Machine Learning Meets Cancer.

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.

Structural transition and migration of incoherent twin boundary in diamond.

Grain boundaries (GBs), with their diversity in both structure and structural transitions, play an essential role in tailoring the properties of polycrystalline materials1-5. As a unique GB subset, {112} incoherent twin boundaries (ITBs) are ubiquitous in nanotwinned, face-centred cubic materials6-9. Although multiple ITB configurations and transitions have been reported7,10, their transition mechanisms and impacts on mechanical properties remain largely unexplored, especially in regard to covalent materials. Here we report atomic observations of six ITB configurations and structural transitions in diamond at room temperature, showing a dislocation-mediated mechanism different from metallic systems11,12. The dominant ITBs are asymmetric and less mobile, contributing strongly to continuous hardening in nanotwinned diamond13. The potential driving forces of ITB activities are discussed. Our findings shed new light on GB behaviour in diamond and covalent materials, pointing to a new strategy for development of high-performance, nanotwinned materials.

A heterostructural MoS2QDs@UiO-66 nanocomposite for the highly efficient photocatalytic degradation of methylene blue under visible light and simulated sunlight.

The development of recyclable photocatalysts with high activity and stability has piqued the interest of researchers in the field of wastewater treatment. In this study, an ultrasonic probe approach was used to immerse a sequence of heterojunctions formed by metal-organic frameworks (UiO-66) and different amounts of molybdenum disulfide quantum dots (MoS2QDs), resulting in a highly recyclable MoS2QDs@UiO-66 photocatalyst. Multiple advanced techniques, such as XPS, XRD, TEM, XRF, and UV-vis spectrophotometry, were used to characterize and confirm the successful preparation of UIO-66 impregnated with MoS2QDs. The results indicated that the best heterostructure catalyst exhibited superior efficiency in the photocatalytic degradation of methylene blue (MB) in water, achieving approximately 99% removal within 30 minutes under simulated sunlight, while approximately 97% removal under visible light. The outstanding photocatalytic performance is predominantly attributed to the photoinduced separation of carriers in this heterostructure system. This study proposes a unique, simple, and low-cost method for improving the degradation performance of organic contaminants in water.

Ultrastrong conductive in situ composite composed of nanodiamond incoherently embedded in disordered multilayer graphene.

Traditional ceramics or metals cannot simultaneously achieve ultrahigh strength and high electrical conductivity. The elemental carbon can form a variety of allotropes with entirely different physical properties, providing versatility for tuning mechanical and electrical properties in a wide range. Here, by precisely controlling the extent of transformation of amorphous carbon into diamond within a narrow temperature-pressure range, we synthesize an in situ composite consisting of ultrafine nanodiamond homogeneously dispersed in disordered multilayer graphene with incoherent interfaces, which demonstrates a Knoop hardness of up to ~53 GPa, a compressive strength of up to ~54 GPa and an electrical conductivity of 670-1,240 S m-1 at room temperature. With atomically resolving interface structures and molecular dynamics simulations, we reveal that amorphous carbon transforms into diamond through a nucleation process via a local rearrangement of carbon atoms and diffusion-driven growth, different from the transformation of graphite into diamond. The complex bonding between the diamond-like and graphite-like components greatly improves the mechanical properties of the composite. This superhard, ultrastrong, conductive elemental carbon composite has comprehensive properties that are superior to those of the known conductive ceramics and C/C composites. The intermediate hybridization state at the interfaces also provides insights into the amorphous-to-crystalline phase transition of carbon.

Integrated Video and Acoustic Emission Data Fusion for Intelligent Decision Making in Material Surface Inspection System.

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.

Glycopolymer-Based Materials: Synthesis, Properties, and Biosensing Applications.

Glycopolymer materials have emerged as a significant biopolymer class that has piqued the scientific community's attention due to their potential applications. Recently, they have been found to be a unique synthetic biomaterial; glycopolymer materials have also been used for various applications, including direct therapeutic methods, medical adhesives, drug/gene delivery systems, and biosensor applications. Therefore, for the next stage of biomaterial research, it is essential to understand current breakthroughs in glycopolymer-based materials research. This review discusses the most widely utilized synthetic methodologies for glycopolymer-based materials, their properties based on structure-function interactions, and the significance of these materials in biosensing applications, among other topics. When creating glycopolymer materials, contemporary polymerization methods allow precise control over molecular weight, molecular weight distribution, chemical activity, and polymer architecture. This review concludes with a discussion of the challenges and complexities of glycopolymer-based biosensors, in addition to their potential applications in the future.

Structural, Electronic, Reactivity, and Conformational Features of 2,5,5-Trimethyl-1,3,2-diheterophosphinane-2-sulfide, and Its Derivatives: DFT, MEP, and NBO Calculations.

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.

Operando Photo-Electrochemical Catalysts Synchrotron Studies.

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.

Rational Functionalization of UiO-66 with Pd Nanoparticles: Synthesis and In Situ Fourier-Transform Infrared Monitoring.

Functionalization of metal-organic frameworks (MOFs) with noble metal nanoparticles (NPs) is a challenging task. Conventional impregnation by metals often leads to agglomerates on the surface of MOF crystals. Functional groups on linkers interact with metal precursors and promote the homogeneous distribution of NPs in the pores of MOFs, but their uncontrolled localization can block channels and thus hinder mass transport. To overcome this problem, we created nucleation centers only in the defective pores of the UiO-66 MOF via the postsynthesis exchange. First, we have introduced defects into UiO-66 using benzoic acid as a modulator. Second, the modulator was exchanged for amino-benzoic acid. As a result, amino groups have decorated mainly the defective pores and attracted the Pd precursor after impregnation. The interaction of the metal precursor with amino groups and the growth of NPs were monitored by in situ infrared spectroscopy. Three processes were distinguished: the gaseous HCl release, NH2 reactivation, and growth of extended Pd surfaces. Uniform Pd NPs were located in the pores because of the homogeneous distribution of the precursor and pore diffusion-limited nucleation rate. Our work demonstrates an alternative approach of controlled Pd incorporation into UiO-66 that is of great importance for the rational design of heterogeneous catalysts.

Synthesis, Characterization and Biodistribution of GdF3:Tb3+@RB Nanocomposites.

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.

Discovery of carbon-based strongest and hardest amorphous material.

Carbon is one of the most fascinating elements due to its structurally diverse allotropic forms stemming from its bonding varieties (sp, sp 2 and sp 3). Exploring new forms of carbon has been the eternal theme of scientific research. Herein, we report on amorphous (AM) carbon materials with a high fraction of sp 3 bonding recovered from compression of fullerene C60 under high pressure and high temperature, previously unexplored. Analysis of photoluminescence and absorption spectra demonstrates that they are semiconducting with a bandgap range of 1.5-2.2 eV, comparable to that of widely used AM silicon. Comprehensive mechanical tests demonstrate that synthesized AM-III carbon is the hardest and strongest AM material known to date, and can scratch diamond crystal and approach its strength. The produced AM carbon materials combine outstanding mechanical and electronic properties, and may potentially be used in photovoltaic applications that require ultrahigh strength and wear resistance.

Application of XAFS and XRD methods for describing the copper and zinc adsorption characteristics in hydromorphic soils.

Modeling metal sorption in soils is of great importance to predict the fate of heavy metals and to assess the actual risk driven from pollution. The present study focuses on adsorption of HM ions on two types of hydromorphic soils, including calcaric fluvisols loamic and calcaric fluvic arenosols. The individual and competitive adsorption behaviors of Cu and Zn on soils and soil constituents are evaluated comprehensively. It is established that the sorption processes were best described with the Langmuir model. The results suggest that the calcaric fluvic arenosols are more vulnerable to heavy metal input compared to fluvisols loamic. In all cases, Cu had a higher range of values of the adsorption process parameters relative to Zn. The Zn is likely to be the most critical environmental factor in such soils since it exhibited a decreased sorption under competitive conditions. The retention mechanisms of HM in hydromorphic soils are considered. Based on theoretical calculations of ion activity in soil solutions using solubility diagrams of Cu and Zn compounds, the possibility of precipitation of Cu hydroxide and Zn carbonate in the studied soils is shown. Direct physical methods of nondestructive testing (XAFS and XRD) are applied to experimentally prove the formation of these HM compounds on the surface of montmorillonite, the dominant mineral in hydromorphic soils, and calcite. Thus, the combination of both physicochemical methods and direct physical methods can provide a large amount of real information about the mechanisms of HM retain with solid phases.

Revisited Ti2Nb2O9 as an Anode Material for Advanced Li-Ion Batteries.

Ti2Nb2O9 with a tunnel-type structure is considered as a perspective negative electrode material for Li-ion batteries (LIBs) with theoretical capacity of 252 mAh g-1 corresponding to one-electron reduction/oxidation of Ti and Nb, but only ≈160 mAh g-1 has been observed practically. In this work, highly reversible capacity of 200 mAh g-1 with the average (de)lithiation potential of 1.5 V vs Li/Li+ is achieved for Ti2Nb2O9 with pseudo-2D layered morphology obtained via thermal decomposition of the NH4TiNbO5 intermediate prepared by K+→ H+→ NH4+ cation exchange from KTiNbO5. Using operando synchrotron powder X-ray diffraction (SXPD), single-phase (de)lithiation mechanism with 4.8% unit cell volume change is observed. Operando X-ray absorption near-edge structure (XANES) experiment revealed simultaneous Ti4+/Ti3+ and Nb5+/Nb4+ reduction/oxidation within the whole voltage range. Li+ migration barriers for Ti2Nb2O9 along [010] direction derived from density functional theory (DFT) calculations are within the 0.15-0.4 eV range depending on the Li content that is reflected in excellent C-rate capacity retention. Ti2Nb2O9 synthesized via the ion-exchange route appears as a strong contender to widely commercialized Ti-based negative electrode material Li4Ti5O12 in the next generation of high-performance LIBs.

Photoswitchable Zirconium MOF for Light-Driven Hydrogen Storage.

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.

The Recent Progress on Silver Nanoparticles: Synthesis and Electronic Applications.

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.

Green Synthesized of Ag/Ag2O Nanoparticles Using Aqueous Leaves Extracts of Phoenix dactylifera L. and Their Azo Dye Photodegradation.

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.

Deciphering the Phillips Catalyst by Orbital Analysis and Supervised Machine Learning from Cr Pre-edge XANES of Molecular Libraries.

Unveiling the nature and the distribution of surface sites in heterogeneous catalysts, and for the Phillips catalyst (CrO3/SiO2) in particular, is still a grand challenge despite more than 60 years of research. Commonly used references in Cr K-edge XANES spectral analysis rely on bulk materials (Cr-foil, Cr2O3) or molecules (CrCl3) that significantly differ from actual surface sites. In this work, we built a library of Cr K-edge XANES spectra for a series of tailored molecular Cr complexes, varying in oxidation state, local coordination environment, and ligand strength. Quantitative analysis of the pre-edge region revealed the origin of the pre-edge shape and intensity distribution. In particular, the characteristic pre-edge splitting observed for Cr(III) and Cr(IV) molecular complexes is directly related to the electronic exchange interactions in the frontier orbitals (spin-up and -down transitions). The series of experimental references was extended by theoretical spectra for potential active site structures and used for training the Extra Trees machine learning algorithm. The most informative features of the spectra (descriptors) were selected for the prediction of Cr oxidation states, mean interatomic distances in the first coordination sphere, and type of ligands. This set of descriptors was applied to uncover the site distribution in the Phillips catalyst at three different stages of the process. The freshly calcined catalyst consists of mainly Cr(VI) sites. The CO-exposed catalyst contains mainly Cr(II) silicates with a minor fraction of Cr(III) sites. The Phillips catalyst exposed to ethylene contains mainly highly coordinated Cr(III) silicates along with unreduced Cr(VI) sites.

Self-Driving Laboratories for Development of New Functional Materials and Optimizing Known Reactions.

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.

Hydrogenation of ethylene over palladium: evolution of the catalyst structure by operando synchrotron-based techniques.

Palladium-based catalysts are exploited on an industrial scale for the selective hydrogenation of hydrocarbons. The formation of palladium carbide and hydride phases under reaction conditions changes the catalytic properties of the material, which points to the importance of operando characterization for determining the relation between the relative fractions of the two phases and the catalyst performance. We present a combined time-resolved characterization by X-ray absorption spectroscopy (in both near-edge and extended regions) and X-ray diffraction of a working palladium-based catalyst during the hydrogenation of ethylene in a wide range of partial pressures of ethylene and hydrogen. Synergistic coupling of multiple techniques allowed us to follow the structural evolution of the palladium lattice as well as the transitions between the metallic, hydride and carbide phases of palladium. The nanometric dimensions of the particles resulted in the considerable contribution of both surface and bulk carbides to the X-ray absorption spectra. During the reaction, palladium carbide is formed, which does not lead to a loss of activity. Unusual contraction of the unit cell parameter of the palladium lattice in the spent catalyst was observed upon increasing hydrogen partial pressure.

Data-Centric Architecture for Self-Driving Laboratories with Autonomous Discovery of New Nanomaterials.

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.