Deep neural network analysis of nanoparticle ordering to identify defects in layered carbon materials

Determining the orderliness of carbon materials with nanoparticle imaging and explainable machine learning

Carbon materials have paramount importance in various fields of materials science, from electronic devices to industrial catalysts. The properties of these materials are strongly related to the distribution of defects-irregularities in electron density on their surfaces. Different materials have various distributions and quantities of these defects, which can be imaged using a procedure that involves depositing palladium nanoparticles. The resulting scanning electron microscopy (SEM) images can be characterized by a key descriptor-the ordering of nanoparticle positions. This work presents a highly interpretable machine learning approach for distinguishing between materials with ordered and disordered arrangements of defects marked by nanoparticle attachment. The influence of the degree of ordering was experimentally evaluated on the example of catalysis via chemical reactions involving carbon-carbon bond formation. This represents an important step toward automated analysis of SEM images in materials science.

Automated Recognition of Nanoparticles in Electron Microscopy Images of Nanoscale Palladium Catalysts

Automated computational analysis of nanoparticles is the key approach urgently required to achieve further progress in catalysis, the development of new nanoscale materials, and applications. Analysis of nanoscale objects on the surface relies heavily on scanning electron microscopy (SEM) as the experimental analytic method, allowing direct observation of nanoscale structures and morphology. One of the important examples of such objects is palladium on carbon catalysts, allowing access to various chemical reactions in laboratories and industry. SEM images of Pd/C catalysts show a large number of nanoparticles that are usually analyzed manually. Manual analysis of a statistically significant number of nanoparticles is a tedious and highly time-consuming task that is impossible to perform in a reasonable amount of time for practically needed large amounts of samples. This work provides a comprehensive comparison of various computer vision methods for the detection of metal nanoparticles. In addition, multiple new types of data representations were developed, and their applicability in practice was assessed.

Toward Totally Defined Nanocatalysis: Deep Learning Reveals the Extraordinary Activity of Single Pd/C Particles

Homogeneous catalysis is typically considered "well-defined" 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 10⁹ 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.

Evidence for the “cocktail” nature of platinum-catalyzed alkyne and alkene hydrosilylation reactions

Evidence of the involvement of a “cocktail”-type catalytic system in the alkyne and alkene hydrosilylation reaction in the presence of platinum on a carbon support is reported.

How to Make a Cocktail of Palladium Catalysts with Cola and Alcohol: Heteroatom Doping vs. Nanoscale Morphology of Carbon Supports

Sparkling drinks such as cola can be considered an affordable and inexpensive starting material consisting of carbohydrates and sulfur- and nitrogen-containing organic substances in phosphoric acid, which makes them an excellent precursor for the production of heteroatom-doped carbon materials. In this study, heteroatom-doped carbon materials were successfully prepared in a quick and simple manner using direct carbonization of regular cola and diet cola. The low content of carbon in diet cola allowed reaching a higher level of phosphorus in the prepared carbon material, as well as obtaining additional doping with nitrogen and sulfur due to the presence of sweeteners and caffeine. Effects of carbon support doping with phosphorus, nitrogen and sulfur, as well as of changes in textural properties by ball milling, on the catalytic activity of palladium catalysts were investigated in the Suzuki–Miyaura and Mizoroki–Heck reactions. Contributions of the heteroatom doping and specific surface area of the carbon supports to the increased activity of supported catalysts were discussed. Additionally, the possibility of these reactions to proceed in 40% potable ethanol was studied. Moreover, transformation of various palladium particles (complexes and nanoparticles) in the reaction medium was detected by mass spectrometry and transmission electron microscopy, which evidenced the formation of a cocktail of catalysts in a commercial 40% ethanol/water solution.