Meet Petr Vozka

Petr is drawn to “analytically messy” samples – plastic-derived fuels, microplastics, environmental materials, and other mixtures containing hundreds or thousands of compounds. Using comprehensive two-dimensional gas chromatography, mass spectrometry, and chemometrics, his group turns that complexity into information relevant to environmental, forensic, and engineering questions.

“A GC×GC chromatogram can contain thousands of peaks. That is beautiful, but a beautiful chromatogram is not the answer,” he says. Petr wants to know which compounds matter, whether they can be measured reliably, and what they reveal about a material’s origin, transformation, risk, or performance. His plastics research examines both how waste can be converted into useful chemicals and fuels and what happens to microplastics and their associated compounds in the environment. “We now have instruments capable of showing us extraordinary chemical detail. The next frontier is deciding what that detail means.”

AI will make good analytical chemistry more important, not less, Petr argues. “AI can find a convincing pattern in bad data just as efficiently as it can find one in good data,” he says. Its successful use will require scientists who understand instrumentation, chemistry, statistics, uncertainty, and data science well enough to ask not only what a model predicts, but whether its answer should be believed.

“Peak count is not a measure of scientific quality,” he adds. “I would rather report 50 compounds that we can identify, quantify, and connect to a meaningful question than 5,000 features that end with ‘tentatively assigned.’” The same principle applies to nontargeted analysis: “The fewer assumptions we make before an experiment, the more disciplined we should be about what we claim afterward.”

Moving from the Czech Republic to Purdue University for his PhD introduced Petr to GC×GC and established an idea that continues to guide his research: “Composition is interesting. Composition connected to behavior is powerful.” Teaching provides a regular test of that understanding. “Strip away the impressive instrument, the colorful chromatogram, and the complicated statistics and ask: What exactly did we measure? How well did we measure it? And why does it matter? Those three questions have saved me from a surprising amount of bad science.”