Deep-learning-enabled online mass spectrometry of the reaction product of a single catalyst nanoparticle

Henrik Klein Moberg, Giuseppe Abbondanza, Ievgen Nedrygailov, David Albinsson, Joachim Fritzsche and Christoph Langhammer ()
Additional contact information
Henrik Klein Moberg: Chalmers University of Technology
Giuseppe Abbondanza: Chalmers University of Technology
Ievgen Nedrygailov: Chalmers University of Technology
David Albinsson: Chalmers University of Technology
Joachim Fritzsche: Chalmers University of Technology
Christoph Langhammer: Chalmers University of Technology

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract Extracting weak signals from noise is a generic challenge in experimental science. In catalysis, it manifests itself as the need to quantify chemical reactions on nanoscopic surface areas, such as single nanoparticles or even single atoms. Here, we address this challenge by combining the ability of nanofluidic reactors to focus reaction product from tiny catalyst surfaces towards online mass spectrometric analysis with the high capacity of a constrained denoising auto-encoder to discern weak signals from noise. Using CO oxidation and C2H4 hydrogenation on Pd as model reactions, we demonstrate that the catalyst surface area required for online mass spectrometry can be reduced by ≈ 3 orders of magnitude compared to state of the art, down to a single nanoparticle with 0.0072 ± 0.00086 μm2 surface area. These results advocate deep learning to improve resolution in mass spectrometry in general and for online reaction analysis in single-particle catalysis in particular.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-62602-3 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-62602-3

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-62602-3

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().