jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Marco Dompé | Francisco J. Cedano‐Serrano | Mehdi Vahdati | Ugo Sidoli | Olaf Heckert | Alla Synytska | Dominique Hourdet | Costantino Creton | Jasper van der Gucht | Thomas E. Kodger | Marleen Kamperman
Source: International Journal of Molecular Sciences (article, 2019-12-21)
Field: Materials Science — Surfaces, Coatings and Films

Tuning the Interactions in Multiresponsive Complex Coacervate-Based Underwater Adhesives In this work, we report the systematic investigation of a multiresponsive complex coacervate-based underwater adhesive, obtained by combining polyelectrolyte domains and thermoresponsive poly(<i>N</i>-isopropylacrylamide) (PNIPAM) units. This material exhibits a transition from liquid to solid but, differently from most reactive glues, is completely held together by non-covalent interactions, i.e., electrostatic and hydrophobic. Because the solidification results in a kinetically trapped morphology, the final mechanical properties strongly depend on the preparation conditions and on the surrounding environment. A systematic study is performed to assess the effect of ionic strength and of PNIPAM content on the thermal, rheological and adhesive properties. This study enables the optimization of polymer composition and environmental conditions for this underwater adhesive system. The best performance with a work of adhesion of 6.5 J/m<sup>2</sup> was found for the complex coacervates prepared at high ionic strength (0.75 M NaCl) and at an optimal PNIPAM content around 30% mol/mol. The high ionic strength enables injectability, while the hydrated PNIPAM domains provide additional dissipation, without softening the material so much that it becomes too weak to resist detaching stress. Coacervate ; Adhesive ; Ionic strength ; Materials science ; Adhesion ; Ionic bonding ; Chemical engineering ; Polymer ; Rheology ; Polyelectrolyte ; Softening ; Nanotechnology ; Composite material ; Chemistry ; Aqueous solution ; Organic chemistry ; Layer (electronics) Polymer Surface Interaction Studies ; Hydrogels: synthesis, properties, applications ; biodegradable polymer synthesis and properties
Authors: N. Kishore | V. Nagarajan | R. Chandiramouli
Source: Processing and Application of Ceramics (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Mechanical properties and band structure of CdSe and CdTe nanostructures at high pressure - a first-principles study First-principles calculations for CdSe and CdTe nanostructures were carried out to study their mechanical properties and band structure under the uniaxial pressure range of 0 to 50GPa. It was presumed that the CdSe and CdTe nanostructures exist in the zinc-blende phase under high pressure. The mechanical properties, such as elastic constants, bulk modulus, shear modulus and Young?s modulus, were explored. Furthermore, Cauchy pressure, Poisson?s ratio and Pugh?s criterion were studied under high pressure for both CdSe and CdTe nanostructures, and the results show that they exhibit ductile property. The band structure studies of CdSe and CdTe were also investigated. The findings show that the mechanical properties and the band structures of CdSe and CdTe can be tailored with high pressure. Cadmium telluride photovoltaics ; Materials science ; Nanostructure ; Bulk modulus ; Shear modulus ; Electronic band structure ; Modulus ; Elastic modulus ; Condensed matter physics ; Nanotechnology ; Composite material Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Advanced Semiconductor Detectors and Materials
Authors: Giovanna Grosso | Enrico M. Troisi | Nick O. Jaensson | Gwm Gerrit Peters | Patrick D. Anderson
Source: Polymer (article, 2019-09-18)
Field: Materials Science — Polymers and Plastics

Modelling flow induced crystallization of IPP: Multiple crystal phases and morphologies Crystallization ; Materials science ; Rheology ; Tacticity ; Isothermal process ; Crystallinity ; Flow properties ; Crystallization of polymers ; Mechanical engineering ; Polymer ; Computer science ; Mechanics ; Composite material ; Thermodynamics ; Physics ; Engineering ; Polymerization Polymer crystallization and properties ; Rheology and Fluid Dynamics Studies ; Polymer Foaming and Composites
Authors: Amy Lai | Jamie Clifton | Paula L. Diaconescu | Natalie Fey
Source: Chemical Communications (article, 2019-01-01)
Field: Chemistry — Organic Chemistry

Computational mapping of redox-switchable metal complexes based on ferrocene derivatives DFT calculations were used to capture the properties of redox-switchable metal complexes relevant to the ring-opening polymerisation of cyclic esters by varying the metals, donors, linkers, and substituents in both accessible ferrocene oxidation states. A map of this chemical space highlights that modifying the ligand architecture and the metal has a larger impact on structural changes than changing the oxidation state of the ferrocene backbone. Ferrocene ; Redox ; Metal ; Chemistry ; Combinatorial chemistry ; Inorganic chemistry ; Electrochemistry ; Organic chemistry ; Physical chemistry ; Electrode Chemical Synthesis and Reactions ; Metal complexes synthesis and properties ; Ferrocene Chemistry and Applications
Authors: Koji Hagihara | Keitaro Mori | Takayoshi Nakano
Source: Scripta Materialia (article, 2019-07-20)
Field: Materials Science — Biomaterials

Enhancement of plastic anisotropy and drastic increase in yield stress of Mg-Li single crystals by Al-addition followed by quenching Strong orientation dependence of yield stress was found in a body-centered cubic (bcc)-structured Mg-Li alloy single crystal by adding 5 at.% of Al combined with rapid quenching, which was negligible in Mg-Li binary single crystals. Furthermore, the addition of 5 at.% of Al combined with rapid quenching caused an extreme increase in yield stress up to ~470 MPa; this compares to ~50 MPa in a Mg-Li binary crystal. Increased valence-electron to atom ratio and development of chemical modulation in the alloy by Al-addition are probable causes of the enhancement of plastic anisotropy and the drastic increase in yield stress, respectively. Materials science ; Quenching (fluorescence) ; Alloy ; Anisotropy ; Yield (engineering) ; Crystallography ; Single crystal ; Valence electron ; Metallurgy ; Analytical Chemistry (journal) ; Electron ; Optics ; Chemistry Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Advanced Welding Techniques Analysis
Authors: Zebulun Arendsee | Jing Li | Urminder Singh | Priyanka Bhandary | Arun S. Seetharam | Eve Syrkin Wurtele
Source: BMC Bioinformatics (article, 2019-08-27)
Field: Materials Science — Biomaterials

fagin: synteny-based phylostratigraphy and finer classification of young genes fagin augments standard phylostratigraphy, and extends synteny-based phylostratigraphy with an automated, customizable, and detailed contextual analysis. By comparing synteny-based phylostrata to standard phylostrata, fagin systematically identifies those orphans and lineage-specific genes that are well-supported to have originated de novo. Analyzing within-species genomes should distinguish orphan genes that may have originated through rapid divergence from de novo orphans. Fagin also delineates whether a gene has no syntenic homolog because of technical or biological reasons. These analyses indicate that some orphans may be associated with regions of high genomic perturbation. Synteny ; Biology ; Genome ; Gene ; Genetics ; Homology (biology) ; Indel ; Comparative genomics ; Computational biology ; Genomics Diatoms and Algae Research ; Photosynthetic Processes and Mechanisms ; Genomics and Phylogenetic Studies
Authors: Susanne Fritsch‐Decker | Zhen An | Jin Yan | Iris Hansjosten | Marco Al‐Rawi | Ravindra Peravali | Silvia Diabaté | Carsten Weiß
Source: Nanomaterials (article, 2019-08-16)
Field: Materials Science — Materials Chemistry

Silica Nanoparticles Provoke Cell Death Independent of p53 and BAX in Human Colon Cancer Cells Several in vitro studies have suggested that silica nanoparticles (NPs) might induce adverse effects in gut cells. Here, we used the human colon cancer epithelial cell line HCT116 to study the potential cytotoxic effects of ingested silica NPs in the presence or absence of serum. Furthermore, we evaluated different physico-chemical parameters important for the assessment of nanoparticle safety, including primary particle size (12, 70, 200, and 500 nm) and surface modification (-NH<sub>2</sub> and -COOH). Silica NPs triggered cytotoxicity, as evidenced by reduced metabolism and enhanced membrane leakage. Automated microscopy revealed that the silica NPs promoted apoptosis and necrosis proportional to the administered specific surface area dose. Cytotoxicity of silica NPs was suppressed by increasing amount of serum and surface modification. Furthermore, inhibition of caspases partially prevented silica NP-induced cytotoxicity. In order to investigate the role of specific cell death pathways in more detail, we used isogenic derivatives of HCT116 cells which lack the pro-apoptotic proteins p53 or BAX. In contrast to the anticancer drug cisplatin, silica NPs induced cell death independent of the p53-BAX axis. In conclusion, silica NPs initiated cell death in colon cancer cells dependent on the specific surface area and presence of serum. Further studies in vivo are warranted to address potential cytotoxic actions in the gut epithelium. The unintended toxicity of silica NPs as observed here could also be beneficial. As loss of p53 in colon cancer cells contributes to resistance against anticancer drugs, and thus to reoccurrence of colon cancer, targeted delivery of silica NPs could be envisioned to also deplete p53 deficient tumor cells. Cytotoxicity ; Apoptosis ; Programmed cell death ; Cytotoxic T cell ; Cancer cell ; Cisplatin ; In vivo ; Chemistry ; Cell culture ; Cell ; Colorectal cancer ; In vitro ; Biophysics ; Cancer research ; Cell biology ; Cancer ; Biochemistry ; Biology ; Medicine ; Chemotherapy ; Internal medicine Nanoparticles: synthesis and applications ; Nanoparticle-Based Drug Delivery ; Arsenic contamination and mitigation
Authors: Kenneth J. Reed | Nathan Bush | Zachary Burns | Gwendolyn Doherty | Thomas Foley | Matthew Milone | Kara L. Maki | Michael Cromer
Source: Biomolecules (article, 2019-09-04)
Field: Materials Science — Materials Chemistry

Modeling the Kinetic Behavior of Reactive Oxygen Species with Cerium Dioxide Nanoparticles The world of medicinal therapies has been historically, and remains to be, dominated by the use of elegant organic molecular structures. Now, a novel medical treatment is emerging based on CeO<sub>2</sub> nano-crystals that are discrete clusters of a few hundred atoms. This development is generating a great deal of exciting and promising research activity, as evidenced by this Special Issue of <i>Biomolecules</i>. In this paper, we provide both a steady-state and time-dependent mathematical description of a sequence of reactions: superoxide generation, superoxide dismutase, and hydrogen peroxide catalase and ceria regeneration. This sequence describes the reactive oxygen species (ROS); superoxide, O<sub>2</sub><sup>-</sup>, molecular oxygen, O<sub>2</sub>, hydroxide ion OH<sup>-</sup> and hydrogen peroxide, H<sub>2</sub>O<sub>2</sub>, interacting with the Ce<sup>3+,</sup> and Ce<sup>4+</sup> surface cations of nanoparticle ceria, CeO<sub>2</sub>. Particular emphasis is placed on the predicted time-dependent role of the Ce<sup>3+</sup>/Ce<sup>4+</sup> ratio within the crystal. The net reaction is succinctly described as: H<sub>2</sub>O<sub>2</sub> + 2O<sub>2</sub><sup>-</sup> + 2H<sup>+</sup> → 2H<sub>2</sub>O + 2O<sub>2</sub>. The chemical equations and mathematical treatment appears to align well with several critical in vivo observations such as; direct and specific superoxide dismutase (SOD), ROS control, catalytic regeneration, ceria self-regulation and self-limiting behavior. However, in contrast to experimental observations, the model predicts that the 4+ ceric ion state is the key SOD agent. Future work is suggested based on these calculations. Cerium ; Kinetic energy ; Nanoparticle ; Cerium oxide ; Oxygen ; Reactive oxygen species ; Chemistry ; Chemical engineering ; Materials science ; Inorganic chemistry ; Nanotechnology ; Catalysis ; Organic chemistry ; Biochemistry ; Engineering ; Physics Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Gas Sensing Nanomaterials and Sensors
Authors: Chaofei Gao | Dongxin He | Yifan Zhou | Wei Wang | Peng Wang
Source: IEEE Access (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

A Study on the Space Charge Characteristics of AC Sliced XLPE Cables Cross-linked polyethylene (XLPE) cables are widely used in power systems due to their excellent electrical and mechanical properties. Space charge is an important factor in cable aging and breakdown. At present, XLPE cables in the power system are mainly ac XLPE cables, so it is necessary to study the space charge characteristics under ac voltage. In this paper, an electro-thermal aging platform for XLPE cable was built and a 10-kV cable was aged. XLPE slices were obtained from the aging cable and ac space charge experiments were carried out on the XLPE slices at different stages of aging. The trap energy levels were calculated and the effects of different electric fields on the ac space charge characteristics were compared. The experimental results show that the trap energy level increases with the aging process and the non-averaged electric field is a necessary condition for charge migration. Space charge ; Cross-linked polyethylene ; Polyethylene ; Materials science ; Electric field ; Power cable ; Voltage ; Electrical engineering ; Composite material ; Thermal ; High voltage ; Physics ; Engineering High voltage insulation and dielectric phenomena ; Electrostatic Discharge in Electronics ; Electrical Fault Detection and Protection
Authors: Kiran Sasikumar | Henry Chan | Badri Narayanan | Subramanian K. R. S. Sankaranarayanan
Source: Chemistry of Materials (article, 2019-04-10)
Field: Materials Science — Materials Chemistry

Machine Learning Applied to a Variable Charge Atomistic Model for Cu/Hf Binary Alloy Oxide Heterostructures Alloy oxide heterostructures are essential for a wide variety of energy applications, including anticorrosion coatings, bifunctional catalysis, energy storage, as well as emerging platforms for multilevel nonvolatile memory and neuromorphic devices. The key role played by oxide composition, density, and stoichiometry in governing electrochemical reactions, interface structure, and ionic transport in alloy oxides remains largely unknown. This is primarily due to the lack of variable charge interatomic potential models that can adequately describe the various atomic/ionic interactions in alloy oxides within a unified framework. Here, we introduce a charge transfer ionic potential (CTIP) model for Cu/Hf/O alloy system and demonstrate its ability to accurately capture the complex potential energy surface owing to dynamically varying interactions, including metallic (Cu/Hf), ionic (Cu/Hf/Ox), mixed environment (interfaces). We leverage supervised machine learning methods powered by genetic algorithms coupled with local simplex optimization to efficiently navigate through a high-dimensional parameter space and identify an optimum set of an independent set of CTIP parameters. We train our model against an extensive first-principles based data set that includes lattice constants, cohesive energies, equations of state, and elastic constants of various experimentally observed polymorphs of hafnia, copper oxide, hafnium, as well as copper. Our machine-learned CTIP model captures the structure, elastic properties, thermodynamics, and energetic ordering of various polymorphs. It also accurately predicts the surface properties of both oxides and their metals. To demonstrate the suitability of our CTIP model for investigating dynamic processes, we employed it to identify the atomistic scale mechanisms associated with the initial nanoscale oxidation and surface oxide growth on Cu-doped hafnia surfaces. Our machine-learned CTIP model can be used to probe the dynamic response of Cu/Hf/O alloy interfaces subjected to external stimuli (e.g., electric field, pressure, temperature, strain, etc.) and a variety of atomistic phenomena including the dynamics of switching in emerging neuromorphic platforms. Materials science ; Ionic bonding ; Oxide ; Heterojunction ; Chemical physics ; Alloy ; Chemistry ; Ion ; Optoelectronics ; Metallurgy Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Semiconductor materials and devices
Authors: Jiechun Liang | Yanheng Xu | Rulin Liu | Xi Zhu
Source: Scientific Data (article, 2019-10-18)
Field: Materials Science — Materials Chemistry

QM-sym, a symmetrized quantum chemistry database of 135 kilo molecules Applying deep learning methods in materials science research is an important way of solving the time-consuming problems of typical ab initio quantum chemistry methodology, but due to the size of large molecules, large and uncharted fields still exist. Implementing symmetry information can significantly reduce the calculation complexity of structures, as they can be simplified to the minimum symmetric units. Because there are few quantum chemistry databases that include symmetry information, we constructed a new one, named QM-sym, by designing an algorithm to generate 135k organic molecules with the C<sub>n</sub>h symmetry composite. Those generated molecules were optimized to a stable state using Gaussian 09. The geometric, electronic, energetic, and thermodynamic properties of the molecules were calculated, including their orbital degeneracy states and orbital symmetry around the HOMO-LUMO. The basic symmetric units were also included. This database p rovides consistent and comprehensive quantum chemical properties for structures with C<sub>n</sub>h symmetries. QM-sym can be used as a benchmark for machine learning models in quantum chemistry or as a dataset for training new symmetry-based models. Quantum chemistry ; Ab initio ; Molecule ; Organic molecules ; Symmetry (geometry) ; Quantum ; Chemistry ; Ab initio quantum chemistry methods ; Computational chemistry ; Theoretical physics ; Computer science ; Database ; Physics ; Quantum mechanics ; Mathematics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Kei Terayama | Koji Tsuda | Ryo Tamura
Source: Japanese Journal of Applied Physics (article, 2019-07-25)
Field: Materials Science — Materials Chemistry

Efficient recommendation tool of materials by an executable file based on machine learning Abstract To accelerate the discoveries of novel materials, an easy-to-use materials informatics tool is essential. We develop materials informatics applications, which can be executed on a Windows computer without any special settings. Our applications efficiently perform Bayesian optimization to optimize materials properties and uncertainty sampling to complete a new phase diagram. We introduce the usage of these applications and show the sampling results for a ternary phase diagram. Executable ; Computer science ; Bayesian optimization ; Informatics ; Sampling (signal processing) ; Materials informatics ; Diagram ; Phase diagram ; Data mining ; Software engineering ; Machine learning ; Phase (matter) ; Programming language ; Database ; Health informatics ; Computer vision ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Artem A. Trofimov | Alison A. Pawlicki | Nikolay Borodinov | Shovon Mandal | Teresa Mathews | Mark Hildebrand | Maxim Ziatdinov | Katherine A. Hausladen | P. Urbanowicz | Chad A. Steed | Anton V. Ievlev | Alex Belianinov | Joshua K. Michener | Rama K. Vasudevan | Olga S. Ovchinnikova
Source: npj Computational Materials (article, 2019-06-13)
Field: Materials Science — Biomaterials

Deep data analytics for genetic engineering of diatoms linking genotype to phenotype via machine learning Abstract Genome engineering for materials synthesis is a promising avenue for manufacturing materials with unique properties under ambient conditions. Biomineralization in diatoms, unicellular algae that use silica to construct micron-scale cell walls with nanoscale features, is an attractive candidate for functional synthesis of materials for applications including photonics, sensing, filtration, and drug delivery. Therefore, controllably modifying diatom structure through targeted genetic modifications for these applications is a very promising field. In this work, we used gene knockdown in Thalassiosira pseudonana diatoms to create modified strains with changes to structural morphology and linked genotype to phenotype using supervised machine learning. An artificial neural network (NN) was developed to distinguish wild and modified diatoms based on the SEM images of frustules exhibiting phenotypic changes caused by a specific protein (Thaps3_21880), resulting in 94% detection accuracy. Class activation maps visualized physical changes that allowed the NNs to separate diatom strains, subsequently establishing a specific gene that controls pores. A further NN was created to batch process image data, automatically recognize pores, and extract pore-related parameters. Class interrelationship of the extracted paraments was visualized using a multivariate data visualization tool, called CrossVis, and allowed to directly link changes in morphological diatom phenotype of pore size and distribution with changes in the genotype. Diatom ; Thalassiosira pseudonana ; Genome ; Frustule ; Computational biology ; Artificial intelligence ; Computer science ; Biology ; Nanotechnology ; Biological system ; Gene ; Materials science ; Genetics Diatoms and Algae Research ; Genomics and Phylogenetic Studies
Authors: Jiawei Zhang | Jianfu Chen | P. Hu | Haifeng Wang
Source: Chinese Chemical Letters (article, 2019-12-03)
Field: Materials Science — Materials Chemistry

Identifying the composition and atomic distribution of Pt-Au bimetallic nanoparticle with machine learning and genetic algorithm Bimetallic strip ; Nanomaterial-based catalyst ; Cluster (spacecraft) ; Nanoparticle ; Catalysis ; Atom (system on chip) ; Stability (learning theory) ; Nanotechnology ; Materials science ; Composition (language) ; Chemical physics ; Chemistry ; Crystallography ; Computer science ; Machine learning Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Yu-chen Liu | Benjamin Afflerbach | Ryan Jacobs | Shih-kang Lin | Dane Morgan
Source: MRS Communications (article, 2019-05-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Exploring effective charge in electromigration using machine learning Electromigration ; Extrapolation ; Reliability (semiconductor) ; Charge (physics) ; Interconnection ; Effective nuclear charge ; Set (abstract data type) ; Function (biology) Copper Interconnects and Reliability ; Machine Learning in Materials Science ; Electronic Packaging and Soldering Technologies