jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Stefano Boscarino | Maria Censabella | Melanie Micali | Marco Russo | A. Terrasi | Maria Grazia Grimaldi | F. Ruffino
Source: Micromachines (article, 2022-02-01)
Field: Materials Science — Materials Chemistry

Morphology, Electrical and Optical Properties of Cu Nanostructures Embedded in AZO: A Comparison between Dry and Wet Methods Herein, Cu nanostructures are obtained by solid-state dewetting of 9 nm copper layer (dry) or by ablating copper target, using a nanosecond pulsed laser at 1064 nm, in acetone and isopropyl alcohol (wet). The Cu nanostructures are embedded in aluminum-doped zinc oxide layer. Then, the electrical, optical, and morphological properties of the two kinds of systems, as a function of their synthesis parameters, are investigated. The aim is to compare the two fabrication methods and select the main conditions to achieve the best system for photovoltaic applications. The main differences, exhibited by the wet and dry processes, were in the shape and size of the Cu nanostructures. Dewetting in nitrogen produces faceted nanoparticles, with an average size below 150 nm, while laser ablation originates spherical and smaller nanoparticles, below 50 nm. Dry system underwent to thermal annealing, which improves the electrical properties, compared to the wet system, with a sheet resistance of 10<sup>3</sup> vs. 10<sup>6</sup> Ω/sq, respectively; finally, the dry system shows a maximum transmittance of 89.7% at 697 nm, compared to the wet system in acetone, 88.4% at 647 nm, as well as in isopropyl alcohol, 86.9% at 686 nm. Moreover, wet systems show higher transmittance in NUV. Morphology (biology) ; Materials science ; Nanostructure ; Nanotechnology ; Chemical engineering ; Composite material ; Geology ZnO doping and properties ; Copper-based nanomaterials and applications ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Bianca Cavalcante Silva | Renato de Mello Prado | Fábio Henrique Rojo Baio | Cid Naudi Silva Campos | Larissa Pereira Ribeiro Teodoro | Paulo Eduardo Teodoro | Thiago Feliph Silva Fernandes
Source: Research Square (preprint, 2022-12-29)
Field: Chemistry — Analytical Chemistry

New approach for predicting nitrogen and pigments in maize from hyperspectral data and machine learning models Abstract Fast diagnostics from hyperspectral data and machine learning (ML) models to predict nitrogen (N) content and pigments in maize crop is a challenge to optimize nitrogen fertilization. Therefore, this field research identifies which ML algorithms accurately estimate nitrogen, chlorophyll, and carotenoid contents in maize crop leaves at different phenological phases using hyperspectral band data. The treatments were arranged in a factorial scheme having four N doses (0, 54, 108, and 216 kg ha − 1 ) combined with five leaf collection seasons at phenological stages V6 to V14. The ML models tested were artificial neural networks – ANN, decision tree adapted for prediction problems – M5P, REPTree decision tree, random forest - RF, polynomial support vector machine – SVMP, and ZeroR - ZR (control). Pearson correlations were estimated between in situ variables and those predicted by ML models. Spectral bands 530–550 nm and 690–750 nm are effective wavelengths as well as RF and SVM are effective models for the estimation of nitrogen and pigment contents. These methods can be applied in precision farming practices and assist in decision making for N management in maize crop. Hyperspectral imaging ; Support vector machine ; Decision tree ; Machine learning ; Artificial intelligence ; Random forest ; Nitrogen ; Artificial neural network ; Mathematics ; Precision agriculture ; Computer science ; Chemistry ; Agriculture ; Ecology ; Biology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Remote Sensing in Agriculture
Authors: Belén Larrán | Marta López‐Alonso | Michele Miranda | Víctor Pereira | Lucas Rigueira | M. Suárez | Carlos Herrero
Source: Scientific Reports (article, 2022-08-08)
Field: Chemistry — Analytical Chemistry

Measuring haemolysis in cattle serum by direct UV–VIS and RGB digital image-based methods A simple, rapid procedure is required for the routine detection and quantification of haemolysis, one of the main sources of unreliable results in serum analysis. In this study, we compared two different approaches for the rapid determination of haemolysis in cattle serum. The first consisted of estimating haemolysis via a simple direct ultraviolet-visible (UV-VIS) spectrophotometric measurement of serum samples. The second involved analysis of red, green, blue (RGB) colour data extracted from digital images of serum samples and relating the haemoglobin (Hb) content by means of both univariate (R, G, B and intensity separately) and multivariate calibrations (R, G, B and intensity jointly) using partial least squares regression and artificial neural networks. The direct UV-VIS analysis and RGB-multivariate analysis using neural network methods were both appropriate for evaluating haemolysis in serum cattle samples. The procedures displayed good accuracy (mean recoveries of 100.7 and 102.1%, respectively), adequate precision (with coefficients of variation from 0.21 to 2.68%), limit of detection (0.14 and 0.21 g L<sup>-1</sup>, respectively), and linearity of up to 10 g L<sup>-1</sup>. Haemolysis ; RGB color model ; Univariate ; Partial least squares regression ; Multivariate statistics ; Computer science ; Artificial intelligence ; Mathematics ; Statistics ; Biology ; Immunology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Effects of Environmental Stressors on Livestock
Authors: Greta Koumarianou | Irène Wang | Lincoln Satterthwaite | David Patterson
Source: Communications Chemistry (article, 2022-03-14)
Field: Chemistry — Spectroscopy

Assignment-free chirality detection in unknown samples via microwave three-wave mixing Straightforward identification of chiral molecules in multi-component mixtures of unknown composition is extremely challenging. Current spectrometric and chromatographic methods cannot unambiguously identify components while the state of the art spectroscopic methods are limited by the difficult and time-consuming task of spectral assignment. Here, we introduce a highly sensitive generalized version of microwave three-wave mixing that uses broad-spectrum fields to detect chiral molecules in enantiomeric excess without any prior chemical knowledge of the sample. This method does not require spectral assignment as a necessary step to extract information out of a spectrum. We demonstrate our method by recording three-wave mixing spectra of multi-component samples that provide direct evidence of enantiomeric excess. Our method opens up new capabilities in ultrasensitive phase-coherent spectroscopic detection that can be applied for chiral detection in real-life mixtures, raw products of chemical reactions and difficult to assign novel exotic species. Mixing (physics) ; Chirality (physics) ; Microwave ; Enantiomer ; Component (thermodynamics) ; Molecule ; Spectral line ; Chemistry ; Biological system ; Enantiomeric excess ; Analytical Chemistry (journal) ; Chromatography ; Physics ; Organic chemistry ; Thermodynamics ; Catalysis ; Quantum mechanics Molecular Spectroscopy and Structure ; Molecular spectroscopy and chirality ; Terahertz technology and applications
Authors: Mohammad AlShaikh Saleh | Shady S. Refaat | Sunil P. Khatri | Ali Ghrayeb
Source:  (article, 2022-03-20)
Field: Materials Science — Materials Chemistry

Detection and Classification of Defects in XLPE Power Cable Insulation via Machine Learning Algorithms Due to high electric stresses in power equipment, insulation degradation has been prevalent as a result of increased PD exposure. In this paper, we study different machine learning (ML) methods for the detection and classification of partial discharges (PDs) for assessing the reliability of insulation systems. We introduce and examine a set of features using selected machine learning-based algorithms. The aim is to detect and classify PDs transpiring within insulation systems. Therefore, this paper presents tools to detect defects using suitable PD sensors and Machine Learning algorithms to facilitate diagnostics and enhance isolation system design. Experiments are being conducted on several voids in the insulator with varying shapes and sizes. A PD sensor is used for detecting the PDs taking place. Due to the presence of noise and other external interferences, appropriate filters and denoising methods are implemented. After that, the relevant PD features, such as the PD magnitude, PD repetition rate, statistical features, wavelet features, etc., are extracted. This study attempts to emphasize the importance of classifying the type of defect, as this will allow engineers to determine the severity of the fault taking place, and take the proper countermeasures. Reliability (semiconductor) ; Fault detection and isolation ; Algorithm ; Computer science ; Machine learning ; Noise (video) ; Artificial intelligence ; Noise reduction ; Partial discharge ; Insulator (electricity) ; Power (physics) ; Engineering ; Electrical engineering ; Voltage High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Carmen Cerchia | Emanuela Roscetto | Rosarita Nasso | Maria Rosaria Catania | Emmanuele De Vendittis | Antonio Lavecchia | Mariorosario Masullo | Rosario Rullo
Source: Antioxidants (article, 2022-04-15)
Field: Chemistry — Inorganic Chemistry

In Silico Identification of Novel Inhibitors Targeting the Homodimeric Interface of Superoxide Dismutase from the Dental Pathogen Streptococcus mutans The microaerophile <i>Streptococcus mutans</i>, the main microaerophile responsible for the development of dental plaque, has a single cambialistic superoxide dismutase (<i>Sm</i>SOD) for its protection against reactive oxygen species. In order to discover novel inhibitors of <i>Sm</i>SOD, possibly interfering with the biofilm formation by this pathogen, a virtual screening study was realised using the available 3D-structure of <i>Sm</i>SOD. Among the selected molecules, compound <b>ALS-31</b> was capable of inhibiting <i>Sm</i>SOD with an IC<sub>50</sub> value of 159 µM. Its inhibition power was affected by the Fe/Mn ratio in the active site of <i>Sm</i>SOD. Furthermore, <b>ALS-31</b> also inhibited the activity of other SODs. Gel-filtration of <i>Sm</i>SOD in the presence of <b>ALS-31</b> showed that the compound provoked the dissociation of the <i>Sm</i>SOD homodimer in two monomers, thus compromising the catalytic activity of the enzyme. A docking model, showing the binding mode of <b>ALS-31</b> at the dimer interface of <i>Sm</i>SOD, is presented. Cell viability of the fibroblast cell line BJ5-ta was not affected up to 100 µM <b>ALS-31</b>. A preliminary lead optimization program allowed the identification of one derivative, <b>ALS-31-9</b>, endowed with a 2.5-fold improved inhibition power. Interestingly, below this concentration, planktonic growth and biofilm formation of <i>S. mutans</i> cultures were inhibited by <b>ALS-31</b>, and even more by its derivative, thus opening the perspective of future drug design studies to fight against dental caries. Streptococcus mutans ; Biofilm ; Superoxide dismutase ; Virtual screening ; Chemistry ; Docking (animal) ; In silico ; Microbiology ; Pathogen ; Enzyme ; Dental plaque ; Biochemistry ; Biology ; Drug discovery ; Bacteria Metal-Catalyzed Oxygenation Mechanisms ; Bacterial biofilms and quorum sensing ; Enzyme function and inhibition
Authors: Addisalem Adefris Legesse | Dawit Desalegn | Senthil Kumaran Selvaraj | Velmurugan Paramasivam | Utkarsh Chadha
Source: Advances in Materials Science and Engineering (article, 2022-04-07)
Field: Materials Science — Polymers and Plastics

Experimental Investigation of Sorghum Stalk and Sugarcane Bagasse Hybrid Composite for Particleboard This study evaluated the physical-mechanical properties of three-layer particleboard made from sorghum stalk and sugarcane bagasse hybrid reinforced bonded with urea-formaldehyde resin. The production of particleboards using the sorghum and bagasse particles has been established at the ratio of (25 : 50 : 75) sorghum stalk and sugarcane bagasse with urea-formaldehyde as a matrix at different concentrations (50 kg/m3, 60 kg/m3, and 70 kg/m3) and pressing pressure (18 MPa, 20 MPa, and 22 MPa) through hand mixing from 3 to 5 minutes and then hot press within a temperature range of 160°C to 180°C for 4 minutes. The particleboards were produced with their proportions through the Taguchi design of the experiment (L9) approach. The experimental results were analyzed using Taguchi design and ANOVA with a general linear model. The experimental results showed that internal bonding, flexural strength, modulus of elasticity, and moisture content were significantly improved with high resin content and pressing pressure from 18 MPa to 22 MPa. The thickness swelling value also dropped where the resin concentration varies from 50 kg/m3 to 70 kg/m3 and shows better board stability. In contrast, the water absorption increased as resin content was increased. Results from ANOVA provide that SS3B1P3UFR3 (3 : 1 ratio of Sorghum with bagasse, 22 MPa of pressing load, and 70 kg/m3 urea-formaldehyde concentration) is the most optimal combination of variables for the better performance of particleboard. Bagasse ; Materials science ; Stalk ; Urea-formaldehyde ; Taguchi methods ; Composite material ; Flexural strength ; Absorption of water ; Swelling ; Formaldehyde ; Pressing ; Composite number ; Young's modulus ; Water content ; Pulp and paper industry ; Horticulture ; Chemistry ; Layer (electronics) ; Adhesive Natural Fiber Reinforced Composites ; Nanocomposite Films for Food Packaging ; Wood Treatment and Properties
Authors: RAMIZ HASANLI | ILHAM ALIYEV | NIZAMI POLADOV | LALA AZIMOVA | TALEH TAGIYEV
Source: Scientific Herald of Uzhhorod University Series Physics (article, 2022-05-26)
Field: Materials Science — Materials Chemistry

Isothermal Transformations in High-Strength Cast Iron Relevance. The technological process of manufacturing parts from high-strength cast irons is simpler and more economical than the process of manufacturing parts from steel. Cast irons are less sensitive to stress concentrators and strike loads. Spheroidal graphite cast irons can achieve DI 70 grades even in the cast state. Through hardening heat treatment or additional alloying, it is possible to produce cast irons of higher strength (grades DI 80 and above). As the strength properties of cast irons increase, disadvantages in the form of low ductility and plasticity become increasingly apparent. These problems can be compensated by providing an ausferritic, bainite or bainite-austenitic structure of the metal matrix of cast irons. A good solution is to obtain cast irons with a complex structure of the bainite-ausferrite type. In this regard, the relevance of this work is due to the fact that, in the practice of modern mechanical engineering, high-strength cast irons are increasingly used. Cast iron ; Bainite ; Materials science ; Metallurgy ; Austenite ; Ductility (Earth science) ; Hardening (computing) ; Work hardening ; Austempering ; Microstructure ; Composite material Metal Alloys Wear and Properties ; Microstructure and Mechanical Properties of Steels ; Engineering and Environmental Studies
Authors: Mengmeng Yang | Jianan Hu | Shuo Cao | Guang Feng | Yi Yang | Renci Liu | Shujun Li | Fu Zhao | Aihan Feng | Qing‐Miao Hu | Aijun Huang | Hao Wang
Source: Metals (article, 2022-09-26)
Field: Materials Science — Materials Chemistry

Influence of Nonmetallic Interstitials on the Phase Transformation between FCC and HCP Titanium: A Density Functional Theory Study In addition to the common stable and metastable phases in titanium alloys, the face-centered cubic phase was recently observed under various conditions; however, its formation remains largely unclarified. In this work, the effect of nonmetallic interstitial atoms O, N, C and B on the formation of the face-centered cubic phase of titanium was investigated with the density functional theory. The results indicate that the occupancy of O, N, C and B on the octahedral interstitial sites reduces the energy gap between the hexagonal-close-packed (HCP) and face-centered cubic (FCC) phases, thus assisting the formation of FCC-Ti under elevated temperature or plastic deformation. Such a gap further decreases with the increase in the interstitial content, which is consistent with the experimental observation of FCC-Ti under high interstitial content. The relative stability of the interstitial-containing HCP-Ti and FCC-Ti was studied against the physical and chemical origins, e.g., the lattice distortion and the electronic bonding. Interstitial O, N, C and B also reduce the stacking fault energy, thus further benefiting the formation of FCC-Ti. Materials science ; Interstitial defect ; Titanium ; Metastability ; Density functional theory ; Crystallography ; Octahedron ; Stacking fault ; Cubic crystal system ; Phase (matter) ; Stacking-fault energy ; Lattice (music) ; Chemical physics ; Crystal structure ; Metallurgy ; Dislocation ; Computational chemistry ; Composite material ; Microstructure ; Chemistry Titanium Alloys Microstructure and Properties ; Metal and Thin Film Mechanics ; Nuclear Materials and Properties
Authors: Tomasz Lipiński | Jacek Pietraszek | A. Wach
Source: Engineering for Rural Development (article, 2022-05-25)
Field: Materials Science — Materials Chemistry

Influence of oxygen content in medium carbon steel on bending fatigue strength Commercial iron alloys apart of typical chemical elements contain phosphorus, sulfur, oxygen as well as nonmetallic inclusions. These elements can form solutions in liquid metal, or they can form separate phases. The physical and chemical reactions that occur in the process of steel melting and solidification produce non-metallic compounds and phases, referred to as inclusions. Inclusions as impurities found in steel can affect its performance characteristics. The article discusses the results of a study investigating the effect of oxygen on the fatigue strength of structural steel during rotary bending. The study was performed on 7 heats produced in an industrial plant. Fourteen heats were produced in 100 ton oxygen converter. All heats were desulfurized. The heats from the converter were subjected to vacuum circulation degassing. The experimental variants were compared in view of the heat treatment options. The examination was realized by the rotatory curving machine about the frequency of pendulum cycles: 6000 periods on minute. For basis was accepted on fatigue defining endurance level 107 cycles. The level of the fatigue-inducing load was adapted to the strength properties of steel from 540 to 650 MPa. The fatigue strength of steel with tested for oxygen content in steel was determined during rotary bending. The results revealed that fatigue strength is determined by the contents of oxygen impurity spaces and tempering temperature. A reduction in the fatigue strength during rotary bending of low-carbon steel from 368 to 252 MPa was observed when the tempering temperature changed from 200 to 600oC. It was found that with an increase in the content from 0.0023%, 0.003% of the bending transfer (for all temperatures from 298 to 328 MPa) can be transferred. Tempering ; Materials science ; Fatigue limit ; Metallurgy ; Oxygen ; Impurity ; Bending ; Composite material ; Carbon fibers ; Chemistry Material Properties and Failure Mechanisms ; Fatigue and fracture mechanics ; Microstructure and Mechanical Properties of Steels
Authors: Mingzhu Tao | Yong He | Xiulin Bai | Xiaoyun Chen | Yuzhen Wei | Peng Cheng | Xuping Feng
Source: Frontiers in Plant Science (article, 2022-08-08)
Field: Chemistry — Analytical Chemistry

Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification Glyphosate is one of the most widely used non-selective herbicides, and the creation of glyphosate-resistant cultivars solves the problem of limited spraying area. Therefore, it is of great significance to quickly identify resistant cultivars without destruction during the development of superior cultivars. This work took maize seedlings as the experimental object, and the spectral indices of leaves were calculated to construct a model with good robustness that could be used in different experiments. Compared with no transfer strategies, transferability of support vector machine learning model was improved by randomly selecting 14% of source domain from target domain to train and applying transfer component analysis algorithm, the accuracy on target domain reached 83% (increased by 71%), recall increased from 10 to 100%, and F1-score increased from 0.17 to 0.86. The overall results showed that both transfer component analysis algorithm and updating source domain could improve the transferability of model among experiments, and these two transfer strategies could complement each other's advantages to achieve the best classification performance. Therefore, this work is beneficial to timely understanding of the physiological status of plants, identifying glyphosate resistant cultivars, and ultimately provides theoretical basis and technical support for new cultivar creation and high-throughput selection. Cultivar ; Transferability ; Robustness (evolution) ; Computer science ; Glyphosate ; Support vector machine ; Artificial intelligence ; Transfer of learning ; Biotechnology ; Machine learning ; Agronomy ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Armin Taheri | Simone Pisana | Chandra Veer Singh
Source: Nanoscale Advances (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Extraordinary lattice thermal conductivity of gold sulfide monolayers Gold sulfide monolayers (α-, β-Au<sub>2</sub>S, α-, β-, γ-AuS) have emerged as a new class of two-dimensional (2D) materials with appealing properties such as high thermal and dynamical stability, oxidation resistance, and excellent electron mobility. However, their thermal properties are still unexplored. In this study, based on first-principles calculations and the Peierls-Boltzmann transport equation, we report the lattice thermal conductivity (<i>κ</i>) and related phonon thermal properties of all members of this family. Our results show that gold sulfide monolayers have lattice thermal conductivity spanning almost three orders of magnitude, from 0.04 W m<sup>-1</sup> K<sup>-1</sup> to 10.62 W m<sup>-1</sup> K<sup>-1</sup>, with different levels of anisotropy. Particularly, our results demonstrate that β-Au<sub>2</sub>S with ultralow <i>κ</i> <sub><i>aa</i></sub> = 0.06 W m<sup>-1</sup> K<sup>-1</sup> and <i>κ</i> <sub><i>bb</i></sub> = 0.04 W m<sup>-1</sup> K<sup>-1</sup> along the principal in-plane directions, has one of the lowest <i>κ</i> values that have been reported for a 2D material, well below that of PbSe. This extremely low lattice thermal conductivity can be attributed to its flattened phonon branches and low phonon group velocity, high anharmonicity, and short phonon lifetimes. Our results may provide insight into the application of gold sulfide monolayers as thermoelectric materials, and motivate future <i>κ</i> measurements of gold sulfide monolayers. Monolayer ; Phonon ; Anharmonicity ; Thermal conductivity ; Anisotropy ; Materials science ; Condensed matter physics ; Sulfide ; Thermoelectric effect ; Lattice (music) ; Thermodynamics ; Nanotechnology ; Physics ; Composite material ; Optics ; Metallurgy 2D Materials and Applications ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials