jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gopinath Singaram | K. Sathiyasekar | Sanjeevikumar Padmanaban | C. Bharatiraja
Source: IET Generation Transmission & Distribution (article, 2018-09-20)
Field: Materials Science — Materials Chemistry

Insulation condition assessment of high‐voltage rotating machines using hybrid techniques The enduring life span of the machine insulation will be decided based on degradation level in motor and generator stator windings. The non‐destructive diagnostic tools like dielectric loss and capacitance test and partial discharge (PD) analysis, recognized to access the deterioration in the insulation system of rotating machines. The experiments reveal various characteristic parameters such as leakage current, dielectric dissipation factor, the capacitance value, and PD magnitude. The integrity of the rotating machine can be find out by analyzing these parameters. This research study shows the hybrid method for prediction of insulation condition in the stator winding by utilizing the artificial neural network (ANN) with gravitational search algorithm in comparison with ANN and ANN–genetic algorithm. The advent of expert systems ensures the quality assurance and service life assessment of the high‐voltage assets. It offers a predictive maintenance solution to personnel dealt with power utilities thereby increasing the uptime, reliability, and productivity, which in turn reducing the operating costs, downtime and unplanned outages. For testing and predicting the insulation status, several 11 kV machines are considered. The predicted results using hybrid techniques extend a close agreement with reference to the data obtained from the experiments performed. The proposed method indicate the competent and trustworthy, by the presented test results. High voltage ; Voltage ; Reliability engineering ; Electrical engineering ; Engineering ; Mechanical engineering ; Automotive engineering ; Computer science ; Materials science High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Vacuum and Plasma Arcs
Authors: Leroy Cronin | S. Hessam M. Mehr | Jarosław M. Granda
Source: Chem (article, 2018-08-01)
Field: Materials Science — Materials Chemistry

Catalyst: The Metaphysics of Chemical Reactivity Metaphysics ; Reactivity (psychology) ; Catalysis ; Chemistry ; Organic chemistry ; Polymer science ; Philosophy ; Medicine ; Epistemology ; Pathology Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Daniel Reker | Gonçalo J. L. Bernardes | Tiago Rodrigues
Source: ChemRxiv (preprint, 2018-11-02)
Field: Materials Science — Materials Chemistry

Evolving and Nano Data Enabled Machine Intelligence for Chemical Reaction Optimization Optimizing reaction conditions is an essential routine in synthetic chemistry. However, selecting appropriate experiments remains tightly connected to expert chemistry knowledge. Here, to streamline the reaction yield optimization process and disconnect it from chemical intuition, we developed an adaptive machine intelligence to navigate multidimensional reaction conditions’ spaces. Our approach (LabMate.AI) employs an interpretable algorithm and requires only <0.05% of all search space as input data. LabMate.AI optimizes many reaction parameters simultaneously, and uses minimal computational resources and time. We demonstrate how LabMate.AI can identify optimal conditions for a Ugi and a C–N cross-coupling reaction in a more efficient and faster manner than human experts, while affording reactivity insights. Our approach formalizes chemical intuition, and acquires expert chemistry knowledge autonomously, thereby providing an innovative framework towards informed and automated experiment selection. The results support machine learning for hastening experimental design, democratizing synthetic chemistry, and freeing chemists for non-routine tasks. Intuition ; Computer science ; Artificial intelligence ; Machine learning ; Chemical space ; Chemistry ; Cognitive science ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Ryan‐Rhys Griffiths | Philippe Schwaller | Alpha A. Lee
Source: ChemRxiv (preprint, 2018-11-21)
Field: Materials Science — Materials Chemistry

Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design Datasets in the Natural Sciences are often curated with the goal of aiding scientific understanding and hence may not always be in a form that facilitates the application of machine learning. In this paper, we identify three trends within the fields of chemical reaction prediction and synthesis design that require a change in direction. First, the manner in which reaction datasets are split into reactants and reagents encourages testing models in an unrealistically generous manner. Second, we highlight the prevalence of mislabelled data, and suggest that the focus should be on outlier removal rather than data fitting only. Lastly, we discuss the problem of reagent prediction, in addition to reactant prediction, in order to solve the full synthesis design problem, highlighting the mismatch between what machine learning solves and what a lab chemist would need. Our critiques are also relevant to the burgeoning field of using machine learning to accelerate progress in experimental Natural Sciences, where datasets are often split in a biased way, are highly noisy, and contextual variables that are not evident from the data strongly influence the outcome of experiments. Computer science ; Outlier ; Artificial intelligence ; Machine learning ; Field (mathematics) ; Natural (archaeology) ; Chemist ; Data science ; Chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software Engineering Research
Authors: Nirlipta Saha | Gonzalo Astray | S. Dutta Gupta
Source: Journal of Cluster Science (article, 2018-07-14)
Field: Materials Science — Materials Chemistry

Modelling and Optimization of Biogenic Synthesis of Gold Nanoparticles from Leaf Extract of Swertia chirata Using Artificial Neural Network Colloidal gold ; Artificial neural network ; Nanoparticle ; Response surface methodology ; Nanochemistry ; Biosynthesis ; Mean squared error ; Biological system ; Chemistry ; Mathematics ; Materials science ; Computer science ; Nanotechnology ; Chromatography ; Organic chemistry ; Biology ; Artificial intelligence ; Statistics Nanoparticles: synthesis and applications ; Medicinal Plants and Neuroprotection
Authors: Glenn Jones
Source: Nature Catalysis (article, 2018-05-08)
Field: Materials Science — Materials Chemistry

Industrial computational catalysis and its relation to the digital revolution Industrial Revolution ; Field (mathematics) ; Digital transformation ; Relation (database) ; Transformation (genetics) ; Digital Revolution ; Computer science ; Data science ; Artificial intelligence ; Management science ; Biochemical engineering ; Industrial engineering ; Nanotechnology ; Engineering ; Political science ; Materials science ; Chemistry ; Mathematics ; Data mining ; World Wide Web ; Telecommunications Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Florian Gruber | Philipp Wollmann | Wulf Grählert | Stefan Kaskel
Source: Journal of Imaging (article, 2018-09-21)
Field: Chemistry — Analytical Chemistry

Hyperspectral Imaging Using Laser Excitation for Fast Raman and Fluorescence Hyperspectral Imaging for Sorting and Quality Control Applications A hyperspectral measurement system for the fast and large area measurement of Raman and fluorescence signals was developed, characterized and tested. This laser hyperspectral imaging system (Laser-HSI) can be used for sorting tasks and for continuous quality monitoring. The system uses a 532 nm Nd:YAG laser and a standard pushbroom HSI camera. Depending on the lens selected, it is possible to cover large areas (e.g., field of view (FOV) = 386 mm) or to achieve high spatial resolutions (e.g., 0.02 mm). The developed Laser-HSI was used for four exemplary experiments: (a) the measurement and classification of a mixture of sulphur and naphthalene; (b) the measurement of carotenoid distribution in a carrot slice; (c) the classification of black polymer particles; and, (d) the localization of impurities on a lead zirconate titanate (PZT) piezoelectric actuator. It could be shown that the measurement data obtained were in good agreement with reference measurements taken with a high-resolution Raman microscope. Furthermore, the suitability of the measurements for classification using machine learning algorithms was also demonstrated. The developed Laser-HSI could be used in the future for complex quality control or sorting tasks where conventional HSI systems fail. Hyperspectral imaging ; Laser ; Materials science ; Sorting ; Remote sensing ; Optics ; Raman spectroscopy ; Image resolution ; Artificial intelligence ; Computer science ; Geology ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Н. В. Печищева | K. Yu. Shunyaev | O. V. Melchakova
Source: Reviews in Analytical Chemistry (article, 2018-05-17)
Field: Chemistry — Analytical Chemistry

Zirconium in modern analytical chemistry Abstract Detailed monographs on the analytical chemistry of zirconium, which is widely used in all fields of modern science and technology, were published in the 1960s–1970s of the last century. This review summarizes information on the modern methods for determination of zirconium in a great variety of natural, technical, and biological objects. Focus is made on the works published in scientific periodicals after 2005. Spectroscopic techniques of zirconium determination including molecular and atomic spectrometry, X-ray fluorescence analysis, and electrochemical and activation methods are described. The paper also describes the applications of zirconium compounds, in particular, in analytical chemistry. Zirconium ; Chemistry ; Nanotechnology ; Analytical Chemistry (journal) ; Computer science ; Environmental chemistry ; Materials science ; Inorganic chemistry Analytical chemistry methods development ; Electrochemical Analysis and Applications ; Radioactive element chemistry and processing
Authors: Pongsakorn Chasing | Phornphimon Maitarad | Hongmin Wu | Dengsong Zhang | Liyi Shi | Vinich Promarak
Source: Catalysts (article, 2018-09-28)
Field: Chemistry — Organic Chemistry

Straightforward Design for Phenoxy-Imine Catalytic Activity in Ethylene Polymerization: Theoretical Prediction The quantitative structure-activity relationship (QSAR) of 18 Ti-phenoxy-imine (FI-Ti)-based catalysts was investigated to clarify the role of the structural properties of the catalysts in polyethylene polymerization activity. The electronic properties of the FI-Ti catalysts were analyzed based on density functional theory with the M06L/6-31G** and LANL2DZ basis functions. The analysis results of the QSAR equation with a genetic algorithm showed that the polyethylene catalytic activity mainly depended on the highest occupied molecular orbital energy level and the total charge of the substituent group on phenylimine ring. The QSAR models showed good predictive ability (R2) and R2 cross validation (R2cv) values of greater than 0.927. The design concept is “head-hat”, where the hats are the phenoxy-imine substituents, and the heads are the transition metals. Thus, for the newly designed series, the phenoxy-imine substituents still remained, while the Ti metal was replaced by Zr or Ni transition metals, entitled FI-Zr and FI-Ni, respectively. Consequently, their polyethylene polymerization activities were predicted based on the obtained QSAR of the FI-Ti models, and it is noteworthy that the FI-Ni metallocene catalysts tend to increase the polyethylene catalytic activity more than that of FI-Zr complexes. Therefore, the new designs of the FI-Ni series are proposed as candidate catalysts for polyethylene polymerization, with their predicted activities in the range of 35,000–48,000 kg(PE)/mol(Cat.)·MPa·h. This combined density functional theory and QSAR analysis is useful and straightforward for molecular design or catalyst screening, especially in industrial research. Quantitative structure–activity relationship ; Polyethylene ; Imine ; Substituent ; Polymerization ; Density functional theory ; Catalysis ; Computational chemistry ; Chemistry ; Transition metal ; Materials science ; Polymer chemistry ; Organic chemistry ; Stereochemistry ; Polymer Organometallic Complex Synthesis and Catalysis ; Carbon dioxide utilization in catalysis ; Polymer crystallization and properties
Authors: Jonas Skovlund Madsen | Søren A. Jensen | Jesper Nygård | Poul‐Erik Hansen
Source: Optics Express (article, 2018-12-20)
Field: Materials Science — Surfaces, Coatings and Films

Replacing libraries in scatterometry Diffraction gratings have a wide array of applications in optics, diagnostics, food science, sensing, and process inspection. Scattering effects from defects can severely degrade the performance of such gratings. In this paper, we consider three classes of defects: Two classes introduced at the grating/air interface, as a change in line heights, and one class introduced as a sinusoidal variation of the grating/substrate interface. The scattering properties of the gratings are modelled using rigorous coupled wave analysis, and defects are approximated with a new semi-analytical model and a neural network. The new methods make it possible to avoid the time consuming library generation/search strategy commonly used in scatterometry. The method does not introduce new numerical parameters, and therefore no new parameter correlations. This work enables improved grating reconstruction, especially of non-diffracting short pitch gratings. It is found that two of the defect classes can be adequately described by the semi-analytical model, while the third defect is accurately reconstructed by a neural network. The network is demonstrated to be faster than a library search and more versatile for related structures. Optics ; Materials science ; Computer science ; Physics Optical Coatings and Gratings ; Photonic Crystals and Applications ; Acoustic Wave Resonator Technologies
Authors: F.P.C. Gomes | Michael R. Thompson
Source: Polymer Degradation and Stability (article, 2018-09-18)
Field: Chemistry — Analytical Chemistry

Nondestructive evaluation of sintering and degradation for rotational molded polyethylene Materials science ; Polyethylene ; Mold ; Nondestructive testing ; Degradation (telecommunications) ; Composite material ; Fourier transform infrared spectroscopy ; Ultrasonic sensor ; Polymer degradation ; Polymer ; Sintering ; Characterization (materials science) ; Polypropylene ; Process engineering ; Acoustics ; Chemical engineering ; Computer science ; Nanotechnology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Industrial Vision Systems and Defect Detection
Authors: Siva Selvaraju | Sampathkumar Senthamaraikannan | Sudha Jayaprakasham | Anirudh Ramesh Madiq
Source: Materials Research (article, 2018-05-24)
Field: Materials Science — Materials Chemistry

Effect of Process Parameters on Microstructure and Mechanical Properties of Friction Stir Welded Cast Nickel Aluminum Bronze Alloy (C95800) In the present study, Friction Stir Welding (FSW) of Nickel Aluminum Bronze (NAB) alloy was carried out by varying the axial load, rotation speed and welding speed rate. Micro-structural analyses of these samples revealed the different grain sizes and shapes of the various zones. Hardness and tensile strength tests were carried out for the samples welded at different conditions such as axial load, welding speed and rotational speed. Grain refinement in the weld nugget zones was achieved for all welding conditions. The refined grains in weld nugget zone (WNZ) make a main contribution to the increase of mechanical properties of FSW welded NAB alloy. The microhardness of the stir zone and Thermo-Mechanically Affected Zone (TMAZ) was found to be higher than that of the base metal. Transverse tensile strength of weld joint was higher than that of the base metal. On the other hand, a lower or similar tensile strength value compared to the base metal was seen from other variable welding parameters. This was due to the tunnel defect in the welded nugget zone. ANOVA test result was used for finding out the contribution of the process parameter. Materials science ; Welding ; Friction stir welding ; Rotational speed ; Metallurgy ; Ultimate tensile strength ; Indentation hardness ; Alloy ; Base metal ; Heat-affected zone ; Grain size ; Microstructure ; Composite material ; Joint (building) ; Aluminium ; Structural engineering Metallurgy and Material Science ; Aluminum Alloys Composites Properties ; Nanoporous metals and alloys
Authors: Xueying Li | Yan Liu | Meirong Lv | Yan Zou | Pingping Fan
Source: Journal of Spectroscopy (article, 2018-09-25)
Field: Chemistry — Analytical Chemistry

Calibration Transfer of Soil Total Carbon and Total Nitrogen between Two Different Types of Soils Based on Visible-Near-Infrared Reflectance Spectroscopy Although visible-near-infrared spectroscopy can rapidly and accurately determine soil nutrients without sample destruction, some problems remain unresolved, such as the mismatch of the established spectral model with different types of samples, limiting the wide application of this technology. Here, we took riverside and mountain soils as examples to explore the calibration transfer between two different types of soils by the WMPDS-S/B algorithm (wavelet multiscale piecewise direct standardization combined with Slope/Bias correction method) and by adding new samples. The predicted TN and TC concentrations improved significantly after being transformed. Compared with adding new samples, the WMPDS-S/B algorithm obtained more accurate results. The average relative errors dropped from 440.2% (without transformation) to approximately 6% for TN and from 342.0% to approximately 7% for TC. The maximum relative errors were reduced from 538.1% to less than 20% for TN and from 403.7% to less than 20% for TC. The RMSEP decreased from 2.42 to approximately 0.04 for TN and from 15.74 to approximately 0.4 for TC. The WMPDS-S/B algorithm had advantages in selecting fewer known samples and obtaining better prediction results. In contrast to past studies, which resolved the calibration transfer between different spectrometers and the measurement environment for the same samples, our study resolved the calibration transfer between different types of samples under the same spectrometer and the measurement environment. The former could only be used for correction among instruments, while the latter fundamentally solved the problem of model sharing across different samples. Calibration ; Soil water ; Spectroscopy ; Analytical Chemistry (journal) ; Spectrometer ; Diffuse reflectance infrared fourier transform ; Soil test ; Nitrogen ; Environmental science ; Soil science ; Mineralogy ; Remote sensing ; Materials science ; Chemistry ; Mathematics ; Optics ; Statistics ; Geology ; Environmental chemistry ; Physics Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Zhe Ji | Chengjin Shen | Fuxiang Wei | Hongwei Li
Source: Metals (article, 2018-04-26)
Field: Materials Science — Materials Chemistry

Dependence of Macro- and Micro-Properties on α Plates in Ti-6Al-2Zr-1Mo-1V Alloy with Tri-Modal Microstructure Tri-modal microstructure is often the target microstructure of titanium alloy components. In this study, the parameters, such as volume fraction, size of primary equiaxed α (αp), secondary lamellar α (αs) and thin α plates (αt) in tri-modal microstructure of Ti-6Al-2Zr-1Mo-1V alloy are adjusted by double heat treatment procedures. The properties of these constituent phases and their effect on the macro-properties are then investigated. The results show that the hardness of primary α (αp) varies slightly with heat treatment routes. The hardness of α plates region including αs and αt increases with increasing αt volume fraction and not with decreasing thickness of αs and αt. Using volume average of the thickness of αs and αt as effective thickness (teff), the hardness of α plates region and teff follow the Hall-Petch relationship. Meanwhile, as αt volume fraction increases, colony structure of αs is gradually destroyed, which makes each αs have large deformation degree by multiple directions slip. When volume fraction ratio of αp, αs and αt is about 1:1:1, αp and αs can undergo relatively large deformation and αt can contribute relatively large strength to the alloy and therefore the alloy has both good strength and ductility. Microstructure ; Materials science ; Volume fraction ; Alloy ; Equiaxed crystals ; Lamellar structure ; Titanium alloy ; Ductility (Earth science) ; Metallurgy ; Composite material ; Deformation (meteorology) ; Creep Titanium Alloys Microstructure and Properties ; Metal and Thin Film Mechanics ; Additive Manufacturing Materials and Processes
Authors: L. Rogers | R.A. Clark | B.J.P. Jones | A.D. McDonald | D.R. Nygren | F. Psihas | C. Adams | V. Álvarez | L. Arazi | C.D.R Azevedo | K. Bailey | F. Ballester | J.M. Benlloch-Rodríguez | F.I.G.M. Borges | A. Botas | S. Cárcel | J.V. Carrión | S. Cebrián | C.A.N. Conde | J. Díaz | M. Diesburg | J. Escada | R. Esteve | R. Felkai | A.F.M. Fernandes | L.M.P. Fernandes | P. Ferrario | A.L. Ferreira | E.D.C. Freitas | J. Generowicz | A. Goldschmidt | J.J. Gómez-Cadenas | D. González-Díaz | R. Guenette | R.M. Gutiérrez | K. Hafidi | J. Hauptman | C.A.O. Henriques | A.I. Hernandez | J.A. Hernando Morata | V. Herrero | S. Johnston | M. Kekic | L. Labarga | A. Laing | P. Lebrun | N. López-March | M. Losada | R.D.P. Mano | J. Martín-Albo | A. Martínez | G. Martínez-Lema | F. Monrabal | C.M.B. Monteiro | F.J. Mora | J. Muñoz Vidal | M. Musti | M. Nebot-Guinot | P. Novella | B. Palmeiro | A. Para | J. Pérez | M. Querol | J. Renner | J. Repond | S. Riordan | L. Ripoll | J. Rodríguez | C. Romo-Luque | F.P. Santos | J.M.F. dos Santos | A. Simón | C. Sofka | M. Sorel | T. Stiegler | J.F. Toledo | J. Torrent | J.F.C.A. Veloso | R. Webb | J.T. White | N. Yahlali
Source: Journal of Instrumentation (article, 2018-10-01)
Field: Materials Science — Materials Chemistry

High voltage insulation and gas absorption of polymers in high pressure argon and xenon gases High pressure gas time projection chambers (HPGTPCs) are made with a variety of materials, many of which still await proper characterization in high pressure noble gas environments. As HPGTPCs increase in size toward ton-scale detectors, assemblies become larger and more complex, creating a need for detailed understanding of how structural supports and high voltage insulators behave. This includes identification of materials with predictable mechanical properties and without surface charge accumulation that may lead to field deformation or sparking. This paper explores the mechanical and electrical effects of high pressure gas environments on insulating polymers PTFE, HDPE, PEEK, POM and UHMW in argon and xenon, including studying gas absorption, swelling and high voltage insulation strength. High voltage ; Argon ; High pressure ; Xenon ; Polymer ; Deformation (meteorology) ; Absorption (acoustics) ; Voltage High voltage insulation and dielectric phenomena ; Polymer crystallization and properties ; Phase Equilibria and Thermodynamics