Justin R. Chimka
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
Also affiliated: University of Pittsburgh (1997–2020); University of Oklahoma (2002–2020)
Faculty Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Justin R. Chimka's research focuses on analytical chemistry, particularly concerning water quality and chemometric modeling. His work investigates methods for quantifying specific chemical compounds in drinking water, addressing challenges such as interferences from natural organic matter and the presence of emerging contaminants like per- and polyfluoroalkyl substances (PFAS).
Chimka has published research on techniques for nitrite quantification using second derivative chemometric models, which help mitigate interferences under chloraminated drinking water distribution system conditions. His publications also explore model selection strategies for regression analyses, including both multiple linear regression and logistic regression, particularly within budget constraints. Further research addresses the quantitation of chloronitramide anions in tap water and the use of diffusive gradients in thin-film passive samplers for PFAS measurement to facilitate compliance monitoring.
His scholarly contributions are reflected in a h-index of 9, with 73 total publications and 1,450 citations. Chimka is a Co-Principal Investigator on a $7,000,000 National Science Foundation grant for the E-RISE Rll: Arkansas Smart Transportation Research Incubator through Data Engineering and Science. He leads a research group and collaborates with several faculty members at the University of Arkansas at Fayetteville, including Julian L. Fairey and Ronald L. Rardin.
Metrics
- h-index: 9
- Publications: 73
- Citations: 1,454
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)
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A Note on Pure Error and Its Effect on Regression Model Significance (2020)
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An "Eia" Approach To Support Laboratory Learning Environments (2020)
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Integrated Auto Id Technology For Multi Disciplinary Undergraduate Studies (I Atmus) (2020)
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Investigating Engineering Student Estimation Processes: A Pilot Study (2020)
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An On Line Rfid Laboratory Learning Environment And The Assessment Of Its Users’ Education (2020)
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Gender and Self-Selection Among Engineering Students (2018)
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Gender and Self-Selection Among Engineering Students (2018)
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Statistical effects of waterway lock unavailability on commodity flow (2018)
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Emerging investigators series: trihalomethane, dihaloacetonitrile, and total N-nitrosamine precursor adsorption by modified carbon nanotubes (CNTs) and CNT micropillars (2017)
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Revealing a Size-Resolved Fluorescence-Based Metric for Tracking Oxidative Treatment of Total <i>N</i>-Nitrosamine Precursors in Waters from Wastewater Treatment Plants (2017)
Federal Grants 1 $7,000,000 total
E-RISE Rll: Arkansas Smart Transportation Research Incubator through Data Engineering and Science
Collaboration Network
Top Collaborators
- 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
- PFAS quantitation with diffusive gradients in thin-film passive samplers: Capturing time-weighted average concentrations around maximum contaminant levels to facilitate compliance
- 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
- PFAS quantitation with diffusive gradients in thin-film passive samplers: Capturing time-weighted average concentrations around maximum contaminant levels to facilitate compliance
- 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
- PFAS quantitation with diffusive gradients in thin-film passive samplers: Capturing time-weighted average concentrations around maximum contaminant levels to facilitate compliance
- PFAS quantitation with diffusive gradients in thin-film passive samplers: Capturing time-weighted average concentrations around maximum contaminant levels to facilitate compliance
- PFAS quantitation with diffusive gradients in thin-film passive samplers: Capturing time-weighted average concentrations around maximum contaminant levels to facilitate compliance
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