jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yu. M. Milekhin | А. А. Коптелов | И. А. Коптелов | А. А. Рогозина
Source: Physical and Chemical Aspects of the Study of Clusters Nanostructures and Nanomaterials (article, 2019-12-16)
Field: Materials Science — Materials Chemistry

THERMAL DECOMPOSITION KINETICS OF AN ENERGY COMPOSITE MATERIAL ON THE BASIS OF INERT BINDING The results are presented of the study of thermal decomposition kinetics of the K – 2 model mixed energetic material, the main components of which are ammonium perchlorate, octogen, aluminum and an inert binder, polybutadiene, plasticized with transformer oil. The activation energy E was determined by isoconversion methods and the Kissinger method according to thermal analysis at heating rates b < 5 K / min. In an open system, the evaporation of plasticizer (Emin = 65 kJ / mol), thermal decomposition of octogen (Emax = 192 kJ/mol) and ammonium perchlorate are successively realized in the samples. When limiting the free exit of gaseous reaction products, the peaks of the maximum heat release rate in octogen shift to the region of higher temperatures, the thermal decomposition of K – 2 is completed earlier than in the open system. An explanation of the mechanism of this phenomenon is proposed. Kinetics ; Thermal decomposition ; Inert ; Composite number ; Basis (linear algebra) ; Decomposition ; Materials science ; Activation energy ; Binding energy ; Inert gas ; Chemistry ; Thermodynamics ; Chemical engineering ; Composite material ; Physical chemistry ; Mathematics ; Physics ; Organic chemistry ; Engineering ; Atomic physics ; Classical mechanics ; Geometry Thermal and Kinetic Analysis ; Energetic Materials and Combustion ; Material Properties and Applications
Authors: Sunny Chaudhary | Thomas Andritsch | A. S. Vaughan
Source: ePrints Soton (University of Southampton) (article, 2019-01-15)
Field: Materials Science — Materials Chemistry

Investigating the role of particle interphase on the electrical behavior in nanodielectric materials Interphase ; Particle (ecology) ; Materials science ; Biology ; Ecology Ferroelectric and Piezoelectric Materials ; Photonic Crystals and Applications ; Chemical and Physical Properties of Materials
Authors: Dietmar Kissinger | Natanael Ayllon | Denis Barataud | Frank Van Den Bogaart | Stphane Bila | Herv Leblond | Anthony Ghiotto | Alexandru Takacs | Kissinger | Yves Mancuso | Farid Medjdoub | Herv Blanck | Jean-Christophe Nallatamby | Joaquin Portilla
Source:  (article, 2019-10-01)
Field: Chemistry — Organic Chemistry

Program Welcome to France where the Declaration of the Rights of Man and the Citizen was written in 1789 and Welcome to Paris "La Ville Lumire"! It is our great pleasure to welcome you to the 22nd European Microwave Week ( Pleasure ; Declaration ; Political science ; Computer science ; Law ; Media studies ; Sociology ; Psychology Microwave-Assisted Synthesis and Applications ; ICT Impact and Policies ; Educational Technology and Optimization
Authors: Ruud van Deursen | Guillaume Godin
Source: ChemRxiv (preprint, 2019-09-28)
Field: Materials Science — Materials Chemistry

Deep Generative Model for Sparse Graphs using Text-Based Learning with Augmentation in Generative Examination Networks Graphs and networks are a key research tool for a variety of science fields, most notably chemistry, biology, engineering and social sciences. Modeling and generation of graphs with efficient sampling is a key challenge for graphs. In particular, the non-uniqueness, high dimensionality of the vertices and local dependencies of the edges may render the task challenging. We apply our recently introduced method, Generative Examination Networks (GENs) to create the first text-based generative graph models using one-line text formats as graph representation. In our GEN, a RNN-generative model for a one-line text format learns autonomously to predict the next available character. The training is stopped by an examination mechanism checking validating the percentage of valid graphs generated. We achieved moderate to high validity using dense g6 strings (random 67.8 +/- 0.6, canonical 99.1 +/- 0.2). Based on these results we have adapted the widely used SMILES representation for molecules to a new input format, which we call linear graph input (LGI). Apart from the benefits of a short compressible text-format, a major advantage include the possibility to randomize and augment the format. The generative models are evaluated for overall performance and for reconstruction of the property space. The results show that LGI strings are very well suited for machine-learning and that augmentation is essential for the performance of the model in terms of validity, uniqueness and novelty. Lastly, the format can address smaller and larger dataset of graphs and the format can be easily adapted to define another meaning of the characters used in the LGI-string and can address sparse graph problems in used in other fields of science. Computer science ; Generative grammar ; Generative model ; Artificial intelligence ; Theoretical computer science ; Machine learning ; Curse of dimensionality ; Representation (politics) Machine Learning in Materials Science ; Genetics, Bioinformatics, and Biomedical Research ; Advanced Graph Neural Networks
Authors: Ruud van Deursen | Guillaume Godin
Source: ChemRxiv (preprint, 2019-09-27)
Field: Materials Science — Materials Chemistry

Deep Generative Model for Sparse Graphs using Text-Based Learning with Augmentation in Generative Examination Networks Graphs and networks are a key research tool for a variety of science fields, most notably chemistry, biology, engineering and social sciences. Modeling and generation of graphs with efficient sampling is a key challenge for graphs. In particular, the non-uniqueness, high dimensionality of the vertices and local dependencies of the edges may render the task challenging. We apply our recently introduced method, Generative Examination Networks (GENs) to create the first text-based generative graph models using one-line text formats as graph representation. In our GEN, a RNN-generative model for a one-line text format learns autonomously to predict the next available character. The training is stopped by an examination mechanism checking validating the percentage of valid graphs generated. We achieved moderate to high validity using dense g6 strings (random 67.8 +/- 0.6, canonical 99.1 +/- 0.2). Based on these results we have adapted the widely used SMILES representation for molecules to a new input format, which we call linear graph input (LGI). Apart from the benefits of a short compressible text-format, a major advantage include the possibility to randomize and augment the format. The generative models are evaluated for overall performance and for reconstruction of the property space. The results show that LGI strings are very well suited for machine-learning and that augmentation is essential for the performance of the model in terms of validity, uniqueness and novelty. Lastly, the format can address smaller and larger dataset of graphs and the format can be easily adapted to define another meaning of the characters used in the LGI-string and can address sparse graph problems in used in other fields of science. Computer science ; Generative grammar ; Generative model ; Artificial intelligence ; Theoretical computer science ; Machine learning ; Curse of dimensionality ; Representation (politics) ; Graph Machine Learning in Materials Science ; Genetics, Bioinformatics, and Biomedical Research ; Advanced Graph Neural Networks
Authors: Iman A. Gad El‐karim | Nabel A. Negm | Samir H. Shafek | Khaled A. M. Abd El-aal
Source: Journal of Basic and Environmental Sciences (article, 2019-04-01)
Field: Chemistry — Organic Chemistry

Synthesis, characterization, surface and antimicrobial activityof some nonionic di-Schiff base surface-active agents A series of nonionic Schiff base surfactants were prepared by the reaction between polyethylene glycol, glyoxalic acidand phenylene diamine. The prepared compounds were characterized using elemental analysis, FTIR and 1H-NMRspectroscopy. The surface activities of the prepared nonionic surfactants were determined using surface and interfacialtension measurements. The thermodynamic properties of these surfactants in adsorption and micellization werecalculated at 25oC. The results showed that the increase in the polyethylene glycol chain length (nonionic chains)increases their adsorption tendencies at the interface. Schiff base ; Antimicrobial ; Chemistry ; Combinatorial chemistry ; Polymer chemistry ; Organic chemistry Organometallic Compounds Synthesis and Characterization
Authors: Chloé Maury
Source: Le dépôt institutionnel (Université du Québec à Trois-Rivières) (article, 2019-01-01)
Field: Materials Science — Polymers and Plastics

Développement d'intercalaires à base de nanocellulose pour armer des vitrages sécuritaires et de protection balistique Je remercie, tout d' abord, le Professeur ric Loranger, mon directeur de recherche pour m'avoir offert l'opportunit de travailler mon projet de recherche dans son laboratoire ainsi que pour le soutien et les connaissances qu' il m'a apports.Je remercie galement trs sincrement le Professeur Frank Crispino, mon co-directeur de recherche, pour toute l'expertise balistique qu ' il m'a apporte, les discussions enrichissantes que nous avons eues ainsi que pour son coute attentive.Je remercie le Professeur Claude Daneault, pour la fabrication du matriel ncessaire mon projet ainsi que pour Nanocellulose ; Chemistry Flame retardant materials and properties ; Advanced Cellulose Research Studies
Authors: V. Hamel
Source: Archipelago (University of Quebec in Montreal) (article, 2019-07-01)
Field: Chemistry — Organic Chemistry

+Vers la synthèse de la (+/-)-valiolamine Le but de ce mémoire était de mettre au point une nouvelle approche de synthèse courte, rapide et efficace tout en innovant dans la façon de la synthétiser. Le but était de se démarquer dans l'originalité et l'élégance de la synthèse tout en gardant en tête une perspective de chimie verte. En permettant d'offrir une nouvelle voie de synthèse pour avoir accès à cette famille de molécules. Notre approche vers la synthèse de la ( +/-) valiolamine utilise 13 étapes et ce à partir d'un produit simple, abondant et très abordable ne possédant aucun centre stéréo génique tel que la 4-hydroxyacétophénone. La ( + )-valiolamine est un produit naturel de la famille des carbasucres. Cette molécule est un composé possédant une&#13;\nactivité biologique permettant d'inhiber des enzymes responsables de la dégradation des polysaccharides. Cette nouvelle voie de synthèse permet un accès rapide au corps principal d'un produit naturel qui est à ce jour commercialisé comme traitement contre le diabète de type deux qui affecte une grande partie de la population. Il est donc possible de conclure que cette synthèse permet d'obtenir le produit naturel sous une forme protégée. La structure principale de la molécule a été réalisée ainsi que quatre des cinq centres stéréogéniques ont été mis en place. Il restera donc du&#13;\ntravail à effectuer pour obtenir la confirmation du dernier centre chiral et ainsi que pour la déprotection de l'amine et de l'alcool primaire pour obtenir la (+/-)valiolamine. &#13;\n_____________________________________________________________________________ &#13;\nMOTS-CLÉS DE L’AUTEUR : (+)-Valiolamine, Carbasucre, Polysaccharides, Diabète, Chimie verte, Produit naturel Humanities ; Art ; Philosophy Carbohydrate Chemistry and Synthesis
Authors: Elizabeth Schwegler
Source: ODU Digital Commons (Old Dominion University) (article, 2019-05-15)
Field: Chemistry — Spectroscopy

The Use of Proteomic Technologies to Identify Serum Glycoproteins for the Early Detection of Liver and Prostate Cancers The application of proteomic technologies to identify serum glycoproteins is an emerging technique to identify new biomarkers indicative of disease severity. Many of these newly evolving protein-profiling methodologies have evolved from previous global protein expression profiling studies such as those involving SELDI-TOF-MS technologies. Though the SELDI approach could distinguish disease from normal by utilizing protein patterns as shown herein with the HCC study of chapter II, it was unable to offer sequence information on the selected peaks, and did not have the ability to analyze the entire dynamic range of the serum/plasma proteome. To address these deficiencies, new strategies that incorporate the use of differential lectin-based glycoprotein capture and targeted immuno-based assays have been developed. The carbohydrate binding specificities of different lectins offers a biological affinity approach that both complements existing mass spectrometer capabilities and retains automated throughput options. A prostate cancer study using disease stratified samples is utilized herein to determine whether lectin capture can identify glycoproteins, which are indicative of different stages of prostate disease. By utilizing upfront lectin fractionation we show here evidence of glycoproteins and glycoprotein isoforms, which are specific to cancer progression. In addition, the incorporation of lectin fractionation followed by albumin depletion allows for a more in depth analysis of the entire dynamic range of the human serum and plasma proteome. Taken together we believe this approach is an attractive strategy for the discovery of proteins indicative of the early detection of liver and prostate cancers. Prostate ; Glycoprotein ; Prostate cancer ; Medicine ; Oncology ; Internal medicine ; Biology ; Cancer ; Molecular biology Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Aveek Kapat
Source: IDEALS (University of Illinois Urbana-Champaign) (article, 2019-04-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Effects of porosity on surface chemistry, wetting, and percolation in porous tungsten, liquid lithium hybrid plasma facing component system Tungsten continues to be the material of choice for plasma facing components (PFC) in the divertor region of future plasma-burning fusion nuclear devices due to favorable properties such as high melting point, high thermal conductivity and low sputtering yield. However, challenges remain for the use of monolithic tungsten under future fusion reactor-relevant conditions including enhanced hydrogen retention, surface cracking and surface morphology evolution that may lead to macro-level impurity emission into the plasma. An alternative approach to conventional monolithic tungsten-based PFC materials is to introduce designs to mitigate known shortcomings of tungsten. This work explores the concept of protecting the plasma from high-Z material emission by integrating a low-Z component that is in the liquid phase.&#13;\n&#13;\nPlasma-surface interaction properties of a porous tungsten-liquid metal hybrid system, having the favorable bulk thermomechanical properties of W while serving as a scaffold for a liquid metal with self-healing and radiative vapor shielding characteristics, is examined. W-substrates with 70% density of bulk W and 1-5m sized pores have been fabricated with 50-nm W powders using spark plasma sintering. Lithium surface chemistry was observed to change in favor of forming Li-O-D as opposed to lithium oxide. Enhanced lithium wettability driven by percolation through the porous tungsten architecture is demonstrated with in-situ liquid lithium drop measurements. Results show complete wetting of liquid Li at 250C in the porous W, 100C lower temperature than what has been observed on traditional tungsten surfaces. Accompanying experiments using in-operando 7.2keV O3+, 8.9keV O4+ ERD in the DIONISOS platform, capable of 1020 m-2s-1, demonstrate Li signals as deep as one micron indicating percolation during exposure to a 250eV/amu D+ plasma. The results presented here demonstrate the feasibility of incorporating, a liquid metal into a porous tungsten substrate, both in wetting and percolation. Furthermore, this integration has shown some favorable PMI characteristics, such as the potentially controllable retention of deuterium Li-O-D. Porosity ; Wetting ; Tungsten ; Component (thermodynamics) ; Percolation (cognitive psychology) ; Lithium (medication) ; Porous medium ; Materials science ; Chemistry ; Chemical engineering ; Composite material ; Metallurgy ; Physics ; Thermodynamics ; Engineering Copper Interconnects and Reliability ; Semiconductor materials and devices ; Aluminum Alloys Composites Properties
Authors: Islem Ishak Dridi
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2019-12-18)
Field: Chemistry — Organic Chemistry

Synthèse stéréosélective et réactivité des 4-hydroxy-1-boryl-1-allénylsilanes. Synthèse et étude théorique de nouveaux dérivés de dimères benzimidazolo-1,3,5,2-triazaphosphorine-2-oxydes This thesis has focused on two different topics. Firstly, the synthesis of 1-silylalleneborane was studied, for the first time, from acetylenic epoxide and a silylborane. This method allows the stereoselective and stereospecific preparation of allenylboranes through an anti SNi mechanism. Subsequently, propargylation of the aldehydes using allenes afforded stereoselective access to the corresponding 2-alkynyldiols-1,3. A six-member closed transition state has been proposed for this last transformation which proceeds via a SE2' type mechanism. The scope and limitations of these two reactions have been explored.Finally, the electrophilic and nucleophilic reactivity of imidates resulting from the condensation of 2-aminobenzimidazole on a variety of orthoesters was studied as a route for the preparation of N-benzimidazole dimers. We also studied the synthesis of [1,2a] benzimidazolo-1,3,5,2-triazaphosphorin-2-oxide dimers through the action of hexamethylphosphoramide on N-benzimidazole dimers, the evaluation of the antioxidant activity of these new compounds formed (amidic and phospho-nitrogen dimers) is in progress. Stereoselectivity ; Reactivity (psychology) ; Chemistry ; Organic chemistry ; Combinatorial chemistry ; Catalysis ; Medicine Organophosphorus compounds synthesis ; Synthesis and Characterization of Heterocyclic Compounds ; Synthesis and Reactivity of Sulfur-Containing Compounds
Authors: T.C. Blanken
Source: Data Archiving and Networked Services (DANS) (article, 2019-01-21)
Field: Materials Science — Materials Chemistry

Model-based estimation and control of the particle distribution and discharge supervision in nuclear fusion reactors Het onderzoek dat in dit proefschrift wordt beschreven is uitgevoerd in overeenstemming met de TU/e Gedragscode Wetenschapsbeoefening. Estimation ; Fusion ; Nuclear engineering ; Particle filter ; Environmental science ; Computer science ; Engineering ; Artificial intelligence ; Systems engineering ; Kalman filter Fusion materials and technologies ; Magnetic confinement fusion research ; Nuclear reactor physics and engineering
Authors: Geoffroy Goujon
Source: theses.fr (ABES) (preprint, 2019-10-22)
Field: Materials Science — Materials Chemistry

Innovative cerium oxide nanoparticles for cerebral ischemia Cerium oxide ; Nanoparticle ; Cerium ; Ischemia ; Materials science ; Oxide ; Medicine ; Nanotechnology ; Business ; Internal medicine ; Metallurgy Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Nanoparticle-Based Drug Delivery
Authors: Kristof T. Schütt | Michael Gastegger | Alexandre Tkatchenko | Klaus‐Robert Müller | Reinhard J. Maurer
Source: DepositOnce (article, 2019-09-25)
Field: Materials Science — Materials Chemistry

Hamiltonian datasets: "Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions"" Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force-field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of molecular structures for targeting electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry. Wave function ; Artificial neural network ; Artificial intelligence ; Hamiltonian (control theory) ; Quantum chemistry ; Quantum ; Machine learning ; Deep learning ; Computer science ; Physics ; Quantum mechanics ; Mathematics ; Molecule ; Mathematical optimization Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Wibe A. de Jong | Muammar El Khatib Rodriguez
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

ML4Chem: Machine Learning for Chemistry and Materials v0.0.1 ML4Chem is a free, and open-source machine learning package for chemistry and materials sciences written in Python. It allows scientists to focus on applying the most commonly used machine learning models in chemistry and materials sciences with minimum effort. Chemistry ; Computer science ; Data science Machine Learning in Materials Science
Authors: Benjamin Rizkin | Albert Shkolnik | Neil Ferraro | Ryan L. Hartman
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2019-11-11)
Field: Materials Science — Materials Chemistry

Combining automated microfluidic experimentation with machine learning for efficient polymerization design Polymerization reactions have traditionally been a notoriously difficult field of chemistry and chemical engineering due to the complexity of the systems, hazards associated with the reagents, environmental footprint of the operations and the highly non-linear topology of the reaction spaces. In this work, we aim to present a new paradigm for studying such complex reactions using machine-learning-assisted automated microchemical reactors. A custom-designed rapidly prototyped microreactor is used in conjunction with <em>in situ</em> infrared thermography and efficient, high-speed experimentation to map the reaction space for a metallocene polymerization catalyst. The study was able to reduce the volume of chemical waste generated by two orders of magnitude and provide the necessary data in an hour instead of months of traditional experimentation. Here we show that efficient microfluidic technology can be coupled with machine learning algorithms to obtain high-fidelity datasets on a complex chemical reaction. Microfluidics ; Computer science ; Polymerization ; Nanotechnology ; Artificial intelligence ; Process engineering ; Materials science ; Engineering ; Polymer ; Composite material Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation