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The role of machine learning (a part of artificial intelligence—AI) in the diagnosis and treatment of various types of oncology is steadily increasing. It is expected that the use of AI in oncology will speed up both diagnostic and treatment planning processes. This review describes recent applications of machine learning in oncology, including medical image analysis, treatment planning, patient survival prognosis, and the synthesis of drugs at the point of care. The fast and reliable analysis of medical images is of great importance in the case of fast-flowing forms of cancer. The introduction of ML for the analysis of constantly growing volumes of big data makes it possible to improve the quality of prescribed treatment and patient care. Thus, ML is expected to become an essential technology for medical specialists. The ML model has already improved prognostic prediction for patients compared to traditional staging algorithms. The direct synthesis of the necessary medical substances (small molecule mixtures) at the point of care could also seriously benefit from the application of ML. We further review the main trends in the use of artificial intelligence-based technologies in modern oncology. This review demonstrates the future prospects of using ML tools to make progress in cancer research, as well as in other areas of medicine. Despite growing interest in the use of modern computer technologies in medical practice, a number of unresolved ethical and legal problems remain. In this review, we also discuss the most relevant issues among them.

A multichannel microfluidic cell (MFC) obtained using 3D printing for studying the structure of complex solutions by small-angle X-ray scattering (SAXS) is described. MFC was tested at the BioMUR beamline of the Kurchatov synchrotron. A comparative analysis of SAXS signal from the standard capillary and from the developed MFC was carried out, with MFC showing significant advantages. The dynamics of SAXS scattering curves for lysozyme solutions with NaCl precipitant were studied when the protein and precipitant concentrations changed. The obtained time series of data are well consistent with the known data for the lysozyme solution.

Photocatalytic hydrogen (H2) production is a promising route for alternative energetics. Understanding structure–activity relationships is a crucial step towards the rational design of photocatalysts, which requires the application of operando spectroscopy under relevant working conditions. We performed an operando investigation on a catalytic system during the photodeposition of Pt on TiO2 and photostimulated H2 production, using simultaneous laboratory X-ray absorption spectroscopy (XAS), UV–Vis spectroscopy, and mass spectrometry. XAS showed a progressive increase in Pt fluorescence for Pt deposited on TiO2 for over an hour, which is correlated with the signal of the produced H2. The final Pt/TiO2 catalyst contained Pt(0) particles. The electronic features corresponding to the Pt4+ species in the UV–Vis spectrum of the solution disappear as soon as UV radiation is applied in the presence of formic acid, which acts as a hole scavenger, resulting in the presence of Pt(0) particles in solution.

Microfluidic devices have opened new opportunities for functional material chemical synthesis in a few applications. The screening of microfluidic synthesis processes is an urgent task of the experimental process in terms of automation and intellectualization. This study proposes a methodology and software for extracting the morphological and dynamic characteristics of generated monodisperse droplets from video data streams obtained from a digital microscope. For this purpose, the paper considers an approach to generating an extended feature space characterizing the process of droplet generation using a microfluidic device based on the creation of synthetic image datasets. YOLOv7 was used as an algorithm for detecting objects in the images. When training this algorithm, the values in the test dataset mAP@0.5 0.996 were obtained. The algorithms proposed for image processing and analysis implement the basic functionality to extract the morphological and dynamic characteristics of monodisperse droplets in the synthesis process. Laboratory validation and verification of the software demonstrated high results of the identification of key characteristics of the monodisperse droplets generated by the microfluidic device with the average deviation from the real values not exceeding 8%.

In the field of intelligent surface inspection systems, particular attention is paid to decision making problems, based on data from different sensors. The combination of such data helps to make an intelligent decision. In this research, an approach to intelligent decision making based on a data integration strategy to raise awareness of a controlled object is used. In the following article, this approach is considered in the context of reasonable decisions when detecting defects on the surface of welds that arise after the metal pipe welding processes. The main data types were RGB, RGB-D images, and acoustic emission signals. The fusion of such multimodality data, which mimics the eyes and ears of an experienced person through computer vision and digital signal processing, provides more concrete and meaningful information for intelligent decision making. The main results of this study include an overview of the architecture of the system with a detailed description of its parts, methods for acquiring data from various sensors, pseudocodes for data processing algorithms, and an approach to data fusion meant to improve the efficiency of decision making in detecting defects on the surface of various materials.

Microscopic tissue analysis is the key diagnostic method needed for disease identification and choosing the best treatment regimen. According to the Global Cancer Observatory, approximately two million people are diagnosed with colorectal cancer each year, and an accurate diagnosis requires a significant amount of time and a highly qualified pathologist to decrease the high mortality rate. Recent development of artificial intelligence technologies and scanning microscopy introduced digital pathology into the field of cancer diagnosis by means of the whole-slide image (WSI). In this work, we applied deep learning methods to diagnose six types of colon mucosal lesions using convolutional neural networks (CNNs). As a result, an algorithm for the automatic segmentation of WSIs of colon biopsies was developed, implementing pre-trained, deep convolutional neural networks of the ResNet and EfficientNet architectures. We compared the classical method and one-cycle policy for CNN training and applied both multi-class and multi-label approaches to solve the classification problem. The multi-label approach was superior because some WSI patches may belong to several classes at once or to none of them. Using the standard one-vs-rest approach, we trained multiple binary classifiers. They achieved the receiver operator curve AUC in the range of 0.80–0.96. Other metrics were also calculated, such as accuracy, precision, sensitivity, specificity, negative predictive value, and F1-score. Obtained CNNs can support human pathologists in the diagnostic process and can be extended to other cancers after adding a sufficient amount of labeled data.

We have designed, built, and tested two cells for in situ and, potentially, operando X-ray absorption spectroscopy experiments in transmission and fluorescence modes. The cells were developed for high-pressure and high-temperature conditions to study the catalytic processes under relevant industrial conditions. Operation of the cells was tested for Ru and Rh-based homogeneous and heterogeneous catalytic systems. Using synchrotron-based in situ X-ray absorption spectroscopy we tracked the evolution of active metal species during catalytic reactions. Our setup proved that it was capable to investigate liquid-state homogeneous and heterogenous systems under elevated temperatures, high pressures of reactive gasses, and in the presence of corrosive reagents.

Summary: New carbon forms that exhibit extraordinary physicochemical properties can be generated from nanostructured precursors under extreme pressure. Nevertheless, synthesis of such fascinating materials is often not well understood. That is the case of the C60 precursor, with irreproducible results that impede further progress in the materials design. Here, the semiconducting amorphous carbon, having band gaps of 0.1–0.3 eV and the advantages of isotropic superhardness and superior toughness over single-crystal diamond and inorganic glasses, is produced from fullerene at high pressure and moderate temperatures. A systematic investigation of the structure and bonding evolution is carried out with complementary characterization methods, which helps to build a model of the transformation that can be used in further high-pressure/high-temperature (high p,T) synthesis of novel nano-carbon systems for advanced applications. The amorphous carbon materials produced have the potential of accomplishing the demanding optoelectronic applications that diamond and graphene cannot achieve.

In this study, we used density functional theory (DFT) and natural bond orbital (NBO) analysis to determine the structural, electronic, reactivity, and conformational features of 2,5,5-trimethyl-1,3,2-di-heteroatom (X) phosphinane-2-sulfide derivatives (X = O (compound 1), S (compound 2), and Se (compound 3)). We discovered that the features improve dramatically at 6-31G** and B3LYP/6-311+G** levels. The level of theory for the molecular structure was optimized first, followed by the frontier molecular orbital theory development to assess molecular stability and reactivity. Molecular orbital calculations, such as the HOMO–LUMO energy gap and the mapping of molecular electrostatic potential surfaces (MEP), were performed similarly to DFT calculations. In addition, the electrostatic potential of the molecule was used to map the electron density on a surface. In addition to revealing molecules’ size and shape distribution, this study also shows the sites on the surface where molecules are most chemically reactive.

As is known from molecular dynamics simulation, lysozyme oligomers in crystallization solutions are most stable when taking into account as many precipitant ions as possible embedded in the corresponding crystal structure. Therefore, the number of precipitant ions associated with crystallographic oligomer models can play a role during the modeling of small-angle X-ray scattering (SAXS) data. This hypothesis has been tested in the present work. As a result, it turned out that the best fit quality to the experimental SAXS data is reached when using oligomers without precipitant ions at all or with embedded chlorine ions. Molecular dynamics (MD) simulation shows that the stability of crystallization oligomers depends on the consideration of anions and cations in oligomer structure. Thus, it is chlorine ions which stabilize dimer and octamers in lysozyme crystallization solution. As SAXS is more sensitive to the role of cations and MD shows the role of anions which are “light” for X-rays, it has been shown that precipitant cations most likely do not bind to monomers, but to already-formed oligomers.

Single-walled carbon nanotubes (SWCNTs) were functionalized by ferrocene through ethyleneglycol chains of different lengths (FcETGn) and the functionalized SWCNTs (f-SWCNTs) were characterized by different complementary analytical techniques. In particular, high-resolution scanning electron transmission microscopy (HRSTEM) and electron energy loss spectroscopy (EELS) analyses support that the outer tubes of the carbon-nanotube bundles were covalently grafted with FcETGn groups. This result confirms that the electrocatalytic effect observed during the oxidation of the reduced form of nicotinamide adenine dinucleotide (NADH) co-factor by the f-SWCNTs is due to the presence of grafted ferrocene derivatives playing the role of a mediator. This work clearly proves that residual impurities present in our SWCNT sample (below 5 wt. %) play no role in the electrocatalytic oxidation of NADH. Moreover, molecular dynamic simulations confirm the essential role of the PEG linker in the efficiency of the bioelectrochemical device in water, due to the favorable interaction between the ETG units and water molecules that prevents π-stacking of the ferrocene unit on the surface of the CNTs. This system can be applied to biosensing, as exemplified for glucose detection. The well-controlled and well-characterized functionalization of essentially clean SWCNTs enabled us to establish the maximum level of impurity content, below which the f-SWCNT intrinsic electrochemical activity is not jeopardized.

The attempts to develop efficient methods of solar energy conversion into chemical fuel are ongoing amid climate changes associated with global warming. Photo-electrocatalytic (PEC) water splitting and CO2 reduction reactions show high potential to tackle this challenge. However, the development of economically feasible solutions of PEC solar energy conversion requires novel efficient and stable earth-abundant nanostructured materials. The latter are hardly available without detailed understanding of the local atomic and electronic structure dynamics and mechanisms of the processes occurring during chemical reactions on the catalyst–electrolyte interface. This review considers recent efforts to study photo-electrocatalytic reactions using in situ and operando synchrotron spectroscopies. Particular attention is paid to the operando reaction mechanisms, which were established using X-ray Absorption (XAS) and X-ray Photoelectron (XPS) Spectroscopies. Operando cells that are needed to perform such experiments on synchrotron are covered. Classical and modern theoretical approaches to extract structural information from X-ray Absorption Near-Edge Structure (XANES) spectra are discussed.

Herein we report the development of a nanocomposite for X-ray-induced photodynamic therapy (X-PDT) and computed tomography (CT) based on PEG-capped GdF3:Tb3+ scintillating nanoparticles conjugated with Rose Bengal photosensitizer via electrostatic interactions. Scintillating GdF3:Tb3+ nanoparticles were synthesized by a facile and cost-effective wet chemical precipitation method. All synthesized nanoparticles had an elongated “spindle-like” clustered morphology with an orthorhombic structure. The structure, particle size, and morphology were determined by transmission electron microscopy (TEM), X-ray diffraction (XRD), and dynamic light scattering (DLS) analysis. The presence of a polyethylene glycol (PEG) coating and Rose Bengal conjugates was proved by Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TG), and ultraviolet–visible (UV-vis) analysis. Upon X-ray irradiation of the colloidal PEG-capped GdF3:Tb3+–Rose Bengal nanocomposite solution, an efficient fluorescent resonant energy transfer between scintillating nanoparticles and Rose Bengal was detected. The biodistribution of the synthesized nanoparticles in mice after intravenous administration was studied by in vivo CT imaging.

Artificial intelligence (AI) approaches continue to spread in almost every research and technology branch. However, a simple adaptation of AI methods and algorithms successfully exploited in one area to another field may face unexpected problems. Accelerating the discovery of new functional materials in chemical self-driving laboratories has an essential dependence on previous experimenters’ experience. Self-driving laboratories help automate and intellectualize processes involved in discovering nanomaterials with required parameters that are difficult to transfer to AI-driven systems straightforwardly. It is not easy to find a suitable design method for self-driving laboratory implementation. In this case, the most appropriate way to implement is by creating and customizing a specific adaptive digital-centric automated laboratory with a data fusion approach that can reproduce a real experimenter’s behavior. This paper analyzes the workflow of autonomous experimentation in the self-driving laboratory and distinguishes the core structure of such a laboratory, including sensing technologies. We propose a novel data-centric research strategy and multilevel data flow architecture for self-driving laboratories with the autonomous discovery of new functional nanomaterials.

Here, we report a new photosensitive metal–organic framework (MOF) that was constructed via the modification of UiO-66-NH2 with diarylethene molecules (DAE, 4-(5-Methoxy-1,2-dimethyl-1H-indol-3-yl)-3-(2,5-dimethylthiophen-3-yl)-4-furan-2,5-dione). The material that was obtained was a highly crystalline porous compound. The photoresponse of the modified MOF was observed via UV–Vis and IR spectroscopy. Most of the DAE molecules inside of the UiO-66-pores had an open conformation after synthesis. However, the equilibrium was able to be shifted further toward an open conformation using visible light irradiation with a wavelength of 520 nm. Conversely, UV-light with a wavelength of 450 nm initiated the transformation of the photoresponsive moieties inside of the pores to a closed modification. We have shown that this transformation could be used to stimulate hydrogen adsorption–desorption processes. Specifically, visible light irradiation increased the H2 capacity of modified MOF, while UV-light decreased it. A similar hybrid material with DAE moieties in the UiO-66 scaffold was applied for hydrogen storage for the first time. Additionally, the obtained results are promising for smart H2 storage that is able to be managed via light stimuli.

Nanoscience enables researchers to develop new and cost-effective nanomaterials for energy, healthcare, and medical applications. Silver nanoparticles (Ag NPs) are currently increasingly synthesized for their superior physicochemical and electronic properties. Good knowledge of these characteristics allows the development of applications in all sensitive and essential fields in the service of humans and the environment. This review aims to summarize the Ag NPs synthesis methods, properties, applications, and future challenges. Generally, Ag NPs can be synthesized using physical, chemical, and biological routes. Due to the great and increasing demand for metal and metal oxide nanoparticles, researchers have invented a new, environmentally friendly, inexpensive synthetic method that replaces other methods with many defects. Studies of Ag NPs have increased after clear and substantial support from governments to develop nanotechnology. Ag NPs are the most widely due to their various potent properties. Thus, this comprehensive review discusses the different synthesis procedures and electronic applications of Ag NPs.

In this study, silver/silver oxide nanoparticles (Ag/Ag2O NPs) were successfully biosynthesized using Phoenix dactylifera L. aqueous leaves extract. The effect of different plant extract/precursor contractions (volume ratio, v/v%) on Ag/Ag2O NP formation, their optical properties, and photocatalytic activity towards azo dye degradation, i.e., Congo red (CR) and methylene blue (MB), were investigated. X-ray diffraction confirmed the crystalline nature of Ag/Ag2O NPs with a crystallite size range from 28 to 39 nm. Scanning electron microscope images showed that the Ag/Ag2O NPs have an oval and spherical shape. UV–vis spectroscopy showed that Ag/Ag2O NPs have a direct bandgap of 2.07–2.86 eV and an indirect bandgap of 1.60–1.76 eV. Fourier transform infrared analysis suggests that the synthesized Ag/Ag2O NPs might be stabilized through the interactions of -OH and C=O groups in the carbohydrates, flavonoids, tannins, and phenolic acids present in Phoenix dactylifera L. Interestingly, the prepared Ag/Ag2O NPs showed high catalytic degradation activity for CR dye. The photocatalytic degradation of the azo dye was monitored spectrophotometrically in a wavelength range of 250–900 nm, and a high decolorization efficiency (84.50%) was obtained after 50 min of reaction. As a result, the use of Phoenix dactylifera L. aqueous leaves extract offers a cost-effective and eco-friendly method.

Abstract Pathway activity assessment-based approaches are becoming highly influential in various fields of biology and medicine. However, these approaches mostly rely on analysis of mRNA expression, and total mRNA from a given locus is measured in the majority of cases. Notably, a significant portion of protein-coding genes produces more than one transcript. This biological fact is responsible for significant noise when changes in total mRNA transcription of a single gene are analyzed. The NFE2L2/AP-1 pathway is an attractive target for biomedical applications. To date, there is a lack of data regarding the agreement in expression of even classical target genes of this pathway. In the present paper we analyzed whether transcript variants of GPX2, NQO1 and SQSTM1 were characterized by individual features of expression when HeLa cells were exposed to pro-oxidative stimulation with hydrogen peroxide. We found that all the transcripts (10 in total) appeared to be significantly individually regulated under the conditions tested. We conclude that individual transcripts, rather than total mRNA, are best markers of pathway activation. We also discuss here some biological roles of individual transcript regulation.

The paper discusses the prospects for connecting the universities to use Mega- Science research facilities as an important tool for the integration of world level science and education. The need for such integration of leading universities with research organizations was noted in the RF Government Decision of 19 June 2015 on the results of the joint meeting of the boards of trustees of the Southern and Siberian Federal University.

We report a series of Pd K-edge and Pt L3-edge X-ray absorption spectra (XAS) collected in situ during thermal treatment of functionalized UiO-67-Pd and UiO-67-Pt metal-organic frameworks in inert and reducing atmospheres. We present raw synchrotron data from three subsequent experiments at different beamlines, normalized XAS spectra and k2-weighted oscillatory χ(k) functions extracted from one of the datasets. Pd K-edge spectra were collected for the samples in 5% H2/He, 3% H2/He and pure He in the temperature range from room temperature (RT) to 450 °C. Pt L3-edge were collected for the samples in 3% H2/He, 10% H2/He and pure He in the temperature range from RT to 300 °C. All spectra are reported together with the used atmosphere and temperature. For the analysis of all reported datasets, please see “Evolution of Pt and Pd species in functionalized UiO-67 metal-organic frameworks”. Fourier-analysis of Pd K-edge is reported in “Formation and growth of Pd nanoparticles in UiO-67 MOF by in situ EXAFS”. Keywords: EXAFS, XANES, MOFs, Nanoparticles

We report the series of Pd K-edge X-ray absorption spectra collected during hydrogenation of ethylene with variable ethylene/hydrogen ratio over carbon supported palladium nanoparticles. The data presented in this article includes normalized X-ray absorption spectra, k2-weighted oscillatory χ(k) functions extracted from the extended X-ray absorption fine structure (EXAFS) and k2-weighted Fourier-transformed EXAFS data, χ(R). Each spectrum is reported together with the hydrogen, ethylene and helium flow rates, adjusted during its collection. In addition, time evolution of the ratio of m/Z signals of 30 and 28 registered by online mass spectrometer is presented. The data analysis is reported in Bugaev et al., Catal. Today, 2019 [1].

Innovations often play an essential role in the acceleration of the new functional materials discovery. The success and applicability of the synthesis results with new chemical compounds and materials largely depend on the previous experience of the researcher himself and the modernity of the equipment used in the laboratory. Artificial intelligence (AI) technologies are the next step in developing the solution for practical problems in science, including the development of new materials. Those technologies go broadly beyond the borders of a computer science branch and give new insights and practical possibilities within the far areas of expertise and chemistry applications. One of the attractive challenges is an automated new functional material synthesis driven by AI. However, while having many years of hands-on experience, chemistry specialists have a vague picture of AI. To strengthen and underline AI’s role in materials discovery, a short introduction is given to the essential technologies, and the machine learning process is explained. After this review, this review summarizes the recent studies of new strategies that help automate and accelerate the development of new functional materials. Moreover, automatized laboratories’ self-driving cycle could benefit from using AI algorithms to optimize new functional nanomaterials’ synthetic routes. Despite the fact that such technologies will shape material science in the nearest future, we note the intelligent use of algorithms and automation is required for novel discoveries.

Synthesis of the MIL-100 metal-organic framework particles was carried out by hydrothermal (HT) and microwave (MW)-assisted methods. Transmission electron microscopy showed formation of microparticles in the course of hydrothermal synthesis and nanoparticles for microwave-assisted synthesis. Powder X-ray diffraction confirmed formation of larger crystallites for hydrothermal synthesis. Particle aggregation in aqueous solution was observed by dynamic light scattering. However, the stability of both samples could be improved in acetic acid solution. Nitrogen sorption isotherms showed high porosity of the particles. ᶫ-leucine molecule was used as a model molecule for loading in the porous micro- and nanoparticles. Loading was estimated by FTIR spectroscopy and thermogravimetric analysis. UV-VIS spectroscopy quantified ᶫ-leucine release from the particles in aqueous solution. Cytotoxicity studies using the HeLa cell model showed that the original particles were somewhat toxic, but ᶫ-leucine loading ameliorated the toxic effects, likely due to signaling properties of the amino acid.

We report here fast (15 min) microwave-assisted solvothermal synthesis of zeolitic imidazolate framework material (ZIF-7). We have optimized solvent composition to achieve high porosity and hydrogen capacity and narrow particle size distribution. It was shown that synthesis in N,N-diethylformamide (DEF) results in a layered ZIF-7 III phase, while N,N-dimethylformamide (DMF) as solvent leads to a pure ZIF-7 phase in microwave conditions. A mixture of toluene with DMF allows the production of pure ZIF-7 material only with the triethylamine additive. Obtained materials were comprehensively characterized. We have pointed out that both X-ray diffraction and infrared spectroscopy could be used for the identification of ZIF-7 or ZIF-7 III phases. Although samples obtained in DMF, and in a mixture of DMF, toluene, and triethylamine were assigned to the pure ZIF-7 phase, solvent composition significantly affected the size of particles in the material and nitrogen and hydrogen adsorption process.

Adsorption of ethylene on palladium, a key step in various catalytic reactions, may result in a variety of surface-adsorbed species and formation of palladium carbides, especially under industrially relevant pressures and temperatures. Therefore, the application of both surface and bulk sensitive techniques under reaction conditions is important for a comprehensive understanding of ethylene interaction with Pd-catalyst. In this work, we apply in situ X-ray absorption spectroscopy, X-ray diffraction and infrared spectroscopy to follow the evolution of the bulk and surface structure of an industrial catalysts consisting of 2.6 nm supported palladium nanoparticles upon exposure to ethylene under atmospheric pressure at 50 °C. Experimental results were complemented by ab initio simulations of atomic structure, X-ray absorption spectra and vibrational spectra. The adsorbed ethylene was shown to dehydrogenate to C2H3, C2H2 and C2H species, and to finally decompose to palladium carbide. Thus, this study reveals the evolution pathway of ethylene on industrial Pd-catalyst under atmospheric pressure at moderate temperatures, and provides a conceptual framework for the experimental and theoretical investigation of palladium-based systems, in which both surface and bulk structures exhibit a dynamic nature under reaction conditions.

We report here a simple two-stage synthesis of zinc–cobalt oxide nanoparticles. We used Zn/Co-zeolite imidazolate framework (ZIF)-8 materials as precursors for annealing and optional impregnation with a silicon source for the formation of a protective layer on the surface of oxide nanoparticles. Using bimetallic ZIFs allowed us to trace the phase transition of the obtained oxide nanoparticles from wurtzite ZnO to spinel Co3O4 structures. Using (X-ray diffraction) XRD and (X-ray Absorption Near Edge Structure) XANES techniques, we confirmed the incorporation of cobalt ions into the ZnO structure up to 5 mol.% of Co. Simple annealing of Zn/Co-ZIF-8 materials in the air led to the formation of oxide nanoparticles of about 20–30 nm, while additional treatment of ZIFs with silicon source resulted in nanoparticles of about 5–10 nm covered with protective silica layer. We revealed the incorporation of oxygen vacancies in the obtained ZnO nanoparticles using FTIR analysis. All obtained samples were comprehensively characterized, including analysis with a synchrotron radiation source.

The formation of palladium hydrides is a well-known phenomenon, observed for both bulk and nanosized samples. The kinetics of hydrogen adsorption/desorption strongly depends on the particle size and shape, as well as the type of support and/or coating of the particles. In addition, the structural properties of hydride phases and their distribution also depend on the particle size. In this work, we report on the in situ characterization of palladium nanocubes coated with HKUST-1 metal-organic framework (Pd@HKUST-1) during desorption of hydrogen by means of synchrotron-based time-resolved X-ray powder diffraction. A slower hydrogen desorption, compared to smaller sized Pd nanoparticles was observed. Rietveld refinement of the time-resolved data revealed the remarkable stability of the lattice parameters of α- and β-hydride phases of palladium during the α- to β- phase transition, denoting the behavior more similar to the bulk materials than nanoparticles. The stability in the crystal sizes for both α- and β-hydride phases during the phase transition indicates that no sub-domains are formed within a single particle during the phase transition.

The size and shape distribution of metal nanoparticles (NPs) are important parameters that need to be tuned in order to achieve desired properties of materials for practical applications. In the current work, we present the synthesis of palladium NPs supported on silica by three different methods, applying reduction by sodium borohydride, hydrazine vapors, and polyethylene glycol (PEG). The synthesized materials were characterized by X-ray diffraction, X-ray fluorescence, transmission electron microscopy, surface area and porosity measurements, and thermogravimetric analysis. Similar nanoparticle sizes with narrow size distribution centered at 8 nm were obtained after reduction by sodium borohydride and hydrazine vapors, whereas the smallest particle size of about 4.8 nm was obtained after reduction by PEG. The effect of modification of the initial palladium chloride compound by ammonium hydroxide was found to lead to the formation of larger particles with average size of 15 nm and broader size distribution. In addition, the process of the reduction of palladium by PEG at different reduction stages was monitored by UV-Vis spectroscopy. CO-stripping voltammetry showed that reduction in hydrazine and in PEG allowed the preparation of Pd NPs with high electrochemically-active surface area. Such NPs are promising materials for electrocatalysis.