jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuzhi Zhang | Chang Gao | Qianrui Liu | Linfeng Zhang | Handong Wang | Mohan Chen
Source: Physics of Plasmas (article, 2020-12-01)
Field: Materials Science — Materials Chemistry

Warm dense matter simulation via electron temperature dependent deep potential molecular dynamics Simulating warm dense matter that undergoes a wide range of temperatures and densities is challenging. Predictive theoretical models, such as quantum-mechanics-based first-principles molecular dynamics (FPMD), require a huge amount of computational resources. Herein, we propose a deep learning based scheme called electron temperature dependent deep potential molecular dynamics (TDDPMD), which can be readily applied to study larger systems with longer trajectories, yielding more accurate properties. We take warm dense beryllium (Be) as an example with the training data from FPMD simulations spanning a wide range of temperatures (0.4–2500 eV) and densities (3.50–8.25 g/cm3). The TDDPMD method well reproduces the principal Hugoniot curve and radial distribution functions from the FPMD method. Furthermore, it depicts the reflection point of the Hugoniot curve more smoothly and provides more converged diffusion coefficients. We also show the new model can yield static structure factors and dynamic structure factors of warm dense Be. Physics ; Molecular dynamics ; Warm dense matter ; Range (aeronautics) ; Statistical physics ; Computational physics ; Diffusion ; Electron ; Quantum mechanics ; Aerospace engineering Machine Learning in Materials Science ; High-pressure geophysics and materials ; Quantum, superfluid, helium dynamics
Authors: Joerg Doellinger | Christian Blumenscheit | Andy Schneider | Peter Lasch
Source: Analytical Chemistry (article, 2020-08-25)
Field: Chemistry — Spectroscopy

Isolation Window Optimization of Data-Independent Acquisition Using Predicted Libraries for Deep and Accurate Proteome Profiling <i>In silico</i> spectral library prediction of all possible peptides from whole organisms has a great potential for improving proteome profiling by data-independent acquisition (DIA) and extending its scope of application. In combination with other recent improvements in the field of mass spectrometry (MS)-based proteomics, including sample preparation, peptide separation, and data analysis, we aimed to uncover the full potential of such an advanced DIA strategy by optimization of the data acquisition. The results demonstrate that the combination of high-quality <i>in silico</i> libraries, reproducible and high-resolution peptide separation using micropillar array columns, as well as neural network supported data analysis enables the use of long MS scan cycles without impairing the quantification performance. The study demonstrates that mean coefficient of variations of 4% were obtained even at only 1.5 data points per peak (full width at half-maximum) across different gradient lengths, which in turn improved proteome coverage up to more than 8000 proteins from HeLa cells using empirically corrected libraries and more than 7000 proteins using a whole human <i>in silico</i> predicted library. These data were obtained using a Q Exactive orbitrap mass spectrometer with moderate scanning speed (12 Hz) and perform very well in comparison to recent studies using more advanced MS instruments, which underline the high potential of this optimization strategy for various applications in clinical proteomics, microbiology, and molecular biology. Orbitrap ; Proteome ; Chemistry ; In silico ; Mass spectrometry ; Proteomics ; Computational biology ; Profiling (computer programming) ; Biological system ; Chromatography ; Computer science ; Biology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Bryce A. Tappan | Richard L. Brutchey
Source: ChemNanoMat (article, 2020-08-25)
Field: Materials Science — Materials Chemistry

Polymorphic Metastability in Colloidal Semiconductor Nanocrystals Abstract Metastable polymorphs of inorganic solids often possess material properties not present in the corresponding thermodynamic polymorphs, making them targets for the development of new functional materials. In contrast with isolating metastable bulk materials, syntheses of metastable polymorphs on the nanoscale are aided by fast non‐equilibrium reaction kinetics and the favorable thermodynamic influence of surface energies, giving rise to greater ease of access to metastable high‐temperature polymorphs and, in some cases, new polymorphs that do not exist in the bulk. The syntheses of metastable semiconductor nanocrystals are of interest for their potentially unique optoelectronic and physicochemical properties. However, in many material systems, synthesizing nanocrystalline products away from thermodynamic equilibrium in a predictable manner remains an outstanding challenge. This review outlines direct synthetic methodologies that have been developed to enable control over the nucleation and growth of metastable polymorphs of semiconductor nanocrystals by tailoring reaction conditions, precursor kinetics, ligand and surface effects, and other synthetic levers. The case studies reviewed herein expound on the direct syntheses of metastable ZnSe, Cu 2 SnSe 3 , CuInSe 2 , Ag 2 Se, and AgInSe 2 nanocrystals, and although there remain numerous examples of metastable nanocrystal syntheses outside of these metal chalcogenide systems, the concepts discussed are of general utility to the field of metastable nanocrystal syntheses as a whole. Explicit examples in which new functional properties are afforded by metastable polymorphs of the aforementioned material systems are presented within the context of applications for solar cells, photonics, and optical sensing. Finally, the factors that affect the kinetic persistence of metastable nanocrystalline polymorphs are discussed at length for these material systems. Metastability ; Nanocrystal ; Nucleation ; Materials science ; Chemical physics ; Chalcogenide ; Nanotechnology ; Semiconductor ; Chemistry ; Thermodynamics ; Optoelectronics ; Physics ; Organic chemistry Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Copper-based nanomaterials and applications
Authors: Binze Zhou | Mengjia Liu | Yanwei Wen | Yun Li | Rong Chen
Source: Opto-Electronic Advances (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Atomic layer deposition for quantum dots based devices Quantum dots (QDs) are promising candidates for the next-generation optical and electronic devices due to the outstanding photoluminance efficiency, tunable bandgap and facile solution synthesis. Nevertheless, the limited optoelectronic performance and poor lifetime of QDs devices hinder their further applications. As a gas-phase surface treatment method, atomic layer deposition (ALD) has shown the potential in QDs surface modification and device construction owing to the atomic-level control and excellent uniformity/conformality. In this perspective, the attempts to utilize ALD techniques in QDs modification to improve the photoluminance efficiency, stability, carrier mobility, as well as interfacial carrier utilization are introduced. ALD proves to be successful in the photoluminance quantum yield (PLQY) enhancement due to the elimination of QDs surface dangling bonds and defects. The QDs stability and devices lifetime are improved greatly through the introduction of ALD barrier layers. Furthermore, the carrier transport is ameliorated efficiently by infilling interstitial spaces during ALD process. Attributed to the ultra-thin and dense coating on the interface, the improvement on optoelectronic performance is achieved. Finally, the challenges of ALD applications in QDs at present and several prospects including ALD process optimization, in-situ characterization and computational simulations are proposed. Atomic layer deposition ; Quantum dot ; Materials science ; Dangling bond ; Nanotechnology ; Surface modification ; Optoelectronics ; Coating ; Deposition (geology) ; Band gap ; Layer (electronics) ; Silicon ; Chemical engineering Quantum Dots Synthesis And Properties ; Semiconductor materials and devices ; ZnO doping and properties
Authors: Salih Ozbay | Nursev Erdoğan | Fuat Erden | Merve Ekmekçioğlu | Mehtap Özdemir | Gülnur Aygün | L. Özyüzer
Source: Applied Surface Science (article, 2020-07-03)
Field: Materials Science — Surfaces, Coatings and Films

Surface free energy analysis of ITO/Au/ITO multilayer thin films on polycarbonate substrate by apparent contact angle measurements Contact angle ; Wetting ; X-ray photoelectron spectroscopy ; Polycarbonate ; Surface energy ; Thin film ; Materials science ; Analytical Chemistry (journal) ; Substrate (aquarium) ; Sputter deposition ; Sputtering ; Oxide ; Chemistry ; Composite material ; Chemical engineering ; Nanotechnology ; Chromatography Surface Modification and Superhydrophobicity ; ZnO doping and properties ; Plasma Diagnostics and Applications
Authors: Yolanda Piñeiro | Manuel González Gómez | Lisandra de Castro Alves | Ángela Arnosa Prieto | Pelayo García‐Acevedo | Román Seco Gudiña | Julieta Puig | Carmen Teijeiro | Susana Yáñez Vilar | J. Rivas
Source: Magnetochemistry (article, 2020-01-10)
Field: Materials Science — Biomaterials

Hybrid Nanostructured Magnetite Nanoparticles: From Bio-Detection and Theragnostics to Regenerative Medicine Nanotechnology offers the possibility of operating on the same scale length at which biological processes occur, allowing to interfere, manipulate or study cellular events in disease or healthy conditions. The development of hybrid nanostructured materials with a high degree of chemical control and complex engineered surface including biological targeting moieties, allows to specifically bind to a single type of molecule for specific detection, signaling or inactivation processes. Magnetite nanostructures with designed composition and properties are the ones that gather most of the designs as theragnostic agents for their versatility, biocompatibility, facile production and good magnetic performance for remote in vitro and in vivo for biomedical applications. Their superparamagnetic behavior below a critical size of 30 nm has allowed the development of magnetic resonance imaging contrast agents or magnetic hyperthermia nanoprobes approved for clinical uses, establishing an inflection point in the field of magnetite based theragnostic agents. Nanotechnology ; Superparamagnetism ; Magnetite ; Biocompatibility ; Materials science ; Nanoparticle ; Regenerative medicine ; Magnetic nanoparticles ; Nanostructure ; Magnetic hyperthermia ; In vivo ; Chemistry ; Magnetic field ; Magnetization ; Biology Nanoparticle-Based Drug Delivery ; Characterization and Applications of Magnetic Nanoparticles ; Graphene and Nanomaterials Applications
Authors: Ruben Van Lommel | Jianyu Zhao | Wim M. De Borggraeve | Frank De Proft | Mercedes Alonso
Source: Chemical Science (article, 2020-01-01)
Field: Materials Science — Biomaterials

Molecular dynamics based descriptors for predicting supramolecular gelation Four molecular dynamics-based descriptors were derived able to classify gelator–solvent combinations as a gel, precipitate or clear solution. Supramolecular chemistry ; Molecular dynamics ; Dynamics (music) ; Solvent ; Molecular recognition ; Chemistry ; Molecular descriptor ; Computational chemistry ; Biological system ; Materials science ; Nanotechnology ; Chemical physics ; Molecule ; Organic chemistry ; Stereochemistry ; Quantitative structure–activity relationship ; Physics ; Biology Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Chunlin Liu | Weiying Lu | Boyan Gao | Hanae Kimura | Yanfang Li | Jing Wang
Source: Food Science & Nutrition (article, 2020-03-03)
Field: Chemistry — Analytical Chemistry

Rapid identification of chrysanthemum teas by computer vision and deep learning Seven commercial Chinese chrysanthemum tea products were classified by computer vision combined with machine learning algorithms. Without the need of building any specific hardware, the image acquisition was achieved in two computer vision approaches. In the first approach, a series of multivariate classification models were built after morphological feature extraction of the image. The best prediction accuracies when classifying flowering stages and tea types were respectively 90% and 63%. In comparison, the deep neural network was applied directly on the raw image, yielded 96% and 89% correct identifications when classifying flowering stage and tea type, respectively. The model can be applied for rapid and automatic quality determination of teas and other related foods. The result indicated that computer vision, especially when combined with deep learning or other machine learning techniques can be a convenient and versatile method in the evaluation of food quality. Artificial intelligence ; Deep learning ; Computer science ; Artificial neural network ; Identification (biology) ; Pattern recognition (psychology) ; Feature extraction ; Feature (linguistics) ; Machine vision ; Machine learning ; Computer vision ; Botany Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Umar Muhammad Ghali | A. G. Usman | Z. M. Chellube | Mohamed Alhosen Ali Degm | Kujtesa Hoti | Huzaifa Umar | Sani I. Abba
Source: SN Applied Sciences (article, 2020-10-23)
Field: Chemistry — Analytical Chemistry

Advanced chromatographic technique for performance simulation of anti-Alzheimer agent: an ensemble machine learning approach Support vector machine ; Artificial intelligence ; Computer science ; Mean squared error ; Machine learning ; Multilayer perceptron ; Perceptron ; Artificial neural network ; Ensemble learning ; Boosting (machine learning) ; Correlation coefficient ; Pattern recognition (psychology) ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Water Quality Monitoring and Analysis
Authors: Oğuz Uncu | Banu Özen
Source: Trends in Food Science & Technology (article, 2020-04-25)
Field: Chemistry — Organic Chemistry

Importance of some minor compounds in olive oil authenticity and quality Olive oil ; Quality (philosophy) ; Pulp and paper industry ; Authentication (law) ; Food science ; Chemistry ; Biochemical engineering ; Environmental science ; Computer science ; Engineering Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food
Authors: Wen Li | Lei Zhang | Mutian Zhang | Shougang Chen
Source: Industrial & Engineering Chemistry Research (article, 2020-11-11)
Field: Materials Science — Materials Chemistry

Structures of Graphene-Reinforced Epoxy Coatings and the Dynamic Diffusion of Guest Water: A Molecular Dynamics Study Recently, increasing research interest has been initiated to investigate graphene (GN)- or graphene oxide (GO)-reinforced epoxy coatings for corrosion protection. However, hardly any study has been devoted to studying their anticorrosion mechanism at a molecular level. In this work, molecular dynamics simulations were first conducted to investigate the structural properties of GN and GO nanoflake-reinforced epoxy coatings under different temperatures. The results show that the introduced GN or GO flakes can effectively improve the compactness of the epoxy coatings, while high temperature will enhance the porosity of the composite coatings. Furthermore, compared with GN-reinforced coating, more compacted coating is obtained for the GO-reinforced epoxy coating because of the stronger interfacial binding forces originating from the hydrogen bonds and van der Waals and electrostatic interactions between the polar groups in the GO flake and epoxy molecules. As a typical corrosion medium, dynamic diffusion of guest water in the composite coatings was also simulated to estimate the effects of the added GO or GN flakes on corrosion protection. The results show that the added GN or GO flakes can act as a physical barrier for water diffusion, while the GO flake can further adsorb the guest water and restrain its movement. Finally, a jump-diffusion model of water in epoxy coating is unraveled. The results obtained in this work deepen our understanding of the anticorrosion mechanism of GN- and GO-reinforced epoxy coatings. Epoxy ; Graphene ; Materials science ; Corrosion ; Coating ; Composite material ; Oxide ; Diffusion ; Composite number ; Molecular dynamics ; van der Waals force ; Molecule ; Nanotechnology ; Metallurgy ; Chemistry ; Computational chemistry ; Organic chemistry High voltage insulation and dielectric phenomena ; Corrosion Behavior and Inhibition ; Graphene research and applications
Authors: Cameron McElfresh | Yinan Cui | S. L. Dudarev | Giacomo Po | Jaime Marian
Source: International Journal of Plasticity (article, 2020-09-02)
Field: Materials Science — Materials Chemistry

Discrete stochastic model of point defect-dislocation interaction for simulating dislocation climb Climb ; Vacancy defect ; Dislocation ; Kinetic Monte Carlo ; Materials science ; Crystallographic defect ; Statistical physics ; Molecular dynamics ; Creep ; Mechanics ; Thermal ; Condensed matter physics ; Monte Carlo method ; Classical mechanics ; Physics ; Thermodynamics ; Mathematics ; Composite material Microstructure and mechanical properties ; Fusion materials and technologies ; Nuclear Materials and Properties
Authors: James A. Frank | Marc‐Joseph Antonini | Po‐Han Chiang | Andrés Canales | David B. Konrad | Indie C. Garwood | Gabriela Rajic | Florian Koehler | Yoel Fink | Polina Anikeeva
Source: ACS Chemical Neuroscience (article, 2020-10-27)
Field: Materials Science — Materials Chemistry

<i>In Vivo</i> Photopharmacology Enabled by Multifunctional Fibers Photoswitchable ligands can add an optical switch to a target receptor or signaling cascade and enable reversible control of neural circuits. The application of this approach, termed photopharmacology, to behavioral experiments has been impeded by a lack of integrated hardware capable of delivering both light and compounds to deep brain regions in moving subjects. Here, we devise a hybrid photochemical genetic approach to target neurons using a photoswitchable agonist of the capsaicin receptor TRPV1, <i>red</i>-AzCA-4. Using multifunctional fibers with optical and microfluidic capabilities, we delivered a transgene coding for TRPV1 into the ventral tegmental area (VTA). This sensitized excitatory VTA neurons to <i>red</i>-AzCA-4, allowing us to optically control conditioned place preference in mice, thus extending applications of photopharmacology to behavioral experiments. Applied to endogenous receptors, our approach may accelerate future studies of molecular mechanisms underlying animal behavior. Neuroscience ; Ventral tegmental area ; Optogenetics ; Excitatory postsynaptic potential ; Biological neural network ; Transgene ; Chemistry ; Biology ; Computer science ; Inhibitory postsynaptic potential ; Dopamine Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Neurobiology and Insect Physiology Research
Authors: Jinbo Pan | Yanfang Zhang | Jingda Zhang | Huta Banjade | Jie Yu | Liping Yu | Shixuan Du | Adrienn Ruzsinszky | Zhenpeng Hu | Qimin Yan
Source: npj Computational Materials (article, 2020-10-14)
Field: Materials Science — Materials Chemistry

Auxetic two-dimensional transition metal selenides and halides Abstract Auxetic two-dimensional (2D) materials provide a promising platform for biomedicine, sensors, and many other applications at the nanoscale. In this work, utilizing a hypothesis-based data-driven approache, we identify multiple materials with remarkable in-plane auxetic behavior in a family of buckled monolayer 2D materials. These materials are transition metal selenides and transition metal halides with the stoichiometry MX (M = V, Cr, Mn, Fe, Co, Cu, Zn, Ag, and X = Se, Cl, Br, I). First-principles calculations reveal that the desirable auxetic behavior of these 2D compounds originates from the interplay between the buckled 2D structure and the weak metal–metal interaction determined by their electronic structures. We observe that the Poisson’s ratio is sensitive to magnetic order and the amount of uniaxial stress applied. A transition from positive Poisson’s ratio (PPR) to negative Poisson’s ratio (NPR) for a subgroup of MX compounds under large uniaxial stress is predicted. The work provides a guideline for the future design of 2D auxetic materials at the nanoscale. Auxetics ; Materials science ; Monolayer ; Transition metal ; Halide ; Nanoscopic scale ; Poisson's ratio ; Stoichiometry ; Metal ; Nanotechnology ; Condensed matter physics ; Poisson distribution ; Crystallography ; Composite material ; Chemistry ; Metallurgy ; Inorganic chemistry ; Physical chemistry ; Physics ; Mathematics ; Catalysis MXene and MAX Phase Materials ; Cellular and Composite Structures ; 2D Materials and Applications
Authors: Flavio A. Franchina | Delphine Zanella | Lena Dubois | Jean‐François Focant
Source: Journal of Separation Science (review, 2020-10-27)
Field: Chemistry — Spectroscopy

The role of sample preparation in multidimensional gas chromatographic separations for non‐targeted analysis with the focus on recent biomedical, food, and plant applications In this review, we consider and discuss the affinity and complementarity between a generic sample preparation technique and the comprehensive two-dimensional gas chromatography process. From the initial technical development focus (e.g., on the GC×GC and solid-phase microextraction techniques), the trend is inevitably shifting toward more applied challenges, and therefore, the preparation of the sample should be carefully considered in any GC×GC separation for an overreaching research. We highlight recent biomedical, food, and plant applications (2016-July 2020), and specifically those in which the combination of tailored sample preparation methods and GC×GC-MS has proven to be beneficial in the challenging aspects of non-targeted analysis. Specifically on the sample preparation, we report on gas-phase, solid-phase, and liquid-phase extractions, and derivatization procedures that have been used to extract and prepare volatile and semi-volatile metabolites for the successive GC×GC analysis. Moreover, we also present a milestone section reporting the early works that pioneered the combination of sample preparation techniques with GC×GC for non-targeted analysis. Sample preparation ; Chromatography ; Gas chromatography ; Derivatization ; Chemistry ; Gas phase ; Sample (material) ; Stationary phase ; Solid-phase microextraction ; Gas chromatography–mass spectrometry ; Mass spectrometry ; Organic chemistry Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Analytical chemistry methods development