EAS Award Winner Nicholas Snow Highlights Key Trends Shaping GC

Gas chromatography (GC) continues to evolve, and Nicholas H. Snow—recipient of the 2025 EAS Award for Outstanding Achievements in Separation Science—identifies three major trends shaping the field today: AI, miniaturization, and automation. With over three decades of experience and more than 100 publications, Snow is uniquely positioned to comment on both the fundamentals and the practical applications of GC and GC-MS.

According to Snow, artificial intelligence is becoming impossible to ignore. AI is increasingly integrated into computer-aided method development, helping analysts optimize complex workflows. In mass spectrometry, AI-assisted tools are proving particularly valuable for spectral interpretation, enabling scientists to deconvolute the growing complexity of unknown samples with greater speed and accuracy.

Miniaturization is another key trend, driven by interest in green instrumentation, smaller lab footprints, and lower energy consumption. Snow encourages analysts to consider smaller, more efficient platforms for routine analyses, noting that full-size research-grade instruments are not always necessary. Smaller systems can offer comparable performance while reducing costs, energy use, and space requirements.

Finally, automation continues to advance, offering clear benefits in efficiency and reproducibility. However, Snow notes that automation can still present challenges, particularly around capital costs, which can rival the price of the GC instrument itself. Despite these hurdles, he believes automation will remain a central trend in modern laboratories.

While GC instrumentation has advanced dramatically, the fundamental theories of gas chromatography have changed little since its invention in the 1950s. Today’s developments in AI, miniaturization, and automation are finally allowing the technology to fully match the potential of these longstanding principles.

For Snow, the future of GC lies in combining strong fundamentals with these next-generation tools—smarter, smaller, and more automated systems that empower analysts to tackle increasingly complex samples efficiently and sustainably.