jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jie Yuan | Valentin Stanev | Chen Gao | Ichiro Takeuchi | Kui Jin
Source: Superconductor Science and Technology (article, 2019-10-28)
Field: Materials Science — Materials Chemistry

Recent advances in high-throughput superconductivity research Superconducting materials find applications in a rapidly growing number of technological areas, and searching for novel superconductors continues to be a major scientific task. However, the steady increase in the complexity of candidate materials presents a big challenge to the researchers in the field. In particular, conventional experimental methods are not well suited to efficiently search for candidates in compositional space exponentially growing with the number of elements; neither do they permit quick extraction of reliable multidimensional phase diagrams delineating the physical parameters that control superconductivity. New research paradigms that can boost the speed and the efficiency of superconducting materials research are urgently needed. High-throughput methods for rapid screening and optimization of materials have demonstrated their utility for accelerating research in bioinformatics and pharmaceutical industry, yet remain rare in quantum materials research. In this paper, we will briefly review the history of high-throughput research paradigm and then focus on some recent applications of this paradigm in superconductivity research. We consider the role these methods can play in all stages of materials development, including high-throughput computation, synthesis, characterization, and the emerging field of machine learning for materials. The high-throughput paradigm will undoubtedly become an indispensable tool of superconductivity research in the near future. Superconductivity ; Field (mathematics) ; Space (punctuation) ; Focus (optics) ; Quantum ; Phase (matter) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Iron-based superconductors research
Authors: Jian Li | Jie Wang | Yanli Pei | Gang Wang
Source: Ceramics International (article, 2019-04-16)
Field: Materials Science — Materials Chemistry

Study on the uniformity of ZnO films grown by MOCVD Metalorganic vapour phase epitaxy ; Materials science ; Deposition (geology) ; Chemical vapor deposition ; Substrate (aquarium) ; Consistency (knowledge bases) ; Process (computing) ; Volumetric flow rate ; Epitaxy ; Thin film ; Composite material ; Flow (mathematics) ; Optoelectronics ; Nanotechnology ; Mechanics ; Computer science ZnO doping and properties ; Fluid Dynamics and Thin Films ; Surface Roughness and Optical Measurements
Authors: François‐Xavier Coudert
Source: Advanced Theory and Simulations (article, 2019-09-18)
Field: Materials Science — Materials Chemistry

Materials Databases: The Need for Open, Interoperable Databases with Standardized Data and Rich Metadata Abstract Driven by the recent rapid increase in the number of materials databases published (open and commercial), some perspectives on the growing need for standardized, interoperable, open databases are discussed here. The field of computational materials discovery is quickly expanding, and recent advances in data mining, high‐throughput screening, and machine learning highlight the potential of open databases. Metadata ; Interoperability ; Field (mathematics) ; Data curation ; Ontology Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Materials Characterization Techniques
Authors: Ahmed S. Saad | Eman S. Elzanfaly | Michael K. Halim | Khadiga M. Kelani
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2019-05-01)
Field: Chemistry — Analytical Chemistry

Comparing the predictability of different chemometric models over UV-spectral data of isoxsuprine and its toxic photothermal degradation products Overfitting ; Partial least squares regression ; Calibration ; Artificial neural network ; Applicability domain ; Computer science ; Chemometrics ; Data set ; Multivariate statistics ; Artificial intelligence ; Statistics ; Biological system ; Mathematics ; Machine learning ; Quantitative structure–activity relationship Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Analytical Chemistry and Sensors
Authors: Omar Elkhatib | Wattana Chaisoontornyotin | Masakazu Gesho | Lamia Goual
Source: Energy & Fuels (article, 2019-12-19)
Field: Chemistry — Analytical Chemistry

Nanoscale Investigation of Asphaltene Deposition under Capillary Flow Conditions This study proposes a novel approach to investigate the diffusion-limited deposition of asphaltenes in flow lines to better understand their nanoscale behavior at interfaces and aid in the development of more accurate remediation methods and modeling tools. Experiments were first designed by flowing asphaltene-in-toluene solutions through capillary polyetheretherketone tubes and imaging their cross-sectional areas using high-resolution scanning electron microscopy. A two-step digital image analysis using machine-learning concepts was applied and consisted of a (1) denoising process by analyzing the local and global bias and variance and (2) binarization process to improve the quality of image segmentation. As a result of polydispersity, particles on the tube surface were categorized into nanoaggregates (1.5–4 nm), small clusters (SCs, 4–10 nm), medium clusters (MCs, 10–20 nm), large clusters (LCs, 20–100 nm), and extra-large clusters (XLCs, >100 nm). A Langmuir adsorption isotherm was measured in toluene with an adsorption free energy of −29 kJ/mol, in agreement with previous work. Nanoaggregates and SCs were the main constituents of the adsorption layer as a result of their high mass diffusivity. A competing behavior between aggregation and adsorption was observed as the asphaltene concentration increased in toluene. Enhanced self-assembly in the bulk phase led to a continuous decrease in the number of adsorbed particles. Adding n-heptane to toluene at different volume ratios prompted the deposition of MCs with a peak in the particle size, number, and mass density observed in the vicinity of the onset of precipitation. These clusters are potential precursors to fouling because they constitute the building blocks of larger particles that grow over time on the surface. Limiting their deposition could be achieved by either increasing the flow rate or introducing chemical inhibitors that promote the formation of larger aggregates in the bulk phase under given flow conditions. The novel insights gained from this study reveal that MC-rich petroleum fluids are more prone to flow assurance challenges and that effective flow enhancers are those that promote the aggregation of MCs into particles that are too large to deposit. Toluene ; Adsorption ; Deposition (geology) ; Chemical engineering ; Analytical Chemistry (journal) ; Materials science ; Chemistry ; Chromatography ; Organic chemistry Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Benoît Creton | Isabelle Lévêque | Fanny Oukhemanou
Source: Oil & Gas Science and Technology – Revue d’IFP Energies nouvelles (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Equivalent alkane carbon number of crude oils: A predictive model based on machine learning In this work, we present the development of models for the prediction of the Equivalent Alkane Carbon Number of a dead oil (EACNdo) usable in the context of Enhanced Oil Recovery (EOR) processes. Models were constructed by means of data mining tools. To that end, we collected 29 crude oil samples originating from around the world. Each of these crude oils have been experimentally analysed, and we measured property such as EACNdo, American Petroleum Institute (API) gravity and $ {\mathrm{C}}_{{20}^{-}}$ , saturate, aromatic, resin, and asphaltene fractions. All this information was put in form of a database. Evolutionary Algorithms (EA) have been applied to the database to derive models able to predict Equivalent Alkane Carbon Number (EACN) of a crude oil. Developed correlations returned EACNdo values in agreement with reference experimental data. Models have been used to feed a thermodynamics based models able to estimate the EACN of a live oil. The application of such strategy to study cases have demonstrated that combining these two models appears as a relevant tool for fast and accurate estimates of live crude oil EACNs. Alkane ; USable ; Context (archaeology) ; Crude oil ; Petroleum ; Asphaltene ; Petroleum engineering ; API gravity ; Carbon number ; Enhanced oil recovery ; Carbon fibers ; Chemistry ; Computer science ; Organic chemistry ; Hydrocarbon ; Algorithm ; Geology ; Alkyl Petroleum Processing and Analysis ; Phase Equilibria and Thermodynamics ; Hydrocarbon exploration and reservoir analysis
Authors: Wen‐Sheng Zhao | Pengwei Liu | Huan Yu | Yue Hu | Gaofeng Wang | Madhavan Swaminathan
Source: IEEE Access (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Repeater Insertion to Reduce Delay and Power in Copper and Carbon Nanotube-Based Nanointerconnects Optimal repeater designs are performed for Cu and carbon nanotube (CNT)-based nanointerconnects to reduce the delay and power dissipation. The effects of inductance and metal-CNT contact resistance are treated appropriately. In this paper, the circuit parameters are calculated analytically, while they can be extracted experimentally for a specific foundry at a specific technology node. The particle swarm optimization (PSO) technique is employed to numerically calculate the optimal repeater size and the optimal number of repeaters in the Cu and CNT-based nanointerconnects. The results are verified against the analytical and genetic algorithm results. To facilitate CAD design, the machine-learning neural network (NN) is adopted. The data obtained using the PSO algorithm are used to train the NN and the feasibility of the NN is investigated and validated. Repeater (horology) ; Particle swarm optimization ; Inductance ; Carbon nanotube ; Genetic algorithm ; Computer science ; Dissipation ; Node (physics) ; Power (physics) ; Artificial neural network ; Materials science ; Optimal design ; Electronic engineering ; Electrical engineering ; Algorithm ; Nanotechnology ; Engineering ; Physics ; Artificial intelligence ; Acoustics Graphene research and applications ; Carbon Nanotubes in Composites ; Low-power high-performance VLSI design
Authors: Hongze Lin | Zejian Li | Huajin Lu | Shujuan Sun | Fengnong Chen | Kaihua Wei | Dazhou Ming
Source: Sensors (article, 2019-10-28)
Field: Chemistry — Analytical Chemistry

Robust Classification of Tea Based on Multi-Channel LED-Induced Fluorescence and a Convolutional Neural Network A multi-channel light emitting diode (LED)-induced fluorescence system combined with a convolutional neural network (CNN) analytical method was proposed to classify the varieties of tea leaves. The fluorescence system was developed employing seven LEDs with spectra ranging from ultra-violet (UV) to blue as excitation light sources. The LEDs were lit up sequentially to induce a respective fluorescence spectrum, and their ability to excite fluorescence from components in tea leaves were investigated. All the spectral data were merged together to form a two-dimensional matrix and processed by a CNN model, which is famous for its strong ability in pattern recognition. Principal component analysis combined with k-nearest-neighbor classification was also employed as a baseline for comparison. Six grades of green tea, two types of black tea and one kind of white tea were verified. The result proved a significant improvement in accuracy and showed that the proposed system and methodology provides a fast, compact and robust approach for tea classification. Fluorescence ; Convolutional neural network ; Light-emitting diode ; Principal component analysis ; Black tea ; Channel (broadcasting) ; Pattern recognition (psychology) ; Near-infrared spectroscopy ; Artificial intelligence ; Computer science ; Biological system ; Matrix (chemical analysis) ; Chemistry ; Materials science ; Optoelectronics ; Physics ; Optics ; Chromatography ; Telecommunications ; Food science ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Tea Polyphenols and Effects
Authors: Larissa Sobral Hilário | Raoni Batista dos Anjos | Henrique Borges de Moraes Juviniano | Djalma Ribeiro da Silva
Source: Materials (article, 2019-11-25)
Field: Materials Science — Surfaces, Coatings and Films

Evaluation of Thermally Treated Calotropis Procera Fiber for the Removal of Crude Oil on the Water Surface Biosorbents have been highlighted as an alternative method for the removal of contaminants from spills or leaks of oil and its derivatives, since they are biodegradable, are highly available, low-cost, and have a good sorption capacity. This research investigated the sorption capacity of <i>Calotropis procera</i> fiber in natura (CP) and thermally treated (150 °C and 200 °C) for crude oil removal and recovery. The oil sorption tests were carried out in a dry and water (layer) static systems. The assays revealed that CP fiber has excellent hydrophobic-oil properties and good crude oil sorption capacity, about 75 times its own weight (76.32 g/g). The results of the treated fibers, CPT150 and CPT200, showed oil sorption capacities (in 24 h) higher than CP, between 94.31-103.37 g/g and 124.60-180.95 g/g, respectively. The results from sample CPT200 showed that it can be an excellent biosorbent for the removal of crude oil and other derivatives due to its high hydrophobicity, great reuse/resorption capacity, and ability to retain oil within the fiber lumens. Thus, it can be applied in the recovery, cleaning, and removal of petroleum products and its derivatives from spills and leaks in the future. Calotropis procera ; Procera ; Materials science ; Fiber ; Crude oil ; Composite material ; Pulp and paper industry ; Petroleum engineering ; Traditional medicine ; Botany ; Engineering ; Medicine Surface Modification and Superhydrophobicity ; Polymer composites and self-healing ; Enhanced Oil Recovery Techniques
Authors: Zhu Zhou | Sohrab Rahimi | Stavros Avramidis | Yiming Fang
Source: BioResources (article, 2019-11-18)
Field: Chemistry — Organic Chemistry

Species- and moisture-based sorting of green timber mix with near infrared spectroscopy Methods suitable for the determination and classification of green timber mix (western hemlock and amabilis fir), with respect to species and moisture content, were developed and tested using near infrared spectroscopy and chemometrics. One thousand two hundred samples were distributed into a calibration set (720 samples) and a prediction set (480 samples). Partial least squares (PLS) and least squares-support vector machines (LS-SVM) for both regression (PLSR and LS-SVR) and classification (PLS-DA and LS-SVC) with different spectral preprocessing methods were implemented. LS-SVM outperformed PLS models for both regression and classification. The coefficient of determination (R2p) and root mean square error (RMSEP) of prediction for the best LS-SVR model with spectra pretreated by smooth and first derivative were 0.9824 and 8.7%, respectively, for wood moisture content prediction in the range of 30% to 253%. The best classification model was LS-SVC with spectra pretreated by smooth and second derivative, with overall accuracies of 99.8% in the prediction set, when the samples were divided into four classes. NIRS combined with LS-SVM can be used as a rapid alternative method for qualitative and quantitative analysis of green hem-fir mix before kiln drying. The results could be helpful for sorting green hem-fir mixes with an on-line application. Partial least squares regression ; Chemometrics ; Support vector machine ; Mathematics ; Calibration ; Sorting ; Least squares support vector machine ; Mean squared error ; Moisture ; Cross-validation ; Linear discriminant analysis ; Coefficient of determination ; Water content ; Pattern recognition (psychology) ; Analytical Chemistry (journal) ; Statistics ; Artificial intelligence ; Algorithm ; Chemistry ; Computer science ; Engineering ; Machine learning ; Chromatography Wood and Agarwood Research ; Wood Treatment and Properties ; Spectroscopy and Chemometric Analyses
Authors: Yi Zhu | Tobias Weiß | Qiushi Zhang | Rui Sun | Bo Wang | Zhicheng Wu | Qing Zhong | Xiao Yi | Huanhuan Gao | Xue Cai | Guan Ruan | Tiansheng Zhu | Chao Xu | Sai Lou | Xiaoyan Yu | Ludovic Gillet | Peter Blattmann | Karim Saba | Christian D. Fankhauser | Michael B. Schmid | Dorothea Rutishauser | Jelena Ljubičić | Ailsa J. Christiansen | Christine Fritz | Niels J. Rupp | Cédric Poyet | Elisabeth J. Rushing | Michael Weller | Patrick Roth | Eugenia Haralambieva | Silvia Höfer | Chen Chen | Wolfram Jochum | Xiaofei Gao | Xiaodong Teng | Lirong Chen | Peter J. Wild | Ruedi Aebersold | Tiannan Guo
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2019-06-11)
Field: Chemistry — Spectroscopy

High-throughput proteomic analysis of FFPE tissue samples facilitates tumor stratification Abstract Formalin-fixed, paraffin-embedded (FFPE), biobanked tissue samples offer an invaluable resource for clinical and biomarker research. Here we developed a pressure cycling technology (PCT)-SWATH mass spectrometry workflow to analyze FFPE tissue proteomes and applied it to the stratification of prostate cancer (PCa) and diffuse large B-cell lymphoma (DLBCL) samples. We show that the proteome patterns of FFPE PCa tissue samples and their analogous fresh frozen (FF) counterparts have a high degree of similarity and we confirmed multiple proteins consistently regulated in PCa tissues in an independent sample cohort. We further demonstrate temporal stability of proteome patterns from FFPE samples that were stored between one to 15 years in a biobank and show a high degree of the proteome pattern similarity between two types histological region of small FFPE samples, i.e. punched tissue biopsies and thin tissue sections of micrometer thickness, despite the existence of certain degree of biological variations. Applying the method to two independent DLBCL cohorts we identified myeloperoxidase (MPO), a peroxidase enzyme, as a novel prognostic marker. In summary, this study presents a robust proteomic method to analyze bulk and biopsy FFPE tissues and reports the first systematic comparison of proteome maps generated from FFPE and FF samples. Our data demonstrate the practicality and superiority of FFPE over FF samples for proteome in biomarker discovery. Promising biomarker candidates for PCa and DLBCL have been discovered. Proteome ; Biomarker discovery ; Biomarker ; Computational biology ; Biology ; Proteomics ; Prostate cancer ; Pathology ; Bioinformatics ; Cancer ; Medicine ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Molecular Biology Techniques and Applications
Authors: Yuhui Zhao | Ziheng Zhao | Peng Shan | Silong Peng | Jinlong Yu | Shuli Gao
Source: Molecules (article, 2019-05-09)
Field: Chemistry — Analytical Chemistry

Calibration Transfer Based on Affine Invariance for NIR without Transfer Standards Calibration transfer is an important field for near-infrared (NIR) spectroscopy in practical applications. However, most transfer methods are constructed with standard samples, which are expensive and difficult to obtain. Taking this problem into account, this paper proposes a calibration transfer method based on affine invariance without transfer standards (CTAI). Our method can be utilized to adjust the difference between two instruments by affine transformation. CTAI firstly establishes a partial least squares (PLS) model of the master instrument to obtain score matrices and predicted values of the two instruments, and then the regression coefficients between each of the score vectors and predicted values are computed for the master instrument and the slave instrument, respectively. Next, angles and biases are calculated between the regression coefficients of the master instrument and the corresponding regression coefficients of the slave instrument, respectively. Finally, by introducing affine transformation, new samples are predicted based on the obtained angles and biases. A comparative study between CTAI and the other five methods was conducted, and the performances of these algorithms were tested with two NIR spectral datasets. The obtained experimental results show clearly that, in general CTAI is more robust and can also achieve the best Root Mean Square Error of test sets (RMSEPs). In addition, the results of statistical difference with the Wilcoxon signed rank test show that CTAI is generally better than the others, and at least statistically the same. Affine transformation ; Calibration ; Wilcoxon signed-rank test ; Partial least squares regression ; Mathematics ; Transformation (genetics) ; Statistics ; Affine combination ; Linear regression ; Rank (graph theory) ; Algorithm ; Computer science ; Geometry Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Stanislas de Lambert | Guillaume Campioni | Vincent Faucher | Bertrand Leturcq | Jérome Cardolaccia
Source: Annals of Nuclear Energy (article, 2019-06-21)
Field: Materials Science — Materials Chemistry

Modeling the consequences of fuel assembly bowing on PWR core neutronics using a Monte-Carlo code The effect of assemblies bowing in PWR nuclear reactors onto core neutronics is an observed phenomenon still poorly understood which can lead to Power Ratio Tilt. Studies of the consequences of rod/assembly bowing involve many different fields addressed by nuclear power plant, such as neutronics, thermohydraulics, mechanics… in a complex combination of multi-physical interactions. For the neutronic part, the modeling of bowed assemblies in Monte Carlo codes must allow to correctly describe the shape of fuel rods. In this article, two discrete ways to model bowed geometries are tested: the first one consists in a stacking of vertical small cylinders following the shape of the fuel rod by small shifts between neighboring cylinders; the second one, newly introduced in the present research, consists in a sequence of rotated cylindrical segments arranged to as to follow the shape of the fuel rod more closely. Both models are used to reproduce two specific bowing patterns, namely C-shape and S-shape, for which a reference modeling involving an analytical toroidal volume cut by planes is available for use with CEA’s Monte Carlo code Tripoli-4®. Results of comparisons between both models and analytical reference show that, even if the segment modeling requires a specific effort to handle implementation constraints, it appears preferable compared to stacking modeling. It provides accuracy with fewer discrete entities and is therefore computationally affordable and it is far more robust when increasing bowing deflection. This approach is thus only considered ready for its application to any kind of bowing patterns. Bowing ; Neutron transport ; Monte Carlo method ; Rod ; Deflection (physics) ; Nuclear reactor core ; Mechanics ; Structural engineering ; Computer science ; Nuclear engineering ; Neutron ; Physics ; Nuclear physics ; Engineering ; Mathematics ; Optics Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear and radioactivity studies
Authors: Alexey Zhokh | P. E. Strizhak
Source: Microporous and Mesoporous Materials (article, 2019-03-14)
Field: Chemistry — Inorganic Chemistry

Crossover between Fickian and non-Fickian diffusion in a system with hierarchy Microporous material ; Fick's laws of diffusion ; Mesoporous material ; Diffusion ; Zeolite ; Cyclohexane ; Thermal diffusivity ; Chemistry ; Molecule ; Thermodynamics ; Desorption ; Materials science ; Adsorption ; Physical chemistry ; Organic chemistry Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Diffusion Coefficients in Liquids