jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aymeric Ousset | Pierre-François Chavez | Joke Meeus | Florent Robin | M. Schubert | Pascal Somville | Kalliopi Dodou
Source: Pharmaceutics (article, 2018-03-07)
Field: Materials Science — Materials Chemistry

Prediction of Phase Behavior of Spray-Dried Amorphous Solid Dispersions: Assessment of Thermodynamic Models, Standard Screening Methods and a Novel Atomization Screening Device with Regard to Prediction Accuracy The evaluation of drug-polymer miscibility in the early phase of drug development is essential to ensure successful amorphous solid dispersion (ASD) manufacturing. This work investigates the comparison of thermodynamic models, conventional experimental screening methods (solvent casting, quench cooling), and a novel atomization screening device based on their ability to predict drug-polymer miscibility, solid state properties (<i>T</i><sub>g</sub> value and width), and adequate polymer selection during the development of spray-dried amorphous solid dispersions (SDASDs). Binary ASDs of four drugs and seven polymers were produced at 20:80, 40:60, 60:40, and 80:20 (<i>w</i>/<i>w</i>). Samples were systematically analyzed using modulated differential scanning calorimetry (mDSC) and X-ray powder diffraction (XRPD). Principal component analysis (PCA) was used to qualitatively assess the predictability of screening methods with regards to SDASD development. Poor correlation was found between theoretical models and experimentally-obtained results. Additionally, the limited ability of usual screening methods to predict the miscibility of SDASDs did not guarantee the appropriate selection of lead excipient for the manufacturing of robust SDASDs. Contrary to standard approaches, our novel screening device allowed the selection of optimal polymer and drug loading and established insight into the final properties and performance of SDASDs at an early stage, therefore enabling the optimization of the scaled-up late-stage development. Amorphous solid ; Materials science ; Phase (matter) ; Chemistry ; Crystallography ; Organic chemistry Material Dynamics and Properties ; Crystallization and Solubility Studies ; Microencapsulation and Drying Processes
Authors: Jens Müller | Tatjana Thomas
Source: Crystals (article, 2018-04-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Low-Frequency Dynamics of Strongly Correlated Electrons in (BEDT-TTF)2X Studied by Fluctuation Spectroscopy Fluctuation spectroscopy measurements of quasi-two-dimensional organic charge-transfer salts (BEDT-TTF) 2 X are reviewed. In the past decade, the method has served as a new approach for studying the low-frequency dynamics of strongly correlated charge carriers in these materials. We review some basic aspects of electronic fluctuations in solids, and give an overview of selected problems where the analysis of 1 / f -type fluctuations and the corresponding slow dynamics provide a better understanding of the underlying physics. These examples are related to (1) an inhomogeneous current distribution due to phase separation and/or a percolative transition; (2) slow dynamics due to a glassy freezing either of structural degrees of freedom coupling to the electronic properties or (3) of the electrons themselves, e.g., when residing on a highly-frustrated crystal lattice, where slow and heterogeneous dynamics are key experimental properties for the vitrification process of a supercooled charge-liquid. Another example is (4), the near divergence and critical slowing down of charge carrier fluctuations at the finite-temperature critical endpoint of the Mott metal-insulator transition. Here also indications for a glassy freezing and temporal and spatial correlated dynamics are found. Mapping out the region of ergodicity breaking and understanding the influence of disorder on the temporal and spatial correlated fluctuations will be an important realm of future studies, as well as the fluctuation properties deep in the Mott or charge-ordered insulating states providing a connection to relaxor or ordered ferroelectric states studied by dielectric spectroscopy. Condensed matter physics ; Mott transition ; Electron ; Mott insulator ; Spectroscopy ; Materials science ; Strongly correlated material ; Physics ; Chemical physics ; Quantum mechanics ; Hubbard model Organic and Molecular Conductors Research ; Magnetism in coordination complexes ; Perovskite Materials and Applications
Authors: Joyner Eke | Katherine A. Elder | Isabel C. Escobar
Source: Membranes (article, 2018-09-07)
Field: Materials Science — Materials Chemistry

Self-Cleaning Nanocomposite Membranes with Phosphorene-Based Pore Fillers for Water Treatment Phosphorene is a two-dimensional material exfoliated from bulk phosphorus and it possesses a band gap. Specifically, relevant to the field of membrane science, the band gap of phosphorene provides it with potential photocatalytic properties, which could be explored in making reactive membranes that can self-clean. The goal of this study was to develop an innovative and robust membrane that is able to control and reverse fouling with minimal changes in membrane performance. To this end, for the first time, membranes have been embedded with phosphorene. Membrane modification was verified by the presence of phosphorus on membranes, along with changes in surface charge, average pore size, and hydrophobicity. After modification, phosphorene-modified membranes were used to filter methylene blue (MB) under intermittent ultraviolet light irradiation. Phosphorene-modified and unmodified membranes displayed similar rejection of MB; however, after reverse-flow filtration was performed to mimic pure water cleaning, the average recovered flux of phosphorene-modified membranes was four times higher than that of unmodified membranes. Furthermore, coverage of MB on phosphorene membranes after reverse-flow filtration was four times lower than that of unmodified membranes, which supports the hypothesis that phosphorene membranes operated under intermittent ultraviolet irradiation can become self-cleaning. Phosphorene ; Membrane ; Materials science ; Chemical engineering ; Filtration (mathematics) ; Fouling ; Membrane fouling ; Nanocomposite ; Band gap ; Nanotechnology ; Chemistry ; Optoelectronics 2D Materials and Applications ; MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques
Authors: José Luís Dores‐Sousa | Jelle De Vos | Wim Th. Kok | Sebastiaan Eeltink
Source: Journal of Chromatography A (article, 2018-07-26)
Field: Chemistry — Spectroscopy

Probing selectivity of mixed-mode reversed-phase/weak-anion-exchange liquid chromatography to advance method development Chemistry ; Selectivity ; van der Waals force ; Ionic strength ; Phase (matter) ; Analytical Chemistry (journal) ; Chromatography ; Wax ; Ion exchange ; Ionic bonding ; Reversed-phase chromatography ; Ion ; High-performance liquid chromatography ; Organic chemistry ; Molecule ; Aqueous solution Analytical Chemistry and Chromatography ; Microfluidic and Capillary Electrophoresis Applications ; Protein purification and stability
Authors: Ravini U. Chandrasena | Charles Louis Flint | Wei-Shih Yang | Arian Arab | Slavomír Nemšák | Mathias Gehlmann | V. Burak Özdöl | F. Bisti | Kanishka Wijesekara | Julia Meyer-Ilse | Eric M. Gullikson | Elke Arenholz | Jim Ciston | Claus M. Schneider | Vladimir N. Strocov | Y. Suzuki | A. X. Gray
Source: Physical review. B./Physical review. B (article, 2018-10-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Depth-resolved charge reconstruction at the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>LaNi</mml:mi><mml:msub><mml:mi mathvariant="normal">O</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mo>/</mml:mo><mml:mi>CaMn</mml:mi><mml:msub><mml:mi mathvariant="normal">O</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math> interface Rational design of low-dimensional electronic phenomena at oxide interfaces is currently considered to be one of the most promising schemes for realizing new energy-efficient logic and memory devices. An atomically abrupt interface between paramagnetic LaNiO&lt;sub&gt;3&lt;/sub&gt; and antiferromagnetic CaMnO&lt;sub&gt;3&lt;/sub&gt; exhibits interfacial ferromagnetism, which can be tuned via a thickness-dependent metal-insulator transition in LaNiO&lt;sub&gt;3&lt;/sub&gt;. Once fully understood, such emergent functionality could turn this archetypal Mott-interface system into a key building block for the above-mentioned future devices. Here, we use depth-resolved standing-wave photoemission spectroscopy in conjunction with scanning transmission electron microscopy and x-ray absorption spectroscopy, to demonstrate a depth-dependent charge reconstruction at the LaNiO&lt;sub&gt;3&lt;/sub&gt;/CaMnO&lt;sub&gt;3&lt;/sub&gt; interface. Our measurements reveal an increased concentration of Mn&lt;sup&gt;3+&lt;/sup&gt; and Ni&lt;sup&gt;2+&lt;/sup&gt; cations at the interface, which create an electronic environment favorable for the emergence of interfacial ferromagnetism mediated via the Mn&lt;sup&gt;4+&lt;/sup&gt;-Mn&lt;sup&gt;3+&lt;/sup&gt; ferromagnetic double exchange and Ni&lt;sup&gt;2+&lt;/sup&gt;-O-Mn&lt;sup&gt;4+&lt;/sup&gt; superexchange mechanisms. Our findings suggest a strategy for designing functional Mott oxide heterostructures by tuning the interfacial cation characteristics via controlled manipulation of thickness, strain, and ionic defect states. Antiferromagnetism ; Superexchange ; Crystallography ; Ferromagnetism ; Materials science ; Condensed matter physics ; Physics ; Mott insulator ; Chemistry Magnetic and transport properties of perovskites and related materials ; Electronic and Structural Properties of Oxides ; Advanced Condensed Matter Physics
Authors: J.F.J. Soetens | Marc van Vijven | Dan L. Bader | Gwm Gerrit Peters | C.W.J. Oomens
Source: Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials (article, 2018-07-11)
Field: Materials Science — Polymers and Plastics

A model of human skin under large amplitude oscillatory shear Skin mechanics is of importance in various fields of research when accurate predictions of the mechanical response of skin is essential. This study aims to develop a new constitutive model for human skin that is capable of describing the heterogeneous, nonlinear viscoelastic mechanical response of human skin under shear deformation. This complex mechanical response was determined by performing large amplitude oscillatory shear (LAOS) experiments on ex vivo human skin samples. It was combined with digital image correlation (DIC) on the cross-sectional area to assess heterogeneity. The skin is modeled as a one-dimensional layered structure, with every sublayer behaving as a nonlinear viscoelastic material. Heterogeneity is implemented by varying the stiffness with skin depth. Using an iterative parameter estimation method all model parameters were optimized simultaneously. The model accurately captures strain stiffening, shear thinning, softening effect and nonlinear viscous dissipation, as experimentally observed in the mechanical response to LAOS. The heterogeneous properties described by the model were in good agreement with the experimental DIC results. The presented mathematical description forms the basis for a future constitutive model definition that, by implementation in a finite element method, has the capability of describing the full 3D mechanical behavior of human skin. Viscoelasticity ; Nonlinear system ; Digital image correlation ; Constitutive equation ; Mechanics ; Softening ; Shear (geology) ; Materials science ; Stiffness ; Amplitude ; Finite element method ; Stiffening ; Structural engineering ; Physics ; Composite material ; Optics ; Engineering Textile materials and evaluations ; Rheology and Fluid Dynamics Studies ; Elasticity and Material Modeling
Authors: Hongli Liu | Shixiong Li | Hongyan Li | Zhong Chen | Jing Li | Yajing Li
Source: Journal of Sol-Gel Science and Technology (article, 2018-10-23)
Field: Chemistry — Spectroscopy

HNTs/SiO2 dual-network aerogels with improved strength and thermal insulation Aerogel ; Materials science ; Compressive strength ; Composite material ; Thermal conductivity ; Thermal insulation ; Scanning electron microscope ; Microstructure Aerogels and thermal insulation ; Supercapacitor Materials and Fabrication ; Layered Double Hydroxides Synthesis and Applications
Authors: Tianyu Gao | John R. Kitchin
Source: Catalysis Today (article, 2018-03-28)
Field: Materials Science — Materials Chemistry

Modeling palladium surfaces with density functional theory, neural networks and molecular dynamics Density functional theory ; Palladium ; Molecular dynamics ; Computational chemistry ; Potential energy surface ; Work (physics) ; Statistical physics ; Transition state theory ; Catalysis ; Diffusion ; Lattice (music) ; Potential energy ; Chemical physics ; Chemistry ; Materials science ; Thermodynamics ; Physical chemistry ; Molecule ; Physics ; Quantum mechanics ; Reaction rate constant ; Kinetics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Anat Milo
Source: Israel Journal of Chemistry (article, 2018-01-19)
Field: Chemistry — Organic Chemistry

The Art of Organic Synthesis in the Age of Automation Abstract Recent progress in the automation of chemical synthesis is part of a broad ongoing trend that is based on incorporating machine learning algorithms to improve automated performance in different tasks. However, what are the implications on the art of organic synthesis? Will robotic chemists replace us in the next 50–100 years? Will they be able to outperform a master organic chemist in the study of mechanisms, the development of new reactions, the optimization of reaction conditions, catalysts, or synthetic routes? What would be our role as human chemists in this automation revolution? In this essay, these questions will be reflected upon through the progress in the synthesis of strychnine since 1954. Chemistry ; Automation ; Chemist ; Organic synthesis ; Biochemical engineering ; Nanotechnology ; Artificial intelligence ; Process engineering ; Organic chemistry ; Catalysis ; Computer science ; Engineering ; Mechanical engineering Chemical synthesis and alkaloids ; Innovative Microfluidic and Catalytic Techniques Innovation ; Microbial Natural Products and Biosynthesis
Authors: Marcel Núñez | Dionisios G. Vlachos
Source: Industrial & Engineering Chemistry Research (article, 2018-12-10)
Field: Materials Science — Materials Chemistry

Multiscale Modeling Combined with Active Learning for Microstructure Optimization of Bifunctional Catalysts A number of studies have recently demonstrated that catalyst microstructure and defect engineering are important to enhance reaction rates, but rigorous microstructure optimization studies are lacking. Kinetic rate prediction requires models that resolve catalyst sites and their coupling arising from surface diffusion, spatial arrangement of multifunctional sites, and lateral interactions. Modeling these effects requires kinetic Monte Carlo (KMC) simulation. The computational demand of KMC simulation makes direct microstructure optimization infeasible. To overcome this challenge, we parametrize the KMC data (reaction rate) using an active learning approach to capture complex structural dependencies among sites at negligible computational cost. We apply our method to a prototype chemistry over bifunctional materials, a case study reminiscent of the ammonia decomposition reaction on defected NiPt (core/shell) structures. We demonstrate that machine learning can effectively develop surrogate models for system tasks in multiscale modeling. Bifunctional ; Kinetic Monte Carlo ; Microstructure ; Multiscale modeling ; Computer science ; Monte Carlo method ; Diffusion ; Catalysis ; Materials science ; Biological system ; Chemistry ; Computational chemistry ; Thermodynamics ; Physics ; Mathematics ; Composite material Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Caroline Desgranges | Jérôme Delhommelle
Source: Physical review. E (article, 2018-12-10)
Field: Materials Science — Materials Chemistry

Crystal nucleation along an entropic pathway: Teaching liquids how to transition We combine machine learning (ML) with Monte Carlo (MC) simulations to study the crystal nucleation process. Specifically, we use ML to infer the canonical partition function of the system over the range of densities and temperatures spanned during crystallization. We achieve this on the example of the Lennard-Jones system by training an artificial neural network using, as a reference dataset, equations of state for the Helmholtz free energy for the liquid and solid phases. The accuracy of the ML predictions is tested over a wide range of thermodynamic conditions, and results are shown to provide an accurate estimate for the canonical partition function, when compared to the results from flat-histogram simulations. Then, the ML predictions are used to calculate the entropy of the system during MC simulations in the isothermal-isobaric ensemble. This approach is shown to yield results in very good agreement with the experimental data for both the liquid and solid phases of argon. Finally, taking entropy as a reaction coordinate and using the umbrella sampling technique, we are able to determine the Gibbs free energy profile for the crystal nucleation process. In particular, we obtain a free energy barrier in very good agreement with the results from previous simulation studies. The approach developed here can be readily extended to molecular systems and complex fluids, and is especially promising for the study of entropy-driven processes. Helmholtz free energy ; Nucleation ; Statistical physics ; Thermodynamics ; Monte Carlo method ; Canonical ensemble ; Gibbs free energy ; Molecular dynamics ; Thermodynamic integration ; Crystallization ; Chemistry ; Physics ; Mathematics ; Computational chemistry Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; nanoparticles nucleation surface interactions
Authors: Peter C. Collins | Santhosh K. Koduri | Vikas Dixit | Hamish L. Fraser
Source: Metallurgical and Materials Transactions A (article, 2018-01-04)
Field: Materials Science — Materials Chemistry

Understanding the Interdependencies Between Composition, Microstructure, and Continuum Variables and Their Influence on the Fracture Toughness of α/β-Processed Ti-6Al-4V Microstructure ; Materials science ; Fracture toughness ; Toughness ; Ultimate tensile strength ; Composite material ; Plasticity ; Plane stress ; Alloy ; Fracture mechanics ; Ductility (Earth science) ; Structural engineering ; Creep ; Finite element method Titanium Alloys Microstructure and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels
Authors: Eric Schmidt | Andrew T. Fowler | James A. Elliott | Paul D. Bristowe
Source: Computational Materials Science (article, 2018-03-23)
Field: Materials Science — Materials Chemistry

Learning models for electron densities with Bayesian regression Density functional theory ; Atom (system on chip) ; Ground state ; Electron ; Statistical physics ; Parametric statistics ; Range (aeronautics) ; Materials science ; Binary number ; Electron density ; Physics ; Atomic physics ; Quantum mechanics ; Computer science ; Mathematics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Electron and X-Ray Spectroscopy Techniques
Authors: A. S. M. Jonayat | Adri C. T. van Duin | Michael J. Janik
Source: ACS Applied Energy Materials (article, 2018-10-23)
Field: Materials Science — Materials Chemistry

Discovery of Descriptors for Stable Monolayer Oxide Coatings through Machine Learning Monolayer metal oxides (MMOs) can provide tunable chemical properties dictated by choice of the support and coating metal oxide. Experimental discovery of (meta)stable coating/support combinations can be accelerated if stability could be predicted based on component physical properties. For such complex systems, machine learning approaches can help to discover underlying principles that dictate system properties. Herein, we use a supervised machine learning (ML) method, regressed against density functional theory-calculated monolayer stabilities, to predict physical properties that are predictive of metal oxide monolayer stability. Monolayer oxide coatings are considered in two classes: (1) “stoichiometric” coatings, in which the monolayer oxide has a stable phase at the same MOx stoichiometry as the substrate, and (2) “nonstoichiometric” coatings. Our ML approach indicates that substrate surface energy, orbital radii, and ionization energies are important for stability of stoichiometric MMOs. The parent oxide stability of the coating material as well as oxidation state differences between coating and support are important descriptors of stability of nonstoichiometric MMOs. Monolayer ; Oxide ; Materials science ; Computer science ; Artificial intelligence ; Nanotechnology ; Metallurgy Machine Learning in Materials Science ; Semiconductor materials and devices ; Electronic and Structural Properties of Oxides
Authors: Victor Kalt | Alma K. González-Alcalde | Soukaina Es-Saidi | Rafael Salas‐Montiel | Sylvain Blaize | Demetrio Macı́as
Source: Journal of the Optical Society of America A (article, 2018-12-12)
Field: Materials Science — Surfaces, Coatings and Films

Metamodeling of high-contrast-index gratings for color reproduction We propose a metamodel-based optimization technique to tailor the chromatic response of high-contrast-index gratings. The algorithm, which couples a population-based metaheuristic with a neural network, is used to retrieve the optimal geometrical parameters of a grating to reproduce a prescribed color. By means of some examples, we assess the possibilities and limitations of our optimization scheme. The numerical evidence found shows that the metamodel approach offers an alternative to traditional metaheuristic techniques that not only provides the best solution for a given geometry and a material but also significantly improves the computing time required for the optimization process. Metamodeling ; Metaheuristic ; Computer science ; Contrast (vision) ; Population ; Artificial neural network ; Mathematical optimization ; Chromatic scale ; Algorithm ; Artificial intelligence ; Mathematics Optical Coatings and Gratings ; Photonic Crystals and Applications ; Optical measurement and interference techniques
Authors: Cristian Monea
Source: Journal of Electrical Engineering, Electronics, Control and Computer Science (article, 2018-09-08)
Field: Materials Science — Materials Chemistry

Signal Processing and Analysis Methods in Nuclear Quadrupole Resonance Spectroscopy The purpose of this paper is to review the signal processing and analysis methods applied in nuclear quadrupole resonance (NQR) spectroscopy. NQR is a radio frequency spectroscopic technique used for detecting solid state compounds containing quadrupolar nuclei, in applications ranging from chemical analysis to explosive and drugs detection. This paper presents the principle of NQR, its applications, the detection methods and an overview of the research done in the field of signal processing and analysis using this technique. Different solutions are described, starting from the techniques developed initially up to state-of-the-art detection algorithms. These are presented in chronological order, also discussing their principles, advantages and disadvantages. This paper proposes several directions for future research and suggests machine learning as a next step in NQR signal analysis. Nuclear quadrupole resonance ; Signal processing ; SIGNAL (programming language) ; Computer science ; Explosive material ; Spectroscopy ; Explosive detection ; Quadrupole ; Field (mathematics) ; Nuclear magnetic resonance ; Digital signal processing ; Physics ; Chemistry ; Mathematics ; Atomic physics ; Computer hardware Solid-state spectroscopy and crystallography ; Advanced NMR Techniques and Applications ; Electron Spin Resonance Studies