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.

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.

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

Smoothness/defectiveness of the carbon material surface is a key issue for many applications, spanning from electronics to reinforced materials, adsorbents and catalysis. Several surface defects cannot be observed with conventional analytic techniques, thus requiring the development of a new imaging approach. Here, we evaluate a convenient method for mapping such "hidden" defects on the surface of carbon materials using 1-5 nm metal nanoparticles as markers. A direct relationship between the presence of defects and the ordering of nanoparticles was studied experimentally and modeled using quantum chemistry calculations and Monte Carlo simulations. An automated pipeline for analyzing microscopic images is described: the degree of smoothness of experimental images was determined by a classification neural network, and then the images were searched for specific types of defects using a segmentation neural network. An informative set of features was generated from both networks: high-dimensional embeddings of image patches and statics of defect distribution.

Electron microscopy dataset for the recognition of nanoscale ordering effects and location of nanoparticles.

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.

Systematic Study of the Behavior of Different Metal and Metal-Containing Particles under the Microwave Irradiation and Transformation of Nanoscale and Microscale Morphology.

In recent years, the application of microwave (MW) irradiation has played an increasingly important role in the synthesis and development of high performance nanoscale catalytic systems. However, the interaction of microwave irradiation with solid catalytic materials and nanosized structures remains a poorly studied topic. In this paper we carried out a systematic study of changes in morphology under the influence of microwave irradiation on nanoscale particles of various metals and composite particles, including oxides, carbides, and neat metal systems. All systems were studied in the native solid form without a solvent added. Intensive absorption of microwave radiation was observed for many samples, which in turn resulted in strong heating of the samples and changes in their chemical structure and morphology. A comparison of two very popular catalytic materials-metal particles (M) and supported metal on carbon (M/C) systems-revealed a principal difference in their behavior under microwave irradiation. The presence of carbon support influences the heating mechanism; the interaction of substances with the support during the heating is largely determined by heat transfer from the carbon. Etching of the carbon surface, involving the formation of trenches and pits on the surface of the carbon support, were observed for various types of the investigated nanoparticles.

Dynamic Behavior of Metal Nanoparticles in Pd/C and Pt/C Catalytic Systems under Microwave and Conventional Heating.

Metal on carbon catalysts (M/C) are ubiquitously used in modern research and industry to carry out a variety of chemical transformations. Stable metal-support frameworks and inertness of the carbon materials are usually taken for granted in these very useful catalytic systems. Initially, the present study was aimed to increase the efficiency of Pd/C and Pt/C catalytic systems under microwave and conventional heating. Interestingly, a dynamic behavior of the metal nanoparticles was revealed, and a series of carbon support transformations occurred during the thermal treatments of the catalysts. Microwave and thermal heating of the M/C catalysts resulted in substantial transformations of the carbon supports via the formation of pits, trenches, nanofibers, and nanowalls. Detailed studies with field-emission scanning electron microscopy were carried out involving statistical averaging over large surface areas. The effects of the dynamic behaviors of the supported metal particles on the catalytic activities of the synthetically useful Mizoroki-Heck and Suzuki-Miyaura reactions were demonstrated. Revealed dynamic behavior and modification of the carbon support due to microwave treatment were observed in a number of M/C systems (M = Pd, Pt, Ni, Co, Cu, Fe, and Au).