jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vemuri Sritejas | Rupak Roy
Source: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (article, 2024-12-24)
Field: Chemistry — Organic Chemistry

Advances in Copper Nanoparticles: Synthesis, Characterization, Applications, and Future Prospects Copper nanoparticles (CuNPs) have emerged as a versatile class of nanomaterials with unique physical, chemical, and biological properties, enabling their application across various scientific and industrial domains. This review provides a comprehensive overview of CuNPs, focusing on their synthesis methods, including physical, chemical, and eco- friendly biological approaches, alongside the advantages and limitations of each. Characterization techniques critical for understanding the structure, composition, and surface properties of CuNPs are discussed in detail. The diverse applications of CuNPs in fields such as biomedicine, catalysis, energy storage, and environmental remediation are highlighted, with particular emphasis on their antimicrobial properties, catalytic efficiency, and potential for sustainable energy solutions. Furthermore, the review addresses concerns regarding the toxicity and environmental impact of CuNPs, underscoring the need for responsible production and use. Finally, the challenges associated with large-scale manufacturing, stability, and biocompatibility are explored, along with future prospects for innovation in synthesis techniques and interdisciplinary applications. This paper aims to provide a critical resource for researchers and industry professionals seeking to harness the potential of copper nanoparticles for scientific advancement and societal benefit. Keywords: Copper nanoparticles, nanomaterials, synthesis methods, characterization techniques, antimicrobial activity, catalysis, energy storage, environmental applications, toxicity, green synthesis. Characterization (materials science) ; Copper ; Nanotechnology ; Nanoparticle ; Materials science ; Metallurgy Nanomaterials for catalytic reactions
Authors: Joseph Lantos | Narendra Kumar | Basudeb Saha
Source: Preprints.org (preprint, 2024-02-28)
Field: Materials Science — Materials Chemistry

Production of fine chemicals using microporous and mesoporous heterogeneous catalysts Fine chemicals are produced in small annual volume batch processes (often &amp;amp;lt;10,000 tonnes per year), with a high associated price (usually &amp;amp;gt; $10/kg). As a result of their usage in the production of speciality chemicals, in areas including agrochemicals, fragrances and pharmaceuticals, their necessity will remain high for the foreseeable future. This review article assesses current methods used to produce fine chemicals with heterogeneous catalysts, including both well-established methods as well as newer experimental methods. A wide range of methods utilising microporous and mesoporous catalysts has been explored, including their preparation and modification before use in industry. Their potential drawbacks, as well as benefits, have been analysed, with their feasibility compared to newer, recently emerging catalysts. The field of heterogeneous catalysis for fine chemical production is a dynamic and ever-changing area of research. This deeper insight into catalytic behaviour and material properties will produce more efficient, selective, and sustainable processes in the fine chemical industry. The findings from this article will provide an excellent foundation for further exploration and a critical review in this field of fine chemical production using micro- and mesoporous heterogeneous catalysts. Microporous material ; Mesoporous material ; Catalysis ; Production (economics) ; Chemical engineering ; Chemistry ; Materials science ; Organic chemistry ; Economics ; Engineering ; Microeconomics Catalytic Processes in Materials Science ; Mesoporous Materials and Catalysis ; Catalysis and Oxidation Reactions
Authors: I.G. Shuttleworth
Source: Oxygen (article, 2024-12-08)
Field: Materials Science — Materials Chemistry

The Modelling of Pt-Bearing ORR and OER-Active Alloys Nanoparticles are a mainstay of heterogeneous catalysis. This is in part due to their mesoscopic structure; they can be grown to have large available surface areas which can be both regenerative and durable in reaction. Their utility is possible by the alloys used in their production—however, analysis of their operation is generally at the DFT or molecular dynamics level. This review will present an overview of the post-DFT methods relevant to materials supporting the ORR and OER reactions. Pt-bearing alloys will then be highlighted with a focus on their application in heterogeneous catalysis and the ORR/OER reactions. The current computational approaches to accurately predicting the band properties of the alloys will then be discussed and both the fundamental and applied importance of this modelling will be highlighted. Mesoscopic physics ; Nanotechnology ; Materials science ; Bearing (navigation) ; Catalysis ; Biochemical engineering ; Computer science ; Chemistry ; Engineering ; Physics ; Artificial intelligence Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Semiconductor materials and devices
Authors: Dereje Bekele Tekliye | Gopalakrishnan Sai Gautam
Source: arXiv (Cornell University) (preprint, 2024-01-19)
Field: Chemistry — Inorganic Chemistry

Accuracy of metaGGA functionals in describing transition metal fluorides Accurate predictions of material properties within the chemical space of transition metal fluorides (TMFs), using density functional theory (DFT) is important for advancing several technological applications. The state-of-the-art semi-local exchange-correlation functionals within DFT include the strongly constrained and appropriately normed (SCAN) and the restored regularized SCAN (r$^2$SCAN), both of which are meta generalized gradient approximation (metaGGA) functionals. Both SCAN and r$^2$SCAN are susceptible to self-interaction errors (SIEs) while modelling correlated $d$ electrons of transition metals. Hence, in this work, we evaluate the accuracy of both functionals in estimating properties of TMFs, including redox enthalpies, lattice geometries, on-site magnetic moments, and band gaps. We observe both SCAN and r$^2$SCAN to exhibit poor accuracy in estimating fluorination enthalpies among TMFs, attributable to SIEs among the $d$ electrons. Thus, we derive optimal Hubbard $U$ corrections for both functionals based on experimental fluorination enthalpies of binary TMFs. Note that the linear response theory yielded unphysical $U$ values for V, Fe, and Ni fluorides. While adding the optimal $U$ to the metaGGA functionals does not significantly affect the lattice volumes and magnetic moments, it does significantly increase the calculated band gaps. Also, we calculate the average Na intercalation voltage in Mn, Fe, Co, and Ni fluorides as a transferability check of our optimal $U$ values. Overall, we recommend using the Hubbard $U$ correction to improve predictions of redox enthalpies in other TMFs, while for band gap predictions, we suggest using the non-corrected functionals. Finally, our study should advance the accuracy of DFT-based screening studies to unearth novel TMFs, which can be used in various applications, including energy storage, catalysis, and magnetic devices. Density functional theory ; Transition metal ; Transferability ; Lattice (music) ; Materials science ; Magnetic moment ; Electron ; Band gap ; Physics ; Condensed matter physics ; Chemistry ; Thermodynamics ; Mathematics ; Quantum mechanics Inorganic Fluorides and Related Compounds ; Machine Learning in Materials Science ; Ga2O3 and related materials
Authors: Lena Angelika Mittmann | Andrea Crovetto
Source: ChemRxiv (preprint, 2024-01-24)
Field: Chemistry — Inorganic Chemistry

Phosphosulfide semiconductors for optoelectronics and solar energy conversion Inorganic phosphosulfides – materials containing phosphorus, sulfur, and at least one metal – are a vast and chemically-versatile family of materials. Benefiting from a wide range of possible phosphorus oxidation states, phosphosulfide semiconductors exist as thiophosphate compounds with various types of P-S polyanions, as genuine multi-anion compounds with or without P-P bonds, as solid solutions, and as many intermediate cases. Since metal phosphides and metal sulfides are among the highest-performing optoelectronic semiconductors, it seems reasonable to consider the phosphosulfide family as a potential pool of materials for solar cells, photoelectrochemical cells, and light-emitting diodes. Nevertheless, phosphosulfide semiconductors have very rarely been characterized with these applications in mind. In this Perspective, we reflect on the potential applicability of known and hypothetical phosphosulfides as light absorbers and emitters in optoelectronic devices. First, we distill the existing knowledge accessible through the Materials Project database, finding promising phosphosulfides among the compounds already present in the database, and identifying what we see as the general advantages and challenges of phosphosulfides as optoelectronic materials. Then, we propose three concrete research directions aimed at finding novel high-quality phosphosulfide semiconductors with high light absorption coefficients, high carrier mobilities, and long carrier lifetimes. In particular, we argue that the versatility of phosphorus in this class of materials could potentially be exploited to engineer defect tolerance. Finally, we devise and describe a custom synthesis setup dedicated to high-throughput exploration of thin-film phosphosulfides. Semiconductor ; Thiophosphate ; Materials science ; Nanotechnology ; Diode ; Optoelectronics ; Compound semiconductor ; Engineering physics ; Computer science ; Chemistry ; Physics Inorganic Chemistry and Materials ; 2D Materials and Applications ; Perovskite Materials and Applications
Source: Malaysian Journal of Chemistry (article, 2024-06-24)
Field: Materials Science — Materials Chemistry

Synthesis and Characterizations of Titanium Carbide (Ti3C2Tx) MXene Electrode for Supercapacitors Transition metal carbides/nitrides and transition metal carbonitrides are collectively known as MXenes.Among the various MXenes available to date, titanium carbide (Ti3C2Tx) MXene is the most explored material due to its unique characteristics such as large surface area, good electronic conductivity, easy processability, etc. to name a few.Herein, we report the synthesis and characterizations of supercapacitor electrode comprising of Ti3C2Tx MXene nanosheets.The Ti3C2Tx MXene-based supercapacitor electrode synthesized via slurry coating followed by drop casting procedure.The electrochemical performances of the Ti3C2Tx MXene-based supercapacitor electrode are examined in a three-electrode cell set-up using characterization techniques such as electrochemical impedance spectroscopy and cyclic voltammetry.The Ti3C2Tx MXene-based supercapacitor electrode delivers a specific capacitance of 108.6 F/g at a scan rate of 5 mV/s.The electrochemical stability of the Ti3C2Tx MXene-based supercapacitor is examined for 2000 continuous cycles and exhibits a 100% capacitance retention even after the cycling study. Titanium carbide ; Supercapacitor ; Materials science ; Electrode ; Carbide ; Titanium ; Metallurgy ; Chemistry ; Electrochemistry MXene and MAX Phase Materials ; Nanomaterials for catalytic reactions ; Aluminum Alloys Composites Properties
Authors: Lena Angelika Mittmann | Andrea Crovetto
Source: ChemRxiv (preprint, 2024-01-25)
Field: Chemistry — Inorganic Chemistry

Phosphosulfide semiconductors for optoelectronics and solar energy conversion Inorganic phosphosulfides – materials containing phosphorus, sulfur, and at least one metal – are a vast and chemically-versatile family of materials. Benefiting from a wide range of possible phosphorus oxidation states, phosphosulfide semiconductors exist as thiophosphate compounds with various types of P-S polyanions, as genuine multi-anion compounds with or without P-P bonds, as solid solutions, and as many intermediate cases. Since metal phosphides and metal sulfides are among the highest-performing optoelectronic semiconductors, it seems reasonable to consider the phosphosulfide family as a potential pool of materials for solar cells, photoelectrochemical cells, and light-emitting diodes. Nevertheless, phosphosulfide semiconductors have very rarely been characterized with these applications in mind. In this Perspective, we reflect on the potential applicability of known and hypothetical phosphosulfides as light absorbers and emitters in optoelectronic devices. First, we distill the existing knowledge accessible through the Materials Project database, finding promising phosphosulfides among the compounds already present in the database, and identifying what we see as the general advantages and challenges of phosphosulfides as optoelectronic materials. Then, we propose three concrete research directions aimed at finding novel high-quality phosphosulfide semiconductors with high light absorption coefficients, high carrier mobilities, and long carrier lifetimes. In particular, we argue that the versatility of phosphorus in this class of materials could potentially be exploited to engineer defect tolerance. Finally, we devise and describe a custom synthesis setup dedicated to high-throughput exploration of thin-film phosphosulfides. Thiophosphate ; Semiconductor ; Materials science ; Diode ; Nanotechnology ; Compound semiconductor ; Optoelectronics ; Engineering physics ; Chemistry ; Physics Inorganic Chemistry and Materials ; 2D Materials and Applications ; Perovskite Materials and Applications
Authors: Rafael G. Uceda | Alfonso Gijón | Sandra Míguez‐Lago | Carlos Moreno-Cruz | Víctor Blanco | Fátima Fernández-Álvarez | Luı́s Álvarez de Cienfuegos | Miguel Molina-Solana | Juan Gómez‐Romero | D. Miguel | Antonio J. Mota | Juan M. Cuerva
Source: ChemRxiv (preprint, 2024-04-16)
Field: Chemistry — Spectroscopy

Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case In this work we predict, among more than a billion possibilities, the best candidates of halogenated [6]helicenes in order to obtain excellent chiroptical properties in terms of the rotatory strength (R). We have used DFT calculations to randomly create derivatives from 1 to 16 halogen atoms, that were then used as a data set to train different deep neural network models. It is worth noting that the simplest model affords a parametrization that allows to easily predict the value of R for any hexahalogenated [6]helicene. The correlation between calculated and predicted data increases together with the complexity of the model. The results show that some positions and halogens are preferred to increase the R value. In this sense, we have also synthesized the derivatives with the higher predicted R, obtaining excellent correlation among the values obtained experimentally, by DFT-calculations and machine learning predictions. Helicene ; Halogen ; Parametrization (atmospheric modeling) ; Artificial neural network ; Value (mathematics) ; Computational chemistry ; Chemistry ; Mathematics ; Statistical physics ; Molecule ; Computer science ; Artificial intelligence ; Machine learning ; Physics ; Quantum mechanics ; Organic chemistry Molecular spectroscopy and chirality ; Synthesis and Properties of Aromatic Compounds ; Advanced NMR Techniques and Applications
Authors: S.N. Nazrin | Halimah Badioze Zaman | N. S. Nirmala Jothi | Doha Jouay | Badreddine Lahrach | M.K. Halimah
Source: JOIV International Journal on Informatics Visualization (article, 2024-09-30)
Field: Materials Science — Materials Chemistry

Atomic Structure Simulation and Properties’ Prediction using Machine Learning on Neodymium Oxide Nanoparticles Zinc Tellurite Glasses Aided by FTIR and TEM Analysis The optical, structural, and physical characteristics of zinc tellurite glasses doped with neodymium oxide nanoparticles, which are produced by the melt-quenching method, were examined in this work. The amorphous character of the glasses was verified by XRD analysis. Using the Pair Distribution Function (PDF) and Monte Carlo simulations and visualisation for precise molecule distribution representation, an intuitive Python interface was created to emphasize these features. The density increased with increasing Nd2O3 concentrations, from 5346 to 5606 kg/cm2. Density data was used to infer the molar volume. The best projected density was achieved by the Gradient Boosting Regressor model, with a R2 of 0.9988 and an RMSE of 0.0032; the best predicted molar volume was achieved by linear regression, with a R2 of 1 and an RMSE of 2.67e-15. These models successfully represent the correlations between dopant concentration and glass properties, advancing our knowledge of the optical properties for further glass technology research. Fourier transform infrared spectroscopy ; Zinc ; Neodymium ; Nanoparticle ; Materials science ; Oxide ; Nanotechnology ; Chemical engineering ; Metallurgy ; Optics ; Engineering ; Physics ; Laser Phase-change materials and chalcogenides ; Glass properties and applications ; X-ray Diffraction in Crystallography
Authors: H.-Q. Jin | Kenneth M. Merz
Source: ChemRxiv (preprint, 2024-02-13)
Field: Chemistry — Organic Chemistry

LigandDiff: 3D Transition Metal Complex Generation with Diffusion Models Transition metal complexes are a class of compounds with varied and versatile properties making them of great technological importance. Their applications cover a wide range of fields, either as metallodrugs in medicine or as materials, catalysts, batteries, solar cells, etc. The demand for the novel design of transition metal complexes with new properties remains of great interest . However, the traditional high-throughput screening approach is inherently expensive and laborious since it depends on human expertise. Here, we present LigandDiff, a generative model to design novel transition metal complexes. Unlike the existing methods which simply extracts and combine ligands to the metal to get new complexes, LigandDiff aims at designing novel ligands from scratch, which opens new pathways for the discovery of organometallic complexes. Moreover, it overcomes the limitations of current methods where the diversity of new complexes highly relies on the diversity of available ligands while LigandDiff can enumerate novel ligands without human intervention. Our results indicate that LigandDiff designs unique and novel ligands under different contexts that are synthetically accessible. Moreover, LigandDiff shows good transferability by generating successful ligands for any transition metal complex. Diffusion ; Transition metal ; Transition (genetics) ; Statistical physics ; Chemistry ; Thermodynamics ; Physics Nanomaterials for catalytic reactions ; Catalytic Processes in Materials Science
Authors: Jonathan Vandermause | Anders Johansson | Yucong Miao | Joost J. Vlassak | Boris Kozinsky
Source: arXiv (Cornell University) (preprint, 2024-01-10)
Field: Materials Science — Materials Chemistry

Phase discovery with active learning: Application to structural phase transitions in equiatomic NiTi Nickel titanium (NiTi) is a protypical shape-memory alloy used in a range of biomedical and engineering devices, but direct molecular dynamics simulations of the martensitic B19' -&gt; B2 phase transition driving its shape-memory behavior are rare and have relied on classical force fields with limited accuracy. Here, we train four machine-learned force fields for equiatomic NiTi based on the LDA, PBE, PBEsol, and SCAN DFT functionals. The models are trained on the fly during NPT molecular dynamics, with DFT calculations and model updates performed automatically whenever the uncertainty of a local energy prediction exceeds a chosen threshold. The models achieve accuracies of 1-2 meV/atom during training and are shown to closely track DFT predictions of B2 and B19' elastic constants and phonon frequencies. Surprisingly, in large-scale molecular dynamics simulations, only the SCAN model predicts a reversible B19' -&gt; B2 phase transition, with the LDA, PBE, and PBEsol models predicting a reversible transition to a previously uncharacterized low-volume phase, which we hypothesize to be a new stable high-pressure phase. We examine the structure of the new phase and estimate its stability on the temperature-pressure phase diagram. This work establishes an automated active learning protocol for studying displacive transformations, reveals important differences between DFT functionals that can only be detected in large-scale simulations, provides an accurate force field for NiTi, and identifies a new phase. Nickel titanium ; Molecular dynamics ; Shape-memory alloy ; Martensite ; Materials science ; Phase (matter) ; Phase transition ; Atom (system on chip) ; Phase diagram ; Statistical physics ; Force field (fiction) ; Condensed matter physics ; Chemical physics ; Thermodynamics ; Artificial intelligence ; Computer science ; Physics ; Computational chemistry ; Chemistry ; Microstructure ; Metallurgy ; Quantum mechanics Machine Learning in Materials Science ; Shape Memory Alloy Transformations ; Force Microscopy Techniques and Applications
Authors: Viktor C. Birschitzky | Michele Reticcioli | Cesare Franchini
Source: arXiv (Cornell University) (preprint, 2024-01-22)
Field: Materials Science — Materials Chemistry

Dataset: Machine Learning Based Prediction of Polaron-Vacancy Patterns on the TiO$_2$(110) Surface The multifaceted physics of oxides is shaped by their composition and the presence of defects, which are often accompanied by the formation of polarons. The simultaneous presence of polarons and defects, and their complex interactions, pose challenges for first-principles simulations and experimental techniques. In this study, we leverage machine learning and a first-principles database to analyze the distribution of surface oxygen vacancies (V$_{\rm O}$) and induced small polarons on rutile TiO$_2$(110), effectively disentangling the interactions between polarons and defects. By combining neural-network supervised learning and simulated annealing, we elucidate the inhomogeneous V$_{\rm O}$ distribution observed in scanning probe microscopy (SPM). Our innovative approach allows us to understand and predict defective surface patterns at previously inaccessible length scales, identifying the specific role of individual types of defects. Specifically, surface-polaron-stabilizing V$_{\rm O}$-configurations are identified, which could have consequences for surface reactivity. Polaron ; Leverage (statistics) ; Rutile ; Materials science ; Annealing (glass) ; Vacancy defect ; Condensed matter physics ; Surface (topology) ; Chemical physics ; Nanotechnology ; Computer science ; Chemistry ; Machine learning ; Physics ; Mathematics ; Quantum mechanics ; Geometry Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques