jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tatsuya Yokoi | Yusuke Noda | Atsutomo Nakamura | Katsuyuki Matsunaga
Source: Physical Review Materials (article, 2020-01-29)
Field: Materials Science — Materials Chemistry

Neural-network interatomic potential for grain boundary structures and their energetics in silicon Artificial neural-network (ANN) interatomic potentials for simulating atomic structures and energetics of grain boundaries (GBs) in silicon were constructed and integrated into structural optimization and molecular dynamics (MD) algorithms. A training dataset including various atomic environments of symmetric tilt GBs was generated by performing density-functional-theory (DFT) calculations. The ANN potential after training was found to be capable of approximating the potential-energy surface at GBs even with dangling bonds and large atomic displacements at high temperatures, which cannot be well reproduced with empirical interatomic potentials. Additionally, reliability of the ANN potential for molecular simulations was evaluated. GB structures optimized or equilibrated by the ANN molecular simulations were also energetically lower for DFT calculations, without significant errors. The ANN potential is therefore expected to greatly reduce structural-optimization iterations and required time steps to acquire stable or equilibrium GB structures in MD simulations, enabling us to address even large-scale systems of general GBs in silicon, with high accuracy and low computational cost. Interatomic potential ; Dangling bond ; Molecular dynamics ; Materials science ; Energetics ; Silicon ; Grain boundary ; Density functional theory ; Artificial neural network ; Statistical physics ; Molecular physics ; Chemical physics ; Computational chemistry ; Thermodynamics ; Computer science ; Physics ; Microstructure ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Semiconductor materials and interfaces ; Semiconductor materials and devices
Authors: Guillermo Reyes | Meri Lundahl | Serguei Alejandro-Martín | Luis E. Arteaga‐Pérez | Claudia Oviedo | Alistair W. T. King | Orlando J. Rojas
Source: Biomacromolecules (article, 2020-01-02)
Field: Materials Science — Biomaterials

Coaxial Spinning of All-Cellulose Systems for Enhanced Toughness: Filaments of Oxidized Nanofibrils Sheathed in Cellulose II Regenerated from a Protic Ionic Liquid Hydrogels of TEMPO-oxidized nanocellulose were stabilized for dry-jet wet spinning using a shell of cellulose dissolved in 1,5-diazabicyclo[4.3.0]non-5-enium propionate ([DBNH][CO<sub>2</sub>Et]), a protic ionic liquid (PIL). Coagulation in an acidic water bath resulted in continuous core-shell filaments (CSFs) that were tough and flexible with an average dry (and wet) toughness of ∼11 (2) MJ·m<sup>-3</sup> and elongation of ∼9 (14) %. The CSF morphology, chemical composition, thermal stability, crystallinity, and bacterial activity were assessed using scanning electron microscopy with energy-dispersive X-ray spectroscopy, liquid-state nuclear magnetic resonance, Fourier transform infrared spectroscopy, thermogravimetric analysis, pyrolysis gas chromatography-mass spectrometry, wide-angle X-ray scattering, and bacterial cell culturing, respectively. The coaxial wet spinning yields PIL-free systems carrying on the surface the cellulose II polymorph, which not only enhances the toughness of the filaments but facilities their functionalization. Cellulose ; Ionic liquid ; Thermogravimetric analysis ; Thermal stability ; Chemical engineering ; Fourier transform infrared spectroscopy ; Crystallinity ; Materials science ; Nanocellulose ; Chemistry ; Regenerated cellulose ; Polymer chemistry ; Organic chemistry ; Composite material Advanced Cellulose Research Studies ; biodegradable polymer synthesis and properties ; Nanocomposite Films for Food Packaging
Authors: Meade Erickson | Marvellous Ngongang | Bakhtiyor Rasulev
Source: Molecules (article, 2020-08-19)
Field: Materials Science — Polymers and Plastics

A Refractive Index Study of a Diverse Set of Polymeric Materials by QSPR with Quantum-Chemical and Additive Descriptors Predicting the activities and properties of materials via in silico methods has been shown to be a cost- and time-effective way of aiding chemists in synthesizing materials with desired properties. Refractive index (n) is one of the most important defining characteristics of an optical material. Presented in this work is a quantitative structure-property relationship (QSPR) model that was developed to predict the refractive index for a diverse set of polymers. A number of models were created, where a four-variable model showed the best predictive performance with R<sup>2</sup> = 0.904 and Q<sup>2</sup><sub>LOO</sub> = 0.897. The robustness and predictability of the best model was validated using the leave-one-out technique, external set and y-scrambling methods. The predictive ability of the model was confirmed with the external set, showing the R<sup>2</sup><sub>ext</sub> = 0.880. For the refractive index, the ionization potential, polarizability, 2D and 3D geometrical descriptors were the most influential properties. The developed model was transparent and mechanistically explainable and can be used in the prediction of the refractive index for new and untested polymers. Quantitative structure–activity relationship ; Refractive index ; Robustness (evolution) ; Predictability ; Polarizability ; Biological system ; Set (abstract data type) ; Polymer ; Computer science ; Quantum chemical ; Materials science ; Mathematics ; Chemistry ; Organic chemistry ; Machine learning ; Molecule ; Statistics ; Composite material ; Optoelectronics Synthesis and properties of polymers ; Photopolymerization techniques and applications ; Conducting polymers and applications
Authors: Cosima Hirschberg | Magnus Edinger | Else Holmfred | Jukka Rantanen | Johan Boetker
Source: Pharmaceutics (article, 2020-09-15)
Field: Chemistry — Analytical Chemistry

Image-Based Artificial Intelligence Methods for Product Control of Tablet Coating Quality Mimicking the human decision-making process is challenging. Especially, many process control situations during the manufacturing of pharmaceuticals are based on visual observations and related experience-based actions. The aim of the present work was to investigate the use of image analysis to classify the quality of coated tablets. Tablets with an increasing amount of coating solution were imaged by fast scanning using a conventional office scanner. A segmentation routine was implemented to the images, allowing the extraction of numeric image-based information from individual tablets. The image preprocessing was performed prior to utilization of four different classification techniques for the individual tablet images. The support vector machine (SVM) technique performed superior compared to a convolutional neural network (CNN) in relation to computational time, and this approach was also slightly better at classifying the tablets correctly. The fastest multivariate method was partial least squares (PLS) regression, but this method was hampered by the inferior classification accuracy of the tablets. Finally, it was possible to create a numerical threshold classification model with an accuracy comparable to the SVM approach, so it is evident that there exist multiple valid options for classifying coated tablets. Artificial intelligence ; Support vector machine ; Computer science ; Preprocessor ; Artificial neural network ; Pattern recognition (psychology) ; Scanner ; Convolutional neural network ; Partial least squares regression ; Process (computing) ; Machine learning ; Segmentation ; Image quality ; Image (mathematics) ; Data mining Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Advanced Chemical Sensor Technologies
Authors: Tritsaris, Georgios A. | Carr, Stephen | Zhu, Ziyan | Xie, Yiqi | Torrisi, Steven B. | Tang, Jing | Mattheakis, Marios | Larson, Daniel | Kaxiras, Efthimios
Source: Open MIND (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Electronic structure calculations of twisted multi-layer graphene superlattices Quantum confinement endows two-dimensional (2D) layered materials with exceptional physics and novel properties compared to their bulk counterparts. Although certain two- and few-layer configurations of graphene have been realized and studied, a systematic investigation of the properties of arbitrarily layered graphene assemblies is still lacking. We introduce theoretical concepts and methods for the processing of materials information, and as a case study, apply them to investigate the electronic structure of multi-layer graphene-based assemblies in a high-throughput fashion. We provide a critical discussion of patterns and trends in tight binding band structures and we identify specific layered assemblies using low-dispersion electronic bands as indicators of potentially interesting physics like strongly correlated behavior. A combination of data-driven models for visualization and prediction is used to intelligently explore the materials space. This work more generally aims to increase confidence in the combined use of physics-based and data-driven modeling for the systematic refinement of knowledge about 2D layered materials, with implications for the development of novel quantum devices. Graphene ; Superlattice ; Materials science ; Electronic structure ; Layer (electronics) ; Nanotechnology ; Visualization ; Space (punctuation) ; Electronic band structure ; Dispersion (optics) ; Computer science ; Physics ; Data mining ; Condensed matter physics ; Optoelectronics ; Quantum mechanics Graphene research and applications ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Ryan P. Badman | Shanna L. Moore | Jessica L. Killian | Tuancheng Feng | Thomas A. Cleland | Fenghua Hu | Michelle D. Wang
Source: Scientific Reports (article, 2020-07-08)
Field: Materials Science — Materials Chemistry

Dextran-coated iron oxide nanoparticle-induced nanotoxicity in neuron cultures Recent technological advances have introduced diverse engineered nanoparticles (ENPs) into our air, water, medicine, cosmetics, clothing, and food. However, the health and environmental effects of these increasingly common ENPs are still not well understood. In particular, potential neurological effects are one of the most poorly understood areas of nanoparticle toxicology (nanotoxicology), in that low-to-moderate neurotoxicity can be subtle and difficult to measure. Culturing primary neuron explants on planar microelectrode arrays (MEAs) has emerged as one of the most promising in vitro techniques with which to study neuro-nanotoxicology, as MEAs enable the fluorescent tracking of nanoparticles together with neuronal electrical activity recording at the submillisecond time scale, enabling the resolution of individual action potentials. Here we examine the dose-dependent neurotoxicity of dextran-coated iron oxide nanoparticles (dIONPs), a common type of functionalized ENP used in biomedical applications, on cultured primary neurons harvested from postnatal day 0-1 mouse brains. A range of dIONP concentrations (5-40 µg/ml) were added to neuron cultures, and cells were plated either onto well plates for live cell, fluorescent reactive oxidative species (ROS) and viability observations, or onto planar microelectrode arrays (MEAs) for electrophysiological measurements. Below 10 µg/ml, there were no dose-dependent cellular ROS increases or effects in MEA bursting behavior at sub-lethal dosages. However, above 20 µg/ml, cell death was obvious and widespread. Our findings demonstrate a significant dIONP toxicity in cultured neurons at concentrations previously reported to be safe for stem cells and other non-neuronal cell types. Nanotoxicology ; Neurotoxicity ; Microelectrode ; Biophysics ; Neuron ; Nanotechnology ; Multielectrode array ; Nanoparticle ; Cell biology ; Chemistry ; Toxicity ; Biology ; Materials science ; Neuroscience Nanoparticles: synthesis and applications ; Neuroscience and Neural Engineering ; Electrochemical Analysis and Applications
Authors: Ke Chen | Yewei Chen | Bo Zhang | Liang Mei | Min Guo | Hong Deng | Shuai Liu | Fengxiang Ma | Zhenfeng Gong | Qingxu Yu
Source: Sensors (article, 2020-02-20)
Field: Chemistry — Spectroscopy

Highly Sensitive Photoacoustic Microcavity Gas Sensor for Leak Detection A highly sensitive photoacoustic (PA) microcavity gas sensor for leak detection is proposed. The miniature and low-cost gas sensor mainly consisted of a micro-electro-mechanical system (MEMS) microphone and a stainless-steel capillary with two small holes opened on the side wall. Different from traditional PA sensors, the designed low-power sensor had no gas valves and pumps. Gas could diffuse into the stainless-steel PA microcavity from two holes. The volume of the cavity in the sensor was only 7.9 μL. We use a 1650.96 nm distributed feedback (DFB) laser and the second-harmonic wavelength modulation spectroscopy (2<i>f</i>-WMS) method to measure PA signals. The measurement result of diffused methane (CH<sub>4</sub>) gas shows a response time of 5.8 s and a recovery time of 5.2 s. The detection limit was achieved at 1.7 ppm with a 1-s lock-in integral time. In addition, the calculated normalized noise equivalent absorption (NNEA) coefficient was 1.2 × 10<sup>-8</sup> W·cm<sup>-1</sup>·Hz<sup>-1/2</sup>. The designed PA microcavity sensor can be used for the early warning of gas leakage. Materials science ; Gas leak ; Leak ; Photoacoustic spectroscopy ; Leakage (economics) ; Optoelectronics ; Microelectromechanical systems ; Detection limit ; Methane ; Absorption (acoustics) ; Optics ; Wavelength ; Laser ; Microphone ; Acoustics ; Chemistry ; Composite material Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Gas Sensing Nanomaterials and Sensors
Authors: Yuping Huang | Yutu Yang | Ye Sun | Haiyan Zhou | Kunjie Chen
Source: Sensors (article, 2020-09-08)
Field: Chemistry — Analytical Chemistry

Identification of Apple Varieties Using a Multichannel Hyperspectral Imaging System This paper reports the nondestructive detection of apple varieties using a multichannel hyperspectral imaging system consisting of an illumination fiber and 30 detection fibers arranged at source-detector distances of 1.5-36 mm over the spectral range of 550-1650 nm. Spatially resolved (SR) spectra were obtained for 1500 apples, 500 each of three varieties from the same orchard to avoid environmental and geographical influences. Partial least squares discriminant analysis (PLSDA) models were developed for single SR spectra and spectral combinations to compare their performance of variety detection. To evaluate the effect of spectral range on variety detection, three types of spectra (i.e., visible region: 550-780 nm, near-infrared region: 780-1650 nm, full region: 550-1650 nm) were analyzed and compared. The results showed that the single SR spectra presented a different accuracy for apple variety classification, and the optimal SR spectra varied with spectral types. Spectral combinations had better accuracies for variety detection with best overall classifications of 99.4% for both spectral ranges in the NIR and full regions; however, the spectral combination could not improve the results over the optimal single SR spectra in the visible region. Moreover, the recognition of golden delicious (GD) was better than those of the other two varieties, with the best classification accuracy of 100% for three types of spectra. Overall, the multichannel hyperspectral imaging system provides more spatial-spectral information for the apples, and the results demonstrate that the technique gave excellent classifications, which suggests that the multichannel hyperspectral imaging system has potential for apple variety detection. Hyperspectral imaging ; Spectral line ; Full spectral imaging ; Remote sensing ; Multispectral image ; Detector ; Pattern recognition (psychology) ; Artificial intelligence ; Near-infrared spectroscopy ; Spectrometer ; Mathematics ; Computer science ; Optics ; Physics ; Geology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Tsung‐Heng Tsai | Meena Choi | Balázs Bánfai | Yansheng Liu | Brendan MacLean | Tom Dunkley | Olga Vitek
Source: Molecular & Cellular Proteomics (article, 2020-03-31)
Field: Chemistry — Spectroscopy

Selection of Features with Consistent Profiles Improves Relative Protein Quantification in Mass Spectrometry Experiments In bottom-up mass spectrometry-based proteomics, relative protein quantification is often achieved with data-dependent acquisition (DDA), data-independent acquisition (DIA), or selected reaction monitoring (SRM). These workflows quantify proteins by summarizing the abundances of all the spectral features of the protein (<i>e.g.</i> precursor ions, transitions or fragments) in a single value per protein per run. When abundances of some features are inconsistent with the overall protein profile (for technological reasons such as interferences, or for biological reasons such as post-translational modifications), the protein-level summaries and the downstream conclusions are undermined. We propose a statistical approach that automatically detects spectral features with such inconsistent patterns. The detected features can be separately investigated, and if necessary, removed from the data set. We evaluated the proposed approach on a series of benchmark-controlled mixtures and biological investigations with DDA, DIA and SRM data acquisitions. The results demonstrated that it could facilitate and complement manual curation of the data. Moreover, it can improve the estimation accuracy, sensitivity and specificity of detecting differentially abundant proteins, and reproducibility of conclusions across different data processing tools. The approach is implemented as an option in the open-source R-based software MSstats. Mass spectrometry ; Selection (genetic algorithm) ; Isobaric labeling ; Chemistry ; Mass spectrometry imaging ; Tandem mass spectrometry ; Chromatography ; Computational biology ; Protein mass spectrometry ; Computer science ; Biology ; Artificial intelligence Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Dong Yan | Lei Hou | Peiyi Wu
Source: Cellulose (article, 2020-01-14)
Field: Chemistry — Organic Chemistry

Exploring the diffusion behavior of urea aqueous solution in the viscose film by ATR-FTIR spectroscopy Viscose ; Aqueous solution ; Fourier transform infrared spectroscopy ; Diffusion ; Materials science ; Urea ; Spectroscopy ; Chemical engineering ; Composite material ; Polymer chemistry ; Analytical Chemistry (journal) ; Chemistry ; Organic chemistry ; Thermodynamics ; Engineering Surfactants and Colloidal Systems ; Analytical Chemistry and Chromatography ; Hydrogels: synthesis, properties, applications
Authors: Anna M. Beiler | Brian D. McCarthy | Ben A. Johnson | Sascha Ott
Source: Nature Communications (article, 2020-11-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Enhancing photovoltages at p-type semiconductors through a redox-active metal-organic framework surface coating Surface modification of semiconductors can improve photoelectrochemical performance by promoting efficient interfacial charge transfer. We show that metal-organic frameworks (MOFs) are viable surface coatings for enhancing cathodic photovoltages. Under 1-sun illumination, no photovoltage is observed for p-type Si(111) functionalized with a naphthalene diimide derivative until the monolayer is expanded in three dimensions in a MOF. The surface-grown MOF thin film at Si promotes reduction of the molecular linkers at formal potentials >300 mV positive of their thermodynamic potentials. The photocurrent is governed by charge diffusion through the film, and the MOF film is sufficiently conductive to power reductive transformations. When grown on GaP(100), the reductions of the MOF linkers are shifted anodically by >700 mV compared to those of the same MOF on conductive substrates. This photovoltage, among the highest reported for GaP in photoelectrochemical applications, illustrates the power of MOF films to enhance photocathodic operation. Photocurrent ; Materials science ; Semiconductor ; Monolayer ; Photoelectrochemistry ; Metal-organic framework ; Cathodic protection ; Surface photovoltage ; Coating ; Organic semiconductor ; Thin film ; Optoelectronics ; Chemical engineering ; Nanotechnology ; Electrode ; Chemistry ; Electrochemistry ; Organic chemistry ; Physical chemistry Ga2O3 and related materials ; Advanced Photocatalysis Techniques ; 2D Materials and Applications
Authors: Ling Lu | Kaiqiang Luo | Wei Yang | Sidian Zhang | Wencai Wang | Haiyan Xu | Sizhu Wu
Source: RSC Advances (article, 2020-01-01)
Field: Materials Science — Polymers and Plastics

Insight into the anti-aging mechanisms of natural phenolic antioxidants in natural rubber composites using a screening strategy based on molecular simulation The failure of materials upon aging has led to the accumulation of waste and environmental pollution. Adding antioxidants (AOs) to the composites is one of the most effective ways to retard aging. However, traditional synthetic AOs are always detrimental to the environment and human health. The selection of antioxidants from streams by experiments will also definitely cost a lot of time and money. In addition, the complexity of thermo-oxidative aging factors along with the lack of quantitative tools significantly hampers its applications. So, building a screening strategy to quickly and easily find an appropriate and eco-friendly AO is imperative. In this study, we chose natural rubber (NR) as a matrix and provided a screening strategy based on diverse natural phenolic antioxidants to evaluate their ability in protecting NR composites. Thymol, α-tocopherol, and lipid-soluble epigallocatechin gallate (lsEGCG) were chosen from 18 natural phenolic antioxidants as potential alternative candidates. They were proved, indeed, to enhance the oxidative time in NR from experiments. Our results emphasized that thymol, α-tocopherol, and lsEGCG were promising alternatives for AOs in NR, and the <i>in vitro</i> toxicity test suggested that they are biocompatible. This study may develop a new strategy preference for screening the antioxidants by combining molecular simulation with the validation of experimental approaches, and therefore guide the AO molecular design with a more accurate theoretical prediction. Natural rubber ; Natural (archaeology) ; Composite material ; Materials science ; Biology Polymer Nanocomposites and Properties ; Polymer crystallization and properties ; Lignin and Wood Chemistry