jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Matthew Ralphs
Source: Digital Commons - USU (Utah State University) (article, 2021-10-08)
Field: Materials Science — Materials Chemistry

Investigating the Effect of Carbon Nanotube Functionalization in a Polydimethylsiloxane Composite Through use of a Stepped Bar Apparatus Thermal interface materials (TIMs) are used as an aid in transporting heat away from a circuit or electronic module. Composite materials are a popular research area for TIMs because they allow the desired properties from the individual constituents to be combined. The composite selected for this study uses carbon nanotubes (CNT) as the filler and an elastomeric polymer for the matrix, specifically a multiwalled carbon nanotube (MWCNT) / polydimethylsiloxane (PDMS) composite. Additionally, functionalization of the CNT may affect the composites’ thermal conductivity because of its effect on the CNT dispersion in the polymer matrix and its effect on the CNT-polymer interface. The objective of this study was to determine the effect CNT functionalization has on the effective thermal conductivity of a MWCNT/PDMS composite. The three functionalization’s used in this study are unfunctionalized, functionalized with a carboxyl group, and functionalized with a hydroxyl group. Secondary objectives were to develop the initial stages of a carbon-polymer composite database and to perform an uncertainty analysis on the stepped bar apparatus used in this study. The database is to be used for visualization of data found in literature to promote data driven research. The uncertainty analysis on the stepped bar apparatus is to qualify the instrument for thermal measurements in this study Initial results showed some increase in thermal properties of the composite, but there was little difference between the thermal conductivity of the three functionalization’s because of the high level of uncertainty used early on in this study. Later results showed an increase in mechanical properties of the composite which offset any thermal advantage with use as a TIM. A stronger composite means less compression under a similar load, resulting in a thicker TIM and higher resistance. However, the mechanical and thermal properties compound to show that -OH functionalized MWCNT present better properties for a TIM than unfunctionalized and -COOH functionalized; none show better results than the polymer by itself. Polydimethylsiloxane ; Carbon nanotube ; Surface modification ; Bar (unit) ; Composite number ; Materials science ; Nanotechnology ; Composite material ; Chemical engineering ; Engineering ; Physics Carbon Nanotubes in Composites ; Smart Materials for Construction ; Thermal properties of materials
Authors: Yating Wang | Ziyan Xin | Jiajun Shen | Lunyu Zhao | Bijia Wang | Xueling Feng | Zhiping Mao | Xiaofeng Sui
Source: SSRN Electronic Journal (article, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Acrylonitrile-Butadiene-Styrene-Based Composites Derived from 'Fish-Net'-Inspired Pickering Emulsion for High-Performance Electromagnetic Interference Shielding and Thermal Management Materials science ; Composite material ; Electromagnetic shielding ; EMI ; Electromagnetic interference ; Pickering emulsion ; Electrical conductor ; Carbon nanotube ; Acrylonitrile butadiene styrene ; Nanotechnology ; Nanoparticle ; Electronic engineering Electromagnetic wave absorption materials ; Dielectric materials and actuators ; Advanced Antenna and Metasurface Technologies
Authors: Vyacheslav Romanov
Source: 2021 IEEE International Conference on Big Data (Big Data) (article, 2021-12-15)
Field: Materials Science — Materials Chemistry

Latent Learning with pyroMind.2020 Computational methods and tools for scientific and engineering applications must be able to ingest and interpret domain knowledge, conceptual constructs, and theoretical frameworks. The artificial intelligence tool built for materials science applications (pyroMind.2020) exploits a variety of novel computational methods to learn latent patterns and empirical relationships to complement scarce experimental data. Learning to predict the latent variables (latent learning) helps to pinpoint the anticipated range-edge anomalies and improves the confidence in data interpretation, interpolation, and extrapolation. Computer science ; Latent variable ; Artificial intelligence ; Exploit ; Machine learning ; Complement (music) ; Extrapolation ; Domain (mathematical analysis) ; Variety (cybernetics) ; Data science ; Mathematics Machine Learning in Materials Science ; High Temperature Alloys and Creep ; Nuclear Materials and Properties
Authors: Takashiro Akitsu | Shintaro Suda | Natsuki Katsuumi
Source: Edelweiss Chemical Science Journal (article, 2021-11-15)
Field: Chemistry — Physical and Theoretical Chemistry

Re-reporting the Crystal Structure of Copper Complex from Another Point of View We faced an example of re-reporting of the crystal structure, which was studied from another perspective. With the development of data-driven science, the efficiency of all researchers may be improved if the rules of data "reuse", which are different from "novelty", are established. In this context, the crystal structure of a copper(II) complex with 2,6-pyridine dicarboxylic acid, C14H8O8CuN2.H2O (monohydrate), was re-determined by us again. It has a different number of crystalline water molecules in a crystal of the same copper(II) complex previously reported (trihydrate). Interestingly, both crystal structures have been reported again and again by many researchers for a long time. What’s novelty for each report? Copper ; Crystal structure ; Novelty ; Context (archaeology) ; Crystal (programming language) ; Crystallography ; Perspective (graphical) ; Reuse ; Molecule ; Materials science ; Computer science ; Chemistry ; Artificial intelligence ; Geography ; Engineering ; Psychology ; Metallurgy ; Organic chemistry Crystallography and molecular interactions ; Computational Drug Discovery Methods ; Radioactive element chemistry and processing
Authors: Xinrui Liu | Yifeng Wu | Douglas L. Irving | Meng Li
Source: arXiv (Cornell University) (preprint, 2021-05-11)
Field: Materials Science — Materials Chemistry

Gaussian graphical models with graph constraints for magnetic moment interaction in high entropy alloys This article is motivated by studying the interaction of magnetic moments in high entropy alloys (HEAs), which plays an important role in guiding HEA designs in materials science. While first principles simulations can capture magnetic moments of individual atoms, explicit models are required to analyze their interactions. This is essentially an inverse covariance matrix estimation problem. Unlike most of the literature on graphical models, the inverse covariance matrix possesses inherent structural constraints encompassing node types, topological distance of nodes, and partially specified conditional dependence patterns. The present article is, to our knowledge, the first to consider such intricate structures in graphical models. In particular, we introduce graph constraints to formulate these structures that arise from domain knowledge and are critical for interpretability, which leads to a Bayesian conditional autoregressive model with graph constraints (CARGO) for structured inverse covariance matrices. The CARGO method enjoys efficient implementation with a modified Gauss-Seidel scheme through proximity operators for closed-form posterior exploration. We establish algorithmic convergence for the proposed algorithm under a verifiable stopping criterion. Simulations show competitive performance of CARGO relative to several other methods and confirm our convergence analysis. In a novel real data application to HEAs, the proposed methods lead to data-driven quantification and interpretation of magnetic moment interactions with high tractability and transferability. Graphical model ; Interpretability ; Computer science ; Covariance ; Estimator ; Theoretical computer science ; Mathematical optimization ; Algorithm ; Applied mathematics ; Mathematics ; Artificial intelligence Machine Learning in Materials Science
Authors: Zachary M. Sparrow | Brian G. Ernst | Trine K. Quady | Robert A. DiStasio
Source: arXiv (Cornell University) (preprint, 2021-09-26)
Field: Chemistry — Inorganic Chemistry

CASE21: Uniting Non-Empirical and Semi-Empirical Density Functional\n Approximation Strategies using Constraint-Based Regularization In this work, we present a general framework that unites the two primary\nstrategies for constructing density functional approximations (DFAs):\nnon-empirical (NE) constraint satisfaction and semi-empirical (SE) data-driven\noptimization. The proposed method employs B-splines -- bell-shaped spline\nfunctions with compact support -- to construct each inhomogeneity correction\nfactor (ICF). This choice offers several distinct advantages over a polynomial\nbasis by enabling explicit enforcement of linear and non-linear constraints as\nwell as ICF smoothness using Tikhonov regularization and penalized B-splines\n(P-splines). As proof of concept, we use this approach to construct CASE21 -- a\nConstrained And Smoothed semi-Empirical hybrid generalized gradient\napproximation that completely satisfies all but one constraint (and partially\nsatisfies the remaining one) met by the PBE0 NE-DFA and exhibits enhanced\nperformance across a diverse set of chemical properties. As such, we argue that\nthe paradigm presented herein maintains the physical rigor and transferability\nof NE-DFAs while leveraging high-quality quantum-mechanical data to improve\nperformance.\n Regularization (linguistics) ; Computer science ; Smoothness ; Mathematical optimization ; Tikhonov regularization ; Spline (mechanical) ; Applied mathematics ; Construct (python library) ; Constraint (computer-aided design) ; Polynomial ; Mathematics ; Inverse problem ; Artificial intelligence ; Mathematical analysis Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Donghui Li | Mychal P. Spencer | Madhusudhan R. Pallaka | Tucker T. Bisel | Yelin Ni | Andy Zwoster | Mark K. Murphy | Leonard S. Fifield
Source:  (article, 2021-06-07)
Field: Materials Science — Materials Chemistry

Principal Component Analysis of Nuclear Cable Insulation Subjected to Elevated Temperature and Gamma Radiation In nuclear power plants (NPPs), the aging of electrical cable insulation occurs due to elevated temperature, ionizing radiation, and other environmental factors. To ensure the safe and efficient operation of NPPs, determination of key indicators of cable aging is critical to predict the remaining useful lifetime of electrical cable insulation. In this work, the effects of simultaneous and sequential thermal and gamma radiation on the aging of cross-linked polyethylene (XLPE) electrical cable insulation are investigated. The chemical changes of the insulation were monitored non-destructively through the use of Fourier transform infrared (FTIR) spectroscopy. The FTIR spectra were measured stepwise after predetermined exposure intervals, with a total irradiation dose up to 320 kGy at a dose rate of 300 Gy/hr in two exposure scenarios; simultaneously aged samples were heated at 150°C during irradiation, while sequentially aged samples were heated at 150°C for designated durations followed by corresponding times of irradiation at ambient temperature. A data-driven approach using principal component analysis (PCA) was developed to highlight changes in the carbonyl region of the infrared spectra of the aged samples due to oxidation and to differentiate oxidation rates under the simultaneous and sequential exposure conditions. Findings indicate that the sequential aging scenario in which irradiation follows thermal aging may be more conservative than the simultaneous aging scenario for XLPE electrical cable insulation. Materials science ; Irradiation ; Fourier transform infrared spectroscopy ; Accelerated aging ; Polyethylene ; Composite material ; Radiation ; Cross-linked polyethylene ; Gamma ray ; Forensic engineering ; Nuclear engineering ; Optics ; Chemical engineering ; Engineering ; Nuclear physics High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Recycling and Waste Management Techniques
Authors: Peter Kraus | Elisabeth H. Wolf | Charlotte Prinz | Giulia Bellini | Annette Trunschke | Robert Schlögl
Source: ChemRxiv (preprint, 2021-10-28)
Field: Materials Science — Materials Chemistry

Towards automation of operando experiments: A case study in contactless conductivity measurements Automation of experiments is a key component on the path of digitalisation in catalysis and related sciences. Here we present the lessons learned and caveats avoided during the automation of our contactless conductivity measurement set-up, capable of operando measurement of catalytic samples. We briefly discuss the motivation behind the work, the technical groundwork required, and the philosophy guiding our design. The main body of this work is dedicated to the detailing of the implementation of the automation, data structures, as well as the modular data processing pipeline. The open-source toolset developed as part of this work allows us to carry out unattended and reproducible experiments, as well as post-process data according to current best practice. This process is illustrated by implementing two routine sample protocols, one of which was included in the Handbook of Catalysis, providing several case studies showing the benefits of such automation, including increased throughput and higher data quality. The datasets included as part of this work contain catalytic and operando conductivity data, and are self-consistent, annotated with metadata, and are available on a public repository in a machine-readable form. We hope the datasets as well as the tools and workflows developed as part of this work will be an useful guide on the path towards automation and digital catalysis. Workflow ; Automation ; Pipeline (software) ; Modular design ; Computer science ; Process (computing) ; Metadata ; Work (physics) ; Engineering ; Database ; World Wide Web ; Mechanical engineering Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Nastaran Saghayimarouf | Majid Monajjemi | Karim Zare | Ali Shamel
Source: Nexo Revista Científica (article, 2021-06-08)
Field: Chemistry — Spectroscopy

Investigation of different SWCNTs interaction with dopamine and serotonin anticancers: a theoretical study Carbon nano tubes (CNTS) have two basic structure as single-walled and multi-walled based on hexagonal plexus of carbon atoms. CNTs can serve as platforms to conjugate other compounds specially in medications purposes by immobilization of biomolecules at their surface. Dopamine and serotonin are two biological molecules which have bifunctional activities as hormone and neurotransmitter. These two molecules have important roles as neurotransmitters in the central and peripheral nervous systems but serotonin functions as a mood regulator, while dopamine is connected to the “pleasure center”. In this article we optimized molecular and structural properties of connected dopamine and serotonin with SWNTS with four different diameters (7.0,7.5,7.7 and 10.0 nm) by using molecular quantum methods such as NMR shielding tensor data by B3LYP level of theory with 6-31 G(d) as a basis set, mk and frequency methods. Theoretical computations were performed to study NMR chemical shift data including magnetic shielding tensor (σ, ppm), shielding asymmetry (η), magnetic shielding anisotropy (σaniso), magnetic shielding isotropy (σiso) , skew of a tensor (Κ) and chemical shift anisotropy (Δσ) and span (Ω) at various rotation angles around a specific rotation , physical and chemical properties of atomic nuclei , frequency data by B3LYP/6-31g level of theory and POP method using gaussian 09 program. Electromagnetic shielding ; Anisotropy ; Dopamine ; Serotonin ; Isotropy ; Computational chemistry ; Chemistry ; Molecular physics ; Materials science ; Nuclear magnetic resonance ; Chemical physics ; Physics ; Neuroscience ; Quantum mechanics ; Psychology ; Composite material Advanced NMR Techniques and Applications
Authors: Haozhe Wang | Zhenpeng Yao | Gang Seob Jung | Qichen Song | Marek Hempel | Tomás Palacios | Gang Chen | Markus J. Buehler | Alán Aspuru‐Guzik | Jing Kong
Source:  (preprint, 2021-08-11)
Field: Materials Science — Materials Chemistry

Frank-van der Merwe Growth in Bilayer Graphene Bilayer graphene has attracted significant interest due to its unique properties, including fascinating electrical behavior when one layer is slightly rotated relative to the other. However, the quality of large-area bilayer graphene is often limited by the layer-plus-island growth mode in which islands of thicker graphene present as unavoidable impurities. Here, we report the observation of the layer-by-layer, Frank-van der Merwe (FM) growth mode in bilayer graphene where multilayer impurities are suppressed. Instead of the conventional surface adhesive energy, it is found that interface adhesive energy is possible to be tuned with an oxidative pretreatment. The FM-grown bilayer graphene is of AB-stacking or with small-twisting-angle (θ = 0-5°), which is more mechanically robust compared to monolayer graphene, facilitating a free-standing wet transfer technology. Bilayer graphene ; Graphene ; Bilayer ; Monolayer ; Materials science ; Stacking ; Layer (electronics) ; Nanotechnology ; Condensed matter physics ; Optoelectronics ; Chemistry ; Membrane ; Physics Graphene research and applications ; Quantum and electron transport phenomena ; Molecular Junctions and Nanostructures
Authors: Saqib Mukhtar
Source: Koroze a ochrana materialu (article, 2021-09-01)
Field: Materials Science — Materials Chemistry

Corrosion in underground infrastructures Abstract There is a significant loss due to corrosion of buried infrastructure. Many pipes have failed due to mistreatment happening within them all around the world. Different soil aeration leads to macro corrosion cells that cause critical levels within the path corrosion leading to a loss of structural integrity of the buried pipes underground. This review paper seeks to address and presents a predetermined model developed by using software COMSOL Multiphysics to identify and characterize the areas experiencing a high rate of corrosion beneath the surface due to differential aeration. The pipe surfaces experience electrochemical reactions and reactant transport mechanisms in the soil and the pipes. Porosity and degree of saturation make the closed-form equations used to create the mass transport properties and electrical properties that constitute three-phase medium using standard soil parameters. The current model enables the study of soil property variations and conditions from the external environment pipeline corrosion. The model results conclude and agree well with the literature and case studies done at pipeline failure sites. The model used in this review will then enable water utilities to develop forecasting tools that may be useful for assessment. Corrosion ; Multiphysics ; Pipeline (software) ; Integrity management ; Aeration ; Environmental science ; Mass transport ; Geotechnical engineering ; Forensic engineering ; Materials science ; Engineering ; Metallurgy ; Finite element method ; Mechanical engineering ; Structural engineering ; Waste management ; Engineering physics Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Geotechnical Engineering and Underground Structures
Authors: A. Wang | Ryan Kingsbury | Matthew J. McDermott | Matthew Horton | Anubhav Jain | Shyue Ping Ong | Shyam Dwaraknath | Kristin A. Persson
Source: ChemRxiv (preprint, 2021-05-19)
Field: Materials Science — Materials Chemistry

A Framework for Quantifying Uncertainty in DFT Energy Corrections In this work, we demonstrate a method to quantify uncertainty in corrections to density functional theory (DFT) energies based on empirical results. Such corrections are commonly used to improve the accuracy of computational enthalpies of formation, phase stability predictions, and other energy-derived properties, for example. We incorporate this method into a new DFT energy correction scheme comprising a mixture of oxidation-state and composition-dependent corrections and show that many chemical systems contain unstable polymorphs that may actually be predicted stable when uncertainty is taken into account. We then illustrate how these uncertainties can be used to estimate the probability that a compound is stable on a compositional phase diagram, thus enabling better-informed assessments of compound stability. Density functional theory ; Stability (learning theory) ; Work (physics) ; Phase diagram ; Statistical physics ; Energy (signal processing) ; Scheme (mathematics) ; Phase (matter) ; Diagram ; Thermodynamics ; Computer science ; Applied mathematics ; Computational chemistry ; Mathematics ; Chemistry ; Physics ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Chemical Thermodynamics and Molecular Structure
Authors: Alexander Lukin | Oğuz Gülseren
Source: Preprints.org (preprint, 2021-03-12)
Field: Materials Science — Materials Chemistry

Tuning the Spatially Controlled Growth, Structural Self-Organizing and Cluster-Assembling of the Carbyne-Enriched Nanostructured Metamaterials during Ion-Assisted Pulse-Plasma Deposition&lt;strong&gt;&lt;strong&gt; &lt;/strong&gt;&lt;/strong&gt; Structural self-organizing and pattern formation are universal and key phenomena observed during growth and cluster-assembling of the carbyne-enriched nanostructured metamaterials at the ion-assisted pulse-plasma deposition. Fine tuning these universal phenomena opens access to designing the properties of the growing carbyne-enriched nano-matrix. The structure of bonds in the grown carbyne-enriched nano-matrices can be programmed by the processes of self-organization and auto-synchronization of nanostructures. We propose the innovative concept, connected with application of the universal Cymatics phenomena during the predictive growth of the carbyne-enriched nanostructured metamaterials. We also propose the self-organization approach for increase stability of the long linear carbon chains. The main idea of suggested concept is manipulating by the self-organized wave patterns excitation phenomenon and their distribution by the spatial structure and properties of the nanostructured metamaterial grows region through the new synergistic effect. Mentioned effect will be provided through the vibration-assisted self-organized wave patterns excitation along with simultaneous manipulating by their properties through the electric field. We propose to use acoustic activation of the plasma zone of nano-matrix growing. Interaction between the inhomogeneous electric field distribution generated on the vibrating layer and the plasma ions will serve as the additional energizing factor controlling the local pattern formation and self-organizing of the nano-structures. Suggested concept makes it possible to provide precise predictive designing the spatial structure and properties of the advanced carbyne-enriched nanostructured metamaterials. Carbyne ; Metamaterial ; Materials science ; Nanostructure ; Nanotechnology ; Plasma ; Cluster (spacecraft) ; Chemical physics ; Optoelectronics ; Chemistry ; Physics ; Computer science ; Organic chemistry Graphene research and applications ; Carbon Nanotubes in Composites ; Electrohydrodynamics and Fluid Dynamics