jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiaxiang Sun | Hongru Zhang | Qingquan Li | Hongshun Liu | Xinbo Lu | Kaining Hou
Source: High Voltage (article, 2020-11-03)
Field: Materials Science — Materials Chemistry

Contamination degree prediction of insulator surface based on exploratory factor analysis‐least square support vector machine combined model Abstract This study presents a combined model based on the exploratory factor analysis (EFA) and the least square support vector machine (LSSVM) to predict the contamination degree of insulator surface. Firstly, EFA method is utilised to reduce numerous influence factor variables of the insulator contamination into a few factor variables, which could decrease the complexity of the model. Then, regarding the above factor variables as new input variables, LSSVM model is established to predict the insulator contamination degree. In order to obtain the optimal predictive value, the non‐dominated sorting genetic algorithm II is applied on the optimization of LSSVM model parameters. The proposed EFA‐LSSVM combined model is compared with the models of LSSVM, back propagation neural network, and multiple linear regression on the model performance. Results indicate that the EFA‐LSSVM combined model in this study effectively overcomes the shortcomings of the other three models mentioned above in computational time, prediction accuracy and generalization ability. Finally, the feasibility of the proposed model in predicting contamination degree of insulator surface is verified by adopting the radar map of the evaluation indexes of model performance. Support vector machine ; Contamination ; Sorting ; Computer science ; Generalization ; Artificial neural network ; Least squares support vector machine ; Data mining ; Mathematics ; Artificial intelligence ; Algorithm High voltage insulation and dielectric phenomena ; Infrastructure Maintenance and Monitoring ; Non-Destructive Testing Techniques
Authors: Yuri Amorim Coutinho | Nico Vervliet | Lieven De Lathauwer | Nele Moelans
Source: npj Computational Materials (article, 2020-01-07)
Field: Materials Science — Materials Chemistry

Combining thermodynamics with tensor completion techniques to enable multicomponent microstructure prediction Abstract Multicomponent alloys show intricate microstructure evolution, providing materials engineers with a nearly inexhaustible variety of solutions to enhance material properties. Multicomponent microstructure evolution simulations are indispensable to exploit these opportunities. These simulations, however, require the handling of high-dimensional and prohibitively large data sets of thermodynamic quantities, of which the size grows exponentially with the number of elements in the alloy, making it virtually impossible to handle the effects of four or more elements. In this paper, we introduce the use of tensor completion for high-dimensional data sets in materials science as a general and elegant solution to this problem. We show that we can obtain an accurate representation of the composition dependence of high-dimensional thermodynamic quantities, and that the decomposed tensor representation can be evaluated very efficiently in microstructure simulations. This realization enables true multicomponent thermodynamic and microstructure modeling for alloy design. Microstructure ; Representation (politics) ; Tensor (intrinsic definition) ; Statistical physics ; Realization (probability) ; Computer science ; Material properties ; Alloy ; Materials science ; Thermodynamics ; Physics ; Mathematics ; Metallurgy ; Geometry Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Magnetic Properties and Applications
Authors: Dan Chicea
Source: Sensors (article, 2020-06-17)
Field: Chemistry — Analytical Chemistry

An Artificial Neural Network Assisted Dynamic Light Scattering Procedure for Assessing Living Cells Size in Suspension Dynamic light scattering (DLS) is an essential technique used for assessing the size of the particles in suspension, covering the range from nanometers to microns. Although it has been very well established for quite some time, improvement can still be brought in simplifying the experimental setup and in employing an easier to use data processing procedure for the acquired time-series. A DLS time series processing procedure based on an artificial neural network is presented with details regarding the design, training procedure and error analysis, working over an extended particle size range. The procedure proved to be much faster regarding time-series processing and easier to use than fitting a function to the experimental data using a minimization algorithm. Results of monitoring the long-time variation of the size of the <i>Saccharomyces cerevisiae</i> during fermentation are presented, including the 10 h between dissolving from the solid form and the start of multiplication, as an application of the proposed procedure. The results indicate that the procedure can be used to identify the presence of bigger particles and to assess their size, in aqueous suspensions used in the food industry. Suspension (topology) ; Dynamic light scattering ; Artificial neural network ; Computer science ; Range (aeronautics) ; Particle size ; Series (stratigraphy) ; Biological system ; Time series ; Algorithm ; Materials science ; Artificial intelligence ; Engineering ; Nanotechnology ; Machine learning ; Mathematics ; Chemical engineering Spectroscopy and Chemometric Analyses ; Polysaccharides Composition and Applications ; Proteins in Food Systems
Authors: Mircea Dragoman | Martino Aldrigo | Daniela Dragoman | Ian M. Povey | S. Iordănescu | Adrian Dinescu | Andrea Di Donato | M. Modreanu
Source: Physica E Low-dimensional Systems and Nanostructures (article, 2020-09-23)
Field: Materials Science — Materials Chemistry

Multifunctionalities of 2D MoS2 self-switching diode as memristor and photodetector Materials science ; Optoelectronics ; Wafer ; Photodetector ; Chemical vapor deposition ; Diode ; Memristor ; Layer (electronics) ; Silicon ; Piezoelectricity ; Nanotechnology ; Composite material ; Electronic engineering 2D Materials and Applications ; Perovskite Materials and Applications ; Advanced Memory and Neural Computing
Authors: Alberto García‐González | Antonio Huerta | Sergio Zlotnik | Pedro Dı́ez
Source: Research Square (preprint, 2020-12-15)
Field: Chemistry — Analytical Chemistry

A kernel Principal Component Analysis (kPCA) Digest&amp;nbsp;with a New Backward Mapping (pre-image reconstruction) Strategy Kernel principal component analysis ; Principal component analysis ; Kernel (algebra) ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Component (thermodynamics) ; Image (mathematics) ; Computer vision ; Kernel method ; Mathematics ; Physics ; Combinatorics ; Support vector machine Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Neural Networks and Applications
Authors: M. Leroux | Zahra Doumandji | Laëtitia Chézeau | Laurent Gaté | Sara Nahle | Romain Hocquel | Vadim Zhernovkov | Sylvie Migot | Jafar Ghanbaja | Céline Bonnet | Raphaël Schneider | Bertrand H. Rihn | Luc Ferrari | Olivier Joubert
Source: International Journal of Molecular Sciences (article, 2020-07-09)
Field: Materials Science — Materials Chemistry

Toxicity of TiO2 Nanoparticles: Validation of Alternative Models There are many studies concerning titanium dioxide (TiO<sub>2</sub>) nanoparticles (NP) toxicity. Nevertheless, there are few publications comparing <i>in vitro</i> and <i>in vivo</i> exposure, and even less comparing air-liquid interface exposure (ALI) with other <i>in vitro</i> and <i>in vivo</i> exposures. The identification and validation of common markers under different exposure conditions are relevant for the development of smart and quick nanotoxicity tests. In this work, cell viability was assessed <i>in vitro</i> by WST-1 and LDH assays after the exposure of NR8383 cells to TiO<sub>2</sub> NP sample. To evaluate <i>in vitro</i> gene expression profile, NR8383 cells were exposed to TiO<sub>2</sub> NP during 4 h at 3 cm<sup>2</sup> of TiO<sub>2</sub> NP/cm<sup>2</sup> of cells or 19 μg/mL, in two settings-submerged cultures and ALI. For the <i>in vivo</i> study, Fischer 344 rats were exposed by inhalation to a nanostructured aerosol at a concentration of 10 mg/m<sup>3</sup>, 6 h/day, 5 days/week for 4 weeks. This was followed immediately by gene expression analysis. The results showed a low cytotoxic potential of TiO<sub>2</sub> NP on NR8383 cells. Despite the absence of toxicity at the doses studied, the different exposures to TiO<sub>2</sub> NP induce 18 common differentially expressed genes (DEG) which are involved in mitosis regulation, cell proliferation and apoptosis and inflammation transport of membrane proteins. Among these genes, we noticed the upregulation of <i>Ccl4</i>, <i>Osm</i>, <i>Ccl7</i> and <i>Bcl3</i> genes which could be suggested as early response biomarkers after exposure to TiO<sub>2</sub> NP. On the other hand, the comparison of the three models helped us to validate the alternative ones, namely submerged and ALI approaches. Nanoparticle ; Toxicity ; Chemistry ; Nanotechnology ; Materials science ; Organic chemistry Nanoparticles: synthesis and applications ; Thermal and Kinetic Analysis ; TiO2 Photocatalysis and Solar Cells
Authors: Marie Sejkorová | Branislav Šarkan | Petr Veselík | Ivana Hurtová
Source: Energies (article, 2020-12-05)
Field: Chemistry — Analytical Chemistry

FTIR Spectrometry with PLS Regression for Rapid TBN Determination of Worn Mineral Engine Oils The TBN (Total Base Number) parameter is generally recognized by both engine oil processors and engine manufacturers as a key factor of oil quality. This is especially true for lubricating oils used in diesel and gas engines, which are exposed to relatively high temperatures and, therefore, require more effective protection against degradation. The FTIR spectrometry method together with a multivariate statistical software helped to create a model for the determination of TBN of worn motor oil SAE 15W-40 ACEA: E5/E7, API: CI-4. The best results were provided using a model FTIR with Partial Least Squares (PLS) regression in an overall range of 4000–650 cm−1 without the use of mathematical adjustments of the scanned spectra by derivation. Individual spectral information was condensed into nine principal components with linear combinations of the original absorbances at given wavenumbers that are mutually not correlated. A correlation coefficient (R) between values of TBN predicted by the FTIR-PLS model and values determined using a potentiometric titration in line with the ČSN ISO 3771 standard reached a value of 0.93. The Root Mean Square Error of Calibration (RMSEC) was determined to be 0.171 mg KOH.g−1, and the Root Mean Square Error of Prediction (RMSEP) was determined to be 0.140 mg KOH.g−1. The main advantage of the proposed FTIR-PLS model can be seen in a rapid determination and elimination of the necessity to work with dangerous chemicals. FTIR-PLS is used mainly in areas of oil analysis where the speed of analysis is often more important than high accuracy. Partial least squares regression ; Fourier transform infrared spectroscopy ; Coefficient of determination ; Linear regression ; Chemistry ; Correlation coefficient ; Analytical Chemistry (journal) ; Principal component regression ; Calibration ; Mathematics ; Statistics ; Chromatography ; Engineering Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Pesticide Residue Analysis and Safety
Authors: Viprav B. Raju | Edward Sazonov
Source: IEEE Access (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Detection of Oil-Containing Dressing on Salad Leaves Using Multispectral Imaging There is a need for non-invasive methods for nutritional analysis of food that can address the drawbacks of current methods such as color photography, which cannot distinguish between energy-dense and zero calorie foods. This paper discusses a novel, multispectral approach for the identification of oil, particularly in salads, and defines a pseudo-reflectance term. A custom-made multispectral camera was used to collect a novel, publicly shared dataset of images of untreated lettuce leaves or leaves treated with vinegar, oil, or a combination of these. The camera captured image data at 10 wavelengths ϵ [380nm,980nm] across the electromagnetic spectrum in the visible and NIR (near-infrared) regions. Imaging was done in a lab environment with the presence of ambient light. Mean spectra were extracted from the regions of interest in the multispectral cube and used to compute pseudo-reflectance. ANOVA (Analysis of Variance) was performed to look for variances in the pseudo-reflectance curves. ANOVA proved that the differences between group means of the four treatment groups (oil, vinegar, oil plus vinegar, control) were statistically significant. Pairs of groups showing the greatest significance were established using a Tukey post hoc test. Sequential Forward Selection (SFS) was used to determine 5 optimal feature wavelengths from the whole feature space (410 nm, 455 nm, 485 nm, 810 nm, and 850 nm). A combination of visible (VIS) and infrared (IR) wavelengths, selected using SFS, showed the greatest potential for discrimination between groups containing oil and groups that do not contain oil with a classification accuracy of 84.20%. The pseudo-reflectance values were statistically proven to be sensitive to the presence of oil as a dressing. This research has demonstrated the feasibility of implementing a multispectral imaging technique for identifying the presence of oil in salads and possibly an energy content detection system. Multispectral image ; Remote sensing ; Near-infrared spectroscopy ; Wavelength ; Reflectivity ; Artificial intelligence ; Computer science ; Optics ; Environmental science ; Mathematics ; Materials science ; Geology ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Kashif Irshad | Abdulmohsen Almalawi | Asif Irshad Khan | Md. Mottahir Alam | Md. Hasan Zahir | Amjad Ali
Source: Sustainability (article, 2020-02-19)
Field: Materials Science — Materials Chemistry

An IoT-Based Thermoelectric Air Management Framework for Smart Building Applications: A Case Study for Tropical Climate This study investigates the performance of the thermoelectric air conditioning (TE-AC) system smartly controlled by the Internet of Things (IoT)-based configuration for real tropical climatic application. Air cooling management was done through thermoelectric coolers, and an Arduino microcontroller with various sensors such as a temperature sensor, simple RF modules, and actuators was used to control the indoor climatic conditions based on outdoor conditions. The result shows that when the input power supply to the IoT-based TE-AC system is increased, the cooling capacity of the framework is also enhanced. Significant power and carbon emission reduction was observed for the IoT-based TE-AC system as compared to the TE-AC system without IoT. The IoT-incorporated system also ensures better microclimatic temperature control. Additionally, the system cooling capacity improves by 14.0%, and the coefficient of performance is increased by 46.3%. Thus, this study provides a smart solution to the two major energy harvesting issues of traditional air conditioners—an increase in energy efficiency by employing a TE-AC system and a further improvement in efficiency by using an IoT-based thermal management system. Air conditioning ; Internet of Things ; Automotive engineering ; Environmental science ; Thermoelectric effect ; Computer science ; Coefficient of performance ; Microcontroller ; Efficient energy use ; Embedded system ; Electrical engineering ; Heat pump ; Engineering ; Mechanical engineering Advanced Thermoelectric Materials and Devices ; Innovative Energy Harvesting Technologies ; Building Energy and Comfort Optimization
Authors: Rahel Jedamski | Jonas Heinzel | Maximilian Rößler | Jérémy Épp | J. Eckebrecht | Jens Gentzen | Matthias Pütz | Bernhard Karpuschewski
Source: tm - Technisches Messen (article, 2020-11-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Potential of magnetic Barkhausen noise analysis for in-process monitoring of surface layer properties of steel components in grinding Abstract Grinding processes are often the last step in the value-added chain of high-performance hardened steel components. However, thermo-mechanical loads which can take place during the process can have a detrimental effect on the surface integrity of ground parts, which are generally tested by post-process measurements. In the present study, two different approaches for an in-process inspection of the workpiece surface integrity were assessed using magnetic Barkhausen noise analysis during cylindrical grinding of hardened workpieces. The results showed that both measuring systems are able to detect changes in the surface state of workpieces in-process or directly after grinding in the grinding machine. After preparations to protect the sensors from influences during the grinding process, changes in the residual stress state and a decrease of hardness could be reliably detected. Due to constant contact conditions between sensor and workpiece a high reproducibility of the measurements was achieved. Barkhausen effect ; Grinding ; Surface integrity ; Materials science ; Residual stress ; Process (computing) ; Barkhausen stability criterion ; Surface layer ; Metallurgy ; Mechanical engineering ; Composite material ; Layer (electronics) ; Magnetic field ; Computer science ; Engineering Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Advanced machining processes and optimization
Authors: Lovrenc Novak | Brane Širok | Marko Hočevar | Pero Gatarić
Source: Drying Technology (article, 2020-03-04)
Field: Materials Science — Polymers and Plastics

Influence of load mass and drum speed on fabric motion and performance of a heat pump tumble dryer The present study investigates fabric motion in the drum and its effect on performance parameters of a domestic heat pump tumble dryer in several operating points, defined by different load masses and drum speeds. Fabric motion in the drum was recorded by using a high-speed camera and evaluated statistically in order to determine the relation between the fabric distribution and the dryer performance parameters. Multiple regression analysis was performed to develop models for condensate mass flow rate as a function of drum speed, load mass, and drying stage. The results enable optimization of dryer operating parameters with respect to energy efficiency, drying time per kilogram of fabric, and condensate mass flow rate. Drum ; Mass flow rate ; Volumetric flow rate ; Mechanics ; Materials science ; Simulation ; Mechanical engineering ; Environmental science ; Automotive engineering ; Engineering ; Physics Textile materials and evaluations ; Food Drying and Modeling ; Heat Transfer Mechanisms
Authors: Sneha N. Tambat | Dinesh J. Ahirrao | Aniruddha B. Pandit | Neetu Jha | Sharad M. Sontakke
Source: Environmental Technology & Innovation (article, 2020-06-30)
Field: Chemistry — Inorganic Chemistry

Hydrothermally synthesized N2-UiO-66 for enhanced and selective adsorption of cationic dyes Adsorption ; Cationic polymerization ; Freundlich equation ; Chemistry ; Methyl orange ; Langmuir ; Langmuir adsorption model ; Diffractometer ; Metal-organic framework ; Nuclear chemistry ; Scanning electron microscope ; BET theory ; Hydrothermal circulation ; Inorganic chemistry ; Chemical engineering ; Materials science ; Organic chemistry ; Catalysis ; Crystal structure Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Nanomaterials for catalytic reactions
Authors: Xihan Chen | Ryan T. Pekarek | Jing Gu | Andriy Zakutayev | Katherine E. Hurst | Nathan R. Neale | Ye Yang | Matthew C. Beard
Source: ACS Applied Materials & Interfaces (article, 2020-08-18)
Field: Materials Science — Materials Chemistry

Transient Evolution of the Built-in Field at Junctions of GaAs Built-in electric fields at semiconductor junctions are vital for optoelectronic and photocatalytic applications since they govern the movement of photogenerated charge carriers near critical surfaces and interfaces. Here, we exploit transient photoreflectance (TPR) spectroscopy to probe the dynamical evolution of the built-in field for n-GaAs photoelectrodes upon photoexcitation. The transient fields are modeled in order to quantitatively describe the surface carrier dynamics that influence those fields. The photoinduced surface field at different types of junctions between n-GaAs and n-TiO<sub>2</sub>, Pt, electrolyte and p-NiO are examined, and the results reveal that surface Fermi-level pinning, ubiquitous for many GaAs surfaces, can have beneficial consequences that impact photoelectrochemical applications. That is, Fermi-level pinning results in the primary surface carrier dynamics being invariant to the contacting layer and promotes beneficial carrier separation. For example, when p-NiO is deposited there is no Fermi-level equilibration that modifies the surface field, but photogenerated holes are promoted to the n-GaAs/p-NiO interface and can transfer into defect midgap states within the p-NiO resulting in an elongated charge separation time and those transferred holes can participate in chemical reactions. In contrast, when the Fermi-level is unpinned via molecular surface functionalization on p-GaAs, the carriers undergo surface recombination faster due to a smaller built-in field, thus potentially degrading their photochemical performance. Materials science ; Photoexcitation ; Fermi level ; Non-blocking I/O ; Chemical physics ; Semiconductor ; Electric field ; Surface states ; Charge carrier ; Optoelectronics ; Surface modification ; Condensed matter physics ; Atomic physics ; Surface (topology) ; Electron ; Chemistry ; Physics ; Excited state ; Physical chemistry Quantum Dots Synthesis And Properties ; Electronic and Structural Properties of Oxides ; Chalcogenide Semiconductor Thin Films