Automatic recognition of Pd ions in high-resolution mass spectra of multicomponent samples without visible fine isotope structures

Automatic recognition of Pd ions in high-resolution mass spectra of multicomponent samples without visible fine isotope structures Научная публикация

Журнал

Nature Communications
ISSN: 2041-1723

Вых. Данные

Год: 2026, DOI: 10.1038/s41467-026-77431-1

Авторы

Gurevich Pavel E. ^1,2 , Kozlov Konstantin S. ² , Silverstov Artem S. ² , Nersesyan Lev E. ² , Kolomoets Nikita I. ² , Ilyushenkova Valentina V. ² , Burykina Julia V. ² , Boiko Daniil A. ² , Vorozhtsov Artem P. ² , Ananikov Valentine P. ²

Организации

1	Center for Energy Science and Technology, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, bld. 1, Moscow, Russia
2	Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky Prospekt 47, Moscow, Russia

Palladium plays a three-fold role in modern science and technology: it serves as a key active center in modern catalytic synthetic methods, occurs as unwanted trace contamination that influences new catalysts development and can lead to false-positive reports, and is an emerging environmental pollutant from automotive catalysts. High activity at ppm/ppb loadings and the necessity to analyze sub-micromolar concentrations of samples are common challenges in these applications, making reliable Pd identification essential. Mass spectrometry (MS) can detect Pd-containing ions in complex mixtures, but post-spectral recognition is difficult when minor Pd signals overlap with signals from other components and fine isotope structures are not visible. The present study introduces a machine-learning-based (ML) approach featuring a graph algorithm for the separation of signals from different multicharged ions and deep-learning classifiers for the automatic recognition of Pd ions in mass spectra of multi-component mixtures down to 10–8 mol L–1. It is applied to Pd intermediates in catalytic synthesis, contaminant catalysis and environmental samples. The scalability of the approach enables extension to Ni, Cu, Ag, Cl and Br. With the developed ML/MS workflow, Pd and other important elements could be automatically revealed; thus, routine MS hardware could be transformed into high-performance scanners.

Gurevich P.E. , Kozlov K.S. , Silverstov A.S. , Nersesyan L.E. , Kolomoets N.I. , Ilyushenkova V.V. , Burykina J.V. , Boiko D.A. , Vorozhtsov A.P. , Ananikov V.P.
Automatic recognition of Pd ions in high-resolution mass spectra of multicomponent samples without visible fine isotope structures
Nature Communications. 2026. DOI: 10.1038/s41467-026-77431-1 OpenAlex

Поступила в редакцию:	22 мая 2025 г.
Опубликована в печати:	11 сент. 2026 г.

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