jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Reem Mahmoud | F. Gitzhofer | Jasmin Blanchard | Nicolas Abatzoglou
Source: Research Square (preprint, 2023-08-07)
Field: Materials Science — Materials Chemistry

In Situ Graphene Synthesis Study in Inductively Coupled Radiofrequency Thermal Plasma Reactor using Methane Precursor Abstract While numerous studies are available on methane pyrolysis chemical kinetics and the effect of plasma parameters on graphene synthesis, a comprehensive understanding of the formation mechanism remains elusive without in situ analysis. This study aims to utilize a sampling probe for the first time to collect graphene locally on transmission electron microscopy grids and perform a localized sampling and analysis of the gas composition (during graphene synthesis) using mass spectrometry. This technique provides a 3D tracking of methane pyrolysis in radiofrequency inductively coupled thermal plasma reactor for graphene production. Response surface methodology based on central composite design is employed to obtain a 3D visualization of the synthesis process. Quadratic and cubic models are developed, followed by comprehensive analysis of variance. A comparison of the gas-phase chemistry resulting from the in situ measurements with thermodynamic equilibrium calculations reveals that the process is controlled by thermochemical kinetics. H 2 , C 2 H 2 , C 2 H 4 , C 3 H 6, and C 6 H 6, as well as residual CH 4 , are the main hydrocarbons found in the graphene nucleation zone. The primary pathway for methane pyrolysis and graphene formation in RF plasma is through H 2 and C 2 hydrocarbons, while graphene nucleation and growth reactions are terminated 350 mm from the plasma torch nozzle exit. Morphology, quality, mean particle size, and the number of layers of the produced graphene samples, locally collected at different locations by 3D axisymmetric probe scanning, were investigated using TEM, high-resolution TEM imaging, and Raman analysis. The gathered information is highly valuable for plasma reactor design. Graphene ; Nucleation ; Methane ; Materials science ; Raman spectroscopy ; High-resolution transmission electron microscopy ; Chemical engineering ; Analytical Chemistry (journal) ; Nanotechnology ; Chemistry ; Transmission electron microscopy ; Organic chemistry Graphene research and applications ; Diamond and Carbon-based Materials Research ; Plasma Diagnostics and Applications
Authors: Wolfgang R. Streit | Pablo Pérez-García | Jennifer Chow | Elisa Costanzi | Marno Gurschke | Jonas Dittrich | Robert F. Dierkes | Violetta Applegate | Golo Feuerriegel | Prince Tete | Dominik Danso | Julia Schumacher | Christopher Pfleger | Holger Gohlke | Sander H. J. Smits | Ruth A. Schmitz | Rebecka Molitor | Stephan Thies | Karl‐Erich Jaeger
Source: Research Square (preprint, 2023-07-28)
Field: Materials Science — Biomaterials

An archaeal lid-containing feruloyl-esterase degrades polyethylene terephthalate (PET) Polyethylene terephthalate ; Esterase ; Materials science ; Polymer science ; Chemistry ; Composite material ; Biochemistry ; Enzyme biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Microbial bioremediation and biosurfactants
Authors: Tzu Yu Wang | Simon P. Neville | Michael S. Schuurman
Source: ChemRxiv (preprint, 2023-06-19)
Field: Materials Science — Materials Chemistry

Machine Learning Seams of Conical Intersection: A Characteristic Polynomial Approach The machine learning of potential energy surfaces (PESs) has undergone rapid progress in recent years. The vast majority of this work, however, has been focused on the learning of ground state PESs. To reliably extend machine learning protocols to excited state PESs, the occurrence of seams of conical intersections between adiabatic electronic states must be correctly accounted for. This introduces a serious problem, for at such points the adiabatic potentials are not differentiable to any order, complicating the application of standard machine learning methods. We show that this issue may be overcome by instead learning the coordinate-dependent coefficients of the characteristic polynomial of a simple decomposition of the potential matrix. We demonstrate that, through this approach, quantitatively accurate machine learning models of seams of conical intersection may be constructed. Conical intersection ; Intersection (aeronautics) ; Differentiable function ; Polynomial ; Conical surface ; Adiabatic process ; Potential energy ; Calculator ; Computer science ; Artificial intelligence ; Excited state ; Algorithm ; Mathematics ; Physics ; Geometry ; Mathematical analysis ; Engineering ; Quantum mechanics ; Aerospace engineering Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Selvaraju Sivamani | Jeanifer Manganaan Ehilla | Saikat Banerjee | M. Vijayanand
Source: Bulletin of the Chemical Society of Ethiopia (article, 2023-05-12)
Field: Chemistry — Organic Chemistry

Regression modelling for concentration dependent mass density and specific volume of aqueous surfactant solutions ABSTRACT. In the present work, mathematical models relating mass density and specific volume of solution with concentration for aqueous solutions of anionic (sodium lauryl sulfate - SLS) and cationic (cetyltrimethylammonium bromide - CTAB) surfactants (0-10% (w/w)) have been investigated. The experimental studies were performed to study the effect of solution concentration on mass density and specific volume of solution. Mass density increases, and specific volume decreases with an increase in solution concentration for both surfactants. Linear, quadratic, cubic, quartic, quintic, and exponential models were fitted to the experimental data to check the applicability. The models were validated based on the coefficient of determination (R2) and the sum of square of error (SSE) to be unity and zero, respectively. Modelling results reveal that the quintic model was fitted to study the effect of density on solution concentration for both surfactants.&#x0D; &#x0D; KEY WORDS: Density, Surfactant, Cetyltrimethylammonium bromide, Sodium lauryl sulfate, Modelling, Regression&#x0D; &#x0D; Bull. Chem. Soc. Ethiop. 2023, 37(4), 1047-1054. &#x0D; DOI: https://dx.doi.org/10.4314/bcse.v37i4.19 Chemistry ; Aqueous solution ; Pulmonary surfactant ; Bromide ; Volume (thermodynamics) ; Sodium dodecyl sulfate ; Mass concentration (chemistry) ; Thermodynamics ; Analytical Chemistry (journal) ; Chromatography ; Inorganic chemistry ; Physical chemistry Surfactants and Colloidal Systems
Authors: Zachary W. Riedel | Daniel P. Shoemaker
Source: ChemRxiv (preprint, 2023-10-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design rules, accurate enthalpy prediction, and synthesis of stoichiometric Eu3+ quantum memory candidates Stoichiometric Eu3+ compounds have recently shown promise for building dense, optically addressable quantum memory as the cations’ long nuclear spin coherence times and shielded 4f electron optical transitions provide reliable memory platforms. Implementing such a system, though, requires ultra-narrow inhomogeneous linewidth compounds. Finding this rare linewidth behavior within a wide range of potential chemical spaces remains difficult, and while exploratory synthesis is often guided by density functional theory (DFT) calculations, lanthanides’ 4f electrons pose unique challenges for stability predictions. Here, we report DFT procedures that reliably reproduce known phase diagrams and that correctly predict two experimentally realized quantum memory candidates. We are the first to synthesize the double perovskite halide Cs2NaEuF6. It is an air-stable compound with a calculated band gap of 6.9 eV that surrounds Eu3+ with mononuclidic elements, which are desirable for avoiding inhomogeneous linewidth broadening. We also analyze computational database entries to identify phosphates and iodates as the next generation of chemical spaces for stoichiometric quantum memory system studies. This work identifies new candidate platforms for exploring chemical effects on quantum memory candidates’ inhomogeneous linewidth while also providing a framework for screening Eu3+ compound stability with DFT. Laser linewidth ; Density functional theory ; Stoichiometry ; Quantum ; Materials science ; Chemistry ; Computational chemistry ; Physics ; Physical chemistry ; Quantum mechanics ; Laser Crystal Structures and Properties ; Advanced Condensed Matter Physics ; Solid-state spectroscopy and crystallography
Authors: Zhe Zhang | Xiaoyu Song
Source: arXiv (Cornell University) (preprint, 2023-04-29)
Field: Materials Science — Biomaterials

Nanoscale creep mechanism of clay through MD modeling with hexagonal particles In this article, we investigate the creep mechanism of clay at the nanoscale. We conduct the molecular dynamics (MD) modeling of clay samples consisting of hexagonal particles under compression and shear. The MD simulations include oedometer creep, shear creep, direct shear tests, and stress relaxation. The numerical results show that the nanoscale creep mechanism of clay is related to particle rotation, translation, and stacking under different loading conditions. The clay sample under creep shows two types of particle arrangements, i.e., the shifted face-to-face configuration and the face-to-edge configuration. The orientation angle of clay particles is computed to track the rotation of individual particles due to creep. The fabric variation of the clay under creep is characterized by the dihedral angle between the basal particle plane and the x-y plane and the order parameter. It is found that the factors affecting the microstructure variation of the clay aggregate include stress levels, loading rates, and particle sizes. In the nanoscale shear creep test, the creep process comprises three stages, i.e., primary, secondary, and tertiary. The microstructure change during creep depends on the initial alignment of clay particles. The clay creep under a more significant stress level results in a more considerable order parameter and a more orientated clay structure. Creep ; Materials science ; Composite material ; Stress relaxation ; Microstructure ; Particle (ecology) ; Stress (linguistics) ; Shear (geology) ; Geology Clay minerals and soil interactions ; Soil and Unsaturated Flow ; Concrete and Cement Materials Research
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Contents list Permissions Request permissions Contents list Phys. Chem. Chem. Phys., 2023, 25, 14647 DOI: 10.1039/D3CP90117C This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given. Read more about how to correctly acknowledge RSC content. Acknowledgement ; Attribution ; Chromatin structure remodeling (RSC) complex ; Computer science ; Library science ; Physics ; Chemistry ; Computer security ; Psychology ; Social psychology Luminescence Properties of Advanced Materials ; Quantum Dots Synthesis And Properties ; Advanced Photocatalysis Techniques
Authors: Zachary W. Riedel | Daniel P. Shoemaker
Source: ChemRxiv (preprint, 2023-10-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design rules, accurate enthalpy prediction, and synthesis of stoichiometric Eu3+ quantum memory candidates Stoichiometric Eu3+ compounds have recently shown promise for building dense, optically addressable quantum memory as the cations’ long nuclear spin coherence times and shielded 4f electron optical transitions provide reliable memory platforms. Implementing such a system, though, requires ultra-narrow inhomogeneous linewidth compounds. Finding this rare linewidth behavior within a wide range of potential chemical spaces remains difficult, and while exploratory synthesis is often guided by density functional theory (DFT) calculations, lanthanides’ 4f electrons pose unique challenges for stability predictions. Here, we report DFT procedures that reliably reproduce known phase diagrams and that correctly predict two experimentally realized quantum memory candidates. We are the first to synthesize the double perovskite halide Cs2NaEuF6. It is an air-stable compound with a calculated band gap of 6.9 eV that surrounds Eu3+ with mononuclidic elements, which are desirable for avoiding inhomogeneous linewidth broadening. We also analyze computational database entries to identify phosphates and iodates as the next generation of chemical spaces for stoichiometric quantum memory system studies. This work identifies new candidate platforms for exploring chemical effects on quantum memory candidates’ inhomogeneous linewidth while also providing a framework for screening Eu3+ compound stability with DFT. Laser linewidth ; Density functional theory ; Stoichiometry ; Quantum ; Materials science ; Chemistry ; Computational chemistry ; Physical chemistry ; Physics ; Quantum mechanics ; Laser Crystal Structures and Properties ; Advanced Condensed Matter Physics ; Solid-state spectroscopy and crystallography
Authors: Samir F. Matar | Vladimir L. Solozhenko
Source: ChemRxiv (preprint, 2023-03-22)
Field: Materials Science — Materials Chemistry

Novel ultrahard carbon allotropes from crystal chemistry and first principles: rhombohedral ene-C21 and yne-C24 Rhombohedral ene-C21 and yne-C24, characterized by the presence of C=C and C=C bonds, respectively, inserted into the 9R diamond polytype C18, are proposed as novel ultrahard carbon allotropes from crystal chemistry and first principles. Like 9R C18, they belong to the cfe topology characteristic of layered SiC polytypes. With ultrahard properties approaching those of diamond and lonsdaleite, ene-C21 and yne-C24 are dynamically stable with phonon signatures identifying C=C and C=C high frequency vibrations similar to molecular allene and acetylene. The electronic band structures correspond to insulating C18, metallic ene-C21 and semiconducting yne-C24. Allene ; Diamond ; Acetylene ; Ene reaction ; Trigonal crystal system ; Trigonal prismatic molecular geometry ; Materials science ; Carbon fibers ; Crystal (programming language) ; Crystallography ; Crystal structure ; Topology (electrical circuits) ; Chemistry ; Chemical physics ; Stereochemistry ; Catalysis ; Organic chemistry ; Composite material Boron and Carbon Nanomaterials Research ; Diamond and Carbon-based Materials Research ; Graphene research and applications
Authors: David A. Rincón | Ana J. Escorcia | Markus Doerr | Martha C. Daza
Source: Theoretical Chemistry Accounts (article, 2023-11-22)
Field: Chemistry — Physical and Theoretical Chemistry

QTAIM based descriptors for the classification of acrylates Abstract Acrylates are used in cosmetics, orthopedics, paints, coatings, adhesives, textiles, and biomedical applications such as contact lenses and bone cements. However, some acrylates are mutagenic and the aim of this article is to explain the mutagenicity in terms of the atomic population redistribution in the molecule using two new descriptors which are based on atomic populations framed in the quantum theory of atoms in molecules. They describe the electron-withdrawing effect of a group of atoms in a molecule. The descriptors consider substituents of prop-2-enoates, the number of the acrolein units and the electrophilicity. The cluster analysis using these descriptors allows to classify acrylates in terms of the number of acrolein backbones and the type of the substituent group. Five main groups can be distinguished: monoacrylates with monomethacrylates, diacrylates with dimethacrylates, triacrylates, trimethacrylate and monoacrylates with electron-rich substituents. The substituents of mutagenic acrylates are electron withdrawing. This makes the acrolein backbone β-carbon more electrophilic and the molecule more reactive. Electrophile ; Acrolein ; Substituent ; Chemistry ; Molecule ; Redistribution (election) ; Polar effect ; Computational chemistry ; Population ; Atoms in molecules ; Stereochemistry ; Organic chemistry ; Catalysis Crystallography and molecular interactions ; Chemical Thermodynamics and Molecular Structure ; Computational Drug Discovery Methods
Authors: Qifeng Shi | Huishu Wu | Peipei Zhang | Dongsheng Wang | Jingwen Wang | Xiaohua Jie
Source: Coatings (article, 2023-09-08)
Field: Materials Science — Materials Chemistry

Balanced Anti-Corrosion Action of Reduced Graphene Oxide in Zn-Al Coating during Medium-Term Exposure to NaCl Solution Considering the electronegativity and shielding anti-sepsis characteristic of reduced graphene oxide (G), we design a Zn-Al coating with embedded G (Zn-G/Al) on low-carbon steel using the low-pressure cold spray (LPCS) method. In this method, G-coated Al powders (G/Al) prepared using in situ reduction and Zn powders were mixed as a raw material for spraying. Embedding G could boost the cathodic protection performance of Zn-Al (70 wt.% zinc and 30 wt.% aluminum) coating, as has been confirmed in previous work. In this work, the microstructure, composition and electrochemical parameters of Zn-G/Al coating during full immersion were measured to investigate G’s effect on the corrosion protection properties of the Zn-Al coating. The test results showed that embedded G could facilitate the generation of many corrosion products and pile on the coating surface to form a corrosion product film during full immersion. The corrosion product film on the Zn-0.2 wt.%G/Al coating surface demonstrated an excellent protective property, which reflects the fact that the Ecorr and icorr values for Zn-0.2 wt.%G/Al after 20d immersion (Ecorr = −1.143 Vvs.SCE, icorr= 49.96 μA/cm2) were lower than the initial value (Ecorr = −1.299 Vvs.SCE, icorr = 82.16 μA/cm2). It can be concluded that adding an appropriate amount of G to the coating can balance the cathodic protection and shielding property of the coating. The equilibrium mechanism was also analyzed in this work. Coating ; Materials science ; Corrosion ; Cathodic protection ; Zinc ; Microstructure ; Immersion (mathematics) ; Oxide ; Metallurgy ; Electrochemistry ; Chemical engineering ; Composite material ; Electrode ; Chemistry Corrosion Behavior and Inhibition ; MXene and MAX Phase Materials ; Magnesium Alloys: Properties and Applications
Authors: Garg, Vikas | Heinonen, Markus | Kaski, Samuel | Verma, Yogesh
Source: Research Explorer (The University of Manchester) (article, 2023-04-01)
Field: Materials Science — Materials Chemistry

Modular Flows:Differential Molecular Generation Generating new molecules is fundamental to advancing critical applications such as drug discovery and material synthesis. Flows can generate molecules effectively by inverting the encoding process, however, existing flow models either require artifactual dequantization or specific node/edge orderings, lack desiderata such as permutation invariance, or induce discrepancy between the encoding and the decoding steps that necessitates post hoc validity correction. We circumvent these issues with novel continuous normalizing E(3)-equivariant flows, based on a system of node ODEs coupled as a graph PDE, that repeatedly reconcile locally toward globally aligned densities. Our models can be cast as message-passing temporal networks, and result in superlative performance on the tasks of density estimation and molecular generation. In particular, our generated samples achieve state-of-the-art on both the standard QM9 and ZINC250K benchmarks. Decoding methods ; Encoding (memory) ; Modular design ; Permutation (music) ; Node (physics) ; Ode Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Pranav Kairon | John Sous | Mona Berciu | Roman V. Krems
Source: arXiv (Cornell University) (preprint, 2023-12-15)
Field: Materials Science — Materials Chemistry

Extrapolation of polaron properties to low phonon frequencies by Bayesian machine learning Feasibility of accurate quantum calculations is often restricted by the dimensionality of the truncated Hilbert space required for the numerical computations. The present work demonstrates Bayesian machine learning (ML) models that use quantum properties in an effectively lower-dimensional Hilbert space to make predictions for the Hamiltonian parameters that require a larger basis set as applied to a classical problem in quantum statistical mechanics, the polaron problem. We consider two polaron models: the Su-Schrieffer-Heeger (SSH) model and the mixed SSH-Holstein model. We demonstrate ML models that can extrapolate polaron properties in the phonon frequency. We consider the sharp transition in the ground-state momentum of the SSH polaron and examine the evolution of this transition from the anti-adiabatic regime to the adiabatic regime. We also demonstrate Bayesian models that use the posterior distributions of highly approximate quantum calculations as the prior distribution for models of more accurate quantum results. This drastically reduces the number of fully converged quantum calculations required to map out the polaron dispersion relations for the full range of Hamiltonian parameters of interest. Polaron ; Hamiltonian (control theory) ; Curse of dimensionality ; Adiabatic process ; Physics ; Statistical physics ; Quantum ; Phonon ; Quantum mechanics ; Hilbert space ; Bayesian optimization ; Mathematics ; Computer science ; Electron ; Mathematical optimization ; Machine learning Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Quantum many-body systems
Authors: Sina Malakpour Estalaki | Tengfei Luo | Khachatur V. Manukyan
Source: arXiv (Cornell University) (preprint, 2023-03-24)
Field: Materials Science — Materials Chemistry

Bayesian Optimization of Metastable Nickel Formation During the Spontaneous Crystallization under Extreme Conditions Spontaneous crystallization of metals under extreme conditions is a unique phenomenon occurring under far-from-equilibrium conditions that could enable the development of revolutionary and disruptive metastable metals with unusual properties. In this work, the formation of the hexagonal close-packed Nickel (hcp-Ni) metastable phase during spontaneous crystallization is studied using non-equilibrium molecular dynamics (MD) simulations, with the goal of maximizing the fraction of this metastable phase in the final state. We employ Bayesian Optimization (BO) with the Gaussian Processes (GP) regression as the surrogate model to maximize the hcp-Ni phase fraction, where temperature and pressure are control variables. MD simulations provide data for training the GP model, which is then used with BO to predict the next simulation condition. Such a BO-guided active learning leads to a maximum hcp-Ni fraction of 43.38% in the final crystalized phase within 40 iterations when a face-centered cubic (fcc) crystallite serves as the seed for crystallization from the amorphous phase. When an hcp seed is used, the maximum hcp-Ni fraction in the final crystal increases to 58.25% with 13 iterations. This study shows the promise of using BO to identify the process conditions that can maximize the rare phases. This method can also be generally applicable to process optimization to achieve target material properties. Metastability ; Crystallization ; Materials science ; Phase (matter) ; Thermodynamics ; Crystallite ; Amorphous metal ; Fraction (chemistry) ; Amorphous solid ; Molecular dynamics ; Bayesian optimization ; Crystal (programming language) ; Crystallography ; Chemistry ; Alloy ; Metallurgy ; Computer science ; Physics ; Mathematics ; Mathematical optimization ; Computational chemistry ; Chromatography Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Injection Molding Process and Properties