Ionization studies

The LC/MS peak heights or areas are weakly related to the concentration of the compounds due to two reasons. Firstly, different compounds ionize to a very different extent in ESI source. The differences more than six orders of magnitude have been pinpointed. Secondly, the ionization efficiency of the compounds may be strongly altered by the co-eluting compounds. This effect is known as matrix effect and mostly results in an ionization suppression. Therefore, today the only way to quantify compounds with mass spectrometry is via the help of the standard substances.

The aim of our work is focused on revealing the ionization mechanisms, predicting ionization efficiencies and matrix effect.

Read more about the recent (semi/)quantification for non/targeted LC/HRMS analysis.

Look at the video!

Synthesis in charged nanodroplets

Synthesis of new compounds is essential for a range of technologies from material science to medicine. An ideal synthesis is fast, cheap, easily scalable, selective, environmentally friendly, and produces desired substances with high yield. Charged  nanodroplets  have  been  used  to  protonate  compounds  due  to  their  superacidic properties  and, recently,  to  conduct  and  accelerate  organic  reactions.  It has been observed that several reactions are accelerated in charged droplets and sometimes new products, compared to the solution-phase syntheses, have been observed.

In our research we are addressing the site selectivity of the reactions carried out in charged nanodroplets.

Research funding

RapMixTox: Rapid and automated prediction of complex mixture toxicity by nontarget instrumental analysis

Anneli Kruve, Magnus Breitholtz, Jonathan Martin, Matthew MacLeod

FORMAS 2021 – 2023

Tandem development of waste textile recycling process and chemical screening for a non-toxic Re:Start

Aji Pallikunnel Mathew & Anneli Kruve

FORMAS 2021 – 2023

Quantification of emerging contaminants with liquid chromatography high resolution mass spectrometry and graph-based machine learning

Anneli Kruve & Meelis Kull

VR 2022 – 2025

Interlaboratory comparison of (semi-)quantitative LC/HRMS non-targeted screening

Anneli Kruve & Nikolaos S. Thomaidis

NORMAN 2020 – 2022