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Barry Lavine

Dr. Barry Lavine

Regents Professor

455A Physical Sciences I 



  • B.A., 1976, Temple University
  • M.S., 1982, Ohio State University
  • Ph.D.,1986, Pennsylvania State University

Research Interests:

  • Vibrational spectroscopy and imaging
  • Spectral library matching
  • Forensic automotive paint analysis
  • Swellable pH sensitive polymers for optical sensing
  • Bioinformatics (cancer marker identification from mass spectral data)

Research in the Lavine group is focused on the development of new techniques in reversed phase liquid chromatography (RPLC) and spectroscopy for use in biomedical, environmental and forensic applications. State-of-the-art data analysis methods are also being developed to extract information from the large amounts of data routinely generated in profiling studies of the type performed in our laboratory in the solution of these problems.    


Screening Method for Renal Cell Carcinoma: RCC, which has the highest mortality rate of any urological cancer, is asymptotic in its early stages.  To improve the prognosis for RCC patients, early detection through development of a methodology based on analysis of urine for molecular markers characteristic of RCC in its initial stages is desirable.  An obvious advantage of this approach to cancer prescreening is that urine samples are inexpensive and readily accessible. Currently, a RPLC method to detect creatinine, quinolinic acid, gentisic acid, 4-hydroxybenzoic acid, and other RCC biomarkers in urine is being developed using aqueous mobile phases with small amounts (1% v/v or less) of medium chain length alcohols (e.g., butanol and pentanol) as mobile phase modifiers.  Better resolution (i.e., higher selectivity and greater chromatographic efficiency) is obtained for these polar organic compounds when water rich mobile phases are used as eluents in lieu of traditional hydro-organic solvent mixtures. A better understanding of the relationship between stationary phase wetting and the chromatographic selectivity is also being gained from these studies.

Chemical Sensors to Study Rising Ocean Acidity: Carbon dioxide emissions have caused the acidity of the ocean waters to rise, altering the concentration of carbonate in the oceans and in turn causing harm to shell building organisms. To better understand this phenomenon, the Lavine Research group is developing optical pH sensors in collaboration with Professor Albert Rosenberger in the Physics Department at OSU using whispering gallery modes of silica hollow bottle resonators to measure pH corrected for the ionic strength in ocean waters. Swellable pH sensitive polymer particles that respond to pH, ionic strength or both can be deposited on the interior surface of a silica hollow bottle resonator. As the pH of the solution in contact with the particles increases, the refractive index of the particles decreases. As a result, the whispering gallery modes with internal evanescent components shift in frequency as a function of pH.  Plots of selected mode frequencies versus pH yield sigmoid shaped titration curves similar to those obtained using turbidity to monitor refractive index changes of the particles as a function of pH.  The response time of 10-15 s and resolution of 0.06 pH units represent marked improvements over previous optical pH sensing methods.


Forensic Examination of Automotive Paints: In the forensic examination of automotive paint, each layer of paint is analyzed individually by FTIR spectroscopy.  Laboratories in North America typically hand section each layer and present each separated layer to the spectrometer for analysis, which is time consuming.  In addition, sampling too close to the boundary between adjacent layers can pose a problem as it produces an IR spectrum that is a mixture of the two layers.  Not having a “pure” spectrum of each layer will prevent a meaningful comparison between each paint layer or in the situation of searching an automotive database will prevent the forensic paint examiner from developing an accurate hit list of potential suspects.  These two problems can be addressed by collecting concatenated IR data from all paint layers in a single analysis by scanning across the cross sectioned layers of the paint sample using a FTIR imaging microscope.  Decatenation of the IR data is achieved by multivariate curve resolution using a Varimax extended rotation to select the starting point (i.e., initial estimates of the reconstructed IR spectra of each layer) for the alternating least squares algorithm to obtain a pure IR spectrum of each automotive paint layer.  Comparing the reconstructed IR spectrum of each layer against the IR spectral library of the PDQ database shows that it is possible to identify the correct line and model of the vehicle from these reconstructed spectra.  This imaging approach to IR analysis of automotive paint, not only saves time and eliminates the need to analyze each layer separately, but has the potential t ensure that the final spectrum of each layer is “pure” and not a mixture. 


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