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.

Using the FDA's Century Mix, we developed a robust QA/QC system for untargeted GC×GC-TOFMS metabolomics, tracking 100 compounds to improve standardization, reproducibility, and long-term performance.

Automated LC and 2D-LC method development is key to wider adoption, but major challenges remain. This talk presents new algorithms and strategies to optimize separations using modeling and machine learning.

Ultrafine particles (UFP) <100 nm pose health risks due to deep lung penetration. This study uses DTD-GC×GC-TOFMS to reveal PAH profiles in ambient air UFP sampled in Augsburg, Germany, in 2023.

In this presentation, we will discuss the outcome of applying RPLC × HILIC hyphenated to UV, IMS and HRMS, to support the compound identification.