jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zahra Ayazi | Zahra Monsef Khoshhesab | Ali Reza Amani‐Ghadim
Source: SHILAP Revista de lepidopterología (article, 2018-01-01)
Field: Chemistry — Organic Chemistry

Synthesis of nickel ferrite nanoparticles as an efficient magnetic sorbent for removal of an azo-dye: Response surface methodology and neural network modeling In this research, nickel ferrite (NiFe2O4) nanoparticles (NFNs) are prepared through coprecipitation method, and applied for adsorption removal of a model organic pollutant, methyl orange (MO). The characterization of the prepared NFNs was performed using scanning electron microscopy (SEM), X-ray diffraction (XRD), vibrating sample magnetometer (VSM) and transmission electron microscopy (TEM). Optimization and modeling of the removal of MO applying NFNs were performed via central composite design (CCD) and the influential parameters including nano-sorbent amount, dye initial concentration, contact time and pH were considered as input variables for CCD. A dye removal percentage of 99 % was achieved under the optimum condition established for MO removal that was in agreeing with the predicted value. Additionally, multi-layer artificial neural network (ML-ANN) was applied to acquire a predictive model of MO removal. The isothermal investigation of MO adsorption was performed by developing Langmuir, Freundlich and Temkin models, and results showed that experimental data were best fit in Freundlich model. Based on the adsorption kinetics studies, the pseudo-second-order kinetic model was the best model to describe the adsorption mechanism of MO onto NFNs. Sorbent ; Response surface methodology ; Nickel ; Nanoparticle ; Ferrite (magnet) ; Materials science ; Artificial neural network ; Magnetic nanoparticles ; Chemical engineering ; Nanotechnology ; Chemistry ; Computer science ; Metallurgy ; Chromatography ; Composite material ; Adsorption ; Organic chemistry ; Artificial intelligence ; Engineering Nanomaterials for catalytic reactions
Authors: Sergey A. Sheptunov | Islam A. Alexandrov | Dmitry Golovatov | Rasul M. Glashev
Source:  (article, 2018-09-01)
Field: Materials Science — General Materials Science

Simulation of Thermoset Heat Conductivity by Means of Artificial Neural Networks The study is relevant due to necessity of improvement of thermal and physical features of composite materials for ensuring the size stability of composite structures. Therefore, this Article is aimed at developing an automatized system for determining the thermal conductivity of thermosetting polymers by modified nanosized carbon structures depending on the process parameters. Since this is a multiple-parameter task lacking formalized solution methods which could ensure a high performance capability and fair accuracy, the method of simulation by means of artificial neural networks was used. This Article describes the procedure of manufacture and research of thermal conductivity of specimens of nano-modified binder. Upon the experimental research results, databases of thermal and physical features and manufacture process parameters of thermosetting polymers were formed what enabled the formation of a neural network model of dependence of thermal and physical features of thermosetting polymers modified with nanosized carbon structures from the manufacture process parameters. The elaborated neural model of dependence of thermal and physical features of thermosetting polymers modified with nanosized carbon structures ensures the total convergence of the obtained values for samples of the tuition, validation and testing -98.77%. Thermosetting polymer ; Artificial neural network ; Conductivity ; Materials science ; Thermal conductivity ; Computer science ; Artificial intelligence ; Composite material ; Polymer science ; Physics Material Properties and Applications ; Ergonomics and Human Factors ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: Felix C. Mocanu | Konstantinos Konstantinou | Tae Hoon Lee | Noam Bernstein | Volker L. Deringer | Gábor Cśanyi | Stephen R. Elliott
Source: Cambridge University Engineering Department Publications Database (article, 2018-09-27)
Field: Materials Science — Materials Chemistry

Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential The phase-change material, Ge2Sb2Te5, is the canonical material ingredient for next-generation storage-class memory devices used in novel computing architectures, but fundamental questions remain regarding its atomic structure and physico-chemical properties. Here, we introduce a machine-learning (ML)-based interatomic potential that enables large-scale atomistic simulations of liquid, amorphous, and crystalline Ge2Sb2Te5 with an unprecedented combination of speed and density-functional theory (DFT) level of accuracy. Two applications exemplify the usefulness of such an ML-driven approach: we generate a 7,200-atom structural model, hitherto inaccessible with DFT simulations, that affords new insight into the medium-range structural order; and we create an ensemble of uncorrelated, smaller structures, for studies of their chemical bonding with statistical significance. Our work opens the way for new atomistic insights into the fascinating and chemically complex class of phase-change materials that are used in real non-volatile memory devices. Phase-change memory ; Amorphous solid ; Uncorrelated ; Interatomic potential ; Chemistry ; Atom (system on chip) ; Density functional theory ; Atomic units ; Phase (matter) ; Statistical physics ; Molecular dynamics ; Chemical physics ; Nanotechnology ; Computer science ; Computational chemistry ; Materials science ; Crystallography ; Physics ; Physical chemistry ; Quantum mechanics Phase-change materials and chalcogenides ; Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films
Authors: Baichuan Sun | Amanda S. Barnard
Source: Journal of Physics Materials (article, 2018-09-18)
Field: Materials Science — Materials Chemistry

Texture based image classification for nanoparticle surface characterisation and machine learning Restricting materials informatics to the numerical parameters output from conventional materials modelling software restricts us to a subset of machine learning methods capable of uncovering structure/property relationships and driving materials discovery and design. Presented here is a simple way of converting materials structures in to unique image-based fingerprints suitable for image processing methods, that does not require subjective pre-assessment of the data and selection of descriptors by the user. This combination of methods is shown to classify the morphologies in a set of 425 silver nanoparticles in a meaningful way, and predict the correlation with the energy of the Fermi level in agreement with other machine learning methods that required user intervention. Moving to an image-based, rather than feature list-based, description of nanoparticles and materials brings us one step closer to using experimental micrographs as inputs for machine learning. Artificial intelligence ; Computer science ; Machine learning ; Texture (cosmology) ; Image (mathematics) ; Feature (linguistics) ; Software ; Set (abstract data type) ; Property (philosophy) ; Image processing ; Feature selection ; Pattern recognition (psychology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Neha Chauhan | Nirmal Yadav | Nisha Arya
Source: International Journal of Current Microbiology and Applied Sciences (article, 2018-04-10)
Field: Materials Science — Polymers and Plastics

Applications of Artificial Neural Network in Textiles An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. The artificial neural network is increasingly used as a powerful tool for many real-world problems. In Textiles and Clothing industries, it involves the interaction of a large number of variables. Because of the high degree of variability in raw materials, multistage processing and a lack of precise control on process parameters, the relation between such variables and the product properties is relied on the human knowledge but it is not possible for human being to remember all the details of the process-related data over the years. ANN has proved its usefulness for resolving many problems in textiles such as prediction of yarn properties, analysis of fabric defects, process optimization etc. The power of neural networks lies in their ability to represent complex relationships and learn them directly from the data being modelled. The ability to predict these properties accurately has become a challenge due to highly non-linear and interactive behaviour of textile materials. The prediction of properties or performance of a process in advance is required to minimize the setup cost and time. The function of ANN is not constant but can be changed dynamically. Artificial neural network ; Process (computing) ; Computer science ; Unison ; Artificial intelligence ; Relation (database) ; Function (biology) ; Key (lock) ; Textile ; Information processing ; Machine learning ; Yarn ; Industrial engineering ; Data mining ; Engineering ; Mechanical engineering Textile materials and evaluations
Authors: Zhen Li | Donghui Wen | Yue Ma | Qing Wang | Guoqing Chen | Ruiqian Zhang | Rui Tang | Huan He
Source: Journal of Iron and Steel Research International (article, 2018-06-29)
Field: Materials Science — Metals and Alloys

Prediction of alloy composition and microhardness by random forest in maraging stainless steels based on a cluster formula Alloy ; Indentation hardness ; Materials science ; Metallurgy ; Valence electron ; Cluster (spacecraft) ; Microstructure ; Electron Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; High Entropy Alloys Studies
Authors: Yeseul Choi | Andrew J. Adamczyk
Source: The Journal of Physical Chemistry A (article, 2018-11-28)
Field: Materials Science — Materials Chemistry

Tuning Hydrogenated Silicon, Germanium, and SiGe Nanocluster Properties Using Theoretical Calculations and a Machine Learning Approach There are limited studies available that predict the properties of hydrogenated silicon-germanium (SiGe) clusters. For this purpose, we conducted a computational study of 46 hydrogenated SiGe clusters (Si <sub>x</sub>Ge <sub>y</sub>H <sub>z</sub>, 1 < X + Y ≤ 6) to predict the structural, thermochemical, and electronic properties. The optimized geometries of the Si <sub>x</sub>Ge <sub>y</sub>H <sub>z</sub> clusters were investigated using quantum chemical calculations and statistical thermodynamics. The clusters contained 6 to 9 fused Si-Si, Ge-Ge, or Si-Ge bonds, i.e., bonds participating in more than one 3- to 4-membered rings, and different degrees of hydrogenation, i.e., the ratio of hydrogen to Si/Ge atoms varied depending on cluster size and degree of multifunctionality. Our studies have established trends in standard enthalpy of formation, standard entropy, and constant pressure heat capacity as a function of cluster composition and structure. A novel bond additivity correction model for SiGe chemistry was regressed from experimental data on seven acyclic Si/Ge/SiGe species to improve the accuracy of the standard enthalpy of formation predictions. Electronic properties were investigated by analysis of the HOMO-LUMO energy gap to study the effect of elemental composition on the electronic stability of Si <sub>x</sub>Ge <sub>y</sub>H <sub>z</sub> clusters. These properties will be discussed in the context of tailored nanomaterials design and generalized using a machine learning approach. Germanium ; Enthalpy ; Materials science ; Silicon ; Cluster (spacecraft) ; Electronic structure ; Cluster expansion ; Nanomaterials ; Dangling bond ; Context (archaeology) ; Band gap ; Physical chemistry ; Thermodynamics ; Computational chemistry ; Nanotechnology ; Chemistry ; Physics ; Optoelectronics Silicon Nanostructures and Photoluminescence ; Semiconductor materials and interfaces ; Semiconductor materials and devices
Authors: Saja Abid | Ahmed Mutanabbi Abdula | Mohammed Al Marjani | Qais Abdulhameed
Source: Egyptian Journal of Chemistry (article, 2018-12-10)
Field: Chemistry — Organic Chemistry

Synthesis, Antimicrobial, Antioxidant and Docking Study of Some Novel 3,5-Disubstituted-4,5-dihydro-1H-pyrazoles Incorporating Imine Moiety A novel series of 3,5-Disubstituted-4,5-Dihydro-1H-pyrazoles (3-9) containing imine moiety were synthesized and characterized using spectral analysis. The synthesized derivatives were In vitro screened against several bacterial species, Staphylococcus aureus, Pseudomonas aeruginosa and Acinetobacter baumannii as well as Candida albicans and revealed moderate to potent activity. The antioxidant study was confirmed for the synthesized derivatives against 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical qualitatively using TLC technique, and quantitatively by spectrophotometric method. The half maximal inhibitory concentration (IC50) was calculated for the active compounds (1-5, 8). Docking study for the potent compounds (2 and 4) against glucosamine-6-phosphate synthase, the target enzyme for the antimicrobial agents, was explored to explain the interactions of the discovered hits within the amino acid residues of the enzyme active side. The docking parameters enhanced the activity of new compounds as promising antimicrobial agents. Moiety ; Docking (animal) ; Imine ; Antimicrobial ; Chemistry ; Combinatorial chemistry ; Antioxidant ; Stereochemistry ; Organic chemistry ; Medicine Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds ; Multicomponent Synthesis of Heterocycles
Authors: Roland Tolulope Loto
Source: PLoS ONE (article, 2018-04-19)
Field: Materials Science — Materials Chemistry

Electrochemical analysis of the corrosion inhibition effect of trypsin complex on the pitting corrosion of 420 martensitic stainless steel in 2M H2SO4 solution Inhibition effect of trypsin complex (TC) on the pitting corrosion of martensitic stainless steel (type 420) in 1M H2SO4 solution was studied with potentiodynamic polarization, open circuit potential measurement and optical microscopy. TC reduced the corrosion rate of the steel with maximum inhibition efficiency of 80.75%. Corrosion potential shifted anodically due to the electrochemical action of TC. The pitting potential increased from 1.088VAg/AgCl (3M) at 0% TC to 1.365VAg/AgCl(3M) at 4% TC. TC shifts the open circuit corrosion potential from -0.270s at 0% TC concentration to -0.255V at 5% TC. The compound completely adsorbed onto the steel according to Langmuir, Frumkin and Temkin isotherms. ATF-FTIR spectroscopy confirmed the inhibition mode to be through surface coverage. Thermodynamic calculations showed physisorption molecular interaction. Corrosion pits are present on the uninhibited 420 morphology in comparison to TC inhibited surface which slightly deteriorated. Corrosion ; Physisorption ; Pitting corrosion ; Materials science ; Electrochemistry ; Open-circuit voltage ; Metallurgy ; Polarization (electrochemistry) ; Martensitic stainless steel ; Dielectric spectroscopy ; Adsorption ; Langmuir ; Martensite ; Nuclear chemistry ; Analytical Chemistry (journal) ; Chemistry ; Microstructure ; Physical chemistry ; Electrode ; Chromatography ; Voltage Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Thermography and Photoacoustic Techniques
Authors: Jun Liu | Tiejun Pan | Zhengyong Zhang
Source: Journal of Spectroscopy (article, 2018-01-01)
Field: Chemistry — Analytical Chemistry

Incremental Support Vector Machine Combined with Ultraviolet-Visible Spectroscopy for Rapid Discriminant Analysis of Red Wine The aim of this work is to develop a new method to overcome the increased training time when a recognition model is updated based on the condition of new features extracted from new samples. As a common complex system, red wine has a rich chemical composition and is used as an object of this research. The novel method based on incremental learning support vector machine (I-SVM) combined with ultraviolet–visible (UV-Vis) spectroscopy was applied to discriminant analysis of the brands of red wine for the first time. In this method, new features included in the new training samples were introduced into the recognition model through iterative learning in each iteration, and the recognition model was rapidly updated without significantly increasing the training time. Experimental results show that the recognition model established by this method obtains a good balance between training efficiency and recognition accuracy. Wine ; Support vector machine ; Linear discriminant analysis ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Machine learning ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Hasan Mousavipazhouh | Mohammad Azadfallah | Iman Rashidi Jouybari
Source: Maderas Ciencia y tecnología (article, 2018-01-01)
Field: Materials Science — Biomaterials

Encapsulation of precipitated calcium carbonate fillers using carboxymethyl cellulose /polyaluminium chloride: preparation and its influence on mechanical and optical properties of paper Inorganic fillers are usually used in papermaking industry to reduce production costs and enhance paper optical properties. As an attempt to improve fillers application in papermaking, carboxymethyl cellulose (CMC) and poly aluminum chloride (PAC) were used to encapsulate precipitated calcium carbonate (PCC) filler and modify its surfaces. In the first step, the optimum conditions for filler treatment (i.e. pH, CMC and PAC contents) were obtained. Through the second step, different amounts of CMCencapsulated PCC were added to fiber suspension and some hand-sheets were made. Then, the effect of modified filler addition in different levels of 20, 25, and 35% on mechanical and optical properties of paper was investigated. The PAC dosage was found to be a critical factor for effective encapsulation of PCC fillers by carboxymethyl cellulose. When the dosages of CMC and PAC were 4% and 0.8% (based on the dry weight of PCC), respectively, filler modification caused to improve significantly the retention by more than twice. Strength properties of the hand-sheets fabricated with the CMC/ PAC-modified PCC were also found to be significantly better than those obtained with the unmodified PCC at similar levels of filler content. In addition, the brightness and opacity of the filled papers at 30% addition of CMC-modified fillers were strikingly enhanced to 88,45 and 77%, respectively. SEM observations of the filled papers also demonstrated that CMC-encapsulated filler particles were clustered and retained efficiently onto the fiber network. The present method for modification of PCC may provide an alternative approach to increase the filler amount in the paper structure by alleviating filler drawbacks. Carboxymethyl cellulose ; Papermaking ; Materials science ; Filler (materials) ; Calcium carbonate ; Hydroxypropyl cellulose ; Chemical engineering ; Cellulose fiber ; Composite material ; Fiber ; Polymer ; Sodium ; Metallurgy Advanced Cellulose Research Studies ; Material Properties and Processing ; Lignin and Wood Chemistry
Authors: Georgia Moschopoulou | Sophie Mavrikou | Diego Valdés | Spyridon Kintzios
Source: Beverages (article, 2018-12-25)
Field: Chemistry — Analytical Chemistry

Comparative Study of a Cell-Based and Electrochemical Biosensor for the Rapid Detection of 2,4,6-Trichloroanisole in Barrel Water Extracts (1) Background: Fungal metabolites such as haloanisoles (especially 2,4,6-tribromoanisole/ 2,4,6-TCA) are contaminants of cork and wood barrels, materials that are widely used in the wine industry. Thus, the accurate and timely detection of these substances is very important for this sector of beverage industry. (2) Methods: Potentiometry was used for the Bioelectric Recognition Assay (BERA)-based experimental approach, where changes in the electric properties of the Vero cells modified with anti-TCA antibodies were recorded in response to the presence of 2,4,6-TCA in different concentrations. Furthermore, a second electrochemical biosensor system based on the cyclic voltammetric (CV) measurement of a reaction taking place on a screen printed electrode was developed in parallel to the customized application and configuration of the cell-based system. (3) Results: The BERA cell-based biosensor was able to quantitatively differentiate among the lower 2,4,6-TCA concentrations (control, 0.25 and 1.25 ng/L) from spiked oak barrel water extracts in an entirely distinct and reproducible manner. In contrast, the CV method was not sensitive enough to differentiate between the samples. (4) Conclusions: The present study indicates that the BERA-based biosensor after further development and optimization could be used for the routine, high throughput detection of 2,4,6-TCA in oak barrel water extracts. Biosensor ; Barrel (horology) ; Wine ; Chromatography ; Electrode ; Chemistry ; Electrochemistry ; Contamination ; Materials science ; Biochemistry ; Biology ; Food science Analytical chemistry methods development ; Analytical Chemistry and Chromatography ; Electrochemical sensors and biosensors