jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Glen R. Loppnow
Source: Journal of Chemical Education (article, 2018-05-17)
Field: Chemistry — Physical and Theoretical Chemistry

The Unknown Exercise: Engaging First-Year University Students in Classroom Discovery and Active Learning on an Iconic Chemistry Question Scientific process thinking is usually lacking in first-year post-secondary general chemistry courses, as is a deep discussion of analytical techniques used to determine much of what we know about modern chemistry. A classroom activity is described here that brings the identification and characterization of a chemical unknown into the classroom, emphasizing self-directed, active, and teamwork learning within a social constructivist framework. In this way, the cognitive processes of identifying and characterizing an unknown can be emphasized separately from the psychomotor skills involved in the laboratory. Students work in pairs using self-directed learning to research the separation and characterization methods used by chemists. Each group advocates for a particular method, the class votes, and the instructor carries out the proposed method. The results are shown to the students (but not analyzed for them), and the cycle repeats until the unknown is identified. Students are assessed both individually and as a group. This activity was performed by 20–30 students in each of 3 years within two different first-year general chemistry contexts. Results show enhanced engagement in course and activity material and equivalent learning to lecture-delivered material based on assessment scores. Mathematics education ; Psychomotor learning ; Active learning (machine learning) ; Teamwork ; Class (philosophy) ; Cooperative learning ; Discovery learning ; Chemistry ; Psychology ; Learning cycle ; Group work ; Identification (biology) ; Critical thinking ; Chemistry education ; Teaching method ; Cognition ; Computer science ; Artificial intelligence ; Social psychology ; Ecology Various Chemistry Research Topics ; Innovative Teaching Methods ; Science Education and Pedagogy
Authors: Kirsten T. Winther | Max J. Hoffmann | Osman Mamun | Jacob R. Boes | Jens K. Nørskov | Michal Bajdich | Thomas Bligaard
Source: ChemRxiv (preprint, 2018-11-16)
Field: Materials Science — Materials Chemistry

Catalysis-hub.org: An Open Electronic Structure Database for Surface Reactions We present a new open repository for chemical reactions on catalytic surfaces, available at https://www.catalysis-hub.org. The featured Surface Reactions database contains more than 100,000 chemical adsorption and reaction energies obtained from electronic structure calculations, and is continuously being updated with new datasets. In addition to providing quantum-mechanical results for a broad range of reactions and surfaces from different publications, the database features a systematic, large-scale study of chemical adsorption and hydrogenation on bimetallic alloy surfaces. The database contains reaction specific information, such as the surface composition and reaction energy for each reaction, as well as the atomic surface geometries used in the calculations together with the calculation parameters and output, which are essential for data reproducibility. Data can be accessed from the web-interface as well as from a Python API providing direct access from a local workstation. This enables researchers to efficiently use the data as a basis for further calculations and to generate surrogate models for accelerating the discovery of catalytic materials for sustainable energy applications. Catalysis ; Bimetallic strip ; Database ; Python (programming language) ; Adsorption ; Computer science ; Workstation ; Materials science ; Chemistry ; Computational science ; Physical chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Fumiaki Tanaka | Hiroshi Satō | Naoki Yoshii | H. Matsui
Source:  (article, 2018-12-01)
Field: Materials Science — Materials Chemistry

Materials Informatics for Process and Material Co-optimization In semiconductor manufacturing, fabrication processes and their materials should be properly co-optimized to achieve required processing results. Unfortunately, it is a very time-consuming procedure, because the number of possible combinations of process/material candidates is very large. Here, we develop a methodology for co-optimization of processes and their materials. We successfully constructed a prediction model for dry-etching of high-k materials (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> )>0.65). By trying only <;0.00001% of all possible process/material candidates with this model and Bayesian optimization, we can find new combinations of gasses and their processes for 15-20 times higher etching rates than that with a traditional gas/process condition. Bayesian optimization ; Process (computing) ; Process optimization ; Etching (microfabrication) ; Materials informatics ; Computer science ; Semiconductor device fabrication ; Process engineering ; Materials science ; Nanotechnology ; Engineering ; Chemical engineering ; Artificial intelligence ; Health informatics ; Programming language Machine Learning in Materials Science ; Semiconductor materials and devices ; Advancements in Photolithography Techniques
Authors: Gabriel Seisson | Vincent Dubois | C. Bolis | Christophe Denoual
Source: EPJ Web of Conferences (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Flow Stress of beryllium: Attempt for a Bayesian Crossed-data Analysis from Hopkinson Bars to Rayleigh-Taylor Instabilities In this paper, we demonstrate by a Bayesian approach the incapacity of the Preston-Tonks-Wallace (PTW) strength model to represent, with the same set of parameters, the flow stress of beryllium in both moder-ate and highly dynamic experiments, and suggest hypotheses explaining that limitation. Usual plasticity models such as Johnson-Cook (JC) and PTW are mostly adjusted onto quasi-static and dynamic uni-axial compression data acquired thanks to compression machines and split Hopkinson pressure bars. Nonetheless, they may be used beyond the range of mechanical loading in which they have been fitted. This is the case of the simulations of solid Rayleigh-Taylor instabilities (RTI) driven by high explosives. A recent work of Henry de Frahan et al. noticed the inability of various plasticity models to stand for the growth of beryllium RTI. Amongst them, the PTW model has been particularly examined through four different sets of parameters, each of them largely un-derestimates the growth of the experimental instability. Thus, this work is an attempt, regarding the plastic flow modeling of beryllium, to conciliate uni-axial compression tests (CT) and RTI by means of a crossed Bayesian analysis. Plasticity ; Beryllium ; Bayesian probability ; Flow stress ; Compression (physics) ; Mechanics ; Instability ; Explosive material ; Stress (linguistics) ; Flow (mathematics) ; Statistical physics ; Materials science ; Physics ; Structural engineering ; Mathematics ; Engineering ; Thermodynamics ; Nuclear physics ; Statistics ; Chemistry High-Velocity Impact and Material Behavior ; Laser-Plasma Interactions and Diagnostics ; Energetic Materials and Combustion
Authors: Xavier Subirats | Lídia Redón | Martı́ Rosés
Source: ADMET & DMPK (article, 2018-04-06)
Field: Chemistry — Spectroscopy

Lipophilicity determination of acidic compounds: MEEKC as a reliable high-throughput methodology In the present study a pressure-assisted MEEKC method with reversed-polarity using a conventional CE instrument with UV detection and uncoated fused silica capillaries is validated as a high-throughput methodology for the lipophilicity determination of the neutral species of acidic compounds (pK&lt;sub&gt;a&lt;/sub&gt; &amp;gt; 3.5). After the calibration of the system with four standard compounds of known log P&lt;sub&gt;o/w&lt;/sub&gt;, mass distribution ratios (log k&lt;sub&gt;MEEKC&lt;/sub&gt;) of new molecules can be directly converted into log P&lt;sub&gt;o/w&lt;/sub&gt; values by means of a simple linear equation (log P&lt;sub&gt;o/w&lt;/sub&gt;=a·log k&lt;sub&gt;MEEKC&lt;/sub&gt;+b). The method was internally and externally validated for a log P&lt;sub&gt;o/w&lt;/sub&gt; range between -1.54 and 4.75, with higher accuracies than conventional liquid chromatographic methods. Lipophilicity ; Chemistry ; Analytical Chemistry (journal) ; Chromatography ; Stereochemistry Analytical Chemistry and Chromatography ; Free Radicals and Antioxidants ; Computational Drug Discovery Methods
Authors: Keith Baggerly
Source: Clinical Chemistry (review, 2018-05-18)
Field: Chemistry — Spectroscopy

Experimental Design, Randomization, and Validation Featured Article: Baggerly KA, Morris JS, Coombes KR. Reproducibility of SELDI-TOF protein patterns in serum: comparing datasets from different experiments. Bioinformatics 2004;20:777–85.2 Before our article discussed here appeared in 2004, one might have thought a golden age in proteomic diagnostics was at hand. The good news began in 2002 (1), when National Cancer Institute (NCI)3/Food and Drug Administration (FDA) researchers claimed to have processed high-throughput measurements of easy-to-get samples with machine learning algorithms to extract previously elusive diagnoses. The high-throughput measurements were from mass spectrometry, specifically surface-enhanced laser desorption and ionization (SELDI) assays. The samples were minimally processed serum. The machine learning algorithms were, broadly, black boxes taking peak intensities (nominally peptide abundances) and producing categorical calls: “disease,” “no disease,” or “something else.” The diagnosis was whether a woman had ovarian cancer. The numbers looked impressive. Starting with 216 samples (100 women with ovarian cancer, 100 healthy controls, and 16 women with … Food and drug administration ; Reproducibility ; Randomization ; Medicine ; Categorical variable ; Disease ; Computer science ; Machine learning ; Artificial intelligence ; Oncology ; Computational biology ; Internal medicine ; Clinical trial ; Chemistry ; Chromatography ; Biology ; Pharmacology Advanced Proteomics Techniques and Applications ; Gene expression and cancer classification ; Metabolomics and Mass Spectrometry Studies
Authors: Guankui Wang | Ernest Groman | Dmitri Simberg
Source: Precision Nanomedicine (article, 2018-04-30)
Field: Materials Science — Biomaterials

Discrepancies in the in vitro and in vivo role of scavenger receptors in clearance of nanoparticles by Kupffer cells Nanoparticles are recognized and cleared by Kupffer cells (KCs) in the liver. This process complicates the development of targeted nanoparticles because of significant reduction of number of nanoparticles that can reach target tissues. Macrophage scavenger receptor SR type AI/II is the central phagocytic receptor that has been shown to promote in vitro uptake of many nanoparticle types. In this paper, the authors set out to clarify the role of SR-AI/II in the in vivo liver clearance of 10kDa dextran superparamagnetic iron oxide (SPIO) Feridex-IV® and 20kDa dextran-coated SPIO nanoworms (SPIO NWs). Feridex showed efficient SR-AI/II-dependent uptake by isolated KCs in vitro, whereas SPIO NWs showed no uptake by KCs. Both Feridex and SPIO NWs showed a very short and nearly identical circulation half-life and efficient uptake by KCs in vivo. The SR-AI/II inhibitor, polyinosinic acid, prolonged the circulation half-life of both Feridex and SPIO NWs, but did not reduce the KC uptake. The circulation half-life and KC uptake of Feridex and SPIO NWs were identical in SR-AI/II-deficient mice and wild-type mice. These data suggest: (1) there is a limited correlation between in vitro and in vivo uptake mechanisms of nanoparticles in KCs; and (2) redundant, SR-AI/II independent mechanisms play a significant role in the nanoparticle recognition by KCs in vivo. Understanding the complexity of nanoparticle clearance assays and mechanisms is an important step to improving the design of “stealthy” nanoparticles. In vivo ; Scavenger receptor ; In vitro ; Nanoparticle ; Biophysics ; Chemistry ; Receptor ; Cell biology ; Nanotechnology ; Materials science ; Biochemistry ; Biology ; Cholesterol Nanoparticle-Based Drug Delivery ; Inhalation and Respiratory Drug Delivery ; Phagocytosis and Immune Regulation
Authors: Xianglin Li | Zhiwei Wang | Chiew Kei Tan | Zexiang Shen | Alfred Iing Yoong Tok
Source: Coatings (article, 2018-12-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ordered Array of Metal Particles on Semishell Separated with Ultrathin Oxide: Fabrication and SERS Properties Metal particles in gap cavities provide an interesting system to achieve hybrid local surface plasmon modes for local field enhancement. Here, we demonstrate a relatively simple method to fabricate Ag nanoparticles positioned on Ag semishells separated by a thin (~5 nm) dielectric layer. The obtained structure can provide strong local electric field enhancement for surface-enhanced Raman scattering (SERS). The fabrication of the ordered array structure was realized by nanosphere self-assembly, atomic layer deposition, and metal thin-film dewetting. Numerical simulation proved that, compared to the conventional metal semishell arrays, the additional Ag particles introduce extra hot spots particularly in the valley regions between adjacent Ag semishells. As a result, the SERS enhancement factor of the metal semishell-based plasmonic structure could be further improved by an order of magnitude. The developed novel plasmonic structure also shows good potential for application in plasmon-enhanced solar water-splitting devices. Dewetting ; Materials science ; Plasmon ; Fabrication ; Raman scattering ; Optoelectronics ; Nanotechnology ; Nanoparticle ; Layer (electronics) ; Dielectric ; Deposition (geology) ; Surface plasmon ; Electric field ; Metal ; Thin film ; Raman spectroscopy ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Solar-Powered Water Purification Methods
Authors: Sandra I. Anjo | Isaura Simões | Pedro Castanheira | Mário Grãos | Bruno Manadas
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2018-04-25)
Field: Chemistry — Spectroscopy

A generic normalization method for proper quantification in untargeted proteomics screening ABSTRACT The label-free quantitative mass spectrometry methods, in particular, the SWATH-MS approach, have gained popularity and became a powerful technique for comparison of large datasets. In the present work, it is introduced the use of recombinant proteins as internal standards for untargeted label-free methods. The proposed internal standard strategy reveals a similar intragroup normalization capacity when compared with the most common normalization methods, with the additional advantage of maintaining the overall proteome changes between groups (which are lost using other methods). Therefore, the proposed strategy is able to maintain a good performance even when large qualitative and quantitative differences in sample composition are observed, such as the ones induced by biological regulation (as observed in secretome and other biofluids’ analyses) or by enrichment approaches (such as immunopurifications). Moreover, this approach corresponds to a cost-effective alternative, easier to implement than the current stable-isotope labeling internal standards, therefore being an appealing strategy for large quantitative screening, as clinical cohorts for biomarker discovery. Normalization (sociology) ; Computer science ; Proteome ; Quantitative proteomics ; Computational biology ; Biomarker discovery ; Label-free quantification ; Proteomics ; Data mining ; Bioinformatics ; Chemistry ; Biology ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Karim Muratov | Ekaterina A. Kuchuk | Sreekumar Vellalath | Oleg I. Afanasyev | Alexei P. Moskovets | Gleb L. Denisov | Denis Chusov
Source: Organic & Biomolecular Chemistry (article, 2018-01-01)
Field: Chemistry — Inorganic Chemistry

Formal reductive addition of acetonitrile to aldehydes and ketones An efficient and highly productive rhodium-catalyzed method for the synthesis of nitriles employing aldehydes or ketones, methyl cyanoacetate, water and carbon monoxide as starting materials has been developed. Simple rhodium chloride without any ligands can be used. The fine tuning of the substrate can lead to the activity higher than 5000 TON. Chemistry ; Acetonitrile ; Combinatorial chemistry ; Ketone ; Organic chemistry Asymmetric Hydrogenation and Catalysis ; Cyclopropane Reaction Mechanisms ; Chemical Synthesis and Analysis
Authors: Shalmali Hui
Source: International Journal for Research in Applied Science and Engineering Technology (article, 2018-02-28)
Field: Materials Science — Materials Chemistry

Morphological and Mechanical Properties of LDPE/EVA Based TPE: Effect of both Modified and Unmodified Nanosilica A comparative study between the modified and unmodified nanosilica to fine tune the morphological and mechanical properties of a model low-density polyethylene (LDPE) -ethylene vinyl acetate copolymer (EVA) thermoplastic elastomer blend system is explored in this article. The nanosilica particles were melt-blended with the LDPE/EVA system at 1.5, 3, and 5 wt % loadings through the variation of the sequence of addition. The blends were compression molded, and their morphological and mechanical properties were evaluated. The incorporation of mSiO 2 produced a drastic improvement of Young's modulus (up to 60%) as compared to the control blend. The morphological studies clearly indicated that modified silica had a homogeneous dispersion in the bi-component polymer matrix leading to strengthening of silica-polymer interface. Interestingly, after modification the sequence did not play a major role in affecting the morphology of this system. Overall, although the properties of the unmodified nanosilica filled blends are strong functions of the sequence of addition, but, interestingly, the properties of the modified nanosilica filled blends do not depend much on the preparation procedure. Low-density polyethylene ; Materials science ; Composite material ; Polyethylene Silicone and Siloxane Chemistry ; Polymer Nanocomposites and Properties ; Advanced Sensor and Energy Harvesting Materials
Authors: Yang Ye | Luis A. Serrano | Stefan Guldin
Source: ChemRxiv (preprint, 2018-02-01)
Field: Materials Science — Materials Chemistry

A Versatile AuNP Synthetic Platform for Decoupled Control of Size and Surface Composition While a plethora of protocols exist for the synthesis of sub-10 nm gold nanoparticles (AuNPs), the independent control over size and surface composition remains restricted. This poses a particular challenge for systematic studies of AuNP structure-function relationships and optimization of crucial design parameters. To this end, we report on a modular 2-step approach based on the synthesis of AuNPs in oleylamine (OAm) followed by the subsequent functionalization with target thiol ligands. The synthesis of OAm-capped AuNPs enables fine tuning of the core size in the range of 2–7nm by varying the reaction temperature. The subsequent thiol-for-OAm ligand-exchange allows a reliable generation of thiol-capped AuNPs with target surface functionality. The compatibility of this approach with a vast library of thiol ligands provides detailed control of mixed ligand composition and solubility in a wide range of solvents ranging from water to hexane. This decoupled control over the AuNP core and ligand shell provides a powerful toolbox for the methodical screening of optimal design parameters and facile preparation of AuNPs with target properties. Oleylamine ; Nanotechnology ; Ligand (biochemistry) ; Surface modification ; Nanoparticle ; Thiol ; Materials science ; Combinatorial chemistry ; Modular design ; Colloidal gold ; Chemistry ; Computer science ; Chemical engineering ; Organic chemistry ; Engineering Nanocluster Synthesis and Applications ; Nanomaterials for catalytic reactions ; Quantum Dots Synthesis And Properties
Authors: I Ferouka | Tijana Šušteršič | Marko Živanović | Nenad Filipović
Source: Journal of the Serbian Society for Computational Mechanics (article, 2018-12-31)
Field: Materials Science — Biomaterials

MATHEMATICAL MODELLING OF POLYMER TRAJECTORY DURING ELECTROSPINNING The most important characteristics of the electrospun fibers are their internal morphology and their diameter. They both depend on polymer's parameters, but also on the process parameters. The motivation for this research is therefore to simulate the jet during electrospinning and analyze the effects of some of the parameters on the jet (and implicitly on fibers), without the necessity to perform the experiments each time. The calculations of the polymer's behavior between the electrodes was based on the discrete model of Reneker. Thus, the jet was modeled as a system of beads connected by viscoelastic elements. The results were compared to the results obtained in the literature and showed good agreement. The results showed how the jet shape during electrospinning depends on each parameter. The ultimate goal was to establish the exact influence of the above-mentioned parameters on the fibers' diameter. This would allow to set rough values of electrospinning parameters, which could be further fine-tuned, in order to obtain the fibers with expected characteristics. The beneficial effect of such simulations are time gain, but also reduced material consumption, maintenance costs etc. Electrospinning ; Trajectory ; Polymer ; Materials science ; Polymer science ; Composite material ; Physics Electrospun Nanofibers in Biomedical Applications ; Electrohydrodynamics and Fluid Dynamics ; Advanced Sensor and Energy Harvesting Materials
Authors: Letian Lyu | Perveshwer Jaswal | Guangyu Xu
Source: AIP Advances (article, 2018-03-01)
Field: Materials Science — Materials Chemistry

Effect of channel-width and chirality on graphene field-effect transistor based real-time biomolecule sensing Graphene field-effect transistors (GFET) hold promise in biomolecule sensing due to the outstanding properties of graphene materials. Charges in biomolecules are transduced into a change in the GFET current, which allows real-time monitoring of the biomolecule concentrations. Here we theoretically evaluate the performance of GFET based real-time biomolecule sensing, aiming to better understand the width-scaling limit in GFET based biosensors. In particular, we study the effect of the channel-width and the chirality on FET sensitivity by taking the percentage change of the FET current per unit charge density as the sensing signal. Firstly, GFETs made of graphene nanoribbons (GNR) and graphene sheets (GS) show comparable sensing signals to each other when gated at 1011 – 1012 cm-2 carrier densities. Sensing signals in GNRs are enhanced when gated near the sub-band thresholds, and increase their values in wider GNRs due to the change in device conductance and quantum capacitance. Secondly, the GNR chirality is found to fine tune the sensing signals. Armchair GNRs with smaller energy bandgaps appear to have an enhanced sensing signal close to 1011 cm-2 carrier densities. These results may help understand the scaling limit in GFET based biosensors along the width direction, and shed light on forming all-electrical bio-arrays. Graphene ; Biomolecule ; Quantum capacitance ; Biosensor ; Field-effect transistor ; Materials science ; Transistor ; Conductance ; Nanotechnology ; Chirality (physics) ; Capacitance ; Scaling ; Graphene nanoribbons ; Optoelectronics ; SIGNAL (programming language) ; Physics ; Condensed matter physics ; Voltage ; Electrode ; Computer science Graphene research and applications ; Nanowire Synthesis and Applications ; Advancements in Semiconductor Devices and Circuit Design