jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sabrina Diehn | Helmut Schlaad | Janina Kneipp
Source: Molecules (article, 2022-08-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multivariate Imaging for Fast Evaluation of In Situ Dark Field Microscopy Hyperspectral Data Dark field scattering microscopy can create large hyperspectral data sets that contain a wealth of information on the properties and the molecular environment of noble metal nanoparticles. For a quick screening of samples of microscopic dimensions that contain many different types of plasmonic nanostructures, we propose a multivariate analysis of data sets of thousands to several hundreds of thousands of scattering spectra. By using non-negative matrix factorization for decomposing the spectra, components are identified that represent individual plasmon resonances and relative contributions of these resonances to particular microscopic focal volumes in the mapping data sets. Using data from silver and gold nanoparticles in the presence of different molecules, including gold nanoparticle-protein agglomerates or silver nanoparticles forming aggregates in the presence of acrylamide, plasmonic properties are observed that differ from those of the original nanoparticles. For the case of acrylamide, we show that the plasmon resonances of the silver nanoparticles are ideally suited to support surface enhanced Raman scattering (SERS) and the two-photon excited process of surface enhanced hyper Raman scattering (SEHRS). Both vibrational tools give complementary information on the in situ formed polyacrylamide and the molecular composition at the nanoparticle surface. Plasmon ; Raman scattering ; Hyperspectral imaging ; Nanoparticle ; Silver nanoparticle ; Materials science ; Surface plasmon resonance ; Plasmonic nanoparticles ; Dark field microscopy ; Raman spectroscopy ; Scattering ; Microscopy ; Surface plasmon ; Nanotechnology ; Optics ; Optoelectronics ; Physics ; Computer science Gold and Silver Nanoparticles Synthesis and Applications ; Optical Imaging and Spectroscopy Techniques ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Mohsen Maddah | Charles P. Unsworth | Gideon Gouws | Natalie O. V. Plank
Source: PLoS ONE (article, 2022-06-16)
Field: Materials Science — Polymers and Plastics

Synthesis of encapsulated ZnO nanowires provide low impedance alternatives for microelectrodes Microelectrodes are commonly used in electrochemical analysis and biological sensing applications owing to their miniaturised dimensions. It is often desirable to improve the performance of microelectrodes by reducing their electrochemical impedance for increasing the signal-to-noise of the recorded signals. One successful route is to incorporate nanomaterials directly onto microelectrodes; however, it is essential that these fabrication routes are simple and repeatable. In this article, we demonstrate how to synthesise metal encapsulated ZnO nanowires (Cr/Au-ZnO NWs, Ti-ZnO NWs and Pt-ZnO NWs) to reduce the impedance of the microelectrodes. Electrochemical impedance modelling and characterisation of Cr/Au-ZnO NWs, Ti-ZnO NWs and Pt-ZnO NWs are carried out in conjunction with controls of planar Cr/Au and pristine ZnO NWs. It was found that the ZnO NW microelectrodes that were encapsulated with a 10 nm thin layer of Ti or Pt demonstrated the lowest electrochemical impedance of 400 ± 25 kΩ at 1 kHz. The Ti and Pt encapsulated ZnO NWs have the potential to offer an alternative microelectrode modality that could be attractive to electrochemical and biological sensing applications. Microelectrode ; Materials science ; Nanotechnology ; Nanowire ; Nanomaterials ; Electrical impedance ; Electrochemistry ; Electrode ; Fabrication ; Dielectric spectroscopy ; Optoelectronics ; Chemistry ; Electrical engineering Conducting polymers and applications ; Advanced battery technologies research ; Neuroscience and Neural Engineering
Authors: Emma King‐Smith | Simon Berritt | Louise Bernier | Xinjun Hou | Jacquelyn Klug‐McLeod | Jason Mustakis | Neal W. Sach | Joseph W. Tucker | Qingyi Yang | Roger M. Howard | Alpha A. Lee
Source: ChemRxiv (preprint, 2022-10-27)
Field: Materials Science — Materials Chemistry

Probing the Chemical "Reactome" with High Throughput Experimentation Data High-throughput experimentation (HTE) has the potential to improve our understanding of organic chemistry by systematically interrogating reactivity across diverse chemical spaces. Notable bottlenecks include few publicly available large-scale datasets and the need for facile interpretation of these data's hidden chemical insights. Herein we report the development of a High Throughput Experimentation Analyzer (HiTEA), a robust and statistically rigorous framework which is applicable to any HTE dataset regardless of size, scope, or target reaction outcome. We improve the HTE data landscape with the disclosure of 47,000+ previously proprietary HTE reactions. HiTEA is validated on this dataset, showcasing the elucidation of hidden relationships between reaction components and outcomes as well as highlighting reaction space that necessitates further investigation. Scope (computer science) ; Throughput ; Computer science ; Chemical space ; Data science ; Interpretation (philosophy) ; Outcome (game theory) ; Chemistry ; Drug discovery ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Wissam Muhsin | Jie Zhang
Source: Processes (article, 2022-07-22)
Field: Chemistry — Analytical Chemistry

Multi-Objective Optimization of a Crude Oil Hydrotreating Process with a Crude Distillation Unit Based on Bootstrap Aggregated Neural Network Models This paper presents the multi-objective optimization of a crude oil hydrotreating (HDT) process with a crude atmospheric distillation unit using data-driven models based on bootstrap aggregated neural networks. Hydrotreating of the whole crude oil has economic benefit compared to the conventional hydrotreating of individual oil products. In order to overcome the difficulty in developing accurate mechanistic models and the computational burden of utilizing such models in optimization, bootstrap aggregated neural networks are utilized to develop reliable data-driven models for this process. Reliable optimal process operating conditions are derived by solving a multi-objective optimization problem incorporating minimization of the widths of model prediction confidence bounds as additional objectives. The multi-objective optimization problem is solved using the goal-attainment method. The proposed method is demonstrated on the HDT of crude oil with crude distillation unit simulated using Aspen HYSYS. Validation of the optimization results using Aspen HYSYS simulation demonstrates that the proposed technique is effective. Hydrodesulfurization ; Artificial neural network ; Distillation ; Process (computing) ; Computer science ; Crude oil ; Process engineering ; Engineering ; Machine learning ; Petroleum engineering ; Chemistry Petroleum Processing and Analysis ; Catalysis and Hydrodesulfurization Studies ; Enhanced Oil Recovery Techniques
Authors: Kuk-Jin Seo | Hyun‐Joon Kim | Dae‐Eun Kim
Source: Friction (article, 2022-11-18)
Field: Materials Science — Materials Chemistry

Feasibility of wear reduction for soft nanostructured thin film through enhanced elastic recoverability and contact stress relief Abstract This work shows that a soft, thin film comprising randomly aligned carbon nanotubes (CNTs) can reduce surface wear more effectively than a homogeneous thin film because of enhanced elastic recoverability and contact stress relief originating from its mesh structure. To investigate the wear characteristics of the mesh structure compared to those of the homogeneous thin film, multi-walled CNTs (MWCNTs) and diamond-like carbon (DLC) thin films were prepared to conduct nanoscale tribological experiments using the atomic force microscopy (AFM). The MWCNT thin film showed unmeasurably low wear compared with the DLC thin film under a certain range of normal load. To demonstrate the wear reduction mechanism of the MWCNT thin film, its indentation and frictional behaviors were assessed. The indentation behavior of the MWCNT thin film revealed repetitive elastic deformation with a wide strain range and a significantly lower elastic modulus than that of the DLC thin film. The permanent deformation of the MWCNT thin film was observed through frictional experiments under relatively high normal load conditions. These results are expected to provide insights into the design of highly wear-resistant surfaces using nanostructures. Materials science ; Thin film ; Composite material ; Elastic modulus ; Tribology ; Indentation ; Diamond-like carbon ; Deformation (meteorology) ; Contact area ; Nanoindentation ; Nanostructure ; Contact mechanics ; Stress (linguistics) ; Carbon nanotube ; Nanotechnology ; Finite element method ; Structural engineering Diamond and Carbon-based Materials Research ; Metal and Thin Film Mechanics ; Tribology and Wear Analysis
Authors: Alon Khabra | Haim Cohen | Gad A. Pinhasi | Xavier Querol | Patricia Córdoba | Tomer Zidki
Source: Catalysts (article, 2022-12-24)
Field: Materials Science — Materials Chemistry

Synthesis of a SiO2/Co(OH)2 Nanocomposite Catalyst for SOX/NOX Oxidation in Flue Gas Sulfur and nitrogen oxides (SOX/NOX) are the primary air toxic gas pollutants emitted during fuel combustion, causing health and environmental concerns. Therefore, their emission in flue gases is strictly regulated. The existing technologies used to decrease SOX/NOX content are flue gas desulfurization, which necessitates high capital and operating costs, and selective catalytic reduction, which, in addition to these costs, requires expensive catalysts and high operating temperatures (350–400 °C). New wet scrubbing processes use O3 or H2O2 oxidants to produce (NH4)2SO4 and NH4NO3 fertilizers upon ammonia injection. However, these oxidants are expensive, corrosive, and hazardous. SiO2/Co(OH)2 nanocomposites are presented here as potential catalysts for SOX/NOX oxidation in wet scrubber reactors to scrub these toxic gases using atmospheric oxygen as the oxidant at relatively low temperatures of 60–90 °C. Several silica-cobalt-oxide-based nanocomposites were synthesized as potential catalysts at different concentrations and temperatures. The nanocomposite catalysts were characterized and exhibited excellent catalytic properties for SOX/NOX oxidation using atmospheric oxygen as the oxidant, replacing the problematic H2O2/O3. We thus propose SiO2-supported Co(OH)2 nanoparticles (NPs) as excellent catalysts for the simultaneous scrubbing of polluting SOX/NOX gases in flue gases using atmospheric O2 as the oxidation reagent at a relatively low-temperature range. NOx ; Flue gas ; Catalysis ; Chemistry ; Wet scrubber ; Selective catalytic reduction ; Flue-gas desulfurization ; Waste management ; Environmental chemistry ; Inorganic chemistry ; Combustion ; Chemical engineering ; Organic chemistry Catalytic Processes in Materials Science ; Industrial Gas Emission Control ; Gas Sensing Nanomaterials and Sensors
Authors: Gregory J. Limburn | Daniel W. Davies | Neil Langridge | Z. Malik | Benjamin A. D. Williamson | David O. Scanlon | Geoffrey Hyett
Source: Journal of Materials Chemistry C (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Investigation of factors affecting the stability of compounds formed by isovalent substitution in layered oxychalcogenides, leading to identification of Ba<sub>3</sub>Sc<sub>2</sub>O<sub>5</sub>Cu<sub>2</sub>Se<sub>2</sub>, Ba<sub>3</sub>Y<sub>2</sub>O<sub>5</sub>Cu<sub>2</sub>S<sub>2</sub>, Ba<sub>3</sub>Sc<sub>2</sub>O<sub>5</sub>Ag<sub>2</sub>Se<sub>2</sub> and Ba<sub>3</sub>In<sub>2</sub>O<sub>5</sub>Ag<sub>2</sub>Se<sub>2</sub> Four novel compositions containing chalcogenide layers, adopting the Ba<sub>3</sub>M<sub>2</sub>O<sub>5</sub>M'<sub>2</sub>Ch<sub>2</sub> layered structure have been identified: Ba<sub>3</sub>Sc<sub>2</sub>O<sub>5</sub>Cu<sub>2</sub>Se<sub>2</sub>, Ba<sub>3</sub>Y<sub>2</sub>O<sub>5</sub>Cu<sub>2</sub>S<sub>2</sub>, Ba<sub>3</sub>Sc<sub>2</sub>O<sub>5</sub>Ag<sub>2</sub>Se<sub>2</sub> and Ba<sub>3</sub>In<sub>2</sub>O<sub>5</sub>Ag<sub>2</sub>Se<sub>2</sub>. A comprehensive comparison of experimental and computational results providing the crystallographic and electronic structure of the compounds under investigation has been conducted. Materials were synthesised at 800 °C under vacuum using a conventional ceramic synthesis route with combination of binary oxide and chalcogenide precursors. We report their structures determined by Rietveld refinement of X-ray powder diffraction patterns, and band gaps determined from optical measurements, which range from 1.44 eV to 3.04 eV. Through computational modelling we can also present detailed band structures and propose that, based on their predicted transport properties, Ba<sub>3</sub>Sc<sub>2</sub>O<sub>5</sub>Ag<sub>2</sub>Se<sub>2</sub> has potential as a visible light photocatalyst and Ba<sub>3</sub>Sc<sub>2</sub>O<sub>5</sub>Cu<sub>2</sub>Se<sub>2</sub> is of interest as a p-type transparent conductor. These four novel compounds are part of a larger series of sixteen compounds adopting the Ba<sub>3</sub>M<sub>2</sub>O<sub>5</sub>M'<sub>2</sub>Ch<sub>2</sub> structure that we have considered, of which approximately half are stable and can be synthesized. Analysis of the compounds that cannot be synthesized from this group allows us to identify why compounds containing either M = La, or silver sulfide chalcogenide layers, cannot be formed in this structure type. Materials science ; Substitution (logic) ; Identification (biology) ; Crystallography ; Stability (learning theory) ; Chemistry ; Computer science Iron-based superconductors research ; Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics
Authors: Andrea Darù | Carlos Martín−Fernández | Jeremy N. Harvey
Source: ACS Catalysis (article, 2022-10-05)
Field: Chemistry — Organic Chemistry

Iron-Catalyzed Kumada Cross-Coupling Reaction Involving Fe<sub>8</sub>Me<sub>12</sub><sup>–</sup> and Related Clusters: A Computational Study We present a mechanistic investigation of the iron-catalyzed Kumada reaction involving clusters as active species. The pioneer scientist who focused experimentally on this reaction was Kochi, who during the mechanistic investigation of the Kumada reaction recorded an EPR spectrum of an Fe(I) species that was broader than expected for a single-metal Fe-complex. In recent years, the species responsible has been isolated and characterized as the Fe8Me12– cluster by the Neidig group. To date, its catalytic behavior has been studied from a spectroscopic perspective but never computationally, and a complete mechanism that considers this cluster has not been suggested yet. We found that the 12-coordinated cluster is involved in the reaction, but it is most likely not the active species of the catalysis. Instead, 11- and 13-coordinated, such as Fe8Me11– and Fe8Me13–, are suggested to be fundamental for initiating the reaction. The initiation step we propose is the most classic one: halogen abstraction, carried out by a Fe8Me11– cluster and generating a carbon-centered radical. This radical can then coordinate to the Fe8Me12– cluster and undergo reductive elimination with one of the methyl groups that surround the cluster. This reductive elimination step yields the final product and regenerates the Fe8Me11– cluster that can restart the catalytic cycle. Cluster (spacecraft) ; Chemistry ; Catalysis ; Reductive elimination ; Coupling reaction ; Halogen ; Catalytic cycle ; Reaction mechanism ; Computational chemistry ; Stereochemistry ; Photochemistry ; Organic chemistry Organometallic Complex Synthesis and Catalysis ; Catalytic Cross-Coupling Reactions ; Inorganic Chemistry and Materials
Authors: Dan Wei | Tiejun Zhou | Yunqing Huang | Kai Jiang
Source: Mathematics (article, 2022-11-25)
Field: Materials Science — Materials Chemistry

A Multi-Category Inverse Design Neural Network and Its Application to Diblock Copolymers In this work, we design a multi-category inverse design neural network to map ordered periodic structures to physical parameters. The neural network model consists of two parts, a classifier and Structure-Parameter-Mapping (SPM) subnets. The classifier is used to identify structures, and the SPM subnets are used to predict physical parameters for desired structures. We also present an extensible reciprocal-space data augmentation method to guarantee the rotation and translation invariant of periodic structures. We apply the proposed network model and data augmentation method to two-dimensional diblock copolymers based on the Landau–Brazovskii model. Results show that the multi-category inverse design neural network has high accuracy in predicting physical parameters for desired structures. Moreover, the idea of multi-categorization can also be extended to other inverse design problems. Inverse ; Computer science ; Artificial neural network ; Extensibility ; Categorization ; Classifier (UML) ; Reciprocal ; Artificial intelligence ; Algorithm ; Translation (biology) ; Mathematics ; Geometry Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization
Authors: Izabela Miturska | Anna Rudawská | Elżbieta Doluk
Source: Materials (article, 2022-11-04)
Field: Materials Science — General Materials Science

Influence of Physical Modification of the Adhesive Composition on the Strength Properties of Aerospace Aluminum Alloy Sheet Adhesive Joints One of the most important design factors in the constitution of adhesive joints is the correct choice of adhesive. Currently, there is a full range of options on the commercial market in this regard, but there is increasing research into modifying adhesives for specific engineering applications. The aim of this study was to analyze the effect of physical modification with fillers on the properties of the adhesive composition and the adhesive joints. The adhesives used in the study were a composition of Epidian 5 epoxy resin and PAC curing agent modified with 1% montmorillonite, 5% calcium carbonate and 20% activated carbon. The adhesive compositions in the cured state were subjected to strength tests and SEM and DSC analyses. Using these compositions, adhesive joints of EN AW 2024 T3 aluminum alloy sheets were also made. The tests carried out showed that, due to the use of different fillers, their effects on certain properties of the adhesive compositions are different types. It was shown that physical modification of the adhesive composition does not always result in positive effects. The study also attempted to determine the correlation between the properties of the adhesive compositions in the cured state and the strength of the adhesive joints. Adhesive ; Materials science ; Composite material ; Epoxy ; Curing (chemistry) ; Epoxy adhesive ; Alloy Material Properties and Applications ; Mechanical Behavior of Composites ; Epoxy Resin Curing Processes
Authors: Taraneh Javanbakht | Subhayu Chakravorty
Source: Journal of Engineering Sciences (article, 2022-01-01)
Field: Chemistry — Spectroscopy

Optimization of Machine Learning Algorithms for Proteomic Analysis Using TOPSIS The present study focuses on a new application of the TOPSIS method for the optimization of machine learning algorithms, supervised neural networks (SNN), the quick classifier (QC), and genetic algorithm (GA) for proteomic analysis. The main hypotheses are that the change in the weights of alternatives could affect the ranking of algorithms. The obtained data confirmed this hypothesis for their ranking. Moreover, adding labor as a cost criterion to the list of criteria did not affect this ranking. This was because candidate 3 had better fuzzy membership degrees than the two other candidates concerning their criteria. This work showed the importance of the value of the fuzzy membership degrees of the cost criterion of the algorithms in their ranks. The values of the fuzzy membership degrees of the algorithms used for proteomic analysis could determine their priority according to their score differences. One of the advantages of this study was that the studied methods could be compared according to their characteristics. Another advantage was that the obtained results could be related to the new ones after improving these methods. The results of this work could be applied in engineering, where the analysis of proteins would be performed with these methods. Machine learning ; Ranking (information retrieval) ; Computer science ; Artificial intelligence ; Classifier (UML) ; Fuzzy logic ; Algorithm ; Data mining ; Artificial neural network Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Fuzzy Logic and Control Systems
Authors: Alexander Skorikov | Kees Joost Batenburg | Sara Bals
Source: Journal of Microscopy (article, 2022-12-26)
Field: Materials Science — Surfaces, Coatings and Films

Analysis of 3D elemental distribution in nanomaterials: Towards higher throughput and dose efficiency Many advanced nanomaterials rely on carefully designed morphology and elemental distribution to achieve their functionalities. Among the few experimental techniques that can directly visualise the 3D elemental distribution on the nanoscale are approaches based on electron tomography in combination with energy-dispersive X-ray spectroscopy (EDXS) and electron energy loss spectroscopy (EELS). Unfortunately, these highly informative methods are severely limited by the fundamentally low signal-to-noise ratio, which makes long experimental times and high electron irradiation doses necessary to obtain reliable 3D reconstructions. Addressing these limitations has been the major research question for the development of these techniques in recent years. This short review outlines the latest progress on the methods to reduce experimental time and electron irradiation dose requirements for 3D elemental distribution analysis and gives an outlook on the development of this field in the near future. Elemental analysis ; Nanomaterials ; Materials science ; Spectroscopy ; Nanotechnology ; Computer science ; Field (mathematics) ; Chemistry ; Physics ; Mathematics Electron and X-Ray Spectroscopy Techniques ; Electronic and Structural Properties of Oxides ; Advanced Electron Microscopy Techniques and Applications