jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Soham Banerjee | Chia-Hao Liu | Kirsten M. Ø. Jensen | Pavol Juhás | Jennifer D. Lee | Marcus A. Tofanelli | Christopher J. Ackerson | Christopher B. Murray | Simon J. L. Billinge
Source: Acta Crystallographica Section A Foundations and Advances (article, 2019-12-17)
Field: Materials Science — Materials Chemistry

<i>Cluster-mining</i>: an approach for determining core structures of metallic nanoparticles from atomic pair distribution function data A novel approach for finding and evaluating structural models of small metallic nanoparticles is presented. Rather than fitting a single model with many degrees of freedom, libraries of clusters from multiple structural motifs are built algorithmically and individually refined against experimental pair distribution functions. Each cluster fit is highly constrained. The approach, called cluster-mining , returns all candidate structure models that are consistent with the data as measured by a goodness of fit. It is highly automated, easy to use, and yields models that are more physically realistic and result in better agreement to the data than models based on cubic close-packed crystallographic cores, often reported in the literature for metallic nanoparticles. Cluster (spacecraft) ; Goodness of fit ; Pair distribution function ; Nanoparticle ; Computer science ; Function (biology) ; Core (optical fiber) ; Data mining ; Distribution function ; Metal ; Distribution (mathematics) ; Key (lock) ; Degrees of freedom (physics and chemistry) ; Statistical physics ; Materials science ; Computational science ; Algorithm ; Biological system ; Physics ; Nanotechnology ; Mathematics ; Thermodynamics ; Mathematical analysis ; Machine learning ; Quantum mechanics X-ray Diffraction in Crystallography ; Nanocluster Synthesis and Applications ; Machine Learning in Materials Science
Authors: Muthusaravanan Sivaramakrishnan | Vivek Jagadeesan Sharavanan | Deenadayalan Karaiyagowder Govindarajan | Yogesan Meganathan | Bala Subramaniam Devaraj | N. Sivarajasekar | Ram Kothandan | Kumaravel Kandaswamy
Source: SN Applied Sciences (article, 2019-02-04)
Field: Materials Science — Materials Chemistry

Green synthesized silver nanoparticles using aqueous leaf extracts of Leucas aspera exhibits antimicrobial and catalytic dye degradation properties Silver nanoparticle ; Chemistry ; Nuclear chemistry ; Aqueous solution ; Bacillus subtilis ; Silver nitrate ; Antimicrobial ; Nanoparticle ; Catalysis ; Nanotechnology ; Organic chemistry ; Materials science ; Bacteria Nanoparticles: synthesis and applications
Authors: Yannick Bleu | Vincent Barnier | F. Christien | Florent Bourquard | A.-S. Loir | Florence Garrelie | Christophe Donnet
Source: Carbon (article, 2019-08-29)
Field: Materials Science — Materials Chemistry

Dynamics of carbon diffusion and segregation through nickel catalyst, investigated by in-situ XPS, during the growth of nitrogen-doped graphene Graphene ; Materials science ; Nickel ; X-ray photoelectron spectroscopy ; Carbon fibers ; Amorphous carbon ; Chemical engineering ; Carbon film ; Diffusion ; Chemical vapor deposition ; Microstructure ; Annealing (glass) ; Surface diffusion ; Thin film ; Amorphous solid ; Nanotechnology ; Metallurgy ; Composite material ; Chemistry ; Physical chemistry ; Crystallography ; Composite number Graphene research and applications ; Semiconductor materials and devices ; Diamond and Carbon-based Materials Research
Authors: Menghuai Wu | Andreas Ludwig | Abdellah Kharicha
Source: Metals (article, 2019-02-14)
Field: Materials Science — Materials Chemistry

Volume-Averaged Modeling of Multiphase Flow Phenomena during Alloy Solidification The most recent developments and applications in volume-averaged modeling of solidification processes have been reviewed. Since the last reviews of this topic by Beckermann and co-workers [Applied Mech. Rev. 1993, p. 1; Annual Rev. Heat Transfer 1995, p. 115], major progress in this area has included (i) the development of a mixed columnar-equiaxed solidification model; (ii) further consideration of moving crystals and crystal dendritic morphology; and (iii) the model applications to analyze the formation mechanisms of macrosegregation, as-cast structure, shrinkage cavity and porosity in different casting processes. The capacity of computer hardware is still a limiting factor. However, many calculation examples, as verified by the laboratory casting experiments, or even by the casting processes at a small industrial scale, show great application potential. Following the trend of developments in computer hardware (projection according to Moore’s law), a full 3D calculation of casting at the industry scale with the multiphase volume-averaged solidification models will become practically feasible in the foreseeable future. Equiaxed crystals ; Casting ; Shrinkage ; Scale (ratio) ; Limiting ; Materials science ; Volume (thermodynamics) ; Mechanics ; Heat transfer ; Flow (mathematics) ; Porosity ; Alloy ; Mechanical engineering ; Metallurgy ; Thermodynamics ; Engineering ; Composite material ; Physics Solidification and crystal growth phenomena ; Metallurgical Processes and Thermodynamics ; Aluminum Alloy Microstructure Properties
Authors: Sivadurgaprasad Chinta | Raghunathan Rengaswamy
Source: Industrial & Engineering Chemistry Research (article, 2019-01-29)
Field: Materials Science — Materials Chemistry

Machine Learning Derived Quantitative Structure Property Relationship (QSPR) to Predict Drug Solubility in Binary Solvent Systems Prediction of drug solubility is a crucial problem in pharmaceutical industries for both drug delivery and discovery purposes. Several theoretical approaches have been proposed to predict drug solubility in mixed solvent systems when the solubility values in pure solvents are known. Quantitative structure property relationship (QSPR) approaches are gaining attention to predict various physical properties due to their robustness and computational tractability. In this work, a machine learning based QSPR approach is proposed to predict drug solubility in binary solvent systems using structural features, such as molar refractivity, McGowan volume, topological surface area, and so forth. A genetic algorithm based feature selection procedure is used to check the dependency between the selected features and to obtain the final set of significant features. Initially, solubility is assumed to behave linearly with respect to the structural features and model parameters are estimated using ordinary least-squares and a weight-based optimization approach. Later, solubility is assumed to be piecewise linear with respect to structural features and multiple model (MM) parameters are identified using a machine learning approach, which is a prediction error based clustering approach. The efficacy of proposed approaches is demonstrated on drug solubility data collected from literature. To compare the efficiency of the proposed MM approach, a neural network based nonlinear model with different configurations using a Levenberg–Marquardt training algorithm has been tested. A novel testing strategy is also proposed to identify a suitable model for a test sample when model parameters are obtained using a prediction error based clustering approach. Quantitative structure–activity relationship ; Solubility ; Computer science ; Binary number ; Cluster analysis ; Artificial neural network ; Biological system ; Artificial intelligence ; Machine learning ; Algorithm ; Mathematics ; Chemistry ; Organic chemistry Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods
Authors: Xue Wang | Shichen Shen | Sailee Rasam | Jun Qu
Source: Mass Spectrometry Reviews (review, 2019-03-28)
Field: Chemistry — Spectroscopy

MS1 ion current‐based quantitative proteomics: A promising solution for reliable analysis of large biological cohorts The rapidly-advancing field of pharmaceutical and clinical research calls for systematic, molecular-level characterization of complex biological systems. To this end, quantitative proteomics represents a powerful tool but an optimal solution for reliable large-cohort proteomics analysis, as frequently involved in pharmaceutical/clinical investigations, is urgently needed. Large-cohort analysis remains challenging owing to the deteriorating quantitative quality and snowballing missing data and false-positive discovery of altered proteins when sample size increases. MS1 ion current-based methods, which have become an important class of label-free quantification techniques during the past decade, show considerable potential to achieve reproducible protein measurements in large cohorts with high quantitative accuracy/precision. Nonetheless, in order to fully unleash this potential, several critical prerequisites should be met. Here we provide an overview of the rationale of MS1-based strategies and then important considerations for experimental and data processing techniques, with the emphasis on (i) efficient and reproducible sample preparation and LC separation; (ii) sensitive, selective and high-resolution MS detection; iii)accurate chromatographic alignment; (iv) sensitive and selective generation of quantitative features; and (v) optimal post-feature-generation data quality control. Prominent technical developments in these aspects are discussed. Finally, we reviewed applications of MS1-based strategy in disease mechanism studies, biomarker discovery, and pharmaceutical investigations. Biomarker discovery ; Proteomics ; Quantitative proteomics ; Chemistry ; Biochemical engineering ; Computational biology ; Computer science ; Nanotechnology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Sunil Mehla | Somanath Kukade | Pramod Kumar | Peddy V.C. Rao | Sriganesh Gandham | Raman Ravishankar
Source: Fuel (article, 2019-01-17)
Field: Chemistry — Inorganic Chemistry

Fine tuning H-transfer and β-scission reactions in VGO FCC using metal promoted dual functional ZSM-5 Naphtha ; Petrochemical ; Olefin fiber ; Gasoline ; Fluid catalytic cracking ; Coke ; Chemistry ; Catalysis ; Fraction (chemistry) ; Chemical engineering ; Cracking ; Zeolite ; Ethylene ; Organic chemistry ; Alkene Zeolite Catalysis and Synthesis ; Catalysis and Hydrodesulfurization Studies ; Catalysis and Oxidation Reactions
Authors: Renée Haver | Lara Tejerina | Hua‐Wei Jiang | Michel Rickhaus | Michael Jirásek | Isabell Grübner | Hannah J. Eggimann | Laura M. Herz | Harry L. Anderson
Source: Journal of the American Chemical Society (article, 2019-04-24)
Field: Materials Science — Materials Chemistry

Tuning the Circumference of Six-Porphyrin Nanorings Most macrocycles are made from a simple repeat unit, resulting in high symmetry. Breaking this symmetry allows fine-tuning of the circumference, providing better control of the host-guest behavior and electronic structure. Here, we present the template-directed synthesis of two unsymmetrical cyclic porphyrin hexamers with both ethyne (C2) and butadiyne (C4) links, and we compare these nanorings with the symmetrical analogues with six ethyne or six butadiyne links. Inserting two extra carbon atoms into the smaller nanoring causes a spectacular change in binding behavior: the template affinity increases by a factor of 3 × 10<sup>9</sup>, to a value of ca. 10<sup>38</sup> M<sup>-1</sup>, and the mean effective molarity is ca. 830 M. In contrast, removing two carbon atoms from the largest nanoring results in almost no change in its template-affinity. The strain in these nanorings is 90-130 kJ mol<sup>-1</sup>, as estimated both from DFT calculation of homodesmotic reactions and from comparing template affinities of linear and cyclic oligomers. Breaking the symmetry has little effect on the absorption and fluorescence behavior of the nanorings: the low radiative rates that are characteristic of a circular delocalized S<sub>1</sub> excited state are preserved in the low-symmetry macrocycles. Nanoring ; Chemistry ; Porphyrin ; Crystallography ; Delocalized electron ; Symmetry breaking ; Annulene ; Symmetry (geometry) ; Dimer ; Excited state ; Chemical physics ; Photochemistry ; Computational chemistry ; Nanotechnology ; Atomic physics ; Organic chemistry Porphyrin and Phthalocyanine Chemistry ; Synthesis and Properties of Aromatic Compounds ; Luminescence and Fluorescent Materials
Authors: Christian Schneider | David Bodesheim | Julian Keupp | Rochus Schmid | Gregor Kieslich
Source: Nature Communications (article, 2019-10-29)
Field: Chemistry — Inorganic Chemistry

Retrofitting metal-organic frameworks The post-synthetic installation of linker molecules between open-metal sites (OMSs) and undercoordinated metal-nodes in a metal-organic framework (MOF) - retrofitting - has recently been discovered as a powerful tool to manipulate macroscopic properties such as the mechanical robustness and the thermal expansion behavior. So far, the choice of cross linkers (CLs) that are used in retrofitting experiments is based on qualitative considerations. Here, we present a low-cost computational framework that provides experimentalists with a tool for evaluating various CLs for retrofitting a given MOF system with OMSs. After applying our approach to the prototypical system CL@Cu<sub>3</sub>BTC<sub>2</sub> (BTC = 1,3,5-benzentricarboxylate) the methodology was expanded to NOTT-100 and NOTT-101 MOFs, identifying several promising CLs for future CL@NOTT-100 and CL@NOTT-101 retrofitting experiments. The developed model is easily adaptable to other MOFs with OMSs and is set-up to be used by experimentalists, providing a guideline for the synthesis of new retrofitted MOFs with modified physicochemical properties. Retrofitting ; CLs upper limits ; Computer science ; Robustness (evolution) ; Materials science ; Metal-organic framework ; Nanotechnology ; Chemistry ; Engineering ; Structural engineering Metal-Organic Frameworks: Synthesis and Applications ; Boron and Carbon Nanomaterials Research ; Magnetism in coordination complexes
Authors: Wannaruedee Wannapakdee | Thittaya Yutthalekha | Pannida Dugkhuntod | Kamonlatth Rodponthukwaji | Anawat Thivasasith | Somkiat Nokbin | Thongthai Witoon | Sitthiphong Pengpanich | Chularat Wattanakit
Source: Catalysts (article, 2019-02-13)
Field: Chemistry — Inorganic Chemistry

Dehydrogenation of Propane to Propylene Using Promoter-Free Hierarchical Pt/Silicalite-1 Nanosheets Propane dehydrogenation (PDH) is the extensive pathway to produce propylene, which is as a very important chemical building block for the chemical industry. Various catalysts have been developed to increase the propylene yield over recent decades; however, an active site of monometallic Pt nanoparticles prevents them from achieving this, due to the interferences of side-reactions. In this context, we describe the use of promoter-free hierarchical Pt/silicalite-1 nanosheets in the PDH application. The Pt dispersion on weakly acidic supports can be improved due to an increase in the metal-support interaction of ultra-small metal nanoparticles and silanol defect sites of hierarchical structures. This behavior leads to highly selective propylene production, with more than 95% of propylene selectivity, due to the complete suppression of the side catalytic cracking. Moreover, the oligomerization as a side reaction is prevented in the presence of hierarchical structures due to the shortening of the diffusion path length. Dehydrogenation ; Propane ; Catalysis ; Materials science ; Context (archaeology) ; Chemical engineering ; Silanol ; Selectivity ; Chemistry ; Organic chemistry Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Yu‐Chen Sun | Marco Chu | Morris Huang | Omer Hegazi | Hani E. Naguib
Source: Macromolecular Materials and Engineering (article, 2019-07-09)
Field: Materials Science — Polymers and Plastics

Hybrid Electroactive Shape Memory Polymer Composites with Room Temperature Deformability Abstract Polymeric blend shape memory polymers (SMPs) can be constructed from two immiscible polymeric matrices. The shape recovery behavior of these composite systems can be easily controlled by varying the ratio of the polymer blends. It has been recently discovered that the functionality of SMPs can be further enhanced with electroactive ability through the use of conductive fillers. However, the fillers may negatively interact with the SMPs and cause a reduction in the elongation at failure thereby diminishing the shape recovery performance. It is proposed that a plasticizer can be utilized to alter the microstructure of the SMPs with conductive fillers. In this study, a new hybrid SMP is developed by combining single‐walled carbon nanotubes (SWCNT) into a poly(lactic acid) (PLA) and thermoplastic polyurethane (TPU) SMP system containing poly(ethylene glycol) (PEG) plasticizer. The incorporation of PEG is able to lower the activation temperature, while enhancing dispersion of SWCNT. The presence of SWCNT can stabilize the SMP system and significantly enhance the shape‐fixing capability after deformation at room temperature conditions. By carefully controlling the formulation, an electroactive SMP can be created by optimizing the amount of SWCNT and PEG plasticizer. Materials science ; Plasticizer ; Thermoplastic polyurethane ; Composite material ; Shape-memory polymer ; Polyurethane ; Polymer ; Composite number ; Ethylene glycol ; Dispersion (optics) ; Elastomer ; Chemical engineering Polymer composites and self-healing ; Marine Sponges and Natural Products ; Conducting polymers and applications
Authors: Wei Wang | Mary Marshall | Evan Collins | Sara Marquez | Chaonan Mu | Kit H. Bowen | Xinxing Zhang
Source: Nature Communications (article, 2019-03-12)
Field: Chemistry — Physical and Theoretical Chemistry

Intramolecular electron-induced proton transfer and its correlation with excited-state intramolecular proton transfer Electron-induced proton transfer depicts the proton motion coupled with the attachment of a low-energy electron to a molecule, which helps to understand copious fundamental chemical processes. Intramolecular electron-induced proton transfer is a similar process that occurs within a single molecule. To date, there is only one known intramolecular example, to the best of our knowledge. By studying the 10-hydroxybenzo[h]quinoline and 8-hydroxyquinoline molecules using anion photoelectron spectroscopy and density functional theory, and by theoretical screening of six other molecules, here we show the intramolecular electron-induced proton transfer capability of a long list of molecules that meanwhile have the excited-state intramolecular proton transfer property. Careful examination of the intrinsic electronic signatures of these molecules reveals that these two distinct processes should occur to the same category of molecules. Intramolecular electron-induced proton transfer could have potential applications such as molecular devices that are responsive to electrons or current. Intramolecular force ; Electron transfer ; Proton-coupled electron transfer ; Proton ; Excited state ; Molecule ; Electron ; Chemical physics ; Chemistry ; Density functional theory ; Atomic physics ; Computational chemistry ; Photochemistry ; Physics ; Stereochemistry Photochemistry and Electron Transfer Studies ; Porphyrin and Phthalocyanine Chemistry ; Radical Photochemical Reactions
Authors: Marilena Vlachou | Stefanos Kikionis | Angeliki Siamidi | Sotiria Kyriakou | Andrew Tsotinis | Efstathia Ioannou | Vassilios Roussis
Source: Pharmaceutics (article, 2019-09-17)
Field: Materials Science — Biomaterials

Development and Characterization of Eudragit®-Based Electrospun Nanofibrous Mats and Their Formulation into Nanofiber Tablets for the Modified Release of Furosemide Furosemide, a chloride channel blocker ordinarily used as a high-ceiling or loop diuretic, is practically insoluble in water and dilute acids. Due to its acidic nature, furosemide is mostly absorbed in the stomach and in the upper small intestine. Efforts have focused on the development of sustained release systems of furosemide in order to improve the effectiveness of the drug, which exhibits poor aqueous solubility and poor permeability. Recently, electrospun nanofibrous drug delivery systems have emerged as promising alternative solid-dosage forms due to their advantages of high porosity, high surface to volume ratio, and high drug-loading efficacy. Herein, a number of nanofibrous mats composed of different types of Eudragit<sup>®</sup> polymers in various concentrations and combinations loaded with furosemide were designed, successfully electrospun, and characterized using SEM, FTIR, DSC, and TGA analyses. The nanofibrous nonwovens were formulated in nanofiber tablets and the release profile of furosemide from them was evaluated at pH 1.2 and 6.8 and compared to that of physical mixture matrix tablets of analogous composition as well as to that of a commercial formulation. It was found that the release of furosemide was compatible with the gastroretentive and slower intestinal release requirements with a well-defined absorption window, while some nanofiber formulations could act as furosemide carriers in emergency situations where a relatively fast onset of its action is required, as in the case of critically ill post-traumatic patients. Furosemide ; Nanofiber ; Solubility ; Chemical engineering ; Aqueous solution ; Materials science ; Diuretic ; Controlled release ; Drug delivery ; Electrospinning ; Polymer ; Porosity ; Chemistry ; Chromatography ; Composite material ; Pharmacology ; Nanotechnology ; Organic chemistry ; Medicine Electrospun Nanofibers in Biomedical Applications ; Drug Solubulity and Delivery Systems ; Hydrogels: synthesis, properties, applications