jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ramakrishnan Kannan | Anton V. Ievlev | Nouamane Laanait | Maxim Ziatdinov | Rama K. Vasudevan | Stephen Jesse | Sergei V. Kalinin
Source: Advanced Structural and Chemical Imaging (review, 2018-04-30)
Field: Materials Science — Materials Chemistry

Deep data analysis via physically constrained linear unmixing: universal framework, domain examples, and a community-wide platform Many spectral responses in materials science, physics, and chemistry experiments can be characterized as resulting from the superposition of a number of more basic individual spectra. In this context, unmixing is defined as the problem of determining the individual spectra, given measurements of multiple spectra that are spatially resolved across samples, as well as the determination of the corresponding abundance maps indicating the local weighting of each individual spectrum. Matrix factorization is a popular linear unmixing technique that considers that the mixture model between the individual spectra and the spatial maps is linear. Here, we present a tutorial paper targeted at domain scientists to introduce linear unmixing techniques, to facilitate greater understanding of spectroscopic imaging data. We detail a matrix factorization framework that can incorporate different domain information through various parameters of the matrix factorization method. We demonstrate many domain-specific examples to explain the expressivity of the matrix factorization framework and show how the appropriate use of domain-specific constraints such as non-negativity and sum-to-one abundance result in physically meaningful spectral decompositions that are more readily interpretable. Our aim is not only to explain the off-the-shelf available tools, but to add additional constraints when ready-made algorithms are unavailable for the task. All examples use the scalable open source implementation from https://github.com/ramkikannan/nmflibrary that can run from small laptops to supercomputers, creating a user-wide platform for rapid dissemination and adoption across scientific disciplines. Non-negative matrix factorization ; Matrix decomposition ; Computer science ; Context (archaeology) ; Weighting ; Domain (mathematical analysis) ; Superposition principle ; Matrix (chemical analysis) ; Scalability ; Factorization ; Algorithm ; Theoretical computer science ; Data mining ; Mathematics ; Chemistry ; Physics Machine Learning in Materials Science ; Spectroscopy Techniques in Biomedical and Chemical Research ; Gene expression and cancer classification
Authors: Claudia Gonzalez Viejo | Sigfredo Fuentes | Damir D. Torrico | Kate Howell | Frank R. Dunshea
Source: Journal of Food Science (article, 2018-03-30)
Field: Chemistry — Analytical Chemistry

Assessment of Beer Quality Based on a Robotic Pourer, Computer Vision, and Machine Learning Algorithms Using Commercial Beers This paper is a novel approach for food science using machine modeling techniques that could contribute significantly to rapid screenings of food and brewage products for the food industry and the implementation of Artificial Intelligence (AI). The use of RoboBEER to assess beer quality showed to be a reliable, objective, accurate, and less time-consuming method to predict sensory descriptors compared to trained sensory panels. Hence, this method could be useful as a rapid screening procedure to evaluate beer quality at the end of the production line for industry applications. Mouthfeel ; Quantitative Descriptive Analysis ; Carbonation ; Artificial intelligence ; Mathematics ; Flavor ; Machine learning ; Food science ; Sensory system ; Organoleptic ; Computer science ; Chemistry ; Psychology Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Sensory Analysis and Statistical Methods
Authors: Hui Xing | Bing Zhao | Yujie Wang | Xiaoyi Zhang | Yang Ren | Ningning Yan | Tieren Gao | Jindong Li | Lanting Zhang | Hong Wang
Source: ACS Combinatorial Science (article, 2018-01-30)
Field: Materials Science — Materials Chemistry

Rapid Construction of Fe–Co–Ni Composition-Phase Map by Combinatorial Materials Chip Approach One hundred nanometer thick Fe-Co-Ni material chips were prepared and isothermally annealed at 500, 600, and 700 °C, respectively. Pixel-by-pixel composition and structural mapping was performed by microbeam X-ray at synchrotron light source. Diffraction images were recorded at a rate of 1 pattern/s. The XRD patterns were automatically processed, phase-identified, and categorized by hierarchical clustering algorithm to construct the composition-phase map. The resulting maps are consistent with corresponding isothermal sections reported in the ASM Alloy Phase Diagram Database, verifying the effectiveness of the present approach in phase diagram construction. Microbeam ; Isothermal process ; Phase diagram ; Synchrotron ; Alloy ; Chemistry ; Phase (matter) ; Diffraction ; Pixel ; Synchrotron light source ; Composition (language) ; X-ray crystallography ; Analytical Chemistry (journal) ; Crystallography ; Optics ; Thermodynamics ; Physics ; Chromatography Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Microstructure and mechanical properties
Authors: Eduardo Guzmán | Javier Tajuelo | Juan Manuel Pastor | Miguel A. Rubio | Francisco Ortega | Ramón G. Rubio
Source: Current Opinion in Colloid & Interface Science (article, 2018-06-15)
Field: Chemistry — Organic Chemistry

Shear rheology of fluid interfaces: Closing the gap between macro- and micro-rheology Rheology ; Shear (geology) ; Viscosity ; Mechanics ; Materials science ; Physics ; Composite material Surfactants and Colloidal Systems ; Material Dynamics and Properties ; Enhanced Oil Recovery Techniques
Authors: Mohammadreza Nasiri | Derek J. Saxon | Theresa M. Reineke
Source: Macromolecules (article, 2018-03-19)
Field: Materials Science — Biomaterials

Enhanced Mechanical and Adhesion Properties in Sustainable Triblock Copolymers via Non-covalent Interactions ABA triblock copolymers are used as thermoplastic elastomers (TPEs) for a wide variety of applications. Herein, we describe incorporation of sugar-based glassy components to create sustainable triblock copolymers as alternatives for commodity TPEs. Poly(glucose-6-acrylate-1,2,3,4-tetraacetate) [PGATA] and poly(acetylated acrylic isosorbide) [PAAI] end blocks were chain-extended from a poly(n-butyl acrylate) [PnBA] midblock. PAAI–PnBA–PAAI exhibited excellent adhesion properties: peel = 8.74 N cm–1, loop tack = 2.96 N cm–2, no shear failure up to 100 h, and shear adhesion failure temperature (SAFT) = 60 °C. Although similar peel adhesion and higher loop tack were observed for PGATA–PnBA–PGATA, the shear strength and SAFT were moderate (18 h and 42 °C, respectively). PAAI–PnBA–PAAI are tough elastomers and demonstrated high stress and elongation at break (σ = 6.5 MPa and ε = 620%, respectively) while the GATA-based analogue exhibited weaker tensile properties (σ = 0.8 MPa and ε = 476%). To address this, the anomeric hydroxyl groups of GATA units were selectively deprotected to promote self-complementary hydrogen bonding in the glassy domains, resulting in 80% enhancement in the ultimate tensile stress at break (σ = 1.5 MPa). This study aims to demonstrate effects of noncovalent interactions, such as chain entanglements and self-complementary hydrogen bonding, to enhance the adhesion and mechanical performance of sugar-derived TPEs. Copolymer ; Ultimate tensile strength ; Materials science ; Polymer chemistry ; Acrylate ; Elastomer ; Thermoplastic elastomer ; Adhesion ; Hydrogen bond ; Composite material ; Chemistry ; Polymer ; Organic chemistry ; Molecule biodegradable polymer synthesis and properties ; Polymer composites and self-healing ; Silicone and Siloxane Chemistry
Authors: Xiaoqing Guo | Li‐Peng Zhou | Li‐Xuan Cai | Qing‐Fu Sun
Source: Chemistry - A European Journal (article, 2018-03-23)
Field: Materials Science — Materials Chemistry

Self‐Assembled Bright Luminescent Lanthanide‐Organic Polyhedra for Ratiometric Temperature Sensing Luminescent lanthanide-containing compounds have a lot of stimulating applications. However, the fine-tuning of the optical properties of multi-nuclear lanthanide edifices is still a big challenge. We report here the coordination self-assembly of a group of bright luminescent lanthanide organic polyhedra (LOPs) with record high emission quantum yields, by using two fully-conjugated ligands featuring the triazole-pyridine-amido (tpa) chelating moiety, easily-accessible from the "Click" reaction. The self-assembled LOPs are characterized by NMR spectroscopy, high-resolution ESI-TOF-MS, and X-ray crystallography. Interestingly, inter-metal energy transfer (ET) is confirmed on the mixed-lanthanide polyhedral molecules, though the metal centers are separated nearly 2 nm apart. A feasible ratiometric luminescent thermometer, with work range from cryogenic to physiological temperatures, has been demonstrated with the mixed Eu/Tb LOPs. We envisage these molecular-level bright luminescent LOPs have great application potential in displaying, labelling/imaging, up-conversion materials etc. Lanthanide ; Luminescence ; Polyhedron ; Materials science ; Luminescent Measurements ; Nanotechnology ; Chemistry ; Optoelectronics ; Organic chemistry ; Combinatorics ; Mathematics ; Ion Luminescence and Fluorescent Materials ; Lanthanide and Transition Metal Complexes ; Molecular Sensors and Ion Detection
Authors: Keitaro Yamamoto | Yutaka Ie | Norimitsu Tohnai | Fumitoshi Kakiuchi | Yoshio Aso
Source: Scientific Reports (article, 2018-11-29)
Field: Chemistry — Organic Chemistry

Antiaromatic character of cycloheptatriene-bis-annelated indenofluorene framework mainly originated from heptafulvene segment Fully π-conjugated polycyclic hydrocarbons with antiaromatic character have attracted research attention because of their unique properties such as narrow energy gaps, and thus should find application as optical and electronic materials. Although antiaromatic 16π-electron frameworks can be constructed by the incorporation of multiple seven-membered rings in a fused fashion to install methylenecycloheptatriene (heptafulvene) segments, the development of corresponding benzo[1,2:4,5]di[7]annulene (BDA)-containing π-conjugated systems remains challenging due to the difficulty of their molecular design and synthesis. In this study, we develop an unprecedented chemical structure of cycloheptatriene-bis-annelated indenofluorene, which possesses both BDA and indenofluorene frameworks in a fused fashion. Physical measurements and X-ray analyses, along with theoretical calculations, indicated that antiaromaticity appeared in the BDA framework. By using the conjugated polycyclic hydrocarbon possessing both seven-membered and five-membered rings, this study provides fundamental insight into the strong antiaromatic nature of heptafulvene-based BDA framework. Antiaromaticity ; Annulene ; Cycloheptatriene ; Conjugated system ; Character (mathematics) ; Ring (chemistry) ; Chemistry ; Computational chemistry ; Aromaticity ; Materials science ; Molecule ; Stereochemistry ; Organic chemistry ; Mathematics ; Polymer ; Geometry Synthesis and Properties of Aromatic Compounds ; Porphyrin and Phthalocyanine Chemistry ; Fullerene Chemistry and Applications
Authors: Xiaxi Li | Kevin Blinn | Dongchang Chen | Meilin Liu
Source: Electrochemical Energy Reviews (article, 2018-08-20)
Field: Materials Science — Materials Chemistry

In Situ and Surface-Enhanced Raman Spectroscopy Study of Electrode Materials in Solid Oxide Fuel Cells Materials science ; Raman spectroscopy ; Electrode ; Anode ; Cathode ; Nanotechnology ; Oxide ; Chemical engineering ; Chemistry ; Metallurgy Advancements in Solid Oxide Fuel Cells ; Electrocatalysts for Energy Conversion ; Electronic and Structural Properties of Oxides
Authors: Ji‐Young Kim | Myung‐Geun Han | Miao-Bin Lien | Sergei Magonov | Yimei Zhu | H. George | Theodore B. Norris | Nicholas A. Kotov
Source: Science Advances (article, 2018-02-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dipole-like electrostatic asymmetry of gold nanorods The symmetry of metallic nanocolloids, typically envisaged as simple geometrical shapes, is rarely questioned. However, the symmetry considerations are so essential for understanding their electronic structure, optical properties, and biological effects that it is important to reexamine these foundational assumptions for nanocolloids. Gold nanorods (AuNRs) are generally presumed to have nearly perfect geometry of a cylinder and therefore are centrosymmetric. We show that AuNRs, in fact, have a built-in electrostatic potential gradient on their surface and behave as noncentrosymmetric particles. The electrostatic potential gradient of 0.11 to 0.07 V/nm along the long axes of nanorods is observed by off-axis electron holography. Kelvin probe microscopy, secondary electron imaging, energy-filtered transmission electron microscopy, and plasmon mapping reveal that the axial asymmetry is associated with a consistently unequal number of cetyltrimethylammonium bromide moieties capping the two ends of the AuNRs. Electrostatic field maps simulated for the AuNR surface reproduce the holography images. The dipole-like surface potential gradient explains previously puzzling discrepancies in nonlinear optical effects originating from the noncentrosymmetric nature of AuNRs. Similar considerations of symmetry breaking are applicable to other nanoscale structures for which the property-governing symmetry of the organic shell may differ from the apparent symmetry of inorganic core observed in standard electron microscopy images. Asymmetry ; Nanorod ; Dipole ; Nanoscopic scale ; Charge (physics) ; Nanotechnology ; Electrostatics ; Physics ; Condensed matter physics ; Materials science ; Quantum mechanics Gold and Silver Nanoparticles Synthesis and Applications ; Electrostatics and Colloid Interactions ; Plasmonic and Surface Plasmon Research
Authors: Jan Frenzel | Gunther Eggeler | Eckhard Quandt | Stefan Seelecke | Manfred Kohl
Source: MRS Bulletin (article, 2018-04-01)
Field: Materials Science — Materials Chemistry

High-performance elastocaloric materials for the engineering of bulk- and micro-cooling devices Materials science ; Engineering physics ; Mechanical engineering ; Nanotechnology ; Engineering Shape Memory Alloy Transformations ; Thermal Expansion and Ionic Conductivity ; Magnetic and transport properties of perovskites and related materials
Authors: Florian P.Y. Barré | Beatriz Rocha | Frédéric Dewez | Mark Towers | P. T. Murray | Emmanuelle Claude | Berta Cillero‐Pastor | Ron M. A. Heeren | Tiffany Porta Siegel
Source: International Journal of Mass Spectrometry (article, 2018-09-18)
Field: Chemistry — Spectroscopy

Faster raster matrix-assisted laser desorption/ionization mass spectrometry imaging of lipids at high lateral resolution Mass spectrometry imaging ; Mass spectrometry ; Chemistry ; Context (archaeology) ; Image resolution ; Orbitrap ; MALDI imaging ; Resolution (logic) ; Desorption ; Matrix-assisted laser desorption/ionization ; Computer science ; Artificial intelligence ; Chromatography ; Geology ; Adsorption Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: So Fujikake | Volker L. Deringer | Tae Hoon Lee | Marcin Krynski | Stephen R. Elliott | Gábor Cśanyi
Source: Apollo (University of Cambridge) (article, 2018-02-27)
Field: Materials Science — Materials Chemistry

Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures. We demonstrate how machine-learning based interatomic potentials can be used to model guest atoms in host structures. Specifically, we generate Gaussian approximation potential (GAP) models for the interaction of lithium atoms with graphene, graphite, and disordered carbon nanostructures, based on reference density functional theory data. Rather than treating the full Li-C system, we demonstrate how the energy and force differences arising from Li intercalation can be modeled and then added to a (prexisting and unmodified) GAP model of pure elemental carbon. Furthermore, we show the benefit of using an explicit pair potential fit to capture "effective" Li-Li interactions and to improve the performance of the GAP model. This provides proof-of-concept for modeling guest atoms in host frameworks with machine-learning based potentials and in the longer run is promising for carrying out detailed atomistic studies of battery materials. Intercalation (chemistry) ; Gaussian ; Lithium (medication) ; Nanostructure ; Statistical physics ; Carbon fibers ; Materials science ; Chemical physics ; Physics ; Nanotechnology ; Chemistry ; Computational chemistry ; Quantum mechanics ; Composite number ; Psychology ; Composite material Graphene research and applications ; Advancements in Battery Materials ; Surface and Thin Film Phenomena
Authors: Adrià Pérez | Gerard Martínez-Rosell | Gianni De Fabritiis
Source: Current Opinion in Structural Biology (review, 2018-02-21)
Field: Materials Science — Materials Chemistry

Simulations meet machine learning in structural biology Petabyte ; Computer science ; Machine learning ; Latency (audio) ; Artificial neural network ; Artificial intelligence ; Field (mathematics) ; Force field (fiction) ; Sampling (signal processing) ; Molecular dynamics ; Big data ; Data mining ; Computational chemistry ; Mathematics ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Shin Kiyohara | Tomohiro Miyata | Koji Tsuda | Teruyasu Mizoguchi
Source: Scientific Reports (article, 2018-09-04)
Field: Materials Science — Materials Chemistry

Data-driven approach for the prediction and interpretation of core-electron loss spectroscopy Spectroscopy is indispensable for determining atomic configurations, chemical bondings, and vibrational behaviours, which are crucial information for materials development. Despite their importance, the interpretation of spectra using "human-driven" methods, such as the manual comparison of experimental spectra with reference/simulated spectra, is difficult due to the explosive increase in the number of experimental spectra to be observed. To overcome the limitations of the "human-driven" approach, we develop a new "data-driven" approach based on machine learning techniques by combining the layer clustering and decision tree methods. The proposed method is applied to the 46 oxygen-K edges of the ELNES/XANES spectra of oxide compounds. With this method, the spectra can be interpreted in accordance with the material information. Furthermore, we demonstrate that our method can predict spectral features from the material information. Our approach has the potential to provide information about a material that cannot be determined manually as well as predict a plausible spectrum from the geometric information alone. Spectral line ; Computer science ; XANES ; Spectroscopy ; Interpretation (philosophy) ; Explosive material ; Cluster analysis ; Core (optical fiber) ; Data mining ; Artificial intelligence ; Materials science ; Chemistry ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Xiaomei Wang | Xiang Mao | Jianhan Huang
Source: Colloids and Surfaces A Physicochemical and Engineering Aspects (article, 2018-08-26)
Field: Materials Science — Materials Chemistry

Hierarchical porous hyper-cross-linked polymers modified with phenolic hydroxyl groups and their efficient adsorption of aniline from aqueous solution Adsorption ; Aniline ; Gibbs free energy ; Chemistry ; Aqueous solution ; Enthalpy ; Langmuir adsorption model ; Thermodynamics ; Diffusion ; Mesoporous material ; Polymer ; Langmuir ; Chemical engineering ; Physical chemistry ; Organic chemistry ; Catalysis Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport
Authors: Yan Huang | Zijie Tang | Zhuoxin Liu | Jun Wei | Hong Hu | Chunyi Zhi
Source: Nano-Micro Letters (article, 2018-02-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Toward Enhancing Wearability and Fashion of Wearable Supercapacitor with Modified Polyurethane Artificial Leather Electrolyte Inspired by the sophisticated artificial leather garment industry and toward enhancing wearability of energy storage devices, we demonstrate a polyurethane artificial leather supercapacitor with large sheet electrodes embedded in the leather layer simultaneously working as a polyelectrolyte. This design totally reserves textiles underneath and thus addresses the well-known challenge of wearing comfortability. It provides a revolutionary configuration of wearable supercapacitors: the artificial leather on garment is also a supercapacitor. Unlike the polyvinyl alcohol-based acidic electrolytes, which are widely used, sodium chloride is used to modify the intrinsically fluorescent polyurethane leather for ionic transportation, which has no harm to human. The fluorescent leather supercapacitor is easily transferrable from any arbitrary substrates to form various patterns, enabling multifunctionalities of practical wearability, fashion, and energy storage. Supercapacitor ; Polyurethane ; Materials science ; Electrolyte ; Polyvinyl alcohol ; Wearable computer ; Nanotechnology ; Energy storage ; Electrode ; Composite material ; Computer science ; Chemistry ; Electrochemistry ; Embedded system Supercapacitor Materials and Fabrication ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Yaoyao Huang | Hu Yang | Lvcun Chen | Tao Yang | Hanfang Huang | Runping Shi | Peng Lu | Chenghua Zhong
Source: PLoS ONE (article, 2018-03-06)
Field: Chemistry — Inorganic Chemistry

Selective biosorption of thorium (IV) from aqueous solutions by ginkgo leaf Low-cost biosorbents (ginkgo leaf, osmanthus leaf, banyan leaf, magnolia leaf, holly leaf, walnut shell, and grapefruit peel) were evaluated in the simultaneous removal of La3+, Ce3+, Pr3+, Nd3+, Sm3+, Eu3+, Gd3+, Yb3+, Lu3+, UO22+, Th4+, Y3+, Co2+, Zn2+, Ni2+, and Sr2+ from aqueous solutions. In single metal systems, all adsorbents exhibited good to excellent adsorption capacities toward lanthanides and actinides. In a simulated multicomponent mixed solution study, higher selectivity and efficiency were observed for Th4+ over other metal cations, with ginkgo leaves providing the highest adsorptivity (81.2%) among the seven biosorbents. Through optimization studies, the selectivity of Th4+ biosorption on ginkgo leaf was found to be highly pH-dependent, with optimum Th4+ removal observed at pH 4. Th4+ adsorption was found to proceed rapidly with an equilibrium time of 120 min and conform to pseudo-second-order kinetics. The Langmuir isotherm model best described Th4+ biosorption, with a maximum monolayer adsorption capacity of 103.8 mg g-1. Thermodynamic calculations indicated that Th4+ biosorption was spontaneous and endothermic. Furthermore, the physical and chemical properties of the adsorbent were determined by scanning electron microscopy, Brunauer-Emmett-Teller, X-ray powder diffraction, and Fourier transform infrared analysis. The biosorption of Th from a real sample (monazite mineral) was studied and an efficiency of 90.4% was achieved from nitric acid at pH 4 using ginkgo leaves. Biosorption ; Aqueous solution ; Chemistry ; Nuclear chemistry ; Adsorption ; Langmuir adsorption model ; Organic chemistry ; Sorption Radioactive element chemistry and processing ; Extraction and Separation Processes ; Geochemistry and Elemental Analysis