jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jarmo Kouko | Marina Jajcinovic | Wolfgang Johann Fischer | Annika Ketola | Ulrich Hirn | Elias Retulainen
Source: Cellulose (article, 2018-12-04)
Field: Materials Science — Biomaterials

Effect of mechanically induced micro deformations on extensibility and strength of individual softwood pulp fibers and sheets Tensile tests of individual bleached kraft pulp fibers and paper sheets of industrial origin were conducted in order to investigate the effect of refining and small-scale fiber deformations on the extensibility of fibers and paper. The shape of the tensile curves of most of the fibers was concave upward (i.e., increasing slope) and consisted of two or three phases suggesting that the fibrillar structure and disordered regions in the fiber wall were straightened out during straining. Only a few of the individual BSKP fiber tensile curves were apparently linear. Elongation of the individual kraft fibers varied from 8 to 32% and the average elongation was not increased by high consistency refining. Tensile test results of laboratory sheets made of the same BSKP pulp suggested that the fiber bonding not only governs paper strength, but also is highly relevant for the elongation of fiber networks. The key conclusion related to this investigation and freely dried sheets was that the increased network elongation and strength after refining is mainly due to increased inter-fiber bonding and a higher shrinkage tendency of the fiber network and not due to the increased elongation or strength of individual fibers. Ultimate tensile strength ; Materials science ; Elongation ; Composite material ; Pulp (tooth) ; Fiber ; Softwood ; Extensibility ; Tensile testing ; Shrinkage Advanced Cellulose Research Studies ; Material Properties and Processing ; Natural Fiber Reinforced Composites
Authors: Nicolai Bache | Philipp E. Geyer | Dorte B. Bekker‐Jensen | Ole Hoerning | Lasse Gaarde Falkenby | Peter V. Treit | Sophia Doll | Igor Paron | Florian Meier | Jesper V. Olsen | Ole Vorm | Matthias Mann
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2018-05-15)
Field: Chemistry — Spectroscopy

A novel LC system embeds analytes in pre-formed gradients for rapid, ultra-robust proteomics ABSTRACT To further integrate mass spectrometry (MS)-based proteomics into biomedical research and especially into clinical settings, high throughput and robustness are essential requirements. They are largely met in high-flow rate chromatographic systems for small molecules but these are not sufficiently sensitive for proteomics applications. Here we describe a new concept that delivers on these requirements while maintaining the sensitivity of current nano-flow LC systems. Low-pressure pumps elute the sample from a disposable trap column, simultaneously forming a chromatographic gradient that is stored in a long storage loop. An auxiliary gradient creates an offset, ensuring the re-focusing of the peptides before the separation on the analytical column by a single high-pressure pump. This simplified design enables robust operation over thousands of sample injections. Furthermore, the steps between injections are performed in parallel, reducing overhead time to a few minutes and allowing analysis of more than 200 samples per day. From fractionated HeLa cell lysates, deep proteomes covering more than 130,000 sequence unique peptides and close to 10,000 proteins were rapidly acquired. Using this data as a library, we demonstrate quantitation of 5200 proteins in only 21 min. Thus, the new system-termed Evosep One-analyzes samples in an extremely robust and high throughput manner, without sacrificing in depth proteomics coverage. Proteomics ; Chromatography ; Analyte ; Elution ; Mass spectrometry ; Proteome ; Robustness (evolution) ; Chemistry ; Computer science Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Protein purification and stability
Authors: Mónica Echeverry‐Rendón | Valentina Duque | David Quintero | Sara M. Robledo | Martin C. Harmsen | Félix Echeverría
Source: Journal of Biomaterials Applications (article, 2018-11-01)
Field: Materials Science — Biomaterials

Improved corrosion resistance of commercially pure magnesium after its modification by plasma electrolytic oxidation with organic additives The optimal mechanical properties render magnesium widely used in industrial and biomedical applications. However, magnesium is highly reactive and unstable in aqueous solutions, which can be modulated to increase stability of reactive metals that include the use of alloys or by altering the surface with coatings. Plasma electrolytic oxidation is an efficient and tuneable method to apply a surface coating. By varying the plasma electrolytic oxidation parameters voltage, current density, time and (additives in the) electrolytic solution, the morphology, composition and surface energy of surface coatings are set. In the present study, we evaluated the influence on surface coatings of two solute additives, i.e. hexamethylenetetramine and mannitol, to base solutes silicate and potassium hydroxide. Results from in vitro studies in NaCl demonstrated an improvement in the corrosion resistance. In addition, coatings were obtained by a two-step anodization procedure, firstly anodizing in an electrolyte solution containing sodium fluoride and secondly in an electrolyte solution with hexamethylenetetramine and mannitol, respectively. Results showed that the first layer acts as a protective layer which improves the corrosion resistance in comparison with the samples with a single anodizing step. In conclusion, these coatings are promising candidates to be used in biomedical applications in particular because the components are non-toxic for the body and the rate of degradation of the surface coating is lower than that of pure magnesium. Plasma electrolytic oxidation ; Materials science ; Anodizing ; Corrosion ; Electrolyte ; Hexamethylenetetramine ; Magnesium ; Coating ; Chemical engineering ; Surface modification ; Chromate conversion coating ; Sodium silicate ; Inorganic chemistry ; Metallurgy ; Aluminium ; Composite material ; Electrode ; Chemistry Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; Hydrogen Storage and Materials
Authors: Xiaolin Li | Yichi Zhang | He Zhao | Craig Burkhart | L. Catherine Brinson | Wei Chen
Source: arXiv (Cornell University) (preprint, 2018-05-08)
Field: Materials Science — Materials Chemistry

A Transfer Learning Approach for Microstructure Reconstruction and Structure-property Predictions Stochastic microstructure reconstruction has become an indispensable part of computational materials science, but ongoing developments are specific to particular material systems. In this paper, we address this generality problem by presenting a transfer learning-based approach for microstructure reconstruction and structure-property predictions that is applicable to a wide range of material systems. The proposed approach incorporates an encoder-decoder process and feature-matching optimization using a deep convolutional network. For microstructure reconstruction, model pruning is implemented in order to study the correlation between the microstructural features and hierarchical layers within the deep convolutional network. Knowledge obtained in model pruning is then leveraged in the development of a structure-property predictive model to determine the network architecture and initialization conditions. The generality of the approach is demonstrated numerically for a wide range of material microstructures with geometrical characteristics of varying complexity. Unlike previous approaches that only apply to specific material systems or require a significant amount of prior knowledge in model selection and hyper-parameter tuning, the present approach provides an off-the-shelf solution to handle complex microstructures, and has the potential of expediting the discovery of new materials. Computer science ; Initialization ; Artificial intelligence ; Convolutional neural network ; Property (philosophy) ; Transfer of learning ; Deep learning ; Range (aeronautics) ; Pruning ; Algorithm ; Materials science Machine Learning in Materials Science ; Machine Learning and Algorithms ; Enhanced Oil Recovery Techniques
Authors: Masashi Tsubaki | Teruyasu Mizoguchi
Source: The Journal of Physical Chemistry Letters (article, 2018-08-06)
Field: Materials Science — Materials Chemistry

Fast and Accurate Molecular Property Prediction: Learning Atomic Interactions and Potentials with Neural Networks The discovery of molecules with specific properties is crucial to developing effective materials and useful drugs. Recently, to accelerate such discoveries with machine learning, deep neural networks (DNNs) have been applied to quantum chemistry calculations based on the density functional theory (DFT). While various DNNs for quantum chemistry have been proposed, these networks require various chemical descriptors as inputs and a large number of learning parameters to model atomic interactions. In this paper, we propose a new DNN-based molecular property prediction that (i) does not depend on descriptors, (ii) is more compact, and (iii) involves additional neural networks to model the interactions between all the atoms in a molecular structure. In the consideration of the molecular structure, we also model the potentials between all the atoms; this allows the neural networks to simultaneously learn the atomic interactions and potentials. We emphasize that these atomic "pair" interactions and potentials are characterized using the global molecular structure, a function of the depth of the neural networks; this leads to the implicit or indirect consideration of atomic "many-body" interactions and potentials within the DNNs. In the evaluation of our model with the benchmark QM9 data set, we achieved fast and accurate prediction performances for various quantum chemical properties. In addition, we analyzed the effects of learning the interactions and potentials on each property. Furthermore, we demonstrated an extrapolation evaluation, i.e., we trained a model with small molecules and tested it with large molecules. We believe that insights into the extrapolation evaluation will be useful for developing more practical applications in DNN-based molecular property predictions. Extrapolation ; Artificial neural network ; Computer science ; Property (philosophy) ; Quantum ; Set (abstract data type) ; Benchmark (surveying) ; Artificial intelligence ; Density functional theory ; Function (biology) ; Statistical physics ; Machine learning ; Biological system ; Physics ; Chemistry ; Computational chemistry ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Hieu‐Chi Dam | Viet-Cuong Nguyen | Tien Lam Pham | Anh Tuan Nguyen | Kiyoyuki Terakura | Takashi Miyake | Hiori Kino
Source: Journal of the Physical Society of Japan (article, 2018-10-29)
Field: Materials Science — Materials Chemistry

Important Descriptors and Descriptor Groups of Curie Temperatures of Rare-earth Transition-metal Binary Alloys We analyze the Curie temperatures of rare-earth transition metal binary alloys using machine learning. In order to select important descriptors and descriptor groups, we introduce a newly developed subgroup relevance analysis and adopt hierarchical clustering in the representation. We execute exhaustive search and demonstrate that our approach results in the successful selection of important descriptors and descriptor groups. It helps us to choose the combination of descriptors and to understand the meaning of the selected combination of descriptors. Representation (politics) ; Rare earth ; Binary number ; Curie temperature ; Curie ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Cluster analysis ; Relevance (law) ; Materials science ; Data mining ; Statistical physics ; Mathematics ; Condensed matter physics ; Physics ; Ferromagnetism Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Metallurgical and Alloy Processes
Authors: Stefan Heisel | Mareike Rolfes | Kerstin Wohlgemuth
Source: Chemical Engineering & Technology (article, 2018-04-12)
Field: Materials Science — Materials Chemistry

Discrimination between Single Crystals and Agglomerates during the Crystallization Process Abstract A setup for online measurement of crystallization processes was developed, where particle images were taken and in combination with artificial neural networks (ANNs) were used to convert the particle size distribution into populations of single crystals and agglomerates. For generation of an ANN that discriminates all particle sizes equally well, the training set composition was investigated while proportional similarity was applied for image descriptor selection. Adipic acid/water served as a model system. The effect of the measurement setup on experiments performed for normal cooling crystallization was evaluated. It was found that the major challenge of measuring crystal agglomerates lies in superimposing aggregates that falsify the online measurement results, as offline measurements demonstrated. Agglomerate ; Crystallization ; Materials science ; Adipic acid ; Particle (ecology) ; Crystal (programming language) ; Biological system ; Particle size ; Artificial neural network ; Process (computing) ; Particle-size distribution ; Process engineering ; Chemical engineering ; Computer science ; Artificial intelligence ; Composite material ; Engineering Crystallization and Solubility Studies ; Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography
Authors: Makarand S. Ballal | Manish Ganesh Wath | H. M. Suryawanshi
Source: IEEE Transactions on Instrumentation and Measurement (article, 2018-12-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Novel Approach for the Error Correction of CT in the Presence of Harmonic Distortion Current transformers (CTs) are extensively used for the measurement of electrical energy, and hence they play a crucial role in the revenue generation for power distribution utilities. The novelty of this paper is that it presents artificial neural network (ANN) application for the ratio and phase angle error corrections of CTs in the presence of harmonic distortions. In this paper, the behaviors of silicon iron alloy and nanocrystalline alloy CTs are examined by the means of a number of experimental tests under the sinusoidal and distorted waveform conditions. The relation between the nature of CTs secondary current waveforms (CTSwf) with their metrological properties, connected burdens, and nature of load currents (linear or nonlinear) is established. The training data are obtained by the nature of CTSwf in terms of amplitude errors, time shifts, and deviations caused by different harmonic contents between input and output quantities. The ANN is trained for various operating conditions. The implementation of the error correction system and data acquisition is done using dSPACE 1104 software. The experimental results illustrate that the proposed technique can be used for error compensation in case of sinusoidal and the distorted waveform composed of fundamental and many harmonics. Total harmonic distortion ; Waveform ; Harmonics ; Electronic engineering ; Metrology ; Artificial neural network ; Harmonic analysis ; Harmonic ; Amplitude ; Computer science ; Control theory (sociology) ; Engineering ; Acoustics ; Mathematics ; Electrical engineering ; Physics ; Voltage ; Artificial intelligence ; Optics ; Statistics Magnetic Properties and Applications ; Advanced Electrical Measurement Techniques ; Power Quality and Harmonics
Authors: Brett Brownfield | Tony Lemos | John H. Kalivas
Source: Analytical Chemistry (article, 2018-03-05)
Field: Chemistry — Analytical Chemistry

Consensus Classification Using Non-Optimized Classifiers Classifying samples into categories is a common problem in analytical chemistry and other fields. Classification is usually based on only one method, but numerous classifiers are available with some being complex, such as neural networks, and others are simple, such as k nearest neighbors. Regardless, most classification schemes require optimization of one or more tuning parameters for best classification accuracy, sensitivity, and specificity. A process not requiring exact selection of tuning parameter values would be useful. To improve classification, several ensemble approaches have been used in past work to combine classification results from multiple optimized single classifiers. The collection of classifications for a particular sample are then combined by a fusion process such as majority vote to form the final classification. Presented in this Article is a method to classify a sample by combining multiple classification methods without specifically classifying the sample by each method, that is, the classification methods are not optimized. The approach is demonstrated on three analytical data sets. The first is a beer authentication set with samples measured on five instruments, allowing fusion of multiple instruments by three ways. The second data set is composed of textile samples from three classes based on Raman spectra. This data set is used to demonstrate the ability to classify simultaneously with different data preprocessing strategies, thereby reducing the need to determine the ideal preprocessing method, a common prerequisite for accurate classification. The third data set contains three wine cultivars for three classes measured at 13 unique chemical and physical variables. In all cases, fusion of nonoptimized classifiers improves classification. Also presented are atypical uses of Procrustes analysis and extended inverted signal correction (EISC) for distinguishing sample similarities to respective classes. Artificial intelligence ; Preprocessor ; Pattern recognition (psychology) ; Set (abstract data type) ; Sample (material) ; Data mining ; Data pre-processing ; Data set ; Data classification ; Computer science ; Machine learning ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Analytical Chemistry and Chromatography
Authors: Ahmed I. Osman | Jehad K. Abu‐Dahrieh
Source: Catalysis Letters (article, 2018-02-15)
Field: Materials Science — Materials Chemistry

Kinetic Investigation of η-Al2O3 Catalyst for Dimethyl Ether Production Herein, kinetic modelling and kinetic parameters were used to study methanol dehydration to dimethyl ether reaction and revealed that the best model to fit the experimental data was the Bercic model. The dehydration reaction undergoes dissociative adsorption Langmuir–Hinshelwood mechanism of methanol on the alumina catalyst surface, with the calculated value of the activation energy was 136.7 kJ mol−1. Moreover, the effect of different kinetic parameters such as the catalyst weight and methanol concentration or water in the feed on the catalytic performance of η-Al2O3 was examined in a fixed bed reactor under the reaction conditions where the temperature ranged from 180 to 350 °C with a WHSV = 12.1 h−1. Dimethyl ether ; Chemistry ; Catalysis ; Methanol ; Dehydration ; Activation energy ; Kinetic energy ; Adsorption ; Ether ; Dehydration reaction ; Inorganic chemistry ; Physical chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Catalysts for Methane Reforming
Authors: Dorota Czarnecka‐Komorowska | Karolina Wiszumirska | Tomasz Garbacz
Source: Advances in Science and Technology – Research Journal (article, 2018-09-01)
Field: Chemistry — Organic Chemistry

FILMS LDPE/LLDPE MADE FROM POST - CONSUMER PLASTICS: PROCESSING, STRUCTURE, MECHANICAL PROPERTIES The paper presents the results of the investigation into the themo-mechanical, rheological and structural properties of films made from recycled polyethylenes in the "foil-to-foil" system, the comparative material were films made from primary raw materials. The films were prepared by melt mixing technique in a single screw extruder. Primary polyethylenes, recyclates and their blends were used. The aim of the research was to determine the quality of the materials derived from the closed process of the recycling of polyethylene film in comparison to virgin plastics. The mechanical properties of the films were measured by tensile test and puncture test. The morphology was studied using polarized light microscopy (POM) and FT-IR measurements. DSC and shrinkage were used to determine thermal behavior and the degree of crystallinity. The results showed that the obtained recycled blends rLDPE/rLLDPE are high-quality material with good processing properties susceptible to the production of thin films. Studies on the mechanical properties of the recycled blends showed an increase in tensile strength and elongation. An increase in the melting temperature and heat of fusion, as determined from DSC, was observed with an increase in the recycled blends. Puncture results demonstrated that the post-consumer recycled film was more strength than the film made from virgin plastics. Thus, these materials may serve as valuable recycled plastics useful for packaging applications. Linear low-density polyethylene ; Low-density polyethylene ; Materials science ; Composite material ; Polyethylene Photopolymerization techniques and applications
Authors: Shao‐Ding Liu | Junyan Liu | Zhaolong Cao | Jin-Li Fan | Dangyuan Lei
Source: Nanophotonics (article, 2018-06-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dynamic tuning of enhanced intrinsic circular dichroism in plasmonic stereo‐metamolecule array with surface lattice resonance Abstract Enhancing the circular dichroism signals of chiral plasmonic nanostructures is vital for realizing miniaturized functional chiroptical devices, such as ultrathin wave plates and high‐performance chiral biosensors. Rationally assembling individual plasmonic metamolecules into coupled nanoclusters or periodic arrays provides an extra degree of freedom to effectively manipulate and leverage the intrinsic circular dichroism of the constituent structures. Here, we show that sophisticated manipulation over the geometric parameters of a plasmonic stereo‐metamolecule array enables selective excitation of its surface lattice resonance mode either by left‐ or right‐handed circularly polarized incidence through diffraction coupling, which can significantly amplify the differential absorption and hence the intrinsic circular dichroism. In particular, since the diffraction coupling requires no index‐matching condition and its handedness can be switched by manipulating the refractive index of either the superstrate or the substrate, it is therefore possible to achieve dynamic tuning and active control of the intrinsic circular dichroism response without the need of modifying structure parameters. Our proposed system provides a versatile platform for ultrasensitive chiral plasmonics biosensing and light field manipulation. Circular dichroism ; Plasmon ; Materials science ; Circular polarization ; Dichroism ; Surface plasmon resonance ; Optics ; Excitation ; Optoelectronics ; Nanotechnology ; Physics ; Chemistry ; Nanoparticle ; Crystallography Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Orbital Angular Momentum in Optics