Fast transient signals – getting the most out of multidimensional data

Björn Meermann

Björn Meermann studied chemistry at the University of Münster and received his doctorate in 2009 in analytical chemistry, followed by a two-year postdoctoral stay at the University of Ghent (Belgium). Since June 2019, Björn Meermann has been Head of Division 1.1 “Inorganic Trace Analysis” (ITALab) at the Federal Institute for Materials Research and Testing (BAM) in Berlin. In 2024, Björn Meermann successfully completed his Habilitation in analytical chemistry at Technical University Bergakademie Freiberg and is now associated with the university as Privatdozent. Björn Meermann's research focuses on investigating material–environment interactions based on ICP-MS, speciation analysis, AAS, and single particle/cell-ICP-ToF-MS.

Lyndsey Hendriks

Lyndsey Hendriks obtained her PhD in analytical chemistry from ETH Zurich and spent several years at TOFWERK developing ICP-TOFMS instruments and applying them to real-world challenges. She is now a postdoctoral researcher at the University of Vienna, where her work focuses on high-resolution laser ablation ICP-TOFMS for biological applications, as well as the development of advanced analytical methods for the detection and characterization of micro- and nanoplastics in environmental and biological systems. With over a decade of experience working with TOFs, she remains fascinated by the richness of information these instruments can provide.

Lukas Schlatt

Lukas Schlatt received his PhD in analytical chemistry from the University of Münster in 2020, specialising in advanced mass spectrometry. He joined Nu Instruments the same year as an Application Scientist for the Vitesse ICP TOF MS, contributing to method development and high-speed multi-element analysis workflows. In 2022, he became Product Manager for Vitesse, where he leads product strategy and engages closely with the scientific community to advance ICP TOF MS applications, including nanoparticle analysis and laser ablation imaging. His work focuses on bridging instrument capabilities with emerging analytical challenges.

Time-of-flight (TOF) mass spectrometry, when coupled to an inductively coupled plasma (ICP), has revolutionized elemental and isotopic analysis by delivering what other mass analysers cannot: the ability to acquire nearly the entire periodic table at exceptional speeds. All elements, isotopes, and even polyatomic species from a single ion packet are recorded quasi-simultaneously. Combined with TOF's rapid detection capability allowing full mass spectra to be recorded at >kHz frequency, rich multiplexed datasets are collected. This multiplexing capability is both the technique's greatest strength and its most significant challenge.

This themed collection addresses this duality head-on: the power of comprehensive detection allowing for fundamental insights, new applications, and the complexity of meaningful interpretation.

Across different contributions, with applications ranging from single-particle (artificial and natural), single-cell and laser ablation applications, ICP-TOF-MS enables fundamental studies related to matrix-independent quantification approaches for imaging, as well as the study of size-dependent transport efficiency of nano- and microparticles.

Within all studies, a common theme emerges: robust data pre-treatment and data reduction are essential precursors to multidimensional data interpretation. Calibration, drift correction, and deconvolution steps remain critical for generating reliable signals from millions of transient spectra. Subsequently, TOF datasets require reduction and simplification. In single-particle and single-cell studies, where samples are highly diluted due to the nature of the samples, only a small fraction of recorded data, often less than 1%, contain meaningful analytical information. Machine learning and data-driven methods are increasingly being applied to help classify and interpret complex signals. In view of increasingly complex and highly interdisciplinary scientific questions, new data analysis strategies enable the analysis of correlations between data sets, thereby yielding new insights. Semi-supervised learning, clustering, and self-organizing maps now assist in distinguishing various populations, identifying true particle events, characterizing nanoparticle types, and even inferring physiological states of single cells from subtle compositional differences.

Finally, the contributions collected in this themed collection demonstrate that high-throughput, multidimensional datasets can reveal new layers of information. Open-source platforms, data-centric thinking, and collaborative tool development are reshaping how the community is approaching transient, multiplexed datasets. As the field advances, the focus will increasingly shift from “measuring everything” toward “understanding everything”.

---