jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lida Swann
Source: Carolina Digital Repository (University of North Carolina at Chapel Hill) (article, 2019-08-12)
Field: Materials Science — Materials Chemistry

The effect of sintering process on zirconia's optical and physical properties Zirconia restorations present an alternative dental restorative option with an average flexural strength of 1000 MPA. One of the challenges of this material is the decreased translucency when compared with less strong ceramic materials like feldespatic porcelain or lithium disilicate. A potential solution to this challenge is to the increase of sintering temperature and or increase holding times. The present study evaluated the effect of different sintering temperatures and holding times on contrast ratio as a measure of translucency, and evaluated if any temperatures or holding times could have a negative effect on materials strength. We also looked at the effect of cyclic loading vs conventional load to failure testing. One hundred and twenty (120) milled zirconia disks with .2mm thickness and 14mm diameter were sintered . Temperatures vary in 50 degree increments from 1450 to1600 F. Each of those temperatures was held for different times including 1h, 2h, 3h. The ziconia disks used were pure white Zenostar, 10 disks were obtained to test each variable. Contrast ration was measured on a spectophotometer for all specimens. 60 specimens (5 per group) were subjected to mechanical cyclic loading under water at a load of 100N for 100,000 cycles at a frequency of 1.5Hz. After fatigue loading samples and control group were tested on an Instron machine until catastrophic failure occurred. Under the conditions measured, increasing temperature and / or holding time did not alter translucency or significantly affected physical properties. Sintering ; Cubic zirconia ; Process (computing) ; Materials science ; Metallurgy ; Computer science ; Ceramic Catalytic Processes in Materials Science ; Advanced materials and composites ; Advanced ceramic materials synthesis
Authors: Shaoru Garner
Source: Digital Scholarship - UNLV (University of Nevada Reno) (article, 2019-05-29)
Field: Materials Science — Surfaces, Coatings and Films

Conservation of charge in secondary electron emission test stand This Thesis reports theoretical and experimental evaluations of the critical components of the UNLV secondary electron emissions test stand. These components include: (1) a source of electrons, an electron gun, (2)methods and instrumentation to obtain a direct measure of electron current supplied by the electron gun, and (3) a position sensitive particle detector that is capable of detecting single electrons and that provides the spatial and temporal distribution of secondary electrons. The overall objective of this project is to study the properties of secondary electron emission and the conservation of charge in the secondary electron emission test stand. Secondary electron emission (SEE) can potentially lead to beam instabilities, material degradation and r.f. breakdown. The part SEE studios have deduced secondary electron yield by invoking a conservation of charge without the need to experimentally account for all measurables. In the spatial distribution studies of SEE, one may not invoke conservation of charge without some knowledge leading to all loss effects. An electron gun in the SEE test stand at UNLV directs the primary electron beam through a beam drift tube piercing the center of a particle position detector with controlling grid towards the sample under test. Secondary electrons emitted by the sample either drift freely or are drawn by a grid potential to the particle position detector. The particle position detector records the number of electrons and the locations of electron impact. Low primary beam currents are necessary in order to perform these measurements within the resolution of the detector. Because the detector surface is flat and finite in dimension, some loss to the chamber walls will result. Consequential SEE loss to the walls is not measurable in a noise environment even if the experimental apparatus (the SEE test stand) is isolated from the earth ground. A Faraday cup is used to monitor the primary beam current. The beam drift tube, grid, and sample currents are monitored and integrated to yield the number of charges collected by each component over rime. Each component of the detector assembly, such as grid, microchannel plates and the data acquisition card will be studied in detail in order to achieve an absolute measurement of the secondary electron emission. Physics ; Charge (physics) ; Electron ; Test (biology) ; Environmental science ; Nuclear physics ; Particle physics ; Biology ; Ecology Electron and X-Ray Spectroscopy Techniques ; Plasma Diagnostics and Applications ; Advancements in Photolithography Techniques
Authors: Jike Lyu
Source: Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Epitaxial ferroelectric thin films on si(001): strain tuning of batio3 and stabilization of polar phase in hf0.5zr0.5o2 Departament responsable de la tesi: Departament de Física. Materials science ; Ferroelectricity ; Epitaxy ; Pulsed laser deposition ; Barium titanate ; Optoelectronics ; Thin film ; Dielectric ; Nanotechnology ; Layer (electronics) Machine Learning in Materials Science ; Ferroelectric and Piezoelectric Materials
Authors: Leonhard Weingrill | Martin Schwald | D. Frühstück | C. Faustmann | Norbert Enzigner
Source: Zavarivanje i zavarene konstrukcije (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

GMA root welding of pearlitic rails using magnetic arc deflection Welding ; Deflection (physics) ; Arc (geometry) ; Materials science ; Metallurgy ; Structural engineering ; Mechanical engineering ; Engineering ; Optics ; Physics Metal Alloys Wear and Properties ; Welding Techniques and Residual Stresses ; Microstructure and Mechanical Properties of Steels
Authors: Stuart R. Jefferys
Source: Carolina Digital Repository (University of North Carolina at Chapel Hill) (article, 2019-08-10)
Field: Chemistry — Spectroscopy

PIE - the protein inference engine Posttranslational modifications are vital to protein function but are hard to study, especially since several modification isoforms may be present simultaneously. Mass spectrometers are a great tool for investigating modified proteins, but the data they generate are often incomplete, ambiguous, and difficult to interpret. Combining data from multiple experimental techniques provides complementary information. Having both top-down (intact protein mass data) and bottom-up (peptide data) is especially valuable. In the context of background knowledge, combined data is used by human experts to interpret what modifications are present and where they are located. However, this process is arduous and for high-throughput applications needs to be automated. To explore a data integration methodology based on Markov chain Monte Carlo and simulated annealing, I developed the PIE (Protein Inference Engine). This java application integrates information using a modular approach which allows different types of data to be considered simultaneously and for new data types to be added as needed. Validation of the PIE was carried out using two realistically imperfect theoretical data sets. The first, based on the L7/L12 ribosomal protein, tested the limits of PIEs performance as intact mass accuracy and peptide coverage decreases. The second set, based on a mix of two modification variants of the H23c Histone protein, tested PIEs ability to handle isoform mixtures and up to eight simultaneous modifications. The PIE was then applied to analysis of experimental data from an investigation of the modification state of the L7/L12 ribosomal protein. This data consisted of a set of peptides identified as associated with some L7/L12 modification variant and nine intact masses measurements identified as an L7/ L12 modification variant. From this data, PIE was able to make consistent predictions, comparable to expert manual interpretation. Software, source code, user manuals, and demo projects replicating the analyses described in the following can be downloaded from http://pie.giddingslab.org/. Computer science ; Inference ; Artificial intelligence Advanced Proteomics Techniques and Applications ; Genetics, Bioinformatics, and Biomedical Research ; Machine Learning in Bioinformatics
Authors: Mohamed Nidal Ben Abdelouahab
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2019-10-14)
Field: Materials Science — Materials Chemistry

Développement d’un procédé super-absorbant pour la décontamination nucléaire en profondeur de matériaux poreux Within the framework of decommissioning of nuclear facilities, various decontamination operations are required. One of the main issues is the decontamination of cementitious materials due to the incorporation of contaminants into their porous structures. Throughout this project, we study the possibility of using a process for decontaminating cementitious materials based on the "poultice" method, which is mainly used nowadays for desalination of buildings. Such a technical adaptation first requires a meticulous study of physical mechanisms that control the different phases of the process. For that aim, we use model systems, composed of poultices (based on kaolin or cellulose fibers) applied to the surface of a model substrate (glass beads packing) in order to understand the whole process, and in particular the water transfers involved. Magnetic Resonance Imaging techniques are therefore used to monitor water distribution within these systems, and provide complete original information. Our approach consists in breaking down our study into three axes. First, we study substrate imbibition from a wet poultice. We show that liquid can invade the substrate even if it has a larger pore size than the poultice. The imbibition process stops when the capillary pressure in the substrate balances the stress needed to further contract the poultice. This in particular means that the liquid penetration in a porous medium from a poultice may be controlled by adjusting the poultice characteristics. Then, we provide the detailed drying mechanisms of initially saturated systems depending on their pore size distribution. We show that poultice can extract a large part (~ 85 %) of substrate liquid thanks to capillary equilibration processes that allow both media to desaturate homogeneously and simultaneously up to the very last stages of drying. Finally, decontamination efficiency of substrates initially contaminated by inactive cesium is evaluated. We show that salt crystals (cesium nitrate) growing during the system poultice/substrate drying can, in some configurations, limit contaminants transport from the substrate to the poultice and thus reduce the decontamination efficiency. Human decontamination ; Process (computing) ; Porosity ; Materials science ; Waste management ; Engineering ; Computer science ; Composite material Graphite, nuclear technology, radiation studies ; Nuclear reactor physics and engineering ; Nuclear and radioactivity studies
Authors: Dmitriy Chernookiy
Source: Carolina Digital Repository (University of North Carolina at Chapel Hill) (article, 2019-08-10)
Field: Chemistry — Spectroscopy

Optimization of the Cylindrical Ion Trap Geometry for Mass Analysis at High Pressure The cylindrical ion trap (CIT) provides many advantages for implementing mass spectrometry at elevated background pressures, presenting a practical route for the realization of a hand-portable device with performance that is suitable for many critical applications. This objective can be achieved through favorable scaling properties whereby high drive frequencies combined with small trap dimensions compensate for the loss of mass resolution from higher collisional damping rates. However, the simplified geometry of the CIT from the ideal hyperbolic electrode profile gives rise to higher-order fields that must be optimized for satisfactory performance. Previous investigations of CIT geometry have addressed performance at relatively low background pressures (ca. 1 mTorr) with only a few trap configurations. In this work, a practical range of CIT geometry parameters was experimentally evaluated at pressures between ca. 20 to 1000 mTorr of helium at a drive frequency of 9 MHz for ring electrodes with a 0.500 mm radius. Mesh-covered endcap electrodes were substituted for the traditional aperture-style design to mitigate alignment concerns, which proved to be robust and not inherently limiting to resolution. The study focused on the major parameters of ring electrode thickness (0.600, 0.650, 0.700, 0.750 and 0.800 mm) and ring-to-endcap spacing (varied between 0.075 to 0.300 mm) for a total of 20 unique traps. The optimization results are reported in terms of these dimensions and are also generalized by correlating performance changes to specific multipole fields. The octopole was found to be strongly correlated with trends in resolution at all pressures, with the dodecapole serving a minor role; the effects of all higher terms were confirmed to be inconsequential on a first-approach basis. At lower pressures resolution was primarily improved through an extension of the overall field linearity and the enhancement of an octopolar nonlinear resonance for double-resonance ejection, while at higher pressures it appears to benefit from the dynamic stabilization offered by more-than-linear fields. Thus, the optimal trap geometry was found to vary with the degree of damping and the mode of mass analysis. However, the spectral peak width was minimized over the widest pressure range with octopole strengths near 10% relative to the quadrupole. Geometry ; Trap (plumbing) ; Ion ; Mechanics ; Physics ; Materials science ; Mathematics ; Meteorology Mass Spectrometry Techniques and Applications ; Laser-induced spectroscopy and plasma ; Atomic and Molecular Physics
Authors: Aowabin Rahman | Matthew S. Radue | Gregory M. Odegard | Michael W. Czabaj | Prathamesh Deshpande | Ashley D. Spear
Source: Bulletin of the American Physical Society (article, 2019-03-07)
Field: Materials Science — Materials Chemistry

Designing High-strength Carbon-nanotube Polymer Composites Using Reinforcement Learning Algorithms Integrated with Molecular Dynamics Simulations Materials science ; Carbon nanotube ; Composite material ; Reinforcement ; Reinforcement learning ; Molecular dynamics ; Polymer ; Computer science ; Artificial intelligence ; Chemistry ; Computational chemistry Carbon Nanotubes in Composites
Authors: Ryo Yoshida
Source: The Japan Society of Applied Physics (article, 2019-07-10)
Field: Materials Science — Materials Chemistry

Materials Informatics: Small Data Problem and Transfer Learning Materials science ; Informatics ; Materials informatics ; Transfer of learning ; Nanotechnology ; Data science ; Health informatics ; Engineering physics ; Computer science ; Artificial intelligence ; Medicine ; Engineering informatics ; Nursing ; Electrical engineering ; Engineering Machine Learning in Materials Science
Authors: Александр Дударев | Aleksandr Podvintsev | JSC “Perm Plant “Mashinostroitel”
Source: Perm National Research Polytechnic University Aerospace Engineering Bulletin (article, 2019-01-01)
Field: Materials Science — General Materials Science

FEATURES OF PERFORMANCE OF PERFORATION HOLES IN THE SHELL-ABSORBING STRUCTURES AIRCRAFT ENGINE "", , . , . , , Shell (structure) ; Perforation ; Engineering ; Aerospace engineering ; Materials science ; Structural engineering ; Computer science ; Aeronautics ; Mechanical engineering Material Properties and Applications ; Engineering Diagnostics and Reliability ; Technical Engine Diagnostics and Monitoring
Authors: John F. Barry | Jennifer M. Schloss | Erik Bauch | Matthew Turner | Connor Hart | Linh Pham | Ronald L. Walsworth
Source: Reviews of Modern Physics (article, 2020-03-31)
Field: Materials Science — Materials Chemistry

Sensitivity optimization for NV-diamond magnetometry Nitrogen-vacancy (NV) centers in diamond carry spin degrees of freedom that enable their use for sensing of weak magnetic fields with high spatial resolution and under ambient conditions . This article reviews measurement technologies and identifies the physical mechanisms that limit the sensitivity with a focus on NV ensembles. Present-day NV sensors are far from theoretical limits imposed by quantum mechanics, and strategies are suggested for improvement of methods and materials. Diamond ; Physics ; Sensitivity (control systems) ; Magnetometer ; Quantum sensor ; Spin (aerodynamics) ; Dephasing ; Broadband ; Nanotechnology ; Quantum computer ; Quantum ; Electronic engineering ; Condensed matter physics ; Magnetic field ; Quantum mechanics ; Quantum simulator ; Optics ; Materials science Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Metal and Thin Film Mechanics
Authors: Qiming Sun | Xing Zhang | Samragni Banerjee | Peng Bao | Marc Barbry | Nick S. Blunt | Nikolay A. Bogdanov | George H. Booth | Jia Chen | Zhi‐Hao Cui | Janus J. Eriksen | Yang Gao | Sheng Guo | Jan Hermann | Matthew R. Hermes | Kevin J. Koh | Peter Koval | Susi Lehtola | Zhendong Li | Junzi Liu | Narbe Mardirossian | James McClain | Mário Motta | Bastien Mussard | Hung Q. Pham | Artem Pulkin | Wirawan Purwanto | Paul J. Robinson | Enrico Ronca | Elvira R. Sayfutyarova | Maximilian Scheurer | Henry F. Schurkus | James E. T. Smith | Chong Sun | Shi-Ning Sun | Shiv Upadhyay | Lucas K. Wagner | Xiao Wang | Alec F. White | James Whitfield | Mark J. Williamson | Sebastian Wouters | Jun Yang | Jason M. Yu | Tianyu Zhu | Timothy C. Berkelbach | Sandeep Sharma | Alexander Yu. Sokolov | Garnet Kin‐Lic Chan
Source: The Journal of Chemical Physics (article, 2020-07-09)
Field: Materials Science — Materials Chemistry

Recent developments in the P<scp>y</scp>SCF program package PySCF is a Python-based general-purpose electronic structure platform that supports first-principles simulations of molecules and solids as well as accelerates the development of new methodology and complex computational workflows. This paper explains the design and philosophy behind PySCF that enables it to meet these twin objectives. With several case studies, we show how users can easily implement their own methods using PySCF as a development environment. We then summarize the capabilities of PySCF for molecular and solid-state simulations. Finally, we describe the growing ecosystem of projects that use PySCF across the domains of quantum chemistry, materials science, machine learning, and quantum information science. Workflow ; Python (programming language) ; Computer science ; Quantum chemistry ; Software engineering ; Computational science ; Systems engineering ; Chemistry ; Molecule ; Programming language ; Database ; Engineering ; Supramolecular chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Karen Manalastas-Cantos | Petr V. Konarev | Nelly R. Hajizadeh | Alexey Kikhney | Maxim V. Petoukhov | Dmitry Molodenskiy | Alejandro Panjkovich | Haydyn D. T. Mertens | Andrey Gruzinov | Clemente Borges | Cy M. Jeffries | Dmitri I. Svergun | Daniel Franke
Source: Journal of Applied Crystallography (article, 2020-12-02)
Field: Materials Science — Materials Chemistry

<i>ATSAS 3.0</i>: expanded functionality and new tools for small-angle scattering data analysis The <i>ATSAS</i> software suite encompasses a number of programs for the processing, visualization, analysis and modelling of small-angle scattering data, with a focus on the data measured from biological macromolecules. Here, new developments in the <i>ATSAS 3.0</i> package are described. They include <i>IMSIM</i>, for simulating isotropic 2D scattering patterns; <i>IMOP</i>, to perform operations on 2D images and masks; <i>DATRESAMPLE</i>, a method for variance estimation of structural invariants through parametric resampling; <i>DATFT</i>, which computes the pair distance distribution function by a direct Fourier transform of the scattering data; <i>PDDFFIT</i>, to compute the scattering data from a pair distance distribution function, allowing comparison with the experimental data; a new module in <i>DATMW</i> for Bayesian consensus-based concentration-independent molecular weight estimation; <i>DATMIF</i>, an <i>ab initio</i> shape analysis method that optimizes the search model directly against the scattering data; <i>DAMEMB</i>, an application to set up the initial search volume for multiphase modelling of membrane proteins; <i>ELLLIP</i>, to perform quasi-atomistic modelling of liposomes with elliptical shapes; <i>NMATOR</i>, which models conformational changes in nucleic acid structures through normal mode analysis in torsion angle space; <i>DAMMIX</i>, which reconstructs the shape of an unknown intermediate in an evolving system; and <i>LIPMIX</i> and <i>BILMIX</i>, for modelling multilamellar and asymmetric lipid vesicles, respectively. In addition, technical updates were deployed to facilitate maintainability of the package, which include porting the <i>PRIMUS</i> graphical interface to Qt5, updating <i>SASpy</i> - a <i>PyMOL</i> plugin to run a subset of <i>ATSAS</i> tools - to be both Python 2 and 3 compatible, and adding utilities to facilitate mmCIF compatibility in future <i>ATSAS</i> releases. All these features are implemented in <i>ATSAS 3.0</i>, freely available for academic users at https://www.embl-hamburg.de/biosaxs/software.html. Python (programming language) ; Computer science ; Computational science ; Algorithm ; Scattering ; Physics ; Optics Enzyme Structure and Function ; Protein Structure and Dynamics ; Bacterial Genetics and Biotechnology
Authors: Yunxing Zuo | Chi Chen | Xiangguo Li | Zhi Deng | Yiming Chen | Jörg Behler | Gábor Cśanyi | Alexander V. Shapeev | Aidan P. Thompson | Mitchell Wood | Shyue Ping Ong
Source: The Journal of Physical Chemistry A (article, 2020-01-09)
Field: Materials Science — Materials Chemistry

Performance and Cost Assessment of Machine Learning Interatomic Potentials Machine learning of the quantitative relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a new frontier in the development of interatomic potentials (IAPs). Here, we present a comprehensive evaluation of machine learning IAPs (ML-IAPs) based on four local environment descriptors-atom-centered symmetry functions (ACSF), smooth overlap of atomic positions (SOAP), the spectral neighbor analysis potential (SNAP) bispectrum components, and moment tensors-using a diverse data set generated using high-throughput density functional theory (DFT) calculations. The data set comprising bcc (Li, Mo) and fcc (Cu, Ni) metals and diamond group IV semiconductors (Si, Ge) is chosen to span a range of crystal structures and bonding. All descriptors studied show excellent performance in predicting energies and forces far surpassing that of classical IAPs, as well as predicting properties such as elastic constants and phonon dispersion curves. We observe a general trade-off between accuracy and the degrees of freedom of each model and, consequently, computational cost. We will discuss these trade-offs in the context of model selection for molecular dynamics and other applications. Density functional theory ; Phonon ; Context (archaeology) ; Basis set ; Atom (system on chip) ; Statistical physics ; Computer science ; Artificial intelligence ; Machine learning ; Materials science ; Physics ; Atomic physics ; Chemistry ; Computational chemistry ; Condensed matter physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions