jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Clovis Lapointe | Thomas D. Swinburne | Louis Thiry | Stéphane Mallat | Laurent Proville | Charlotte Becquart | Mihai‐Cosmin Marinica
Source: Physical Review Materials (article, 2020-06-15)
Field: Materials Science — Materials Chemistry

Machine learning surrogate models for prediction of point defect vibrational entropy The temperature variation of the defect densities in a crystal depends on vibrational entropy. This contribution to the system thermodynamics remains computationally challenging as it requires a diagonalization of the system's Hessian which scales as $O({N}^{3})$ for a crystal made of $N$ atoms. Here, to circumvent such a heavy computational task and make it feasible even for systems containing millions of atoms, the harmonic vibrational entropy of point defects is estimated directly from the relaxed atomic positions through a linear-in-descriptor machine learning approach of order $O(N)$. With a size-independent descriptor dimension and fixed model parameters, an excellent predictive power is demonstrated on a wide range of defect configurations, supercell sizes, and external deformations well outside the training database. In particular, formation entropies in a range of $250{k}_{B}$ are predicted with less than $1.6{k}_{B}$ error from a training database whose formation entropies span only $25{k}_{B}$ (training error less than $1.0{k}_{B}$). This exceptional transferability is found to hold even when the training is limited to a low-energy superbasin in the phase space while the tests are performed for a different liquid-like superbasin at higher energies. Materials science ; Transferability ; Thermodynamics ; Entropy (arrow of time) ; Statistical physics ; Configuration entropy ; Machine learning ; Computer science ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Anita Tarbuk | Katia Grgić | Emilija Toshikj | Daniel Domović | Dejan Dimitrovski | Vesna Dimova | Igor Jordanov
Source: Cellulose (article, 2020-02-01)
Field: Materials Science — Biomaterials

Monitoring of cellulose oxidation level by electrokinetic phenomena and numeric prediction model Electrokinetic phenomena ; Streaming current ; Isoelectric point ; Titration ; Cellulose ; Zeta potential ; Cationic polymerization ; Biological system ; Cellulose acetate ; Adsorption ; Pulmonary surfactant ; Chemical engineering ; Chemistry ; Materials science ; Computer science ; Inorganic chemistry ; Nanotechnology ; Organic chemistry ; Engineering Advanced Cellulose Research Studies ; Dyeing and Modifying Textile Fibers ; Lignin and Wood Chemistry
Authors: Mohammad Zamanifard | Mohammad Taghi Khorasani | Morteza Daliri | Mahmoud Parvazinia
Source: Journal of Biomaterials Science Polymer Edition (article, 2020-05-13)
Field: Materials Science — Biomaterials

Preparation and modeling of electrospun polyhydroxybutyrate/polyaniline composite scaffold modified by plasma and printed by an inkjet method and its cellular study The reconstruction of the nerve tissue engineering scaffold is always of particular interest due to the inability to recover and repair neural tissues after being damaged by diseases or physical injuries. The primary purpose of this study was obtaining a model used to predict the diameter of the fibers of electrospun polyhydroxybutyrate (PHB) scaffolds. Accordingly, the range of operating parameters, namely the applied voltage, the distance between the nozzle to the collector, and solution concentration, was designed for the electrospinning process at three different levels, giving seventeen experiments. These data were modeled utilizing response surface methodology and artificial neural network method using Design Expert and Matlab software.The effect of process parameters on the diameter, as well as their interactions were investigated in detail, and the corresponding models were suggested. Both the RSM and ANN models showed an excellent agreement between the experimental and predicted response values. In the second phase of the study, PHB natural polymer was electrospun into scaffolds with high biocompatibility, resulting in a 224-360 nm diameter range .To further modify the scaffold in order to improve the compatibility of PHB, the fibrous surface of scaffolds was exposed to oxygenated plasma gas radiation under controlled conditions. Next, polyaniline (PANI) nanoparticles were then synthesized and printed on the surface of scaffolds as parallel lines. Then samples were exposed to the electric field. Fourier-transform infrared spectroscopy, water contact angle, optical and electron microscopy, tensile test, and cell viability analysis were performed to study properties of resulting scaffolds. The results indicated the fact that modification of the scaffolds by oxygen plasma and printing PANI nanoparticles in particular patterns had a favorable impact on cell adhesion and direction of cell growth, showing the potential of resulting scaffolds for nerve tissue engineering applications. Electrospinning ; Materials science ; Polyhydroxybutyrate ; Biocompatibility ; Response surface methodology ; Scaffold ; Composite material ; Fourier transform infrared spectroscopy ; Polymer ; Chemical engineering ; Biomedical engineering ; Chemistry ; Chromatography Electrospun Nanofibers in Biomedical Applications ; Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Varuni Dantanarayana | Tahereh Nematiaram | Daniel Vong | John E. Anthony | Alessandro Troisi | Kien Nguyen-Cong | Nir Goldman | Roland Faller | Adam J. Moulé
Source: Journal of Chemical Theory and Computation (article, 2020-05-13)
Field: Materials Science — Materials Chemistry

Predictive Model of Charge Mobilities in Organic Semiconductor Small Molecules with Force-Matched Potentials Charge mobility of crystalline organic semiconductors (OSC) is limited by local dynamic disorder. Recently, the charge mobility for several high mobility OSCs, including TIPS-pentacene, were accurately predicted from a density functional theory (DFT) simulation constrained by the crystal structure and the inelastic neutron scattering spectrum, which provide direct measures of the structure and the dynamic disorder in the length scale and energy range of interest. However, the computational expense required for calculating all of the atomic and molecular forces is prohibitive. Here we demonstrate the use of density functional tight binding (DFTB), a semiempirical quantum mechanical method that is 2 to 3 orders of magnitude more efficient than DFT. We show that force matching a many-body interaction potential to DFT derived forces yields highly accurate DFTB models capable of reproducing the low-frequency intricacies of experimental inelastic neutron scattering (INS) spectra and accurately predicting charge mobility. We subsequently predicted charge mobilities from our DFTB model of a number of previously unstudied structural analogues to TIPS-pentacene using dynamic disorder from DFTB and transient localization theory. The approach we establish here could provide a truly rapid simulation pathway for accurate materials properties prediction, in our vision applied to new OSCs with tailored properties. Pentacene ; Organic semiconductor ; Density functional theory ; Tight binding ; Charge (physics) ; Chemical physics ; Electron mobility ; Materials science ; Charge carrier ; Inelastic neutron scattering ; Scattering ; Molecular physics ; Chemistry ; Inelastic scattering ; Computational chemistry ; Physics ; Electronic structure ; Optoelectronics ; Transistor ; Voltage ; Quantum mechanics Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Organic and Molecular Conductors Research
Authors: Sebastián Caro-Ortiz | Erik Zuidema | Desmond Dekker | Marcello Rigutto | David Dubbeldam | Thijs J. H. Vlugt
Source: The Journal of Physical Chemistry C (article, 2020-09-08)
Field: Chemistry — Inorganic Chemistry

Adsorption of Aromatics in MFI-Type Zeolites: Experiments and Framework Flexibility in Monte Carlo Simulations &lt;p&gt;Computer simulations of adsorption of aromatics in zeolites are typically performed using rigid zeolite frameworks. However, adsorption isotherms for aromatics are very sensitive to small differences in the atomic positions of the zeolite (Chem. Phys. Lett., 1999, 308, 155-159). This article studies the effect of framework flexibility on the adsorption of aromatics in MFI-type zeolites computed by grand-canonical Monte Carlo simulations. New experimental data of adsorption of ethylbenzene in a MFI-type zeolite at 353 K is presented. The adsorption of n-heptane, ethylbenzene, and xylene isomers is computed in three MFI-type zeolite structures. It is observed that the intraframework interactions in flexible framework models induce small but important changes in the atom positions of the zeolite and hence in the adsorption isotherms. Framework flexibility is differently "rigid": flexible force fields produce a zeolite structure that vibrates around a new equilibrium configuration with limited capacity to accommodate to a bulky guest molecule. The vibration of the zeolite atoms only plays a role at high loadings, and the adsorption is mainly dependent on the average positions of the atoms. The simulations show that models for framework flexibility should not be blindly applied to zeolites and a general reconsideration of the parametrization schemes for such models is needed.&lt;/p&gt; Zeolite ; Adsorption ; Ethylbenzene ; Flexibility (engineering) ; Monte Carlo method ; Computational chemistry ; Materials science ; Chemistry ; Molecule ; Physical chemistry ; Organic chemistry ; Benzene ; Catalysis ; Mathematics Zeolite Catalysis and Synthesis ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced NMR Techniques and Applications
Authors: Adriana Samide | Claudia Merişanu | Bogdan Tutunaru | Gabriela Eugenia Iacobescu
Source: Molecules (article, 2020-01-21)
Field: Materials Science — Materials Chemistry

Poly (Vinyl Butyral-Co-Vinyl Alcohol-Co-Vinyl Acetate) Coating Performance on Copper Corrosion in Saline Environment Poly (vinyl butyral-co-vinyl alcohol-co-vinyl acetate) named further PVBA was investigated as a protective coating for copper corrosion in 0.9% NaCl solution using electrochemical measurements such as, electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization associated with atomic force microscopy (AFM). The PVBA coating on the copper surface (Cu-PVBA) was modeled in methanol containing PVBA. Its inhibitory properties against corrosion was comparatively discussed with those of the copper sample treated in methanol without polymer (Cu-Me) and of untreated sample (standard copper). A protective performance of PVBA coating of 80% was computed from electrochemical measurements, for copper corrosion in NaCl solution. Moreover, AFM images designed a specific surface morphology of coated surface with PVBA, clearly highlighting a polymer film adsorbed on the copper surface, which presents certain deterioration after corrosion, but the metal surface was not significantly affected compared to those of untreated samples or treated in methanol, in the absence of PVBA. Vinyl alcohol ; Copper ; Dielectric spectroscopy ; Vinyl acetate ; Corrosion ; Materials science ; Coating ; Electrochemistry ; Methanol ; Polymer ; Adsorption ; Chemical engineering ; Metallurgy ; Composite material ; Chemistry ; Organic chemistry ; Electrode Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Conducting polymers and applications
Authors: Alex C. Wiedenhoeft
Source: IAWA Journal - KU Leuven/IAWA Journal (article, 2020-10-29)
Field: Chemistry — Organic Chemistry

The XyloPhone: toward democratizing access to high-quality macroscopic imaging for wood and other substrates Abstract One rate-limiting factor in the fight against illegal logging is the lack of powerful, affordable, scalable wood identification tools for field screening. Computer vision wood identification using smartphones fitted with customized imaging peripherals offers a potential solution, but to date, such peripherals suffer from one or more weaknesses: low image quality, lack of lighting control, uncontrolled magnification, unknown distortion, and spherical aberration, and/or no access to or publication of the system design. To address cost, optical concerns, and open access to designs and parameters, I present the XyloPhone, a 3D printed research quality macroscopic imaging attachment adaptable to virtually any smartphone. It provides a fixed focal distance, exclusion of ambient light, selection of visible light or UV illumination, uses the lens from a commercially available loupe, is powered by a rechargeable external battery, is fully open-sourced, at a price point of less than USD 110 is a highly affordable tool for the laboratory or the field, and can serve as the foundational hardware for a scalable field-deployable computer vision wood identification system. Identification (biology) ; Computer science ; Scalability ; Quality (philosophy) ; Image quality ; Field (mathematics) ; Computer hardware ; Artificial intelligence ; Database ; Image (mathematics) ; Physics Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Remote Sensing and LiDAR Applications
Authors: Benjamin Apeleo Zubiri | Janis Wirth | Dominik Drobek | Silvan Englisch | Thomas Przybilla | Tobias Weißenberger | Wilhelm Schwieger | Erdmann Spiecker
Source: Advanced Materials Interfaces (article, 2020-10-07)
Field: Materials Science — Surfaces, Coatings and Films

Correlative Laboratory Nano‐CT and 360° Electron Tomography of Macropore Structures in Hierarchical Zeolites Abstract Hierarchical pore structures exhibit morphological features on several length scales, which govern important materials properties in catalysis, such as catalytic activity, diffusivity or selectivity. Correlative tomography offers unique opportunities for a comprehensive and scale‐bridging 3D characterization of such complex pore morphologies, which is crucial to further optimize materials design and synthesis routines. This study explores the capabilities of correlative 360° electron tomography (ET) and lab‐based nano X‐ray computed tomography (Nano‐CT) enabling 3D analyses of volumes of up to (60 µm)³ with down to nm resolution, as demonstrated for zeolite particles with embedded macropores. By first applying the two techniques to the same particle the higher resolution and fidelity of ET are used to improve the segmentation of pore space in the Nano‐CT reconstruction. Extended statistical relevance and access to interparticle pore space are obtained from reconstructions of larger particle agglomerates, using the large‐field‐of‐view mode of the Nano‐CT. The presented correlative approach enables real space analyses of important pore characteristics for comparison with complementary pore characterization techniques. Moreover, by investigating samples from different stages of the synthesis, 360°‐ET and Nano‐CT provide unique insights into the formation mechanism of porous materials, as demonstrated for the steam‐assisted crystallization of the macroporous zeolite particles. Electron tomography ; Materials science ; Characterization (materials science) ; Correlative ; Tomography ; Porosity ; Macropore ; Nanotechnology ; Zeolite ; Nano- ; Particle (ecology) ; Catalysis ; Mesoporous material ; Composite material ; Chemistry ; Optics ; Transmission electron microscopy ; Scanning transmission electron microscopy Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Hydrocarbon exploration and reservoir analysis
Authors: Hwan-Hui Lim | Enok Cheon | Deuk-Hwan Lee | Jun-Seo Jeon | Seung-Rae Lee
Source: Sensors (article, 2020-03-13)
Field: Chemistry — Analytical Chemistry

Classification of Granite Soils and Prediction of Soil Water Content Using Hyperspectral Visible and Near-Infrared Imaging Soil water content is one of the most important physical indicators of landslide hazards. Therefore, quickly and non-destructively classifying soils and determining or predicting water content are essential tasks for the detection of landslide hazards. We investigated hyperspectral information in the visible and near-infrared regions (400-1000 nm) of 162 granite soil samples collected from Seoul (Republic of Korea). First, effective wavelengths were extracted from pre-processed spectral data using the successive projection algorithm to develop a classification model. A gray-level co-occurrence matrix was employed to extract textural variables, and a support vector machine was used to establish calibration models and the prediction model. The results show that an optimal correct classification rate of 89.8% could be achieved by combining data sets of effective wavelengths and texture features for modeling. Using the developed classification model, an artificial neural network (ANN) model for the prediction of soil water content was constructed. The input parameter was composed of Munsell soil color, area of reflectance (near-infrared), and dry unit weight. The accuracy in water content prediction of the developed ANN model was verified by a coefficient of determination and mean absolute percentage error of 0.91 and 10.1%, respectively. Hyperspectral imaging ; Soil water ; Water content ; Infrared ; Remote sensing ; Environmental science ; Content (measure theory) ; Soil science ; Near-infrared spectroscopy ; Geology ; Mathematics ; Geotechnical engineering ; Optics Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping ; Mineral Processing and Grinding
Authors: Mansour Niazi | Arshad Ali | Jawad Ahmad | Fehaid Alqahtani | Fatmah AB Baotham | Fadi Abu-Amara
Source: Electronics (article, 2020-10-02)
Field: Materials Science — Materials Chemistry

Prediction of Critical Flashover Voltage of High Voltage Insulators Leveraging Bootstrap Neural Network Understanding the flashover performance of the outdoor high voltage insulator has been in the interest of many researchers recently. Various studies have been performed to investigate the critical flashover voltage of outdoor high voltage insulators analytically and in the laboratory. However, laboratory experiments are expensive and time-consuming. On the other hand, mathematical models are based on certain assumptions which compromise on the accuracy of results. This paper presents an intelligent system based on Artificial Neural Networks (ANN) to predict the critical flashover voltage of High-Temperature Vulcanized (HTV) silicone rubber in polluted and humid conditions. Various types of learning algorithms are used, such as Gradient Descent (GD), Levenberg-Marquardt (LM), Conjugate Gradient (CG), Quasi-Newton (QN), Resilient Backpropagation (RBP), and Bayesian Regularization Backpropagation (BRBP) to train the ANN. The number of neurons in the hidden layers along with the learning rate was varied to understand the effect of these parameters on the performance of ANN. The proposed ANN was trained using experimental data obtained from extensive experimentation in the laboratory under controlled environmental conditions. The proposed model demonstrates promising results and can be used to monitor outdoor high voltage insulators. It was observed from obtained results that changing of the number of neurons, learning rates, and learning algorithms of ANN significantly change the performance of the proposed algorithm. Backpropagation ; Artificial neural network ; Voltage ; Arc flash ; Gradient descent ; Computer science ; High voltage ; Insulator (electricity) ; Artificial intelligence ; Silicone rubber ; Conjugate gradient method ; Machine learning ; Electronic engineering ; Engineering ; Algorithm ; Materials science ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Energy Load and Power Forecasting
Authors: Amirhossein Hassanzadeh | Jan van Aardt | Sean P. Murphy | Sarah J. Pethybridge
Source: Journal of Applied Remote Sensing (article, 2020-06-05)
Field: Chemistry — Analytical Chemistry

Yield modeling of snap bean based on hyperspectral sensing: a greenhouse study Farmers and growers typically use approaches based on the crop environment and local meteorology, many of which are labor-intensive, to predict crop yield. These approaches have found broad acceptance but lack real-time and physiological feedback for near-daily management purposes. This is true for broad-acre crops, such as snap bean, which is valued at hundreds of millions of dollars in the annual agricultural market. We aim to investigate the relationships between snap bean yield and plant spectral and biophysical information, collected using a hyperspectral spectroradiometer (400 to 2500 nm). The experiment focused on 48 single snap bean plants (cv. Huntington) in a controlled greenhouse environment during the growth period (69 days). We used applicable accuracy and precision metrics from partial least squares regression and cross-validation methods to evaluate the predictive ability of two harvest stages, namely an early-harvest and late-harvest stage, against our yield indicator (bean pod weight). Four different spectral data sets were used to investigate whether such oversampled, hyperspectral data sets could accurately and precisely model observed variability in yield, in terms of the coefficient of determination (R2) and root-mean-square error (RMSE). The objective of our approach hinges on the philosophy that selected spectral bands from this study, i.e., those that best explain yield variability, can be downsampled from a hyperspectral system for use in a more cost-effective, operational multispectral sensor. Our results suggested the optimal period for spectral evaluation of snap bean yield is 20 to 25 or 32 days prior to harvest for the early- and late-harvest stages, respectively, with the best model performing at a low RMSE (3.02 g plant − 1) and a high coefficient of determination (R2 = 0.72). An unmanned aerial systems-mounted, affordable, and wavelength-programmable multispectral imager, with bands corresponding to those identified, could provide a near real-time and reliable yield estimate prior to harvest. Hyperspectral imaging ; Spectroradiometer ; Precision agriculture ; Mean squared error ; Yield (engineering) ; Crop yield ; Greenhouse ; Multispectral image ; Mathematics ; Partial least squares regression ; Snap ; Statistics ; Environmental science ; Remote sensing ; Agricultural engineering ; Computer science ; Agronomy ; Agriculture ; Geography ; Engineering Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement