jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sunday Olakunle Idowu | Amos A. Fatokun
Source: SLAS TECHNOLOGY (article, 2020-10-15)
Field: Chemistry — Organic Chemistry

Artificial Intelligence (AI) to the Rescue: Deploying Machine Learning to Bridge the Biorelevance Gap in Antioxidant Assays Oxidative stress induced by excessive levels of reactive oxygen species (ROS) underlies several diseases. Therapeutic strategies to combat oxidative damage are, therefore, a subject of intense scientific investigation to prevent and treat such diseases, with the use of phytochemical antioxidants, especially polyphenols, being a major part. Polyphenols, however, exhibit structural diversity that determines different mechanisms of antioxidant action, such as hydrogen atom transfer (HAT) and single-electron transfer (SET). They also suffer from inadequate in vivo bioavailability, with their antioxidant bioactivity governed by permeability, gut-wall and first-pass metabolism, and HAT-based ROS trapping. Unfortunately, no current antioxidant assay captures these multiple dimensions to be sufficiently "biorelevant," because the assays tend to be unidimensional, whereas biorelevance requires integration of several inputs. Finding a method to reliably evaluate the antioxidant capacity of these phytochemicals, therefore, remains an unmet need. To address this deficiency, we propose using artificial intelligence (AI)-based machine learning (ML) to relate a polyphenol's antioxidant action as the output variable to molecular descriptors (factors governing in vivo antioxidant activity) as input variables, in the context of a biomarker selectively produced by lipid peroxidation (a consequence of oxidative stress), for example F<sub>2</sub>-isoprostanes. Support vector machines, artificial neural networks, and Bayesian probabilistic learning are some key algorithms that could be deployed. Such a model will represent a robust predictive tool in assessing biorelevant antioxidant capacity of polyphenols, and thus facilitate the identification or design of antioxidant molecules. The approach will also help to fulfill the principles of the 3Rs (replacement, reduction, and refinement) in using animals in biomedical research. Antioxidant ; Machine learning ; Oxidative stress ; Artificial intelligence ; Context (archaeology) ; Chemistry ; Computer science ; Reactive oxygen species ; Polyphenol ; Oxidative damage ; Biochemistry ; Computational biology ; Pharmacology ; Biology Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Antioxidant Activity and Oxidative Stress
Authors: Jovana Krmar | Milan Vukičević | Ana Kovačević | Ana Protić | Mira Zečević | Biljana Otašević
Source: Journal of Chromatography A (article, 2020-04-29)
Field: Chemistry — Spectroscopy

Performance comparison of nonlinear and linear regression algorithms coupled with different attribute selection methods for quantitative structure - retention relationships modelling in micellar liquid chromatography Chemistry ; Feature selection ; Lasso (programming language) ; Linear regression ; Chromatography ; Support vector machine ; Analyte ; Principal component analysis ; Bayesian multivariate linear regression ; Artificial neural network ; Chemometrics ; Artificial intelligence ; Machine learning ; Computer science Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Spectroscopy and Chemometric Analyses
Authors: Xing Sun | Judith L. MacManus‐Driscoll | Haiyan Wang
Source: Annual Review of Materials Research (article, 2020-05-14)
Field: Materials Science — Materials Chemistry

Spontaneous Ordering of Oxide-Oxide Epitaxial Vertically Aligned Nanocomposite Thin Films The emerging field of self-assembled vertically aligned nanocomposite (VAN) thin films effectively enables strain, interface, and microstructure engineering as well as (multi)functional improvements in electric, magnetic, optical, and energy-related properties. Well-ordered or patterned microstructures not only empower VAN thin films with many new functionalities but also enable VAN thin films to be used in nanoscale devices. Comparative ordered devices formed via templating methods suffer from critical drawbacks of processing complexity and potential contamination. Therefore, VAN thin films with spontaneous ordering stand out and display many appealing features for next-generation technological devices, such as electronics, optoelectronics, ultrahigh-density memory systems, photonics, and 3D microbatteries. The spontaneous ordering described in this review contains ordered/patterned structures in both in-plane and out-of-plane directions. In particular, approaches to obtaining spontaneously ordered/patterned structures in-plane are systematically reviewedfrom both thermodynamic and kinetic perspectives. Out-of-plane ordering is also discussed in detail. In addition to reviewing the progress of VAN films with spontaneous ordering, this article also highlights some recent developments in spontaneous ordering approaches and proposes future directions. Materials science ; Thin film ; Nanotechnology ; Nanocomposite ; Microstructure ; Photonics ; Nanoscopic scale ; Oxide ; Optoelectronics ; Engineering physics ; Composite material ZnO doping and properties ; Copper-based nanomaterials and applications ; Semiconductor materials and devices
Authors: Oluwafemi Adeleke Ojo | Israel Olayide | Maureen Chidima Akalabu | Basiru Olaitan Ajiboye | Adebola Busola Ojo | Babatunji Emmanuel Oyinloye | Murugan Ramalingam | K Parveen | V Banse | L Ledwani | H Ahmed | A Abdel-Mohsen | H Emam | R Nair | S Varghese | B Nair | T Maekawa | Y Yoshida | D Sakthi Kumar | A Dey Bhowmik | A Bandyopadhyay | A Chattopadhyay | A Mishra | M Sardar | A Das | M Kamle | A Bharti | P Kumar | E Abbasi | F Aval | A Akbarzadeh | M Muani | T Nasrabadi | W Joo | Y Hanifehpour | K Nejati-Koshki | R Pashaei-Asl | P Karpagavinayagam | Vedhi | A Akbarzadehl | R Rezaei-Sadabady | S Davaran | N Zarghami | M Samiei | M Kouhi | S Walia | A Acharya | S Amini | E Ulloa | S Ghosh | A Atta | K Pramanik | N | V Vorobyova | M Skiba | A Shakun | S Nahirniak | N Khandelwal | A Singh | D Jain | M Upadhyay | H Verma | A Kora | R Sashidhar | J Arunachalam | C Buzea | I Pacheco | K Robbie | S Campisi | M Schiavoni | C Chan-Thaw | A Villa | B Raksha | S Pooja | S Babu | S Sanghi | T Muralikrishna | M Pattanayak | P Nayak | O Ojo | B Oyinloye | A Ojo | O Afolabi | O Peters | O Olaiya | A Fadaka | J Jonathan | O Osunlana | N Prabhu | T Gajendran | B Rajak | R Kumar | M Gogoi | S Patra | Z Ali | R Yahya
Source: Biointerface Research in Applied Chemistry (article, 2020-07-28)
Field: Materials Science — Materials Chemistry

Nanoparticles and their Biomedical Applications Over the years, due to the remarkable functional properties, the nanoparticles have been widely used and being tested in the treatment and diagnosis of diseases such as cancer, diabetes, etc. The green synthesis of these nanoparticles can be achieved by physical, chemical, and biological methods. Nanoparticles biosynthesis is put forth to be advantageous over chemical and physical methods because it is non or minimally toxic, environmentally friendly, and cost-effective. A green biosynthesis is an approach that connects nanotechnology with plants, microorganisms, waste materials, and biomolecules. The biological methods help to eliminate destructive processing situations, via letting the synthesis at biological pH, room temperature, and simultaneously, affordable price. Among various biological alternatives, medicinal plants and plant extracts seem to be the best options. Plants are the chemical factories of nature, the plant extracts contain various secondary metabolites, and it functions as reducing and stabilizing (capping) agent in bio-reduction reaction to synthesize new nanoparticles. Keeping these points in view, the present article reviews the various synthesis methodologies, key factors, characterizations, usages, and foretold antimicrobial approach in a systematic manner, concentrating on several green pathways for nanoparticles synthesis. Biomolecule ; Environmentally friendly ; Biochemical engineering ; Nanotechnology ; Nanoparticle ; Antimicrobial ; Computer science ; Biotechnology ; Chemistry ; Materials science ; Organic chemistry ; Biology ; Engineering Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Nanotechnology research and applications
Authors: Henrik Lund Mortensen | Søren Ager Meldgaard | Malthe Kjær Bisbo | Mads-Peter Verner Christiansen | Bjørk Hammer
Source: Physical review. B./Physical review. B (article, 2020-08-12)
Field: Materials Science — Materials Chemistry

Atomistic structure learning algorithm with surrogate energy model relaxation The recently proposed atomistic structure learning algorithm (ASLA) builds on neural network enabled image recognition and reinforcement learning. It enables fully autonomous structure determination when used in combination with a first-principles total energy calculator, e.g., a density functional theory (DFT) program. To save on the computational requirements, ASLA utilizes the DFT program in a single-point mode, i.e., without allowing for relaxation of the structural candidates according to the force information at the DFT level. In this work, we augment ASLA to establish a surrogate energy model concurrently with its structure search. This enables approximative but computationally cheap relaxation of the structural candidates before the single-point energy evaluation with the computationally expensive DFT program. We demonstrate a significantly increased performance of ASLA for building benzene while utilizing a surrogate energy landscape. Further, we apply this model-enhanced ASLA in a thorough investigation of the $c(4\ifmmode\times\else\texttimes\fi{}8)$ phase of the Ag(111) surface oxide. ASLA successfully identifies a surface reconstruction which has previously only been guessed on the basis of scanning tunneling microscopy images. Relaxation (psychology) ; Computer science ; Energy (signal processing) ; Density functional theory ; Algorithm ; Artificial intelligence ; Point (geometry) ; Mathematics ; Physics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Yiming Zhang | Zeyu Mao | Qi Han | Youbing Li | Mian Li | Shiyu Du | Zhifang Chai | Qing Huang
Source: Materialia (article, 2020-06-29)
Field: Materials Science — Materials Chemistry

The role of Hume-Rothery's rules play in the MAX phases formability Formability ; Ternary operation ; Ceramic ; Figure of merit ; MAX phases ; Carbide ; Plot (graphics) MXene and MAX Phase Materials ; Machine Learning in Materials Science ; Aluminum Alloys Composites Properties
Authors: Ondrej Dyck | Mina Yoon | Lizhi Zhang | Andrew R. Lupini | Jacob L. Swett | Stephen Jesse
Source: ACS Applied Nano Materials (article, 2020-10-28)
Field: Materials Science — Materials Chemistry

Doping of Cr in Graphene Using Electron Beam Manipulation for Functional Defect Engineering Chromium atoms in graphene have been proposed to exhibit magnetic moments and spin-selective conducting states depending on the local bonding geometry within the graphene structure, which could lead to interesting applications in spintronics. Despite this interest, there are few direct experimental reports of Cr dopants in graphene even though it is theorized to be stable. Here, we demonstrate the introduction of single Cr dopant atoms into the graphene lattice and onto graphene edges through the controlled use of a focused electron beam in a scanning transmission electron microscope. We show local control of doping locations, and when coupled with targeted in situ milling during scanning of the e-beam, these strategies demonstrate an important component of the fabrication of tailored nanostructured devices in the electron microscope. The approach is validated with first-principles calculations to understand synthesis pathways during fabrication and reveal the energetics and local properties of Cr atoms embedded in graphene; for example, Cr doping can convert graphene into a magnetic and semiconducting material, which suggests Cr-doped graphene can be used as a building block for potential electronic devices and a means to construct them. Graphene ; Materials science ; Electron ; Cathode ray ; Doping ; Nanotechnology ; Engineering physics ; Optoelectronics ; Physics ; Nuclear physics Graphene research and applications ; Electron and X-Ray Spectroscopy Techniques ; Integrated Circuits and Semiconductor Failure Analysis
Authors: D. R. N. Vos | Shane R. Ellis | B. Balluff | Ron M. A. Heeren
Source: Molecular Imaging and Biology (review, 2020-10-06)
Field: Chemistry — Spectroscopy

Experimental and Data Analysis Considerations for Three-Dimensional Mass Spectrometry Imaging in Biomedical Research Mass spectrometry imaging (MSI) enables the visualization of molecular distributions on complex surfaces. It has been extensively used in the field of biomedical research to investigate healthy and diseased tissues. Most of the MSI studies are conducted in a 2D fashion where only a single slice of the full sample volume is investigated. However, biological processes occur within a tissue volume and would ideally be investigated as a whole to gain a more comprehensive understanding of the spatial and molecular complexity of biological samples such as tissues and cells. Mass spectrometry imaging has therefore been expanded to the 3D realm whereby molecular distributions within a 3D sample can be visualized. The benefit of investigating volumetric data has led to a quick rise in the application of single-sample 3D-MSI investigations. Several experimental and data analysis aspects need to be considered to perform successful 3D-MSI studies. In this review, we discuss these aspects as well as ongoing developments that enable 3D-MSI to be routinely applied to multi-sample studies. Mass spectrometry imaging ; Mass spectrometry ; Computer science ; Chemistry ; Chromatography Mass Spectrometry Techniques and Applications ; Nuclear Physics and Applications ; Ion-surface interactions and analysis
Authors: Xingmei Chen | Limin Dang | Hai Yang | Xianwei Huang | Xinliang Yu
Source: RSC Advances (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Machine learning-based prediction of toxicity of organic compounds towards fathead minnow Predicting the acute toxicity of a large dataset of diverse chemicals against fathead minnows (<i>Pimephales promelas</i>) is challenging. In this paper, 963 organic compounds with acute toxicity towards fathead minnows were split into a training set (482 compounds) and a test set (481 compounds) with an approximate ratio of 1 : 1. Only six molecular descriptors were used to establish the quantitative structure-activity/toxicity relationship (QSAR/QSTR) model for 96 hour <i>p</i>LC<sub>50</sub> through a support vector machine (SVM) along with genetic algorithm. The optimal SVM model (<i>R</i> <sup>2</sup> = 0.756) was verified using both internal (leave-one-out cross-validation) and external validations. The validation results (<i>q</i> <sub>int</sub> <sup>2</sup> = 0.699 and <i>q</i> <sub>ext</sub> <sup>2</sup> = 0.744) were satisfactory in predicting acute toxicity in fathead minnows compared with other models reported in the literature, although our SVM model has only six molecular descriptors and a large data set for the test set consisting of 481 compounds. Minnow ; Pimephales promelas ; Toxicity ; Chemistry ; Environmental chemistry ; Environmental science ; Artificial intelligence ; Computer science ; Biology ; Fishery ; Fish <Actinopterygii> ; Organic chemistry Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Dercílio Júnior Verly Lopes | Greg W. Burgreen | Edward D. Entsminger
Source: Forests (article, 2020-03-07)
Field: Chemistry — Organic Chemistry

North American Hardwoods Identification Using Machine-Learning This technical note determines the feasibility of using an InceptionV4_ResNetV2 convolutional neural network (CNN) to correctly identify hardwood species from macroscopic images. The method is composed of a commodity smartphone fitted with a 14× macro lens for photography. The end-grains of ten different North American hardwood species were photographed to create a dataset of 1869 images. The stratified 5-fold cross-validation machine-learning method was used, in which the number of testing samples varied from 341 to 342. Data augmentation was performed on-the-fly for each training set by rotating, zooming, and flipping images. It was found that the CNN could correctly identify hardwood species based on macroscopic images of its end-grain with an adjusted accuracy of 92.60%. With the current growing of machine-learning field, this model can then be readily deployed in a mobile application for field wood identification. Hardwood ; Convolutional neural network ; Computer science ; Artificial intelligence ; Identification (biology) ; Photography ; Machine learning ; Field (mathematics) ; Random forest ; Pattern recognition (psychology) ; Computer vision ; Mathematics ; Visual arts ; Ecology Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Remote Sensing and LiDAR Applications
Authors: Abrar Hussain | Lihao Yang | Shifeng Mao | Bo Da | K. Tőkési | Zejun Ding
Source: Nuclear Materials and Energy (article, 2020-12-08)
Field: Materials Science — Surfaces, Coatings and Films

Determination of electron backscattering coefficient of beryllium by a high-precision Monte Carlo simulation We present an up-to-date Monte Carlo simulation of electron backscattering coefficient of beryllium, which is an important material in fusion reactor, at an impact energy range of electrons between 100 eV and 100 keV. The Mott’s cross section calculated with more accurate scattering potential and a relativistic dielectric functional formalism with full Penn algorithm and experimental optical data are used in the modelling of electron elastic and inelastic scatterings, respectively. This Monte Carlo simulation modelling enabled us to derive comprehensive theoretical values of backscattering coefficient, which are found significantly smaller than the previous published experimental data particularly at energies below 10 keV. Simulation of electron backscattering spectra was then carried out with and without Auger electron emission, which confirmed that Auger electrons have negligible contribution to backscattering coefficient. To validate our simulation results, we have also performed calculation for amorphous boron and carbon, while the comparison with the available experimental data shows reasonable agreement. Further simulation for carbon and water covered Be sample has revealed that the surface contamination in low vacuum conditions with several atomic/molecular layers can drastically alter the measurement values for beryllium at low energies. The low backscattering coefficient values of beryllium are partly attributed to the extremely strong forward elastic scattering for low atomic number element. Therefore, we recommend to use the here reported backscattering coefficient data of beryllium for applications. Beryllium ; Monte Carlo method ; Atomic physics ; Electron ; Scattering ; Elastic scattering ; Computational physics ; Inelastic mean free path ; Electron scattering ; Materials science ; Inelastic scattering ; Physics ; Nuclear physics ; Optics Electron and X-Ray Spectroscopy Techniques ; X-ray Spectroscopy and Fluorescence Analysis ; Fusion materials and technologies
Authors: Vasisht Venkatesh | Ryan Noraas | Adam L. Pilchak | Sesh Tamirisa | Kayla Calvert | Ayman A. Salem | T. Broderick | M.G. Glavicic | Ian Dempster | Vikas Saraf
Source: MATEC Web of Conferences (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Data Driven Tools and Methods for Microtexture Classification and Dwell Fatigue Life Prediction in Dual Phase Titanium Alloys Microtexture has been linked to large reductions in cold dwell fatigue (CDF) life of specific dual phase titanium alloy aeroengine components. A recently completed Metals Affordability Initiative (MAI) funded program led by Pratt &amp; Whitney (P&amp;W) and includes ATI Forged Products, Boeing, GE Aviation, Rolls Royce (RR), Arconic, Titanium Metals Corporation (TIMET), PCC-Wyman Gordon (PCC-WG), Scientific Forming Technologies (SFTC), Materials Resources LLC (MRL) and The Ohio State University (OSU) has developed improved techniques for the characterization of microtexured regions (MTR) in titanium billet and forgings, and integrated computational materials engineering (ICME). These methods are aimed at developing and integrating process and property modeling tools for the prediction of microtexture and fatigue life in titanium components. These characterization and fatigue life prediction tools have near-term application off ramps that will enable use for process and product development and quality control. Key results for two widely used alloys, Ti-6242 and Ti-64, will be reviewed in this paper. Forging ; Titanium alloy ; Titanium ; Materials science ; Metallurgy ; Mechanical engineering ; Aerospace ; Manufacturing engineering ; Engineering ; Alloy ; Aerospace engineering Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; Aluminum Alloy Microstructure Properties
Authors: Xinyuan Qian | Xuebing Peng | Yuntao Song | Jianjun Huang | Yanpeng Wei | Peng Liu | Xin Mao | Jianwu Zhang | Liang Wang
Source: Nuclear Materials and Energy (article, 2020-06-27)
Field: Materials Science — Materials Chemistry

Dynamic constitutive relationship of CuCrZr alloy based on Johnson-Cook model CuCrZr alloy has been acknowledged as one of the most promising candidate materials for the heat sink of plasma facing components in nuclear fusion devices with its excellent thermal conductivity and specific mechanical strength. For the safety concern, it is vital to provide appropriate safety assessment on these mechanical structures with CuCrZr alloy, such as divertor and blanket, under high strain rate loading conditions like displacement events, disruption, and so on. In this work, the dynamic compressive behavior of the CuCrZr alloy over wide range of strain rates and temperatures was investigated using the split Hopkinson pressure bar technique. Based on the experiment results, a constitutive equation is built by using Johnson-Cook model to describe the dynamical property of CuCrZr alloy. However, the results indicated that the influences of the strain, strain rate and temperature on the dynamic behavior are not independent. Therefore, a modified Johnson-Cook model was further developed to take the coupled effects of strain, strain rate and temperature into account. Compared with the original Johnson-Cook model, the modified Johnson-Cook model calculated results show good agreement with the experimental data. Materials science ; Blanket ; Constitutive equation ; Alloy ; Strain rate ; Divertor ; Split-Hopkinson pressure bar ; Work (physics) ; Composite material ; Mechanics ; Thermodynamics ; Finite element method ; Plasma ; Tokamak ; Physics High-Velocity Impact and Material Behavior ; Nuclear Materials and Properties ; Microstructure and mechanical properties