jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Stef Smeets | Jonas Ångström | C.-O.A. Olsson
Source: steel research international (article, 2018-09-06)
Field: Materials Science — Materials Chemistry

Quantitative Phase Analysis for Carbide Characterization in Steel Using Automated Electron Diffraction Carbides are critical to many different properties in a steel. In this paper, the quantification of a carbide powder extracted from a stainless steel analyzed with profile refinement of X‐ray powder diffraction patterns, is compared to a novel technique for automated electron diffraction pattern collection with a transmission electron microscope. The automated analysis has the advantage of collecting patterns from individual particles, hence avoiding issues such as overlapping reflections in powder diffractograms. Electron diffraction patterns with satisfactory quality are selected through a deep convoluted neural network and matched to a library of pre‐calculated diffraction patterns, corresponding to a set of selected carbide crystal structures. The quantitative results from the automated particle analysis are in good agreement with the phases identified by powder profile refinement. Materials science ; Diffraction ; Carbide ; Selected area diffraction ; Powder diffraction ; Electron diffraction ; Transmission electron microscopy ; Silicon carbide ; Phase (matter) ; Characterization (materials science) ; Scanning electron microscope ; Crystallography ; Analytical Chemistry (journal) ; Metallurgy ; Optics ; Composite material ; Nanotechnology ; Chemistry ; Physics ; Chromatography X-ray Diffraction in Crystallography ; Microstructure and Mechanical Properties of Steels ; Machine Learning in Materials Science
Authors: Gabriele Baiocco | Nadia Ucciardello
Source: Applied Physics A (article, 2018-12-11)
Field: Materials Science — Metals and Alloys

Neural network implementation for the prediction of secondary phase precipitation and mechanical feature in a duplex stainless steel Indentation ; Materials science ; Welding ; Duplex (building) ; Precipitation ; Toughness ; Corrosion ; Artificial neural network ; Microstructure ; Metallurgy ; Composite material ; Structural engineering ; Computer science ; Engineering ; Artificial intelligence ; Physics Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Welding Techniques and Residual Stresses
Authors: Universidad de Antioquia | Yesica Dayana Morcillo-Bolaños | William José Malule-Herrera | Juan Carlos Ortiz-Arango | Aída Luz Villa
Source: Revista Facultad de Ingeniería Universidad de Antioquia (article, 2018-06-01)
Field: Materials Science — Materials Chemistry

Polyurethane flexible foam recycling via glycolysis using Zn/Sn/Al hydrotalcites as heterogeneous catalyst Polyurethane is a highly versatile material used in comfort, packaging and isolation industries. The global production of polyurethane generates several million tons of non-biodegradable wastes. In this research, the chemical recovery of polyol from polyurethane flexible foam (PUF) wastes was explored. The polyol recovery from PUF was carried out via glycolysis with diethylene glycol (DEG) as a glycolysis agent, Zn/Sn/Al hydrotalcite (HTC) as a heterogeneous catalyst under inert atmosphere using several reaction conditions. The most suitable reaction conditions were achieved for 3 h of reaction, PUF/DEG mass ratio of 1.5 and HTC/DEG of 0.001. The HTC was characterized by FTIR, XRD, TEM and chemical analysis. The recovered polyol was characterized using IR, viscometry and GPC; density and water content was also determined. The recovered polyol was used in the synthesis of PUF as a partial replacement of virgin polyols, and the resultant foams were analyzed using compression tests. Polyol ; Polyurethane ; Diethylene glycol ; Materials science ; Hydrotalcite ; Catalysis ; Chemical engineering ; Inert gas ; Inert ; Fourier transform infrared spectroscopy ; Nuclear chemistry ; Chemistry ; Organic chemistry ; Composite material ; Ethylene glycol Layered Double Hydroxides Synthesis and Applications ; Flame retardant materials and properties ; Carbon dioxide utilization in catalysis
Authors: Yuta Yamamoto | Mizuki Hattori | Junya Ohyama | Atsushi Satsuma | Nobuo Tanaka | Shunsuke Muto
Source: Microscopy (article, 2018-07-19)
Field: Materials Science — Materials Chemistry

Twinned/untwinned catalytic gold nanoparticles identified by applying a convolutional neural network to their Hough transformed Z-contrast images In this article, we demonstrate that a convolutional neural network (CNN) can be effectively used to determine the presence of twins in the atomic resolution scanning transmission electron microscopy (STEM) images of catalytic Au nanoparticles. In particular, the CNN screening of Hough transformed images resulted in significantly higher accuracy rates as compared to those obtained by applying this technique to the raw STEM images. The proposed method can be utilized for evaluating the statistical twining fraction of Au nanoparticles that strongly affects their catalytic activity. Convolutional neural network ; Contrast (vision) ; Nanoparticle ; Artificial intelligence ; Colloidal gold ; Hough transform ; Computer science ; Artificial neural network ; Transmission electron microscopy ; Pattern recognition (psychology) ; Catalysis ; Materials science ; Scanning transmission electron microscopy ; Image (mathematics) ; Computer vision ; Chemistry ; Nanotechnology Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Siyong Gu | Mingli Qin | Houan Zhang | Jidong Ma
Source: Materials (article, 2018-11-27)
Field: Materials Science — Materials Chemistry

Fabrication of La2O3 Uniformly Doped Mo Nanopowders by Solution Combustion Synthesis Followed by Reduction under Hydrogen This work reports the preparation of La₂O₃ uniformly doped Mo nanopowders with the particle sizes of 40⁻70 nm by solution combustion synthesis and subsequent hydrogen reduction (SCSHR). To reach this aim, the foam-like MoO₂ precursors (20⁻40 nm in size) with different amounts of La₂O₃ were first synthesized by a solution combustion synthesis method. Next, these precursors were used to prepare La₂O₃ doped Mo nanopowders through hydrogen reduction. Thus, the content of La₂O₃ used for doping can be accurately controlled via the SCSHR route to obtain the desired loading degree. The successful doping of La₂O₃ into Mo nanopowders with uniform distribution were proved by X-ray photon spectroscopy and transmission electron microscopy. The preservation of the original morphology and size of the MoO₂ precursor by the La₂O₃ doped Mo nanopowders was attributed to the pseudomorphic transport mechanism occurring at 600 °C. As shown by X-ray diffraction, the formation of Mo₂C impurity, which usually occurs in the direct H₂ reduction process, can be avoided by using the Ar calcination-H₂ reduction process, when residual carbon is removed by the carbothermal reaction during Ar calcination at 500 °C. Fabrication ; Materials science ; Doping ; Combustion ; Hydrogen ; Reduction (mathematics) ; Chemical engineering ; Composite material ; Chemistry ; Optoelectronics ; Organic chemistry ; Mathematics ; Engineering Catalytic Processes in Materials Science ; Advanced materials and composites ; Electronic and Structural Properties of Oxides
Authors: Lucian Chan | Geoffrey Hutchison | Garrett M. Morris
Source: ChemRxiv (preprint, 2018-10-22)
Field: Materials Science — Materials Chemistry

Bayesian Optimization for Conformer Generation Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here, we present a new stochastic search method using Bayesian Optimization Algorithm (BOA) for finding the lowest energy conformation of a given molecule. We compare BOA with uniform random search, and systematic search as implemented in Confab, to determine which method finds the lowest energy. Energetic difference, root-mean-square deviation (RMSD), and torsion fingerprint deviation (TFD) are used to quantify differences between the conformer search algorithms. In general, we find BOA requires far fewer evaluations than systematic or uniform random search to find low-energy minima. For molecules with four or more rotatable bonds, Confab typically evaluates 104 (median) conformers in its search, while BOA only requires 102 energy evaluations to find top candidates. Despite evaluating fewer conformers, for many molecules, BOA finds lower-energy conformations than an exhaustive systematic Confab search. Conformational isomerism ; Maxima and minima ; Random search ; Cheminformatics ; Search algorithm ; Computer science ; Molecule ; Statistical physics ; Mathematics ; Algorithm ; Computational chemistry ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Zichuan Fan | Fanchen Kong | Yang Zhou | Yiqing Chen | Yalan Dai
Source: BioMed Research International (review, 2018-11-11)
Field: Chemistry — Spectroscopy

Intelligence Algorithms for Protein Classification by Mass Spectrometry Mass spectrometry (MS) is an important technique in protein research. Effective classification methods by MS data could contribute to early and less-invasive diagnosis and also facilitate developments in the bioinformatics field. As MS data is featured by high dimension, appropriate methods which can effectively deal with the large amount of MS data have been widely studied. In this paper, the applications of methods based on intelligence algorithms have been investigated. Firstly, classification and biomarker analysis methods using typical machine learning approaches have been discussed. Then those are followed by the Ensemble strategy algorithms. Clearly, simple and basic machine learning algorithms hardly addressed the various needs of protein MS classification. Preprocessing algorithms have been also studied, as these methods are useful for feature selection or feature extraction to improve classification performance. Protein MS data growing with data volume becomes complicated and large; improvements in classification methods in terms of classifier selection and combinations of different algorithms and preprocessing algorithms are more emphasized in further work. Computer science ; Preprocessor ; Feature selection ; Data pre-processing ; Algorithm ; Statistical classification ; Classifier (UML) ; Machine learning ; Artificial intelligence ; Data mining ; Data classification ; Field (mathematics) ; Mathematics Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Lívia Vieira Carlini Charamba | Rayany Magali da Rocha Santana | Graziele Elisandra do Nascimento | Bruno Vieira Carlini Charamba | Maiara Celine de Moura | Luana Cassandra Breitenbach Barroso Coelho | Julierme Gomes Correia de Oliveira | Marta Maria Menezes Bezerra Duarte | Daniella Carla Napoleão
Source: Water Science & Technology (article, 2018-08-29)
Field: Chemistry — Analytical Chemistry

Application of the advanced oxidative process on the degradation of the green leaf and purple açaí food dyes with kinetic monitoring and artificial neural network modelling The study evaluated the advanced oxidative processes concerning the degradation of green leaf and purple açaí dyes, as well as the prediction of data through artificial neural networks (ANNs). It was verified that percentage of degradation on the wavelengths (λ) of 215, 248, 523 and 627 nm was 5.95, 49.99, 98.17 and 95.99%, respectively, when UV/H<sub>2</sub>O<sub>2</sub> action and UV-C radiation was applied. A non-linear kinetic model proposed by Chan and Chu presented a good fit to the experimental data, reaching an R<sup>2</sup> value between 0.978 and 0.999, for the studied λ. Within the ANN simulations through Statistica 6.0, the multilayer perceptron (MLP) (3-9-4) presented a better fit to the experimental data. However, higher values of R² were obtained when utilizing the sklearn package with Python language and an MLP (4-5-4) model. Assays with Staphylococcus aureus and Staphylococcus pyogenes bacteria isolates were performed and it was verified that after employing the UV/H<sub>2</sub>O<sub>2</sub> process, there was a decrease in the toxicity of the solution of dyes. In evaluating S. aureus toxicity, normal growth was observed. However, for S. pyogenes bacteria, it was found that when using the UV/H<sub>2</sub>O<sub>2</sub> process, toxicity was evidenced at post-treatment solution concentrations of 100, 70 and 50%. Multilayer perceptron ; Artificial neural network ; Chemistry ; Degradation (telecommunications) ; Toxicity ; Staphylococcus aureus ; Biological system ; Nuclear chemistry ; Chromatography ; Bacteria ; Biology ; Computer science ; Organic chemistry ; Artificial intelligence Dye analysis and toxicity ; Water Quality Monitoring and Analysis ; Advanced oxidation water treatment
Authors: Peyman Babakhani | J. Bridge | Tanapon Phenrat | Ruey‐an Doong | Karl R. Whittle
Source: Environmental Science Nano (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Aggregation and sedimentation of shattered graphene oxide nanoparticles in dynamic environments: a solid-body rotational approach Nanoparticle (NP) aggregation is typically investigated in either quiescent or turbulent mixing conditions; neither is fully representative of dynamic natural environments. Graphene ; Sedimentation ; Nanoparticle ; Oxide ; Mixing (physics) ; Chemical engineering ; Materials science ; Turbulence ; Chemical physics ; Nanotechnology ; Mechanics ; Chemistry ; Physics ; Geology ; Sediment ; Geomorphology Pickering emulsions and particle stabilization ; Coagulation and Flocculation Studies ; Electrohydrodynamics and Fluid Dynamics
Authors: David Hicks | Zhengping Zhou | Guoliang Liu | Carolina Tallón
Source: International Journal of Applied Ceramic Technology (article, 2018-09-30)
Field: Materials Science — Biomaterials

Aligned continuous cylindrical pores derived from electrospun polymer fibers in titanium diboride Abstract The use of electrospun polystyrene ( PS ) fibers to create continuous long range ordered multi‐scale porous structures in titanium diboride (TiB 2 ) is investigated in this work. The introduction of electrospun PS fibers as a sacrificial filler into a colloidal suspension of TiB 2 allows for easy control over the pore size, porosity, and long range ordering of the porous structures of the sintered ceramics. Green bodies were formed by vacuum infiltrating an electrospun‐fiber‐filled mold with the colloidal TiB 2 suspension. The size, volume, distribution, and dispersion of the pores were optimized by carefully selecting the sacrificial polymer, the fiber diameter, the solvent, and the solid content of TiB 2 . The green bodies were partially sintered at 2000°C to form a multi‐scale porous structure via the removal of the PS fibers. Aligned continuous cylindrical pores homogeneously distributed across the samples were derived from the PS fibers in a range of ~5‐20 μm and partial sintering produced inter‐particle porosity with sizes of ~0.3‐1 μm. TiB 2 near‐net‐shaped parts with the multi‐scale porosities (~50‐70%) were successfully cast and sintered. This low‐cost processing technique can be used in the production of thermally and mechanically anisotropic structures into near‐net shape parts, for extreme environment applications, such as high temperature insulation. Materials science ; Composite material ; Porosity ; Polymer ; Suspension (topology) ; Fiber ; Polystyrene ; Agglomerate ; Titanium ; Titanium diboride ; Ceramic ; Metallurgy Electrospun Nanofibers in Biomedical Applications ; Advanced ceramic materials synthesis ; Metal and Thin Film Mechanics
Authors: Ricardo F. Caroço | Marta Bevilacqua | Ibrahim Armagan | Paloma A. Santacoloma | Jens Abildskov | Thomas Skov | Jakob Kjøbsted Huusom
Source: Biotechnology Progress (article, 2018-12-03)
Field: Chemistry — Analytical Chemistry

Raw material quality assessment approaches comparison in pectin production Different opportunities are explored to evaluate quality variation in raw materials from biological origin. Assessment of raw materials attributes is an important step in a bio-based production as fluctuations in quality are a major source of process disturbance. This can be due to a variety of biological, seasonal, and supply scarcity reasons. The final properties of a product are invariably linked with the initial properties of the raw material. Thus, the operational conditions of a process can be tuned to drive the product to the required specification based on the quality assessment of the raw material being processed. Process analytical technology tools which enable this assessment in a far more informative and rapid manner than current industrial practices that rely on rule-of-thumb decisions are assessed. An example with citrus peels is used to demonstrate the conceptual and performance differences of distinct quality assessment approaches. The analysis demonstrates the advantage of characterization through multivariate data analysis coupled with a complementary spectroscopic technique, near-infrared spectroscopy. The quantitative comparative analysis of three different approaches, discriminant classification based on expert-knowledge, unsupervised classification, and spectroscopic correlation with reference physicochemical variables, is performed in the same dataset context. © 2018 Her Majesty the Queen in Right of Canada © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2762, 2019. Raw material ; Production (economics) ; Pectin ; Quality (philosophy) ; Pulp and paper industry ; Environmental science ; Food science ; Chemistry ; Biotechnology ; Biology ; Engineering ; Organic chemistry ; Economics ; Physics Spectroscopy and Chemometric Analyses ; Polysaccharides Composition and Applications ; Leaf Properties and Growth Measurement
Authors: Xavier Rimpault | Marek Balazinski | Jean-François Châtelain
Source: Journal of Manufacturing and Materials Processing (article, 2018-09-10)
Field: Chemistry — Analytical Chemistry

Fractal Analysis Application Outlook for Improving Process Monitoring and Machine Maintenance in Manufacturing 4.0 Industry 4.0 has been advertised for a decade as the next disruptive evolution for production. It relies on automation growth and particularly on data exchange using numerous sensors in order to develop faster production with tight monitoring. The huge amount of data generated by clouds of sensors during production is often used to feed machine learning systems in order to detect faults, monitor and find possible ways for improvement. However, the artificial intelligence within machine learning requires finding and selecting key features, such as average and root mean square. While current machine learning has already proven its use in diverse applications, its efficiency could be further improved by generating better characteristics such as fractal parameters. In this paper, fractal analysis concept is presented and its current and future applications in machining are discussed. This sensitive and robust technique is already extracting high performance key features that could fill in monitoring and prediction systems. On top of improving features selection and, thus, improving the overall performance of monitoring and predictive systems in machining, this could lead to a more rapid artificial intelligence implementation into manufacturing. Machining ; Computer science ; Automation ; Process (computing) ; Artificial intelligence ; Key (lock) ; Machine learning ; Fractal ; Industrial engineering ; Engineering ; Mechanical engineering Spectroscopy and Chemometric Analyses ; Neural Networks and Applications ; Currency Recognition and Detection
Authors: Reza Farahmandfar | Mohammad Hossein Naeli | Mehdi Naderi | Maryam Asnaashari
Source: Journal of Food Science and Technology (article, 2018-12-08)
Field: Chemistry — Organic Chemistry

Stabilizing corn oil using the lemon balm (Melissa officinalis) antioxidants extracted by subcritical water Rosmarinic acid ; Chemistry ; Gallic acid ; Acid value ; Peroxide value ; Carnosic acid ; Antioxidant ; Conjugated diene ; Food science ; Extraction (chemistry) ; Chromatography ; Organic chemistry ; Biochemistry Free Radicals and Antioxidants ; Phytochemistry and Biological Activities ; Essential Oils and Antimicrobial Activity
Authors: Yaqi Wang | Yuanzhen Yang | Jiaojiao Jiao | Zhenfeng Wu | Yang Ming
Source: Molecules (article, 2018-09-20)
Field: Chemistry — Analytical Chemistry

Support Vector Regression Approach to Predict the Design Space for the Extraction Process of Pueraria lobata A support vector regression (SVR) method was introduced to improve the robustness and predictability of the design space in the implementation of quality by design (QbD), taking the extraction process of <i>Pueraria lobata</i> as a case study. In this paper, extraction time, number of extraction cycles, and liquid⁻solid ratio were identified as critical process parameters (CPPs), and the yield of puerarin, total isoflavonoids, and extracta sicca were the critical quality attributes (CQAs). Models between CQAs and CPPs were constructed using both a conventional quadratic polynomial model (QPM) and the SVR algorithm. The results of the two models indicated that the SVR model had better performance, with a higher <i>R</i>² and lower root-mean-square error (<i>RMSE</i>) and mean absolute deviation (<i>MA</i><i>D</i>) than those of the QPM. Furthermore, the design space was predicted using a grid search technique. The operational range was extraction time, 24⁻51 min; number of extraction cycles, 3; and liquid⁻solid ratio, 14⁻18 mL/g. This study is the first reported work optimizing the design space of the extraction process of <i>P. lobata</i> based on an SVR model. SVR modeling, with its better prediction accuracy and generalization ability, could be a reliable tool for predicting the design space and shows great potential for the quality control of QbD. Pueraria ; Quality by Design ; Mathematics ; Support vector machine ; Computer science ; Mean squared error ; Robustness (evolution) ; Statistics ; Artificial intelligence ; Chemistry ; Particle size Spectroscopy and Chemometric Analyses ; Food Quality and Safety Studies ; Optimal Experimental Design Methods