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

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.