jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Edyta Turek | Bogumiła Kumanek | Sławomir Boncel | Dawid Janas
Source: Nanomaterials (article, 2019-04-14)
Field: Materials Science — Materials Chemistry

Manufacture of Networks from Large Diameter Single-Walled Carbon Nanotubes of Particular Electrical Character We have demonstrated that the aqueous two-phase extraction (ATPE) can differentiate between large diameter single-walled carbon nanotubes (CNTs) by electrical character. Introduction of "hydration modulators" to the ATPE machinery has enabled us to isolate metallic and semiconducting CNTs with ease. We have also shown that often there is a trade-off between the purity of the obtained fractions and the ability to separate both metallic and semiconducting CNTs at the same time. To isolate the separated CNTs from the matrices, we have proposed a method based on precipitation and hydrolysis, which can eliminate the need to use lengthy dialysis routines. In the final step, we prepared thin free-standing films from the sorted material and probed how electrical charge is transported through such macroscopic ensembles. Carbon nanotube ; Character (mathematics) ; Materials science ; Nanotechnology ; Composite material ; Carbon nanotube actuators ; Chemical engineering ; Mechanical properties of carbon nanotubes ; Nanotube ; Engineering ; Geometry ; Mathematics Carbon Nanotubes in Composites ; Graphene research and applications ; Fullerene Chemistry and Applications
Authors: Christian S. Lentz
Source: Biological Chemistry (review, 2019-07-10)
Field: Chemistry — Organic Chemistry

What you see is what you get: activity-based probes in single-cell analysis of enzymatic activities Molecular imaging methods can provide spatio-temporal information about the distribution of biomolecules or biological processes, such as certain enzymatic activities, in single cells. Within a cell, it is possible to define the subcellular location of a target, its trafficking through the cell, colocalization with other biomolecules of interest and involvement in certain cell biological processes. On the other hand, single-cell imaging promises to distinguish cells that are phenotypically different from each other. The corresponding cellular diversity comprises the presence of functionally distinct cells in a population ('phenotypic heterogeneity'), as well as dynamic cellular responses to external stimuli ('phenotypic plasticity'), which is highly relevant, e.g. during cell differentiation, activation (of immune cells), or cell death. This review focuses on applications of a certain class of chemical probes, the so-called activity-based probes (ABPs), for visualization of enzymatic activities in the single-cell context. It discusses the structure of ABPs and other chemical probes, exemplary applications of ABPs in single-cell studies in human, mouse and bacterial systems and considerations to be made with regard to data interpretation. Colocalization ; Single-cell analysis ; Cell ; Computational biology ; Context (archaeology) ; Cell type ; Biomolecule ; Cell biology ; Population ; Phenotype ; Biology ; Live cell imaging ; Chemistry ; Biochemistry ; Gene Click Chemistry and Applications ; Receptor Mechanisms and Signaling ; Chemical Synthesis and Analysis
Authors: Arunava Sengupta | Begoña Santillana | Seetharaman Sridhar | Michael Auinger
Source: JOM (article, 2019-08-20)
Field: Materials Science — Materials Chemistry

Transient Effect of Fluid Flow on Dendrite Growth Direction in Binary Fe-C Alloys Using Phase Field in OpenFOAM An open-source fluid flow and phase-field coupled solidification model has been developed in OpenFOAM to investigate the transient nature of the interface growth direction at different degrees of undercooling for an Fe-0.15 wt.% C binary alloy under isothermal conditions. Though there are works on melt convection effects in binary alloys, none reported the transient nature of the dendrite growth direction since thermodynamic driving force decreases with time at a particular undercooling. Developing a theoretical relation will be helpful in understanding the competition between the crystallographic growth direction and solute transport. Flow decoupled simulation results have a good quantitative agreement with the literature. The bending angle formulations on the effects of flow velocity and growth speed were separated. At the end, improved theoretical formulations for estimation of the bending angle based on the anisotropy in interface energy were put forward compared with only few available empirical correlations. Materials science ; Supercooling ; Isothermal process ; Mechanics ; Dendrite (mathematics) ; Phase (matter) ; Transient (computer programming) ; Thermodynamics ; Flow (mathematics) ; Directional solidification ; Anisotropy ; Alloy ; Composite material ; Chemistry ; Physics ; Optics ; Geometry ; Mathematics Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Metallurgical Processes and Thermodynamics
Authors: Laurence Bodelot | Luka Pavić | Simon Hallais | Jérôme Charliac | Bérengère Lebental
Source: Scientific Reports (article, 2019-04-02)
Field: Materials Science — Materials Chemistry

Aggregate-driven reconfigurations of carbon nanotubes in thin networks under strain: in-situ characterization This work focuses on the in-situ characterization of multi-walled carbon nanotube (CNT) motions in thin random networks under strain. Many fine-grain models have been devised to account for CNT motions in carbon nanotube networks (CNN). However, the validation of these models relies on mesoscopic or macroscopic data with very little experimental validation of the physical mechanisms actually arising at the CNT scale. In the present paper, we use in-situ scanning electron microscopy imaging and high-resolution digital image correlation to uncover prominent mechanisms of CNT motions in CNNs under strain. Results show that thin and sparse CNNs feature stronger strain heterogeneities than thicker and denser ones. It is attributed to the complex motions of individual CNTs connected to aggregates within thin and sparse CNNs. While the aggregates exhibit a collective homogeneous deformation, individual CNTs connecting them are observed to fold, unwind or buckle, seemingly to accommodate the motion of these aggregates. In addition, looser aggregates feature internal reconfigurations via cell closing, similar to foam materials. Overall, this suggests that models describing thin and sparse CNN deformation should integrate multiphase behaviour (with various densities of aggregates in addition to individual CNTs), heterogeneity across surface, as well as imperfect substrate adhesion. Carbon nanotube ; Mesoscopic physics ; Characterization (materials science) ; Materials science ; Nanotechnology ; Deformation (meteorology) ; Thin film ; In situ ; Composite material ; Biological system ; Physics Carbon Nanotubes in Composites ; Smart Materials for Construction ; Nanopore and Nanochannel Transport Studies
Authors: J. Juraszek | Ł. Bochenek | A. Rudenko | M. Mofazzel Hosen | Marek Daszkiewicz | Z. Wang | J. Wosnitza | Z. Henkie | M. Samsel–Czekała | Madhab Neupane | T. Cichorek
Source: Physical Review Research (article, 2019-11-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nonsaturating extreme magnetoresistance and large electronic magnetostriction in LuAs This work explores the properties of LuAs, a diamagnetic semimetal with a trivial electronic band structure that exhibit an unsaturated and subquadratic extreme magnetoresistance up to nearly 60 T and a very large magnetostriction which provides thermodynamic evidence for a field-induced change of carrier densities. Diamagnetism ; Magnetostriction ; Magnetoresistance ; Condensed matter physics ; Semimetal ; Materials science ; Magnetic field ; Physics ; Band gap ; Quantum mechanics Iron-based superconductors research ; Magnetic and transport properties of perovskites and related materials ; Physics of Superconductivity and Magnetism
Authors: A N Kazakov | Dmitry Blinov | I. A. Romanov | D. O. Dunikov | V. I. Borzenko
Source: E3S Web of Conferences (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Metal hydride technologies for renewable energy Significant progress in the installation of renewable energy requires the improvement of energy production and storage technologies. Hydrogen energy storage systems based on reversible metal hydride materials can be used as an energy backup system. Metal hydride hydrogen storage systems are distinguished by a high degree of safety of their use, since hydrogen is stored in a solid phase, a high volumetric density of stored hydrogen, and the possibility of long-term storage without losses. A distinctive feature of metal hydride materials is the reversible and selective absorption and release of high-purity hydrogen. This paper presents experimental studies of LaNi 5 -based metal hydride materials with a useful hydrogen capacity of 1.0–1.3 wt.% H 2 with equilibrium pressures of 0.025 - 0.05 MPa and 0.1 - 1.2 MPa at moderate temperatures of 295 - 353 K for the hydrogen purification systems and hydrogen long-term storage systems, respectively. The applicability of metal hydride technologies for renewable energy sources as energy storage systems in the form of hydrogen is also shown. Hydrogen storage ; Hydride ; Hydrogen ; Renewable energy ; Energy storage ; Hydrogen fuel ; Materials science ; Cryo-adsorption ; Hydrogen technologies ; Metal ; Hydrogen production ; Chemical engineering ; Chemistry ; Process engineering ; Hydrogen economy ; Metallurgy ; Thermodynamics ; Engineering ; Organic chemistry ; Electrical engineering ; Physics Hydrogen Storage and Materials ; Ammonia Synthesis and Nitrogen Reduction ; Hybrid Renewable Energy Systems
Authors: Xuanxuan Li | Chufeng Li | Haiguang Liu
Source: Journal of Applied Crystallography (article, 2019-05-28)
Field: Materials Science — Materials Chemistry

<i>ClickX</i>: a visualization-based program for preprocessing of serial crystallography data Serial crystallography is a powerful technique in structure determination using many small crystals at X-ray free-electron laser or synchrotron radiation facilities. The large diffraction data volumes require high-throughput software to preprocess the raw images for subsequent analysis. <i>ClickX</i> is a program designated for serial crystallography data preprocessing, capable of rapid data sorting for online feedback and peak-finding refinement by parameter optimization. The graphical user interface (GUI) provides convenient access to various operations such as pattern visualization, statistics plotting and parameter tuning. A batch job module is implemented to facilitate large-data-volume processing. A two-step geometry calibration for single-panel detectors is also integrated into the GUI, where the beam center and detector tilting angles are optimized using an ellipse center shifting method first, then all six parameters, including the photon energy and detector distance, are refined together using a residual minimization method. Implemented in Python, <i>ClickX</i> has good portability and extensibility, so that it can be installed, configured and used on any computing platform that provides a Python interface or common data file format. <i>ClickX</i> has been tested in online analysis at the Pohang Accelerator Laboratory X-ray Free-Electron Laser, Korea, and the Linac Coherent Light Source, USA. It has also been applied in post-experimental data analysis. The source code is available via https://github.com/LiuLab-CSRC/ClickX under a GNU General Public License. Visualization ; Preprocessor ; Computer science ; Computer graphics (images) ; Crystallography ; Data mining ; Programming language ; Chemistry Enzyme Structure and Function ; Advanced X-ray Imaging Techniques ; X-ray Diffraction in Crystallography
Authors: Helge Kragh
Source: Research at the University of Copenhagen (University of Copenhagen) (article, 2019-11-26)
Field: Chemistry — Physical and Theoretical Chemistry

Controversial Elements: Priority Disputes and the Discovery of Chemical Elements There are only a limited number of chemical elements and to be credited with the discovery of a new one is therefore considered of great importance. Adding to the honour and fame is that traditionally the discoverer has the right to name the element in question. For these and other reasons, element discoveries are often followed by controversies regarding priority. While some of these are contemporary with the discovery process, others occur much later and are attempts to rewrite history. But what is a scientific discovery, more precisely, and why does it sometimes become controversial? From a scientific point of view, does it really matter who is recognised for the discovery of a new element? These are some of the questions considered in the paper, together with a few concrete cases from the history of chemistry. As shown by the recent disputes concerning the discoveries of synthetic elements at the end of the periodic system, modern priority disputes differ in some ways from the disputes of the past. On the other hand, there are also significant similarities. Honour ; Element (criminal law) ; Scientific discovery ; Epistemology ; History ; Point (geometry) ; Genealogy ; Philosophy ; Law ; Political science ; Archaeology ; Psychology ; Mathematics ; Cognitive science History and advancements in chemistry ; Chemistry and Stereochemistry Studies
Authors: Peter Katrík | D. H. H. Hoffmann | Е. С. Мустафин | I. Strašík
Source: Matter and Radiation at Extremes (article, 2019-09-01)
Field: Materials Science — Materials Chemistry

Experimental study of residual activity induced in aluminum targets irradiated by high-energy heavy-ion beams: A comparison of experimental data and FLUKA simulations A number of heavy-ion accelerators are either under construction (e.g., the Facility for Antiproton and Ion Research in Darmstadt and the High Intensity Accelerator Facility in China) or already in operation at many places worldwide. For these accelerators, activation of construction components due to beam loss, even during routine machine operation, is a serious issue, especially with the upcoming high-intensity facilities. Aluminum is one of the most commonly used construction materials in beam lines, collimators, and other components. Therefore, we report here on activation experiments on aluminum samples to verify and benchmark simulation codes. The analysis was performed by gamma spectroscopy of the irradiated targets. Our results on the induced activity measured in samples irradiated by uranium beams at 125 MeV/u and 200 MeV/u and a xenon beam at 300 MeV/u show activation levels significantly lower than those predicted by FLUKA simulations. Irradiation ; Beam (structure) ; Benchmark (surveying) ; Nuclear physics ; Nuclear engineering ; Heavy ion ; Ion ; Radiochemistry ; Particle accelerator ; Residual ; Uranium ; Materials science ; Physics ; Aluminium ; Ion beam ; High energy ; Intensity (physics) ; Atomic physics ; Chemistry ; Computer science ; Engineering ; Composite material ; Optics Fusion materials and technologies ; Ion-surface interactions and analysis ; Particle accelerators and beam dynamics
Authors: Jon Paul Janet | Chenru Duan | Tzuhsiung Yang | Aditya Nandy | Heather J. Kulik
Source: ChemRxiv (preprint, 2019-03-27)
Field: Materials Science — Materials Chemistry

Uncertain Times Call for Quantitative Uncertainty Metrics: Controlling Error in Neural Network Predictions for Chemical Discovery Machine learning (ML) models, such as artificial neural networks, have emerged as a complement to high-throughput screening, enabling characterization of new compounds in seconds instead of hours. The promise of ML models to enable large-scale, chemical space exploration can only be realized if it is straightforward to identify when molecules and materials are outside the model’s domain of applicability. Established uncertainty metrics for neural network models are either costly to obtain (e.g., ensemble models) or rely on feature engineering (e.g., feature space distances), and each has limitations in estimating prediction errors for chemical space exploration. We introduce the distance to available data in the latent space of a neural network ML model as a low-cost, quantitative uncertainty metric that works for both inorganic and organic chemistry. The calibrated performance of this approach exceeds widely used uncertainty metrics and is readily applied to models of increasing complexity at no additional cost. Tightening latent distance cutoffs systematically drives down predicted model errors below training errors, thus enabling predictive error control in chemical discovery or identification of useful data points for active learning. Artificial neural network ; Metric (unit) ; Computer science ; Chemical space ; Machine learning ; Artificial intelligence ; Feature (linguistics) ; Complement (music) ; Identification (biology) ; Data mining ; Drug discovery ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Chuhong Wang | Koutarou Aoyagi | Pandu Wisesa | Tim Mueller
Source: ChemRxiv (preprint, 2019-11-15)
Field: Materials Science — Materials Chemistry

Lithium Ion Conduction in Cathode Coating Materials from On-the-Fly Machine Learning Cathode coatings have the potential to significantly improve the performance of solid-state lithium-ion batteries, but computationally identifying materials with suitable ionic conductivity can be challenging. We demonstrate how this problem can be resolved using on-the-fly machine learning, and we validate our approach against experimental data. Based on a screen for high electrochemical stability, low interfacial reactivity and viable lithium ion conduction, we suggest two promising coating materials Li₃Sc₂(PO₄)₃ and Li₃B₇O₁₂. Li₃B₇O₁₂ to our knowledge has not been studied as an ionic conductor and is predicted to have high interfacial stability for a variety of leading electrolyte/cathode material combinations. The workflow we present accelerates the discovery of fast-to-intermediate ionic conductors and can readily be generalized to other types of materials.&lt;br&gt; Cathode ; Materials science ; Coating ; Lithium (medication) ; Ionic conductivity ; Electrolyte ; Ionic bonding ; Ion ; Electrochemistry ; Electrical conductor ; Thermal conduction ; Fast ion conductor ; Conductivity ; On the fly ; Chemical engineering ; Nanotechnology ; Composite material ; Computer science ; Chemistry ; Electrode ; Physical chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Fábio Capela | Vincent Nouchi | Ruud van Deursen | Igor V. Tetko | Guillaume Godin
Source: arXiv (Cornell University) (preprint, 2019-10-29)
Field: Materials Science — Materials Chemistry

Multitask Learning On Graph Neural Networks Applied To Molecular Property Predictions Prediction of molecular properties, including physico-chemical properties, is a challenging task in chemistry. Herein we present a new state-of-the-art multitask prediction method based on existing graph neural network models. We have used different architectures for our models and the results clearly demonstrate that multitask learning can improve model performance. Additionally, a significant reduction of variance in the models has been observed. Most importantly, datasets with a small amount of data points reach better results without the need of augmentation. Multi-task learning ; Computer science ; Graph ; Machine learning ; Artificial neural network ; Artificial intelligence ; Variance (accounting) ; Property (philosophy) ; Task (project management) ; Deep learning ; Theoretical computer science ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Ryther Anderson | Achay Biong | Diego A. Gómez‐Gualdrón
Source: ChemRxiv (preprint, 2019-09-27)
Field: Materials Science — Materials Chemistry

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF. Adsorption ; Krypton ; Molecule ; Artificial neural network ; Xenon ; Metal-organic framework ; Molecular descriptor ; Methane ; Combining rules ; Computer science ; Materials science ; Chemistry ; Statistical physics ; Nanotechnology ; Artificial intelligence ; Machine learning ; Physics ; Physical chemistry ; Organic chemistry ; Mathematics ; Quantitative structure–activity relationship Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography