How AI, Miniaturization, and Automation Are Reshaping Gas Chromatography

Nicholas H. Snow, recipient of the 2025 EAS Award for Outstanding Achievements in Separation Science, has spent over three decades advancing gas chromatography (GC) and GC–MS, contributing more than 100 publications that span both fundamental theory and practical application. Snow is widely recognized for his insight into emerging trends in separation science, identifying artificial intelligence (AI), miniaturization, and automation as the three forces currently reshaping the field.

According to Snow, AI is becoming increasingly integral to modern GC workflows. Computer-aided method development, enhanced by AI, allows analysts to optimize complex protocols with greater efficiency. In mass spectrometry, AI-assisted tools are proving particularly valuable for spectral interpretation, helping scientists deconvolute complex samples with speed and accuracy that was previously unattainable. These innovations enable researchers to focus on higher-level analysis while ensuring reliable results.

Miniaturization represents another transformative trend. Driven by the desire for greener instrumentation, smaller laboratory footprints, and reduced energy consumption, miniaturized platforms can deliver performance comparable to full-scale research instruments. Snow encourages analysts to consider these smaller, more efficient systems for routine analyses, highlighting their potential to reduce cost, waste, and operational complexity without compromising analytical quality.

Automation also continues to advance, streamlining workflows and improving reproducibility. Snow acknowledges that high upfront costs can pose challenges, as automated systems may rival the price of the GC instrument itself. Nonetheless, he sees automation as central to the evolution of laboratory workflows, enabling analysts to manage larger sample volumes and increasingly complex analyses with consistency and reliability.

While GC instrumentation has evolved dramatically, Snow emphasizes that the fundamental principles of separation remain unchanged since the 1950s. He envisions the future of GC as a careful combination of these strong fundamentals with next-generation tools—smarter, smaller, and more automated systems that allow laboratories to tackle complex samples efficiently, sustainably, and with high confidence in data quality.