Mass spectrometry (MS) is a convenient, highly sensitive, and reliable method for the analysis of complex mixtures, which is vital for materials science, life sciences fields such as metabolomics and proteomics, and mechanistic research in chemistry. Although it is one of the most powerful methods for individual compound detection, complete signal assignment in complex mixtures is still a great challenge. The unconstrained formula-generating algorithm, covering the entire spectra and revealing components, is a "dream tool" for researchers. We present the framework for efficient MS data interpretation, describing a novel approach for detailed analysis based on deisotoping performed by gradient-boosted decision trees and a neural network that generates molecular formulas from the fine isotopic structure, approaching the long-standing inverse spectral problem. The methods were successfully tested on three examples: fragment ion analysis in protein sequencing for proteomics, analysis of the natural samples for life sciences, and study of the cross-coupling catalytic system for chemistry.

An approach to the spatially localized characterization of supported catalysts over a reaction course is proposed. It consists of a combination of scanning, transmission, and high-resolution scanning transmission electron microscopy to determine metal particles from arrays of surface nanoparticles to individual nanoparticles and individual atoms. The study of the evolution of specific metal catalyst particles at different scale levels over time, particularly before and after the cross-coupling catalytic reaction, made it possible to approach the concept of 4D catalysis–tracking the positions of catalytic centers in space (3D) over time (+1D). The dynamic behavior of individual palladium atoms and nanoparticles in cross-coupling reactions was recorded with nanometer accuracy via the precise localization of catalytic centers. Single atoms of palladium leach out into solution from the support under the action of the catalytic system, where they exhibit extremely high catalytic activity compared to surface metal nanoparticles. Monoatomic centers, which make up only approximately 1% of palladium in the Pd/C system, provide more than 99% of the catalytic activity. The remaining palladium nanoparticles changed their shape and could move over the surface of the support, which was recorded by processing images of the array of nanoparticles with a neural network and aligning them using automatically detected keypoints. The study reveals a novel opportunity for single-atom catalysiseasier detachment (capture) from (on) the carbon support surface is the origin of superior catalytic activity, rather than the operation of single atomic catalytic centers on the surface of the support, as is typically assumed.

The product of a revealed transformation—NHC‐ethynyl coupling—was observed as a catalyst transformation pathway in the Sonogashira cross‐coupling, catalyzed by Pd/NHC complexes. The 2‐ethynylated azolium salt was isolated in individual form and fully characterized, including X‐ray analysis. A number of possible intermediates of this transformation with common formulae (NHC)nPd(C2Ph) (n=1,2) were observed and subjected to collision‐induced dissociation (CID) and infrared multiphoton dissociation (IRMPD) experiments to elucidate their structure. Measured bond dissociation energies (BDEs) and IRMPD spectra were in an excellent agreement with quantum calculations for coupling product π‐complexes with Pd0. Molecular dynamics simulations confirmed the observed multiple CID fragmentation pathways. An unconventional methodology to study catalyst evolution suggests the reported transformation to be considered in the development of new catalytic systems for alkyne functionalization reactions.

Homogeneous catalysis is typically considered "welldefined" from the standpoint of catalyst structure unambiguity. In contrast, heterogeneous nanocatalysis often falls into the realm of "poorly defined" systems. Supported catalysts are difficult to characterize due to their heterogeneity, variety of morphologies, and large size at the nanoscale. Furthermore, an assortment of active metal nanoparticles examined on the support are negligible compared to those in the bulk catalyst used. To solve these challenges, we studied individual particles of the supported catalyst. We made a significant step forward to fully characterize individual catalyst particles. Combining a nanomanipulation technique inside a field-emission scanning electron microscope with neural network analysis of selected individual particles unexpectedly revealed important aspects of activity for widespread and commercially important Pd/C catalysts. The proposed approach unleashed an unprecedented turnover number of 10 9 attributed to individual palladium on a nanoglobular carbon particle. Offered in the present study is the Totally Defined Catalysis concept that has tremendous potential for the mechanistic research and development of high-performance catalysts.

A unique ordering effect has been observed in functional catalytic nanoscale materials. Instead of randomly arranged binding to the catalyst surface, metal nanoparticles show spatially ordered behavior resulting in formation of geometrical patterns. Understanding of such nanoscale materials and analysis of corresponding microscopy images will never be comprehensive without appropriate reference datasets. Here we describe the first dataset of electron microscopy images comprising individual nanoparticles which undergo ordering on a surface towards the formation of geometrical patterns. The dataset developed in this study spans three levels of nanoscale organization: (i) individual nanoparticles (1-5 nm) and arrays of nanoparticles (5-20 nm), (ii) ordering effects (20-200 nm) and (iii) complex patterns (from nm to μm scales). The described dataset for the first time provides a possibility for the development of machine learning algorithms to study the unique phenomena of nanoparticles ordering and hierarchical organization.