About

Robert E. Synovec obtained his Ph.D. in 1986 from Iowa State University and joined the University of Washington faculty that same year. His research group pioneers the development of novel analytical instrumentation, chemometric data analysis software, and methodologies based upon fundamental separation science, implemented at a problem-solving level. His team complements their interest in developing and applying innovative instrumentation and chemometrics software with a deep focus on modeling separation processes based on theory. This theoretical modeling has provided fundamental insight and guidance for instrumentation design and software improvements. His work includes the application of separations technology in various fields such as metabolomics, forensics, petroleum-based fuels, biofuels, and environmental systems. Synovec was awarded the GC×GC Scientific Achievement Award at the 10th GC×GC International Symposium in May 2013, recognizing his group's pioneering contributions to comprehensive two-dimensional gas chromatography (GC×GC). He also received the Marcel Golay Award at the 40th International Symposium on Capillary Chromatography (ISCC) conference in Riva del Garda, Italy, in recognition of a lifetime of achievement in capillary chromatography.

Research topics

• Chromatography
• Chemistry
• Machine Learning
• Computer Science
• Engineering
• Process engineering
• Biochemical engineering
• Food science

Selected publications

• Untargeted profiling and differentiation of geographical variants of wine samples using headspace solid-phase microextraction flow-modulated comprehensive two-dimensional gas chromatography with the support of tile-based Fisher ratio analysis

  Journal of Chromatography A · 2021 · 44 citations

  Senior authorCorresponding
  • Chemistry
  • Chromatography
  • Food science
  DOI

• Development of gas chromatographic pattern recognition and classification tools for compliance and forensic analyses of fuels: A review

  Analytica Chimica Acta · 2020 · 61 citations

  Senior authorCorresponding
  • Chemistry
  • Chromatography
  • Biochemical engineering
  DOI

• Predictive Modeling of Aerospace Fuel Properties Using Comprehensive Two-Dimensional Gas Chromatography with Time-Of-Flight Mass Spectrometry and Partial Least Squares Analysis

  Energy & Fuels · 2020 · 37 citations

  Senior authorCorresponding
  • Computer Science
  • Machine Learning
  • Chemistry

  Increasingly stringent requirements for aerospace propulsion system performance, reliability, and operability motivate quantitative connections between fuel composition, physical characteristics, and system performance. Chemically accurate assessment of aviation turbine fuels (Jet-A, JP-8, etc.) and kerosene-based rocket propellants (RP-1 and RP-2) is requisite to mature these models. Comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC × GC–TOFMS) is an excellent analytical tool for measuring detailed chemical information contained in complex fuels. Additionally, multivariate data analysis methods, referred to as chemometrics, are ideally suited to relate detailed chemical information contained within the GC × GC–TOFMS data to fuel properties and performance in a predictive manner. Herein, we apply these techniques to a chemically diverse set of 74 distillate and multicomponent aerospace fuels, resulting in an improved understanding of the chemical compositional basis for physical and thermochemical behavior. Informed by GC × GC–TOFMS data, highly reliable partial least squares (PLS) models are developed and employed in the prediction of physical properties (measured separately using conventional test methods). Root-mean-square errors of cross-validation (RMSECV) were relatively low: values of 0.0450 cSt, 41.3 Btu/lbm, 0.130 mass %, and 0.0064 g/mL were obtained for viscosity, heat of combustion, hydrogen content, and density, respectively. The corresponding normalized root-mean-square errors of cross-validation (NRMSECV) were 6.01, 10.3, 8.71, and 7.12%, respectively. Investigation of the linear regression vectors (LRVs) provides valuable insight into the relationship between the chemical composition and physical properties, enabling, in principle, the model-informed selection of fuel chemical composition to achieve desired performance criteria.

  DOI

Frequent coauthors

• Jamin C. Hoggard

  University of Washington

  63 shared

• Bryan J. Prazen

  Insilicos (United States)

  50 shared

• Bob W. Wright

  Pacific Northwest National Laboratory

  45 shared

• Karisa M. Pierce

  Seattle Pacific University

  45 shared

• Kristen J. Skogerboe

  Seattle University

  32 shared

• Janiece L. Hope

  Battelle

  25 shared

• Carlos G. Fraga

  Edwards Air Force Base

  24 shared

• Emilia Bramanti

  National Research Council

  23 shared

Education

• Ph.D.

  Iowa State University

  1986

Awards & honors

• GC×GC Scientific Achievement Award at the 10th GC×GC Interna…
• Marcel Golay Award at the 40th International Symposium on Ca…

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