jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Manuel Eduardo Martínez-Cartagena | Juan Bernal-Martínez | Carlos Alberto Aranda-Sánchez | Arnulfo Banda-Villanueva | José Luis González-Zapata | Antonio Ledezma‐Pérez | A. Aguilar‐Elguézabal | Jorge Romero‐García
Source: Journal of Materials Science and Chemical Engineering (article, 2021-01-01)
Field: Materials Science — Polymers and Plastics

Biomimetic Synthesized Conductive Copolymer EDOT-Pyrrole Electrodes for Electrocardiogram Recording in Humans We report on electrodes fabricated with EDOT-Pyrrole copolymer through electrophoretic deposition and used for recording and sensing bio-electrical signals. We measured the electrical properties of the copolymer deposited on a stainless-steel substrate, and we performed Cyclic Voltammetry (CV) and Scanning Electron Microscopy (SEM) studies to characterize the morphological properties and copolymer distribution on the metal surface. We found that electrodes fabricated with EDOT-Pyrrole copolymer exhibit a high signal-to-noise ratio as well as an accurate and stable conductivity compared with other commonly used electroconductive polymers. Stainless-steel-coated EDOT-Pyrrole electrodes are suitable to record electrocardiograms in humans with high resolution comparable to standard silver-electrodes. Materials science ; Copolymer ; Electrode ; Cyclic voltammetry ; Conductive polymer ; Scanning electron microscope ; Polymer ; Electrical conductor ; Pyrrole ; Substrate (aquarium) ; Polypyrrole ; Composite material ; Chemical engineering ; Electrochemistry ; Polymerization ; Organic chemistry Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Electrochemical sensors and biosensors
Authors: Sterling G. Baird | Tran Diep | Taylor D. Sparks
Source:  (preprint, 2021-10-18)
Field: Materials Science — Materials Chemistry

DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions We present Descending from Stochastic Clustering Variance Regression (DiSCoVeR), a Python tool for identifying high-performing, chemically unique compositions relative to existing compounds using a combination of a chemical distance metric, density-aware dimensionality reduction, and clustering. We introduce several new metrics for materials discovery and validate DiSCoVeR on Materials Project bulk moduli using compound-wise and cluster-wise validation methods. We visualize these via multiobjective Pareto front plots and assign a weighted score to each composition where this score encompasses the trade-off between performance and density-based chemical uniqueness. We explore an additional uniqueness proxy related to property gradients in chemical space. We demonstrate that DiSCoVeR can successfully screen materials for both performance and uniqueness in order to extrapolate to new chemical spaces. Cluster analysis ; Python (programming language) ; Chemical space ; Computer science ; Uniqueness ; Dimensionality reduction ; Data mining ; Metric (unit) ; Metric space ; Clustering high-dimensional data ; Mathematics ; Machine learning ; Drug discovery ; Bioinformatics ; Discrete mathematics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Fionán Davitt | Kamil Rahme | Sreyan Raha | Shane Garvey | Manuel A. Roldán | Achintya Singha | Shery L. Y. Chang | Subhajit Biswas | Justin D. Holmes
Source: Nanotechnology (article, 2021-12-15)
Field: Materials Science — Materials Chemistry

Solution phase growth and analysis of super-thin zigzag tin selenide nanoribbons Tin selenide (SnSe), a highly promising layered material, has been garnering particular interest in recent times due to its significant promise for future energy devices. Herein we report a simple solution-phase approach for growing highly crystalline layered SnSe nanoribbons. Polyvinylpyrrolidone (PVP) was used as a templating agent to selectively passivates the (100) and (001) facets of the SnSe nanoribbons resulting in the unique growth of nanoribbons along their<i>b</i>-axis with a defined zigzag edge state along the sidewalls. The SnSe nanoribbons are few layers thick (∼20 layers), with mean widths of ∼40 nm, and achievable length of >1<i>μ</i>m. Nanoribbons could be produced in relatively high quantities (>150 mg) in a single batch experiment. The PVP coating also offers some resistance to oxidation, with the removal of the PVP seen to lead to the formation of a SnSe/SnO<i><sub>x</sub></i>core-shell structure. The use of non-toxic PVP to replace toxic amines that are typically employed for other 1D forms of SnSe is a significant advantage for sustainable and environmentally friendly applications. Heat transport properties of the SnSe nanoribbons, derived from power-dependent Raman spectroscopy, demonstrate the potential of SnSe nanoribbons as thermoelectric material. Materials science ; Zigzag ; Raman spectroscopy ; Thermoelectric effect ; Nanotechnology ; Tin ; Graphene nanoribbons ; Selenide ; Phase (matter) ; Optoelectronics ; Chemical engineering ; Optics Advanced Thermoelectric Materials and Devices ; Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films
Authors: Bin Ouyang | Jingyang Wang | Tanjin He | Christopher J. Bartel | Haoyan Huo | Yan Wang | Valentina Lacivita | Haegyeom Kim | Gerbrand Ceder
Source: Nature Communications (preprint, 2021-10-01)
Field: Materials Science — Materials Chemistry

Synthetic accessibility and stability rules of NASICONs In this paper we develop the stability rules for NASICON-structured materials, as an example of compounds with complex bond topology and composition. By first-principles high-throughput computation of 3881 potential NASICON phases, we have developed guiding stability rules of NASICON and validated the ab initio predictive capability through the synthesis of six attempted materials, five of which were successful. A simple two-dimensional descriptor for predicting NASICON stability was extracted with sure independence screening and machine learned ranking, which classifies NASICON phases in terms of their synthetic accessibility. This machine-learned tolerance factor is based on the Na content, elemental radii and electronegativities, and the Madelung energy and can offer reasonable accuracy for separating stable and unstable NASICONs. This work will not only provide tools to understand the synthetic accessibility of NASICON-type materials, but also demonstrates an efficient paradigm for discovering new materials with complicated composition and atomic structure. Electronegativity ; Fast ion conductor ; Stability (learning theory) ; Materials science ; Phase (matter) ; Computer science ; Ab initio ; Topology (electrical circuits) ; Thermodynamics ; Chemical physics ; Machine learning ; Chemistry ; Physical chemistry ; Mathematics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Davide Fioravanti | Giovanni Barcaro | Alessandro Fortunelli
Source: Physical Chemistry Chemical Physics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

An augmented (multi-descriptor) grouping algorithm to optimize chemical ordering in nanoalloys We propose the Augmented Grouping Approach (AugGA) and its deployment in the Augmented Grouping GO (AugGGO) scheme, for an efficient exploration of the chemical ordering (or compositional structure) of multi-component (alloyed) nanoparticles. The approach is based on a 'grouping' strategy (previously proposed for high-symmetry structures) by which the number of compositional degrees of freedom of the system is decreased by defining sets of atoms (groups, or orbits, or shells) that are constrained to be populated by the same element. Three fundamental advances are here included with respect to previous proposals: (i) groups are defined on the basis of descriptors (no point-group symmetry is assumed), (ii) bulk groups can exploit general chemical ordering patterns taken from databases, and (iii) sub-grouping is realized <i>via</i> a multi-descriptor strategy (here using two basic descriptors: the atomic energy and a few types of geometry patterns). The AugGGO approach is applied to two prototypical examples of binary nanoalloys: Pd-Pt and Ag-Cu, with a size between ≈500 and ≈1300 atoms, in different configurations, and the convex hull of the mixing energy as a function of composition is derived. It is shown how the three advances here proposed decisively extend the power and scope of the grouping approach: (i) making it applicable to any generic structural framework, (ii) achieving a thorough sampling of the core regions of nanoparticles, and (iii) catching exotic/unexpected chemical ordering arrangements, at a computational cost which is 1-2 orders of magnitude smaller than that of traditional Monte Carlo single-exchange techniques. Component (thermodynamics) ; Computer science ; Scheme (mathematics) ; Software deployment ; Algorithm ; Materials science ; Mathematics ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Koushik Pal | Yi Xia | Jiahong Shen | Jiangang He | Yubo Luo | Mercouri G. Kanatzidis | Chris Wolverton
Source: npj Computational Materials (preprint, 2021-06-03)
Field: Materials Science — Materials Chemistry

Accelerated discovery of a large family of quaternary chalcogenides with very low lattice thermal conductivity Abstract The development of efficient thermal energy management devices such as thermoelectrics and barrier coatings often relies on compounds having low lattice thermal conductivity ( κ l ). Here, we present the computational discovery of a large family of 628 thermodynamically stable quaternary chalcogenides, AMM′Q 3 (A = alkali/alkaline earth/post-transition metals; M/M′ = transition metals, lanthanides; Q = chalcogens) using high-throughput density functional theory (DFT) calculations. We validate the presence of low κ l in these materials by calculating κ l of several predicted stable compounds using the Peierls–Boltzmann transport equation. Our analysis reveals that the low κ l originates from the presence of either a strong lattice anharmonicity that enhances the phonon-scatterings or rattler cations that lead to multiple scattering channels in their crystal structures. Our thermoelectric calculations indicate that some of the predicted semiconductors may possess high energy conversion efficiency with their figure-of-merits exceeding 1 near 600 K. Our predictions suggest experimental research opportunities in the synthesis and characterization of these stable, low κ l compounds. Anharmonicity ; Density functional theory ; Thermoelectric materials ; Thermal conductivity ; Chalcogen ; Phonon ; Lattice (music) ; Condensed matter physics ; Materials science ; Boltzmann equation ; Alkali metal ; Chemistry ; Thermodynamics ; Computational chemistry ; Crystallography ; Physics ; Organic chemistry Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films
Authors: Yasumitsu Orii | Shuichi Hirose | Akihiro Fujita | Masakazu Kobayashi
Source: Journal of Photopolymer Science and Technology (article, 2021-06-11)
Field: Materials Science — Materials Chemistry

Development of Materials Informatics Platform As the use of IT increases importance with big data and AI, the issue of power consumption has been highlighted. Under these circumstances, the development of new materials is more and more important. Materials Informatics (MI) is one of the hottest technologies in the material development field, because of its potential to reduce the time and costs of discovering innovative materials. To achieve this, the key is to collect data that has been accumulated for many years at research institutions and companies, and to make information extracted from the data into knowledge. This article introduces the development of two methods based on AI: the “cognitive approach”, which reads vast amounts of literature information and digitizes data, and the “analytic approach”, which theoretically estimates the structure and physical properties of chemical substances from predictive models. Data science ; Big data ; Materials informatics ; Informatics ; Key (lock) ; Field (mathematics) ; Computer science ; Power consumption ; Health informatics ; Data mining ; Engineering informatics ; Power (physics) ; Engineering ; Computer security Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Luis Casillas-Trujillo | Rickard Armiento | Björn Alling
Source: Physical Review Materials (article, 2021-03-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Identification of materials with strong magnetostructural coupling using computational high-throughput screening Important phenomena such as magnetostriction, magnetocaloric, and magnetoelectric effects arise from, or could be enhanced by, the coupling of magnetic and structural degrees of freedom. The coupling of spin and lattice also influence transport and structural properties in magnetic materials, in particular around phase transitions. In this paper we propose a method for screening materials for a strong magnetostructural coupling by assessing the effect of the local magnetic configuration on the atomic forces using density functional theory. We have employed the disordered local moment approach in a supercell formulation to probe different magnetic local configurations and their forces and performed a high-throughput search on binary and ternary compounds available in the Crystallography Open Database. We identify a list of materials with a strong spin-lattice coupling out of which several are already known to display magnetolattice coupling phenomena such as ${\mathrm{Fe}}_{3}{\mathrm{O}}_{4}$ and CrN. Others, such as ${\mathrm{Mn}}_{2}{\mathrm{CrO}}_{4}$ and $\mathrm{Ca}{\mathrm{Fe}}_{7}{\mathrm{O}}_{11}$, have been less studied and have yet to reveal their potentials in experiments and applications. Supercell ; Coupling (piping) ; Ternary operation ; Magnetic moment ; Inductive coupling ; Lattice (music) ; Spin (aerodynamics) Multiferroics and related materials ; Magnetic and transport properties of perovskites and related materials ; Magnetic properties of thin films
Authors: Rosita Diana | Lucia Sessa | Simona Concilio | Stefano Piotto | Barbara Panunzi
Source: Frontiers in Materials (article, 2021-10-19)
Field: Chemistry — Inorganic Chemistry

Luminescent Zn (II)-Based Nanoprobes: A Highly Symmetric Supramolecular Platform for Sensing of Biological Targets and Living Cell Imaging Zinc (II) cation is an environmentally friendly metal, less expensive, easy to dispose of, and managed. Highly engineered symmetric systems can be built using zinc (II) atoms as the metal nodes of hybrid organic-inorganic supramolecular structures. In biological contexts, luminescent zinc-based nanoprobes are in growing demand. Specifically, they are currently employed to detect biologically and environmentally relevant analytes, in therapeutic drug delivery, and for bioimaging and diagnostic techniques monitoring aspects of cellular functions. This review will provide a systematic and consequential approach to zinc-based nanoprobes, including zinc-based MOFs and other zinc-based organized nanoparticles. A progression from detecting the biological target to the intracellular sensing/marking/carriage has been followed. A selection of significant cutting-edge articles collected in the last five years has been discussed, based on the structural pattern and sensing performance, with special notice to living cell bioimaging as the most targeted and desirable application. Zinc ; Nanotechnology ; Supramolecular chemistry ; Materials science ; Biological imaging ; Luminescence ; Computer science ; Chemistry ; Fluorescence ; Optoelectronics ; Organic chemistry ; Molecule Metal-Organic Frameworks: Synthesis and Applications ; Molecular Sensors and Ion Detection ; Nanocluster Synthesis and Applications
Authors: Ni Zhan | John R. Kitchin
Source:  (preprint, 2021-08-05)
Field: Materials Science — Materials Chemistry

Uncertainty Quantification in Machine Learning and Nonlinear Least Squares Regression Models Machine learning (ML) models are valuable research tools for making accurate predictions. However, ML models often unreliably extrapolate outside their training data. We propose an uncertainty quantification method for ML models (and generally for other nonlinear models) with parameters trained by least squares regression. The uncertainty measure is based on the multiparameter delta method from statistics, which gives the standard error of the prediction. The uncertainty measure requires the gradient of the model prediction and the Hessian of the loss function, both with respect to model parameters. Both the gradient and Hessian can be readily obtained from most ML software frameworks by automatic differentiation. We show that the uncertainty measure is larger for input space regions that are not part of the training data. Therefore this method can be used to identify extrapolation and to aid in selecting training data or assessing model reliability. Hessian matrix ; Extrapolation ; Measure (data warehouse) ; Computer science ; Nonlinear regression ; Nonlinear system ; Regression ; Overfitting ; Least-squares function approximation ; Reliability (semiconductor) ; Machine learning ; Artificial intelligence ; Uncertainty quantification ; Non-linear least squares ; Data mining ; Regression analysis ; Mathematics ; Statistics ; Applied mathematics ; Explained sum of squares ; Artificial neural network Machine Learning in Materials Science ; Fault Detection and Control Systems ; Computational Drug Discovery Methods
Authors: Guorui Xiao | Qinqing Zhu | Yanwei Cui | Bin Liu | Jifeng Wu | Bai-Zhuo Li | Siqi Wu | Guang‐Han Cao | Zhi Ren
Source: Materials Advances (article, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Synthesis and superconductivity of new TiNiSi-type equiatomic germanide ThIrGe A new germanide ThIrGe has been synthesized and characterized by X-ray diffraction, transmission electron microscopy, electrical resistivity, magnetic susceptibility and thermodynamic measurements. Germanide ; Electrical resistivity and conductivity ; Superconductivity ; Materials science ; Transmission electron microscopy ; Diffraction ; Condensed matter physics ; Magnetic susceptibility ; Metallurgy ; Nanotechnology ; Physics ; Optics Iron-based superconductors research ; Rare-earth and actinide compounds ; Inorganic Chemistry and Materials
Authors: Baoning Sha | Shengzhuo Zhao | Minling Gu | Guannan Zhao | Liping Wang | Guo‐Qiang Bi | Zhanhong Du
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-10-19)
Field: Materials Science — Materials Chemistry

Multi-Functionalized Self-Bonding MXene for Minimal-invasive Jet-injected Neural Interface and Tissue Healing Abstract Implantable central and peripheral neural interfaces have great potential in treating various nerve injuries and diseases. Still, limitations of surgery trauma, handling inconvenience, and biocompatibility issues of available materials and techniques significantly hinder the peripheral nerve interface for research and clinical purposes. MXenes have great potential as bioelectronics materials for excellent hydrophilicity, conductivity, and biocompatibility. However, their application in bioelectronic interface has been limited due to the poor oxidation stability and fast tissue clearance. Here, we developed a minimal-invasive jet-injected neural interface using MXene nanosheets with strong redox stability, tissue adhesion, conductivity, and good self-bonding properties. We also develop a minimal-invasive jet injector to implant the optimized MXene suspension into the damaged sciatic nerve and establish a neural interface through tissue adhesion and self-bonding. We use this neural interface to promote nerve regeneration and perform electrophysiology recording on moving mice. We prove that the nanosheets can mitigate cellular inflammation, promote tissue healing, and record high-quality electrophysiology signals for predicting joint movement. Thus, our material and implantation strategy together form a novel minimal-invasive neural interface, facilitating the collection and analysis of large-scale living body data to solve the challenge of neurological diseases of the peripheral or even the central nervous system. Biocompatibility ; Materials science ; Biomedical engineering ; Regeneration (biology) ; Nanotechnology ; Interface (matter) ; Adhesion ; Neuroscience ; Medicine ; Biology ; Composite material MXene and MAX Phase Materials ; Neuroscience and Neural Engineering ; Advanced Sensor and Energy Harvesting Materials
Authors: Nikita Ghosh | Subhadeep Sen | Goutam Biswas | L. Robindro Singh | Prabir Kumar Haldar
Source: Research Square (preprint, 2021-11-22)
Field: Chemistry — Organic Chemistry

Synthesis and Characterization of CuO Nanoparticles and CuO/PVA-PVP Nanocomposites: Adsorption Activity for Malachite Green (MG) Dye and Antibacterial Property in Wastewater Treatment Abstract Contaminated waterways, particularly effluent from the dye industry, are one of the major issues today. Among various methods adsorption being the most straight forward, low-cost, effective, and successful to treat such effluents. For this purpose, Copper oxide nanoparticles (CuO) and Copper oxide nanoparticles embedded within Polyvinyl alcohol (PVA) and Polyvinyl pyrrolidone (PVP) mixed polymer (CuO/PVA-PVP) were synthesized using precipitation methods and different analytical techniques such as dynamic light scattering (DLS), powdered X-ray diffraction (XRD), fourier-transform infrared (FTIR) spectroscopy and transmission electron microscopy (TEM) were used to characterize morphology and size. Synthesized nanomaterials were used for the adsorption of malachite green (MG) dye in aqueous solution by the batch adsorption method. Various experimental conditions e.g., contact time, concentrations, pH, were optimized to obtain best adsorption outcome. Adsorption kinetics and isotherm suggested that it follows Pseudo-second-order-kinetic and Freundlich isotherm. The invitro antibacterial activity was investigated using disk diffusion and minimum inhibitory concentration (MIC) test against Staphylococcus aureus (S.aureus), a gram positive and Escherichia coli (E.coli), a gram negative bacteria, where both the synthesized nanoparticles showed a decent antibacterial activity against both the bacterial strain. Malachite green ; Adsorption ; Nanocomposite ; Characterization (materials science) ; Wastewater ; Nanoparticle ; Chemical engineering ; Materials science ; Antibacterial activity ; Nuclear chemistry ; Chemistry ; Nanotechnology ; Organic chemistry ; Waste management ; Bacteria Nanomaterials for catalytic reactions ; Environmental remediation with nanomaterials ; Nanoparticles: synthesis and applications