jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nirmal Baishnab
Source: BearWorks (Missouri State University) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Synthesis of Amorphous Hydrogenated Boron Carbide from Orthocarborane Using Argon Bombardment: A Reaxff Molecular Dynamics Study In this study, the synthesis process of a-B­xC:Hy using argon bombardment from the orthocarborane precursor was modeled by using reactive molecular dynamics (MD). Utilizing the MD simulations, the formation of free radicals as a result of ion bombardment was identified and quantified. Then, the densification process that is aided by the mixture of free radicals and orthocarborane was analyzed. The densification process by creating the initial structure composed of free radicals and orthocarborane with active sites created by partially removing some of the hydrogen atoms from the icosahedral cage was also modelled. Overall, a better understanding of the mechanism of the densification of hydrogenated boron carbide and the roles of Ar bombardment and radical species toward the deposition process were obtained. ReaxFF ; Boron carbide ; Amorphous solid ; Molecular dynamics ; Argon ; Materials science ; Boron ; Carbide ; Chemical engineering ; Chemistry ; Chemical physics ; Computational chemistry ; Crystallography ; Metallurgy ; Organic chemistry ; Engineering Boron and Carbon Nanomaterials Research ; Boron Compounds in Chemistry ; Diamond and Carbon-based Materials Research
Authors: Yaning Zhong
Source: NCSU Libraries Repository (North Carolina State University Libraries) (article, 2019-03-25)
Field: Chemistry — Analytical Chemistry

Developing Robust Three-Dimensional Single Particle Tracking for Chemical Analysis. ZHONG, YANING.Developing Robust Three-Dimensional Single Particle Tracking for Chemical Analysis.(Under the direction of Dr. Gufeng Wang) Tracking (education) ; Particle filter ; Particle (ecology) ; Computer science ; Artificial intelligence ; Biology ; Psychology ; Kalman filter Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Kohei Shimamura | 冬樹 下條 | Shigenori Tanaka
Source: Institutional Repositories DataBase (IRDB) (article, 2019-12-05)
Field: Materials Science — Materials Chemistry

人工ニューラルネットワーク原子間相互作用ポテンシャルの分子動力学法への応用と課題 分子動力学（MD）法は，原子毎に立てられたNewtonの運動方程式を逐次的に解くことで系全体の原子ダイナミクスを追跡できる計算機シミュレーション手法であり，ミクロな現象の解明に役立つことから材料分野や生物分野では標準的な手法として認識されている．近年，人工ニューラルネットワーク（ANN）の万能近似性を活用して，従来のMD法が抱えていた精度と計算コストの難点を克服するANN原子間相互作用ポテンシャル（ANN potential）の開発が活発に行われ新局面を迎えている．本稿では，ANN potentialの基本的なアルゴリズムについて，応用例を交えながら，現在直面している回帰学習のデータ不均衡問題などの解決すべき課題について述べる． Process (computing) ; Identification (biology) ; Product (mathematics) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: L Coauthors P Pan | T Song | S Pane | S Staufert | J Park | A Wang | D Patel | H Kim | B Paul | M Mamun | A Haque | M Paul | K Ghosh | R Peixoto | L See Grebenstein | S Pennathur | E Hui | A Pina-Barrera | R Alvarez-Roman | J Baez-Gonzalez | C Amaya-Guerra | C Rivas-Morales | C Gallardo-Rivera | S Galindo-Rodriguez | L Pinto | G Soares | K Pister | R Zoll | C Plummer | H See Awan | A Prospero | A Nunes | A Pusane | A Kislal | F Dinc | J Qiu | Y Zhou | Q Wang | T Ruan | J Gao | C Quini | E Quinones-Aguilar | A Garcia-Cruz | N Rady Raz | M Akbarzadeh-T | R Ramos-Gonzalez | S See Garcia Torres | G Rampioni | L Leoni | P Stano | G Ren | Q Zhao | P Rewatkar | D Nath | G Rincon-Enriquez | A See Pina-Barrera | D Rodrigues | A Rodriguez-Paton | B Ru | D Li | Y Hu | L Yao | J See Qiu | M Sadeghi-Goughari | S Jeon | H Kwon | P Sai Kiran | T Saito | H Miki | S Salehi | N Moayedian | E Alarcon | P Sankar Ganesh | C Schindler | R Schober | W See Wicke | R Mosayebi | H Seonwoo
Source: IEEE Transactions on NanoBioscience (article, 2019-10-01)
Field: Materials Science — Materials Chemistry

2019 Index IEEE Transactions on NanoBioscience Vol. 18 Index (typography) ; Computer science ; World Wide Web Advanced Nanomaterials in Catalysis
Authors: Christopher C. Walker
Source: NCSU Libraries Repository (North Carolina State University Libraries) (article, 2019-08-14)
Field: Materials Science — Materials Chemistry

Development of Accurate Coarse-grained Polymer Molecular Models and a Framework for Copolymer Sequence Characterization via the Kerr Effect. Copolymer ; Sequence (biology) ; Characterization (materials science) ; Polymer ; Materials science ; Computer science ; Chemistry ; Nanotechnology ; Composite material Machine Learning in Materials Science
Authors: N. Sakhnenko
Source: Determinations in nanomedicine & nanotechnology. (article, 2019-10-04)
Field: Materials Science — General Materials Science

Simulation of Corrosion Resistance of Nanocomposites Fe (Co)-W Corrosion ; Nanocomposite ; Materials science ; Resistance (ecology) ; Metallurgy ; Composite material ; Biology Material Properties and Applications ; Metal and Thin Film Mechanics ; Aluminum Alloys Composites Properties
Authors: Shan Zhu | Xinyang Sun | Yuxuan Wang | Naiqin Zhao | Junwei Sha
Source: arXiv (Cornell University) (preprint, 2019-07-03)
Field: Chemistry — Electrochemistry

Equivalent Circuit Model Recognition of Electrochemical Impedance Spectroscopy via Machine Learning Electrochemical impedance spectroscopy (EIS) is an effective method for studying the electrochemical systems. The interpretation of EIS is the biggest challenge in this technology, which requires reasonable modeling. However, the modeling of EIS is of great subjectivity, meaning that there may be several models to fit the same set of data. In order to overcome the uncertainty and triviality of human analysis, this research uses machine learning to carry out EIS pattern recognition. Raw EIS data and their equivalent circuit models were collected from the literature, and the support vector machine (SVM) was used to analyze these data. As the result, we addresses the classification of EIS and recognizing their equivalent circuit models with accuracies of up to 78%. This study demonstrates the great potential of machine learning in electrochemical researches. Dielectric spectroscopy ; Equivalent circuit ; Support vector machine ; Computer science ; Electrical impedance ; Artificial intelligence ; Set (abstract data type) ; Pattern recognition (psychology) ; Machine learning ; Electronic engineering ; Engineering ; Electrical engineering ; Electrochemistry ; Voltage ; Chemistry Electrochemical Analysis and Applications ; Fuel Cells and Related Materials ; Conducting polymers and applications
Authors: Clarisse Liné
Source: Open Archive Toulouse Archive Ouverte (University of Toulouse) (preprint, 2019-09-27)
Field: Materials Science — Materials Chemistry

Evaluation of parameters influencing plant response to carbon nanotube contamination Carbon nanotubes (CNTs) are among the most used nanomaterials (NMs) thanks to their excellent physicochemical properties. All along their lifecycle, they may be spread unintentionallyor intentionally in the environment. It is thus essential to assess their behavior and potential impacts on ecosystems and particularly on crop plants. Overall, behaviour and effects of CNTs in plants are not well understood and still very controversial. In this work, we aimed to assess the influence of several parameters on plant response after exposure in a CNT-contaminated soil. We first focused on the analytical challenge of detecting CNT in biological matrices and tested several spectroscopic techniques. Then, we evaluated the response of tomato plants to two different NMs (CNTs and TiO2-NPs). Our results highlight that despite being different for several parameters (i.e. shape, size, surface chemistry), CNT exposure led to a similar response in tomato plants, in particular on the alteration of plant cell wall components. The study of different plant species (tomato, canola, maize and cucumber) exposed to DWCNT contamination highlighted different responses according to plant species, maize (monocot) being the most sensitive. Different types of CNTs are currently available. Five types of CNTs varying in diameter, functionalization and length were used to investigate their impact on canola. Canola was more sensitive to CNTs with the smallest diameters, but it was also observed that the functionalization greatly modulated the plant response. Finally, we tested the impact of a combined stress: canola plants grown in optimal growth conditions were not impacted by CNT exposure at the tested dose, while we observed that plants were more sensitive to CNTs when submitted to a concomitant heat stress. Humanities ; Forestry ; Physics ; Geography ; Philosophy Carbon Nanotubes in Composites
Authors: J.J. Shea
Source: IEEE Electrical Insulation Magazine (article, 2019-02-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Book Reviews [7 Books reviewed] The following 7 books were reviewed: Interval Finite Element Method with MATLAB;S . Nayak and S. Chakraverty; Measurements and Analysis of Overvoltages in Power Systems; J. Li; Power Microelectronics - Device and Process Technologies, 2nd Edition; Y. C. Liang, G. S. Samudra, and C. Huang; Advanced DC/DC Converters, 2nd Edition; F. L. Luo and H. Ye; Analog Electronics for Measuring Systems; D. Bucci; Astrochemistry - Big Bang to the Present Day; C. Vallance; Concepts in Physical Metallurgy; A. Lavakumar. Converters ; Microelectronics ; Electrical engineering ; Aerospace ; Engineering ; Power electronics ; MATLAB ; Engineering physics ; Electronics ; Process (computing) ; Mechanical engineering ; Voltage ; Computer science ; Aerospace engineering Magnetic Properties and Applications
Authors: Kai Trepte | Sebastian Schwalbe
Source: ChemRxiv (preprint, 2019-10-30)
Field: Chemistry — Inorganic Chemistry

porE: A Code for Deterministic and Systematic Analyses of Porosities Accurate numerical calculations of porosities and related properties are of importance when analyzing metal-organic frameworks (MOFs). &lt;br&gt;We present porE, an open-source, general-purpose implementation to compute such properties and discuss &lt;br&gt;all results regarding their sensitivity to numerical parameters.&lt;br&gt;Our code combines the numerical efficiency of Fortran with the user-friendliness of Python.&lt;br&gt;Three different approaches to calculate porosities are implemented in porE, and&lt;br&gt;their advantages and drawbacks are discussed. In contrast to commonly used implementations, &lt;br&gt;our approaches are entirely deterministic and do not require any stochastic averaging.&lt;br&gt;In addition to the calculation of porosities, porE can calculate pore size distributions and offers the possibility to analyze pore windows. &lt;br&gt;The underlying approaches are outlined, and pore windows are discussed concerning their impact on the analyzed porosities. &lt;br&gt;Comparisons with reference values aim for a clear differentiation &lt;br&gt;between void and accessible porosities, which we provide for a small benchmark set consisting of 8 MOFs.&lt;br&gt;In addition, our approaches are used for a bigger benchmark set containing 370 MOFs, &lt;br&gt;where we determine linear relationships within our approaches as well as to reference values. &lt;br&gt;We show how these relationships can be used to derive corrections to a give porosity approach, &lt;br&gt;minimizing its mean error. As a highlight we show how complex workflows can be &lt;br&gt;designed with a few lines of Python code using porE.&lt;br&gt; Benchmark (surveying) ; Porosity ; Python (programming language) ; Workflow ; Fortran ; Algorithm ; Computer science ; Code (set theory) ; Materials science ; Mathematics ; Set (abstract data type) ; Programming language ; Composite material ; Geology ; Database Metal-Organic Frameworks: Synthesis and Applications ; Enhanced Oil Recovery Techniques ; Machine Learning in Materials Science
Authors: Silvia Pandolfi
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2019-12-13)
Field: Materials Science — Materials Chemistry

High-pressure pathways towards new functional Si-based materials with tailored optoelectronic properties and their characterization In this work, high-pressure high-temperature treatment is used to develop and optimize synthetic pathways to new and exotic forms of silicon. The synthesis of new phases of silicon is a key point in the future development of Si-based technology, especially for solar energy. Development of new Si-based materials with high-efficiency could both optimise the performances and reduce the costs. At an industrial level, high-pressure has been mainly used for the synthesis of diamonds and super-hard materials, but recent studies have proven it also efficient in the synthesis of new silicon-based materials with enhanced properties for applications. We tackle this challenge in all its aspects, starting from the study of transition mechanisms at high-pressure and arriving to the synthesis and characterization of a new silicon material. We use in-situ x-ray diffraction at high-pressure high-temperature conditions to monitor and characterize silicon phase transitions and their dynamics. Our results give new insights that clarify some aspects of the silicon phase diagram that were still a matter of debate. We obtain the first synthesis of pure hexagonal silicon, a longstanding challenge in the field. Thanks to our pure-phase sample, we characterize its physical and structural properties. We prove that hexagonal silicon obtained from high-pressure is in the form of 4H polytype (ABCB stacking sequence). Further structural characterizations reveal a hierarchical nanostructure in the pristine morphology of the sample. Discovery and characterization of discrete quasi-2D nanoparticles accessible for manipulation opens new perspectives for the design of new optoelectronic devices. Materials science ; Humanities ; Physics ; Art Silicon Nanostructures and Photoluminescence ; Diamond and Carbon-based Materials Research ; Boron and Carbon Nanomaterials Research
Authors: Eric Hermes | Khachik Sargsyan | Habib N. Najm | Judit Zádor
Source: ChemRxiv (preprint, 2019-08-30)
Field: Materials Science — Materials Chemistry

Accelerated Saddle Point Refinement Through Full Exploitation of Partial Hessian Diagonalization Identification and refinement of first order saddle point (FOSP) structures on the potential energy surface (PES) of chemical systems is a computational bottleneck in the characterization of reaction pathways. Leading FOSP refinement strategies require calculation of the full Hessian matrix, which is not feasible for larger systems such as those encountered in heterogeneous catalysis. For these systems, the standard approach to FOSP refinement involves iterative diagonalization of the Hessian, but this comes at the cost of longer refinement trajectories due to the lack of accurate curvature information. We present a method for incorporating information obtained by an iterative diagonalization algorithm into the construction of an approximate Hessian matrix that accelerates FOSP refinement. We measure the performance of our method with two established FOSP refinement benchmarks and find a 50% reduction on average in the number of gradient evaluations required to converge to a FOSP for one benchmark, and a 25% reduction on average for the second benchmark. Hessian matrix ; Saddle point ; Benchmark (surveying) ; Computer science ; Mathematical optimization ; Reduction (mathematics) ; Bottleneck ; Matrix (chemical analysis) ; Applied mathematics ; Curvature ; Convergence (economics) ; Algorithm ; Mathematics ; Geometry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Surface Chemistry and Catalysis
Authors: Anouar Belhboub | El Hassane Lahrar | Patrice Simon | Céline Merlet
Source: Electrochimica Acta (preprint, 2019-10-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

On the development of an original mesoscopic model to predict the capacitive properties of carbon-carbon supercapacitors We report on the development of a lattice model to predict structural, dynamical and capacitive properties of electrochemical double layer capacitors. The model uses input from molecular simulations, such as free energy profiles to describe the ion adsorption, and experiments, such as energy barriers for transitions between lattice sites. The model developed is approximately 10,000 times faster than common molecular simulations. We apply this model to a set of carbon structures with well-defined pore sizes and investigate the solvation effect by doing simulations with neat ionic liquids as well as acetonitrile-based electrolytes. We show that our model is able to predict quantities of adsorbed ions and capacitances in a range compatible with experimental values. We show that there is a strong dependency of the calculated properties on the pore size and on the presence or absence of solvent. In particular, for neat ionic liquids, larger capacitances are obtained for smaller pores, while the opposite trend is observed for organic electrolytes. Mesoscopic physics ; Supercapacitor ; Electrolyte ; Materials science ; Chemical physics ; Solvation ; Ion ; Carbon fibers ; Ionic bonding ; Ionic liquid ; Thermodynamics ; Nanotechnology ; Capacitance ; Chemistry ; Physical chemistry ; Physics ; Organic chemistry ; Condensed matter physics ; Electrode ; Composite material Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advancements in Battery Materials
Authors: Jawwad Latif | Zulfiqar Ahmad Khan | Mian Hammad Nazir | K.R. Stokes | Joseph Plummer
Source: Bournemouth University Research Online (Bournemouth University) (article, 2019-07-01)
Field: Materials Science — Materials Chemistry

Condition Monitoring and Predictive Modelling of Coating Delamination within Remote and Stationary Assets The ambiguous nature of meteorological parameters in uncontrolled environmental conditions makes it difficult to determine the structural integrity of stationary and mobile assets. The weather conditions for large vehicles, at The Tank Museum at Bovington United Kingdom, which are operating under controlled and uncontrolled environmental conditions are investigated through weather history and corrosion monitoring techniques applied to large military vehicles by using linear polarisation resistance method. Corrosion reactions were found on several occasions during the operation of large vehicle within uncontrolled environment due to critical level of metrological parameters including salinity, relative humidity and rainfall. Comprehensive solutions have been proposed to detect damage initiation at the earliest possible stage to prompt maintenance professionals to take necessary actions to avoid damage. Early detection techniques will help to prolong the service life of large vehicles or metal structures which are operating or installed remotely. The analysis of diffusion of salt particles into coating during summer and winter season is also presented by estimating the salt concentration by taking linear relationship between wind speed and salt deposition rate based on ISO classification of airborne salinity. The proposed solutions can be applied to valuable assets operating in both non- coastal and coastal regions, near the sea, to predict and estimate the damage. Environmental science ; Corrosion ; Relative humidity ; Salinity ; Coating ; Humidity ; Service life ; Meteorology ; Wind speed ; Marine engineering ; Computer science ; Engineering ; Geology ; Reliability engineering ; Oceanography Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques ; Smart Materials for Construction
Authors: Pablo M. Piaggi
Source: Infoscience (Ecole Polytechnique Fédérale de Lausanne) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Entropy as a tool for crystal discovery The computational prediction of crystal structures has emerged as an useful alternative to expensive and often cumbersome experiments. We propose an approach to the prediction of crystal structures and polymorphism based on reproducing the crystallization process on the computer. The main hurdle faced by such an approach is that crystallization usually takes place in timescales much longer than those that can be afforded with standard molecular simulations. In order to circumvent this difficulty we construct a bias potential which is a function of one or more collective variables and whose role is to promote crystallization. This approach can only have true predictive power if the collective variable is crystal structure agnostic, that is to say, it does not include information about the geometry of any particular crystal structure. In order to achieve this goal, we take inspiration from thermodynamics and propose to use an entropy surrogate as collective variable. We use an approximation for the entropy based on the radial distribution function g(r). Using this collective variable we are able to explore polymorphism in simple metals and molecular crystals. We study the case of urea and find a new polymorph stabilized by entropic effects. We also propose a projection of the collective variable onto each atom that is useful to characterize atomic environments. Lastly, we introduce a generalized Kullback-Leibler divergence that measures the distance between two radial distribution functions. We apply this divergence to the automatic classification of the polymorphs that crystallize during the simulations. Computer science ; Entropy (arrow of time) ; Mathematics ; Physics Crystallization and Solubility Studies
Authors: Menglian Zhou
Source: Deep Blue (University of Michigan) (article, 2019-01-01)
Field: Chemistry — Spectroscopy

Development and Application of Two-Dimensional Micro Gas Chromatography The increasing need for rapid and on-site analysis of environmental, pharmaceutical and petrochemical samples has driven the development of micro gas chromatographs (µGC) for the last few decades. To improve the µGC’s ability to separate and analyze complex samples, we implemented multi-channel multi-dimensional µGC technology. This dissertation describes the design, assembly, characterization, operation and chromatogram reconstruction of a 1D & 2D µGC system as well as their applications on water contamination analysis and exhaled breath analysis. The 1D µGC system has sub-parts-per-billion level sensitivity and can analyze a water sample in 15 minutes. This system was coupled with purge-and-trap sampling and provided sensitive and rapid field analysis of water contamination. Quantification results agreed well with those obtained by an analytical lab using standard analytical methods and benchtop instruments. This system offers a lab-on-a-chip solution for sensitive and rapid water analysis with EPA compliant sample collection methods. The 2D µGC system has high peak capacity (>200 for 1-by-2 channel and >430 for 1-by-4 channel) and high sensitivity (sub-ppb). The 1-by-2 channel 2D µGC was used to analyze the exhaled breath of acute respiratory distress syndrome (ARDS) patients. ARDS is the most severe form of acute lung injury, responsible for high mortality and long-term morbidity, and is notably difficult to diagnosis. Breath samples were drawn from mechanical ventilators in the ICU and analyzed by the fully automated 2D µGC within about 30 minutes. A machine learning algorithm was developed to analyze the metabolic information within breath chromatograms and achieved an overall diagnostic accuracy of 87.1% with 94.1% positive predictive value and 82.4% negative predictive value when compared to adjudications performed by physicians based on the Berlin criteria. The ability to continuously and non-invasively monitor exhaled breath for early diagnosis, disease trajectory tracking, and outcome prediction monitoring of ARDS may have a significant impact on changing medical practices and improving patient outcomes. Gas chromatography ; Chromatography ; Environmental science ; Computer science ; Chemistry Analytical Chemistry and Chromatography