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University Cooperative Research Center

CARFS is the only forensic science focused I/UCRC in the country. An I/UCRC is a collaborative effort among industry, universities and government partners for the purpose of conducting pre-competitive research of shared value. This model has been successfully utilized and refined for over 40 years. These stakeholders partner to advance critical technologies from early stage research to the marketplace.

NSF-I/UCRC enables industrially-relevant, pre-competitive research via multimember, sustained partnerships across industry, academe and government. NSF supports the development and evolution of I/UCRCs, by providing a financial and procedural framework for membership and operations. It also promotes best practices learned over decades of fostering public/private partnerships that produce significant value to the nation, industry and university faculty and students.

The mission of NSF-I/UCRCs is to grow the U.S. innovation capacity by developing long-term partnerships among industry, academe, and government. Industry and NSF funds are leveraged to support and train the next generation workforce within a global context. Faculty and students complete user-inspired research projects with continuous feedback from industry partners to solve real-world needs. The Industrial Advisory Board (IAB) votes on research projects to fund. Faculty, students, and industry mentors/collaborators meet regularly and monthly progress reports are generated. At semi-annual meetings there are IAB discussions and brainstorming. CARFS works on research that is transformative and helps reshape the frontiers of knowledge of industry relevance.

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Together partners perform cutting-edge, pre-competitive fundamental research in science, engineering, technology area(s) of interest to industry which can also drive innovation and the U.S. economy. Members guide the direction of Center research through active involvement and mentoring.

I/UCRCs offer a platform for significant leveraging of financial investment by members to accelerate the knowledge base in emerging technologies and manufacturing sectors while developing an industrially savvy workforce to benefit US economy.

Publications

  • 2018

    Gautier, Adam M.; Andel, Todd R.; Benton, Ryan. “On-Device Detection via Anomalous Environmental Factors”. Proceedings of the 8th Software Security, Protection, and Reverse Engineering Workshop (SSPREW-8), San Juan, Puerto Rico, USA, Article 5, 2018.

    Link: 10.1145/3289239.3289246

  • 2019

    Johnsten, Tom; Green, Wyatt, Crook, L.; Chan, Ho Yin; Benton, Ryan G.; Bourrie, David M. “Discovery of action rules for continuously valued data”. 2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI), 2019.

    Link: 10.1109/CogMI48466.2019.00026

    Wang, Ling; Deriu, Chiara; Wu, Wensong; Mebel, Alexander M.; McCord, Bruce. "Surface‐enhanced Raman spectroscopy, Raman, and density functional theoretical analyses of fentanyl and six analogs". Journal of Raman Spectroscopy, v.50, 2019.

    Link: 10.1002/jrs.5656

  • 2020

    Aijala, Jennett Chenevert; Wu, Wensong; DeCaprio, Anthony P. “Application of statistical design of experiments to assess pre-treatment parameters in forensic hair analysis for amphetamine" Forensic Chemistry, v.20, 2020.

    Link: 10.1016/j.forc.2020.100265

    Aijala, Jennett Chenevert; Wu, Wensong; DeCaprio, Anthony P. "Assessing Hair Decontamination Protocols for Diazepam, Heroin, Cocaine and Δ9-Tetrahydrocannabinol by Statistical Design of Experiments". Journal of Analytical Toxicology, v.45, 2020.

    Link: 10.1093/jat/bkaa110

    Green, Wyatt; Johnsten, Tom; Benton, Ryan. G. “TADS: Transformation of Anomalies in Data Streams. In 2020 IEEE International Conference on Big Data (Big Data)”. IEEE International Conference on Big Data, Atlanta, Georgia, USA, 2020.

    Link: 10.1109/BigData50022.2020.9377765

    Torres, Michelle; Valdes, Nicole; Almirall, Jose. "Comparison of portable and benchtop GC–MS coupled to capillary microextraction of volatiles (CMV) for the extraction and analysis of ignitable liquid residues". Forensic chemistry, v.19, 2020.

    Link: 10.1016/j.forc.2020.100240

    Jung Sukhwan; Kandadi, Rachana R.; Datta Rituparna; Benton, Ryan; Segev Aviv. “Identification of Technology-Relevant Entities Based on Trend Curves”. Proceedings of International Conference on Information Technology Convergence and Services, Vancouver, Canada, 2020.

    Link: 10.5121/csit.2020.100501

    Jung Sukhwan; Kandadi, Rachana R.; Datta Rituparna; Benton, Ryan; Segev Aviv. “Identification of Technology-Relevant Entities Based on Trend Curves and Semantic Similarities”. International Journal of Web & Semantic Technology (IJWesT), v. 11(3), 2020.

    Link: 10.2139/ssrn.3873849

    Winokur, Agnes D; Kaufman, Lindsay M.; Almirall, Jose R. "Differentiation and identification of fentanyl analogues using GC-IRD". Forensic Chemistry, v.20, 2020.

    Link: 10.1016/j.forc.2020.100255

    Foots, Courtney; Pal, Palash; Datta, Rituparna; Segev Aviv. “Classification of Computer Hardware and Performance Prediction Using Statistical Learning and Neural Networks”. Proceedings of International Conference on Artificial Intelligence and Machine Learning, Vancouver, Canada, 2020.

    Link: 10.5121/csit.2020.100517

    Foots, Courtney; Pal, Palash; Datta, Rituparna; Segev Aviv. “CPU Hardware Classification and Performance Prediction Using Neural Networks and Statistical Learning”. International Journal of Artificial Intelligence & Applications (IJAIA), v. 11(4), 2020.

    Link: 10.5121/ijaia.2020.11401

  • 2021

    Wang, Ling; Vendrell-Dones, Mario O.; Deriu, Chiara; Doğruer, Sevde; de B. Harrington, Peter; McCord, Bruce. "Multivariate Analysis Aided Surface-Enhanced Raman Spectroscopy (MVA-SERS) Multiplex Quantitative Detection of Trace Fentanyl in Illicit Drug Mixtures Using a Handheld Raman Spectrometer". Applied Spectroscopy, v.75, 2021.

    Link: 10.1177/00037028211032930

    Wells, Jeffrey D.; MacInnis, Amber E.; Dsouza, Maurell A.; Abdin, Zain Ul; Mughawi, Sara Al; Khloofi, Mohammad Al; Sajwani, Mariam; Maidoor, Maryam Al; Saeed, Ashwaq; Ahli, Hamdan; Shamsi, Rawdha Al; Mheiri, Reem Al. "Forensic entomology when the evidence is “no insect.” Best carrion fly species for predicting maximum postmortem interval in the United Arab Emirates". Forensic Science International, v.328, 2021.

    Link: 10.1016/j.forsciint.2021.110999

    Rodriguez, Jacqueline L.; Almirall, José R. "Continuous vapor sampling of volatile organic compounds associated with explosives using capillary microextraction of volatiles (CMV) coupled to a portable GC–MS". Forensic Chemistry, v.26, 2021.

    Link: 10.1016/j.forc.2021.100380

    Acosta, Alexander; Almirall, Jose. “Differentiation between hemp-type and marijuana-type cannabis using the Fast Blue BB colorimetric test”. Forensic Chemistry, v26, 2021.

    Link: 10.1016/j.forc.2021.100376

    Zapico, Sara; Gauthier, Quentin; Antevska, Aleksandra; McCord, Bruce. “Identifying Methylation Patterns in Dental Pulp Aging: Application to Age-at-Death Estimation in Forensic Anthropology”. International journal of Molecular Sciences, v.22, 3717, 2021.

    Link: 10.3390/ijms22073717

  • 2022

    Torres, Michelle N.; Almirall, José R. "Evaluation of capillary microextraction of volatiles (CMV) coupled to a person-portable gas chromatograph mass spectrometer (GC–MS) for the analysis of gasoline residues" Forensic Chemistry, v.27, 2022

    Link: 10.1016/j.forc.2021.100397

    Ferguson, Kimiko; Tupik, Sherri L.; Haddad, Hunter; Perr, Jeannette; Gilbert, Michael; Newman, Reta; Almirall, José. "Utility of gas chromatography infrared spectroscopy (GC-IR) for the differentiation of positional isomers of fentanyl related substances". Forensic Chemistry, v.29, 2022.

    Link: 10.1016/j.forc.2022.100425

    Deriu, Chiara; Bracho, Asier; McCord, Bruce. "Tailored Colloidal Nanostars for Surface-Enhanced Raman Spectroscopy: Optimization of Formulation Components and Study of the Stabilizer–Nanoparticle Interactions". The Journal of Physical Chemistry C, v.126, 2022.

    Link: 10.1021/acs.jpcc.1c08145

    Acosta, Alexander; Li, Li; Weaver, Mike; Capote, Ryan; Perr, Jeannette; Almirall, Jose. “Validation of a combined Fast blue BB and 4-Aminophenol colorimetric test for indication of Hemp-type and Marijuana-type cannabis”. Forensic Chemistry, v 31, 2022.

    Link: 10.1016/j.forc.2022.100448

    Acosta, Alexander; Li, Li; Weaver, Mike; Capote, Ryan; Perr, Jeannette; Almirall, Jose. “Validation of a combined Fast blue BB and 4-Aminophenol colorimetric test for indication of Hemp-type and Marijuana-type cannabis”. Forensic Chemistry, v 31, 2022.

    Link: 10.1016/j.forc.2022.100448

    Bhattarai, Abhished; Veksler, Maryna; Kurt, Ahmet; Sahin, H; Akkaya, Kemal. “Crypto Wallet Artifact Detection on Android Devices using Advanced Machine Learning Techniques” in the Proceedings of EAI International Conference on Digital Forensics & Cyber Crime (ICDF2C 2022), Boston, MA, Nov. 2022.

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    Tarone, Aaron; Mann, Allysson M.; Zhang, Yan; Zascavage, Roxanne R.; Mitchell, Elizabeth A.; Morales, Edgar; Rusch, Travis W.; Allen, Michael S. “The Devil is in the Details: Variable impacts of season, BMI, sampling site temperature, and presence of insects on the post-mortem microbiome”. Frontiers in Microbiology, 2022.

    Link: 10.3389/fmicb.2022.1064904