Solvolysis and microwave-assisted extraction were used to recover materials from wind turbine blades. GC×GC-TOFMS revealed ~60 compounds, aiding sustainable recycling strategies.

First-time quantitative analysis of NSO-HETs in fuels using GC×GC-ToFMS after LC fractionation. The method shows high precision, accuracy, and applicability to real fuel samples.

With high cannabis use among Canadians, this study examines the composition of vape and smoke, their effects on lung cells, and third-hand exposure risks via fabric-absorbed residues.

GC×GC-HRTOFMS enables precise identification of trace-level odorous compounds in complex VOC profiles, supporting material emission characterization and odor reduction strategies in recycled products.

Non-target screening of indoor dust revealed 2500+ contaminants, including phthalates and COVID-19-related chemicals. Samples from 4 cities show distinct pollutant profiles. Workflow under development.

Using GC×GC and statistical tools, researchers highlight differences between alternative and petroleum-based jet fuels, focusing on minor aromatics and heteroatom-containing compounds.

Peak detection is crucial in GC×GC & LC×LC workflows. We study how signal features impact detection performance, helping select tools for both manual and automated method development.

The integration of chromatographic and statistical approaches in GC×GC-MS enhances compound separation, data interpretation, and reliability—crucial for complex sample analysis across many fields.

We developed an AI-based EI mass spectral database with 120 million predicted spectra to improve unknown compound identification in GC×GC-MS beyond commercial library limits.

LC-GC×GC-FID/TOFMS enhances MOSH/MOAH analysis in food ingredients and additives, helping to resolve coelutions and reduce interpretation errors beyond current ISO 20122:2024 limitations.