jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Janine Colling | Magdalena Müller | Elizabeth Joubert | Federico Marini
Source: Journal of Near Infrared Spectroscopy (article, 2023-06-01)
Field: Chemistry — Analytical Chemistry

Investigating partial least squares discriminant analysis and hierarchical modelling of short wave infrared hyperspectral imaging data to distinguish production area and quality of rooibos (<i>Aspalathus linearis</i>) Short wave infrared hyperspectral imaging was tested for its ability to distinguish rooibos tea ( Aspalathus linearis) based on production area and quality grade, with the aim to replace time-consuming sensory analysis in the industry. The number of latent variables and model parameters of the calibration model were optimised by cross-validation. Classification error rates were used to evaluate the performance of the models in classifying rooibos based on production area and quality grade. The production area of rooibos was distinguished by applying a partial least square-discriminant analysis model with second derivative pre-processing, followed by mean centering and inclusion of nine LVs. The model could successfully distinguish between the two production areas and had a classification accuracy of 100% for the prediction set. To distinguish between different quality grades, a hierarchical model with second derivative pre-processing was developed. Grade A could be distinguished successfully from grades B, C and D (class BCD) with 100% accuracy and grade D could be distinguished from grades B and C (class BC) with 96% accuracy. However, the model was less accurate to distinguish between grade B and C samples, with prediction accuracies of 82 and 66% for B and C, respectively. Application of near infrared hyperspectral imaging therefore offers the potential to replace the use of sensory analysis in the rooibos tea industry to predict production area and quality grade of this herbal tea. Hyperspectral imaging ; Partial least squares regression ; Linear discriminant analysis ; Pattern recognition (psychology) ; Mathematics ; Calibration ; Artificial intelligence ; Computer science ; Statistics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Food Quality and Safety Studies
Authors: Ebiere Dode | Samuel J. Bunu | Onyinpere Rita Garando
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-05-30)
Field: Chemistry — Analytical Chemistry

Assessment of different brands of diclofenac tablets: An evaluation utilizing uv spectroscopy and disintegration test methods Diclofenac is a non-steroidal anti-inflammatory drug (NSAID), that blocks the cyclooxygenase-1 (COX-1) and COX-2 enzymes to inhibit the prostaglandins production. It is available as a salt in the form of potassium, sodium, or epolamine. They are typically administered orally, intravenously, intramuscularly, transdermally, or rectally. Diclofenac is one of the most widely used NSAIDs in Nigeria, and due to the increase in adulterated and subpar medications in the market, it needs to be properly quantified. In this study, the weight uniformity test, disintegration, and ultraviolet (UV) spectroscopy were used to assess five different brands of diclofenac potassium labeled DIC 1 – DIC 5. Analytical balance was used to measure each weight and conduct the weight uniformity test. Using a disintegration apparatus and 0.1 N hydrochloric acid as the medium, a disintegration test was conducted. The UV spectroscopy includes the use of a UV spectrophotometer and the concentration range of 10 – 50 µg/ml was prepared using pure sample and methanol as solvents and scanned between wavelength 220 – 400 nm. The various brands with concentrations of 20 µg/ml were prepared and scanned. The weight uniformity test was passed by all the brands. One brand failed the disintegration test, but one brand did not disintegrate because it was enteric coated. Three brands passed the test. All brands, except for DIC 2, had percentage purity within the stipulated range for chemical analysis. Diclofenac ; Spectroscopy ; Test (biology) ; Materials science ; Ultraviolet visible spectroscopy ; Chromatography ; Traditional medicine ; Chemistry ; Medicine ; Pharmacology ; Physics ; Organic chemistry ; Biology Analytical Methods in Pharmaceuticals ; Crystallization and Solubility Studies ; Chemical and Physical Properties in Aqueous Solutions
Authors: C. Bottinger | Stefanie Chmielewski | Robin Roj | Hans‐Jürgen Gittel | Christian Pelshenke | Ralf Theiß | Peter Dültgen
Source: Civil-comp conferences (article, 2023-08-18)
Field: Materials Science — Materials Chemistry

Production of Ultra-Fine-Grain Steel in Saw Blade Bodies aided by Artificial Intelligence In the production of cold work tool steels, components are first subjected to heat treatment to set the desired parameters such as hardness and strength, and then ground to obtain the final geometric shape.Both processes are resource and energy intensive and highly complex due to many adjustable system and process parameters.Therefore, it is difficult to reliably control parameters such as stresses, microstructure, and dimensions due to the interaction between the individual processing steps.In industrial applications, this leads to a significant amount of rejected material that can only be detected late, which has an overall negative impact on overall efficiency.This work focuses on saw blades bodies that are made of 75Cr1 with microstructural grain sizes of approximately 20 µm.Aided by artificial intelligence, the creation of a digital twin is presented to improve the production processes.It is intended to reduce the grain sizes to approximately 5 µm in order to improve the quality and the performance of the final products.The data is fed to the digital twin by data collection from the manufacturing machines, simulations, laboratory investigations, and metallographic results.In addition, a basis is created to be able to map other processes and industrial sectors.By creating multi-scale and cross-process simulations with industrial machining data using artificial intelligence, it is possible to increase the understanding of the entire process chain.All process data is to be recorded and simulations are carried out to improve the process parameters so that defective parts can be reduced and detected earlier.Furthermore, the component quality and the energy efficiency of the process shall be Production of Ultra-Fine-Grain Steel in Saw BladeBodies aided by Artificial Intelligence Machining ; Process (computing) ; Mechanical engineering ; Work (physics) ; Computer science ; Materials science ; Production (economics) ; Quality (philosophy) ; Process engineering ; Engineering Metal Alloys Wear and Properties ; Advanced machining processes and optimization ; Tunneling and Rock Mechanics
Authors: Juan Carlos Marín-Ortiz | Lilliana Hoyos-Carvajal | Verónica Botero | Oscar de Jesús Córdoba‐Gaona | Carlos Felipe Barrera Sánchez
Source: Research Square (preprint, 2023-07-11)
Field: Chemistry — Analytical Chemistry

Characterizing diploid and tetraploid potato cultivars with reflectance spectroscopy Abstract Characterizing potato cultivars provides information for diagnosing the needs of specific production and management. Techniques that enable rapid, objective, and low-cost identification of various aspects of plant physiology are important to producing this information. This study aimed to identify potato cultivars during three phenological phases using reflectance spectroscopy in the visible and near-infrared ranges. Reflectance spectra were measured using a portable spectrometer from leaves of five potato cultivars. Classification models were made to obtain the percentages of correct classification for each cultivar and phenological stage. General patterns in the reflectance spectra were obtained that were similar for all five cultivars in the measured range, with reflectance around 5% in the green region during the vegetative phase, decreasing slightly during the flowering phase and increasing up to 12% during the senescence phase. A decrease from 50–30% in reflectance was recorded in the measured NIR range during flowering with a variable increase that depended on the cultivar towards senescence. The identified wavelengths allowed the plants of each cultivar to be classified during the vegetative, flowering, and senescence phases, with correct classification rates of 87% for Var Paola, 96% for Var Paysandú, 93% for Var Violeta, and 100% for varieties 448 and 440. Var Paola obtained higher percentages of correct classification with values between 85% for Paysandú and Violeta, 90% for cv 440, and 100% for cv 448. The characterization of potato cultivars with spectroscopy techniques provides information that can be used as a basic input in precision agriculture systems. Cultivar ; Phenology ; Reflectivity ; Horticulture ; Biology ; Botany ; Agronomy ; Optics Spectroscopy and Chemometric Analyses ; Potato Plant Research ; Water Quality Monitoring and Analysis
Authors: Tamara Mendes Leite Silva Trindade | Carla Jovânia Gomes Colares | Nelson Luís Gonçalves Dias de Souza
Source: Food Science and Technology (article, 2023-04-28)
Field: Chemistry — Organic Chemistry

Detection and characterization of adulterants in baru and soursop oils The presence of vegetable oils in the pharmaceutical and cosmetic industries boosts their production and commercialization, maximizing the economic potential, however to increase profits, vegetable oils with high added value are the target of adulteration practices. This negatively affects the quality of the product and compromises the bioactive properties of these oils. The objective of this study was to build partial least squares regression models to identify baru oil and soursop oil samples adulterated with soybean oil. For data collection the oils samples were characterized using portable near-infrared spectrometer and Fourier-transform infrared spectrometer. These techniques were chosen because both provide information about the functional groups of chemical compounds, however portable equipment, being cheaper, presents signals with lower spectral resolution. Thus, the objective of the work is also to verify if the results using both equipment are similar. The developed regression models were effective; however, the near-infrared technique presented some limitations in the identification of soursop oil. This is because for the construction of his model it was necessary to use a smaller number of variables and levels of adulteration. Nevertheless, high R² values and relatively low errors were obtained for all models, making it possible to identify the adulteration. Pratica Application: Adulteration analysis of baru and soursop oils by infrared spectroscopy and portable equipment Partial least squares regression ; Commercialization ; Vegetable oil ; Adulterant ; Environmental science ; Process engineering ; Mathematics ; Food science ; Engineering ; Chemistry ; Statistics ; Chromatography ; Business ; Marketing Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Budiman Nasution | Winsyahputra Ritonga | Ruben Cornelius Siagian | Dolfie Paulus Pandara | Lulut Alfaris | Aldi Cahya Muhammad | Arip Nurahman
Source: Jurnal Penelitian Pendidikan IPA (article, 2023-11-25)
Field: Materials Science — Materials Chemistry

Relationship Between BE4DBE2 and Variables n and z: A Comprehensive Analysis Using Linear Regression, Nonparametric Regression, Naive Bayes Classification, Decision Tree Analysis, SVM Analysis, K-Means Clustering, and Bayesian Regression This research employed various statistical techniques, including linear regression, nonparametric regression, Naive Bayes classification, decision tree analysis, Support Vector Machine (SVM) analysis, k-means clustering, and Bayesian regression, to analyze nuclear data. The research aims to explore the relationships between variables, predict binding energy, classify nuclear data, and identify similar groups. The research results revealed that linear regression indicated a significant influence of the intercept and predictor variable 'n' on the variable 'BE4DBE2,' while the variable 'z' was not significant. However, the overall model had limited explanatory power. Nonparametric regression with smoothing functions effectively modeled the relationship between 'BE4DBE2' and variables 'n' and 'z,' explaining approximately 11% of the variability in the response variable. Classification using Naive Bayes successfully categorized nuclear data based on 'n' and 'z,' revealing their relationship. Decision tree analysis evaluated the performance of this classification model and provided insights into accuracy, agreement, sensitivity, specificity, precision, and negative predictive value. SVM analysis successfully built an accurate SVM model with a linear kernel, classifying nuclear data while depicting decision boundaries and support vectors. K-means clustering grouped nuclear data based on 'n' and 'z,' revealing distinct characteristics and enabling the identification of similar clusters. The Bayesian regression model predicted binding energy using 'n' and 'z' as independent variables, capturing the Gaussian distribution of 'BE4DBE2' and providing statistical measures for parameter estimation. Ccomprehensives nuclear data analysis using various statistical approaches provides valuable insights into relationships, predictions, classification, and clustering, contributing to the advancement of nuclear science and facilitating further research in this field. Support vector machine ; Decision tree ; Naive Bayes classifier ; Cluster analysis ; Artificial intelligence ; Regression analysis ; Mathematics ; Bayesian linear regression ; Machine learning ; Nonparametric statistics ; Statistics ; Linear discriminant analysis ; Bayesian probability ; Pattern recognition (psychology) ; Data mining ; Computer science ; Bayesian inference Machine Learning in Materials Science ; Radiomics and Machine Learning in Medical Imaging
Authors: Jonathan T. Weber | Sascha Schäfer
Source: arXiv (Cornell University) (preprint, 2023-08-20)
Field: Materials Science — Surfaces, Coatings and Films

Electron Imaging of Nanoscale Charge Distributions Induced by Femtosecond Light Pulses Surface charging is ubiquitously observable during in situ transmission electron microscopy of nonconducting specimens as a result of electron beam/sample interactions or optical stimuli and often limits the achievable image stability and spatial or spectral resolution. Here, we report on the electron-optical imaging of surface charging on a nanostructured surface following femtosecond multiphoton photoemission. By quantitatively extracting the light-induced electrostatic potential and studying the charging dynamics on relevant time scales, we gain insights into the details of the multiphoton photoemission process in the presence of an electrostatic background field. We study the interaction of the charge distribution with the high-energy electron beam and secondary electrons and propose a simple model to describe the interplay of electron- and light-induced processes. In addition, we demonstrate how to mitigate sample charging by simultaneously optically illuminating the sample. Femtosecond ; Nanoscopic scale ; Electron ; Charge (physics) ; Materials science ; Atomic physics ; Physics ; Optics ; Optoelectronics ; Laser ; Nanotechnology ; Nuclear physics ; Quantum mechanics Electron and X-Ray Spectroscopy Techniques ; Laser-Matter Interactions and Applications ; Advanced Electron Microscopy Techniques and Applications
Authors: Juefei Wu | Bangshuai Zhu | Chi Ding | Cuiying Pei | Qi Wang | Jian Sun | Yanpeng Qi
Source: arXiv (Cornell University) (preprint, 2023-12-16)
Field: Materials Science — Materials Chemistry

Superconducting Ternary Hydridies in Ca-U-H under High Pressure The research on hydrogen-rich ternary compounds attract tremendous attention for it paves new route to room-temperature superconductivity at lower pressures. Here, we study the crystal structures, electronic structures, and superconducting properties of the ternary Ca-U-H system, combining crystal structure predictions with ab-initio calculations under high pressure. We found four dynamically stable structures with hydrogen clathrate cages: CaUH12-Cmmm, CaUH12-Fd-3m, Ca2UH18-P-3m1, and CaU3H32-Pm-3m. Among them, the Ca2UH18-P-3m1 and CaU3H32-Pm-3m are likely to be synthesized below 1 megabar. The f electrons in U atoms make dominant contribution to the electronic density of states around the Fermi energy. The electron-phonon interaction calculations reveal that phonon softening in the mid-frequency region can enhance the electron-phonon coupling significantly. The Tc value of Ca2UH18-P-3m1 is estimated to be 57.5-65.8 K at 100 GPa. Our studies demonstrate that introducing actinides into alkaline-earth metal hydrides provides possibility in designing novel superconducting ternary hydrides. Ternary operation ; Superconductivity ; Phonon ; Ab initio ; Condensed matter physics ; Materials science ; Fermi level ; Electronic structure ; Crystal structure ; Ab initio quantum chemistry methods ; Electron ; Crystallography ; Chemistry ; Physics ; Molecule ; Nuclear physics Hydrogen Storage and Materials ; Superconductivity in MgB2 and Alloys ; High-pressure geophysics and materials
Authors: Akanksha Akanksha | Apoorva
Source: MIGRATION LETTERS (article, 2023-12-20)
Field: Materials Science — Materials Chemistry

Three-dimensional Architecture of Carbon Nanotube Transistors with High Speed and Performance Transistors, essential components of contemporary electronics, are crucial in providing electricity for the technology-dependent society. Given the continuously growing need for enhanced speed capabilities in electronic devices, it has become crucial to investigate innovative materials and structures to fulfill these demands. Carbon Nanotubes (CNTs) have emerged as a desirable contender due to their remarkable electrical characteristics, characterized by elevated carrier mobility and nanoscale dimensions. This study explores the importance of high-speed performance in the contemporary technology-driven environment and emphasizes the crucial role played by transistors in attaining this objective. This research underscores the distinctive attributes of CNTs, including their exceptional electrical conductivity, mechanical robustness, and dimensions, making them a highly suitable contender for advanced transistors in future applications. The current transistor layouts encounter notable obstacles in effectively using the whole capabilities of CNTs, such as the difficulty associated with achieving accurate alignment and addressing connection concerns. To tackle the issues, this study presents a novel three-dimensional CNT Architecture (3D-CNTA) design that utilizes state-of-the-art Computer-Aided Design (CAD) software to achieve accurate spatial modeling. The 3D-CNTA that has been suggested exhibits exceptional performance in several aspects, encompassing power consumption (0.3 Watts), noise (2.6 microvolts), delay (6.6 nanoseconds), gain (36.25 dB), energy efficiency (2.13 femtojoules per transition), and simulation accuracy (95%). Transistor ; Robustness (evolution) ; Electronics ; Carbon nanotube ; Computer science ; Nanotechnology ; Materials science ; Electrical engineering ; Engineering Carbon Nanotubes in Composites ; Quantum-Dot Cellular Automata ; Advanced Memory and Neural Computing
Authors: T. Prabaharan | A. Bovas Herbert Bejaxhin | Sowmya Sankaran | N. Ramanan | Jenaris D.S.
Source: Journal of Mines Metals and Fuels (article, 2023-03-15)
Field: Materials Science — Polymers and Plastics

Comparative Testing of Tensile, Flexural and Impact Analysis on Coated and Uncoated Kenaf Fiber Reinforced Composite Due to its enhanced strength, stiffness, and tensile qualities, fibre reinforced composites are being employed more and more in the aerospace, automotive, plastic and mineral processing industries. Kenaf, jute, Kemp, and other types of reinforcing fibres are frequently utilized and come in strips. The design and analysis of the mechanical characteristics of the Kenaf fibre reinforced composite is the primary goal of this project. Different combinations of fibre and resin are examined for their various mechanical qualities, such as impact resistance, flexural strength, and tensile strength. By altering the fibre orientation and boosting impact strength, the composite is created. By altering the fibre orientation and boosting impact strength, the composite is created. The composite is personally examined using a variety of testing tools, and it is also examined using design software like Ansys. This research aims to increase the composite’s impact strength by adjusting the fibre length. The hydrogen peroxide solution is also coated on the composite before the tests are run. Comparisons are made between the experimental findings for coated and uncoated composites. Kenaf ; Ultimate tensile strength ; Composite number ; Composite material ; Materials science ; Flexural strength ; Izod impact strength test ; Specific strength ; Fiber Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Mechanical Behavior of Composites
Authors: Matthew Witman | Anuj Goyal | Stephan Lany | Pin-Wen Guan | Anthony H. McDaniel | Kriston Brooks | Claudia Zlotea | Vitalie Stavila | Mark D. Allendorf
Source:  (article, 2023-08-01)
Field: Materials Science — Materials Chemistry

From data-driven modeling to systems level co-design: progress in materials discovery and optimization for hydrogen storage and generation Key concepts: Graph neural networks to directly predict relaxed vacancy properties from the host structure High-throughput screening of vacancy formation enthalpies New oxides for water-splitting, fuel cells, CO 2 conversion, and thermochemical energy storage Hydrogen storage ; Computer science ; Systems engineering ; Hydrogen ; Process engineering ; Engineering ; Chemistry Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Catalysis and Oxidation Reactions
Authors: Kaizhu Liu | Hsiang‐Chen Chui | Changsen Sun | Xue Han
Source: arXiv (Cornell University) (preprint, 2023-08-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-Accuracy Prediction of Metal-Insulator-Metal Metasurface with Deep Learning Deep learning prediction of electromagnetic software calculation results has been a widely discussed issue in recent years. But the prediction accuracy was still one of the challenges to be solved. In this work, we proposed that the ResNets-10 model was used for predicting plasmonic metasurface S11 parameters. The two-stage training was performed by the k-fold cross-validation and small learning rate. After the training was completed, the prediction loss for aluminum, gold, and silver metal-insulator-metal metasurfaces was -48.45, -46.47, and -35.54, respectively. Due to the ultralow error value, the proposed network can replace the traditional electromagnetic computing method for calculation within a certain structural range. Besides, this network can finish the training process less than 1,100 epochs. This means that the network training process can effectively lower the design process time. The ResNets-10 model we proposed can also be used to design meta-diffractive devices and biosensors, thereby reducing the time required for the calculation process. The ultralow error of the network indicates that this work contributes to the development of future artificial intelligence electromagnetic computing software. Computer science ; Deep learning ; Software ; Plasmon ; Process (computing) ; Insulator (electricity) ; Metal-insulator-metal ; Work (physics) ; Computer engineering ; Artificial neural network ; Artificial intelligence ; Materials science ; Electronic engineering ; Optoelectronics ; Electrical engineering ; Mechanical engineering ; Engineering ; Voltage Metamaterials and Metasurfaces Applications ; Animal Vocal Communication and Behavior ; Photonic Crystals and Applications