jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chao Yuan | Yanan Zhao | Xingjun Xi | Yisheng Chen
Source: Biosensors (article, 2023-05-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Non-Destructive Screening of Sodium Metabisulfite Residue on Shrimp by SERS with Copy Paper Loaded with AgNP In order to prompt the appearance of the shrimp color, sodium metabisulfite is frequently added in shrimp processing, which is, however, prohibited in China and many other countries. This study aimed to establish a surface-enhanced Raman spectroscopy (SERS) method for screening sodium metabisulfite residues on shrimp surfaces, in a non-destructive manner. The analysis was carried out using a portable Raman spectrometer jointly with copy paper loaded with silver nanoparticles as the substrate material. The SERS response of sodium metabisulfite gives two fingerprint peaks at 620 (strong) and 927 (medium) cm<sup>-1</sup>, respectively. This enabled unambiguous confirmation of the targeted chemical. The sensitivity of the SERS detection method was determined to be 0.1 mg/mL, which was equal to residual sodium metabisulfite on the shrimp surface at 0.31 mg/kg. The quantitative relationship between the 620 cm<sup>-1</sup> peak intensities and the concentrations of sodium metabisulfite was established. The linear fitting equation was y = 2375x + 8714 with R<sup>2</sup> = 0.985. Reaching an ideal balance in simplicity, sensitivity, and selectivity, this study demonstrates that the proposed method is ideally suitable for in-site and non-destructive screening of sodium metabisulfite residues in seafood. Sodium metabisulfite ; Shrimp ; Chemistry ; Raman spectroscopy ; Detection limit ; Sodium ; Silver nanoparticle ; Analytical Chemistry (journal) ; Chromatography ; Materials science ; Nanotechnology ; Nanoparticle ; Organic chemistry ; Optics ; Fishery ; Biology Gold and Silver Nanoparticles Synthesis and Applications ; Identification and Quantification in Food ; Melamine detection and toxicity
Authors: Sebastian Sado
Source: International Journal of Applied Ceramic Technology (article, 2023-09-14)
Field: Materials Science — Ceramics and Composites

Method of raw materials selection for production of the MgO‐C bricks of comparable properties using PCA and K‐medoids Abstract MgO‐C refractories are zonally installed in most of the key heat devices of steel and iron metallurgy with the exposure to various corrosive factors. One of the most significant factors is low‐temperature decarburization resulting in the oxidation of carbon in MgO‐C refractories and loss of its properties. Moreover MgO‐C materials are exposed for direct attack of liquid slag or steel melt which easily infiltrate the porous, decarburized hot face of the bricks. Cost of MgO‐C materials of high quality is important for refractories user. For this reason researches try to develop materials of high quality but with lower cost by substituting fused raw materials with sintered ones or with recycled MgO‐C. In this article, method of selecting the raw materials for production of MgO‐C bricks with comparable properties is shown. Twenty different variants of MgO‐C materials were prepared with the use of various quality raw materials. Basic properties of the industrially produced MgO‐C bricks were measured. To select recipes with similar quality principal components analysis method and K‐medoids algorithm were applied. To verify the results corrosion resistance of selected MgO‐C materials were tested with the use of induction furnace of medium frequency. Materials science ; Raw material ; Decarburization ; Metallurgy ; Porosity ; Composite material Advanced ceramic materials synthesis ; Metallurgical Processes and Thermodynamics ; Magnesium Oxide Properties and Applications
Authors: Haodong Chen | Hanzhao Qin | Fengying Qin | Bo Li | Yang Yu | Chenglin Li
Source: Crystals (article, 2023-01-20)
Field: Materials Science — Materials Chemistry

Hot Deformation Behavior and Microstructure Evolution of Ti–6Cr–5Mo–5V–4Al–1Nb Alloy This study looked into the thermo-mechanical properties and the recrystallization mechanisms of the Ti–5.5Cr–5Mo–5V-4Al–1Nb alloy (wt.%, Ti−65541). Hot compression experiments were conducted at two phase regions (740~950 °C) and strain rates from 0.001 to 1 s−1. The compressive strain–stress curves were corrected by the adiabatic heating effect. The Arrhenius model was established and provided a reliable prediction of the value of stress with a 0.992 correlation coefficient. The constructed processing map demonstrated that when the temperature rose and the strain rate fell, the power dissipation efficiency (η) showed a rising trend. By utilizing electron backscattered diffraction (EBSD), the microstructural evolution and deformation process were analyzed. It was possible to witness both continuous dynamic recrystallization (cDRX) and discontinuous dynamic recrystallization (dDRX). The dynamic recovery (DRV) and dDRX eventually replaced dDRX while η decreased. Moreover, the deformation band (DB) impeded re-crystallization in the low η area. This study can supply a relatively reliable processing interval for the new Ti−65541 alloy. Materials science ; Dynamic recrystallization ; Electron backscatter diffraction ; Recrystallization (geology) ; Strain rate ; Microstructure ; Alloy ; Composite material ; Deformation (meteorology) ; Adiabatic shear band ; Metallurgy ; Adiabatic process ; Hot working ; Thermodynamics ; Geology Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; Intermetallics and Advanced Alloy Properties
Authors: Ashoka Kumar Ratha | Nalini Kanta Barpanda | Prabira Kumar Sethy | G. Sharada | Santi Kumari Behera
Source: Revue d intelligence artificielle (review, 2023-04-30)
Field: Chemistry — Analytical Chemistry

Computer Intelligence-Based Fruit Grading: A Review India is the second-largest fruit producer in the world.But, fruit identification, classification, and grading are carried out manually.Hence, most of the harvested fruit was wasted due to human perception subjectivity because there needed to be more qualified workers.Therefore, the fruit sector must impose an automated fruit detection system to distinguish among different types of fruits based on their variety, class, maturity, and quality.An automated system may be created with the use of appropriate image processing ideas and machine learning strategies for grading and quality inspection of fruits.With an emphasis on the advancement of state-of-the-art, this study provides a quick examination of the methodologies put out in the research publications from the last couple of years.Various methods are used to compare the relevant studies. Grading (engineering) ; Computer science ; Artificial intelligence ; Biology ; Ecology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Lovro Liverić | Tamara Holjevac Grgurić | Vilko Mandić | R. Chulist
Source: Materials (article, 2023-08-24)
Field: Materials Science — Materials Chemistry

Influence of Manganese Content on Martensitic Transformation of Cu-Al-Mn-Ag Alloy The influence of manganese content on the formation of martensite structure and the final properties of a quaternary Cu-Al-Mn-Ag shape memory alloy (SMA) was investigated. Two alloys with designed compositions, Cu- 9%wt. Al- 16%wt. Mn- 2%wt. Ag and Cu- 9%wt. Al- 7%wt. Mn- 2%wt. Ag, were prepared in an electric arc furnace by melting of high-purity metals. As-cast and quenched microstructures were determined by optical microscopy and scanning electron microscopy equipped with EDS. Phases were confirmed by high-energy synchrotron radiation and electron backscatter diffractions. Austenite and martensite transformations were followed by differential scanning calorimetry and hardness was determined using the Vickers hardness test. It was found that the addition of silver contributes to the formation of the martensite structure in the Cu-Al-Mn-SMA. In the alloy with 7%wt. of manganese, stable martensite is formed even in the as-cast state without additional heat treatment, while the alloy with 16%wt. of manganese martensite transforms only after thermal stabilization and quenching. Two types of martensite, β<sub>1'</sub> and γ<sub>1</sub>', are confirmed in the Cu-9Al-7Mn-2Ag specimen. The as-cast SMA with 7%wt. Mn showed significantly lower martensite transformation temperatures, M<sub>s</sub> and M<sub>f</sub>, in relation to the quenched alloy. With increasing manganese content, the M<sub>s</sub> and M<sub>f</sub> temperatures are shifted to higher values and the microhardness is lower. Materials science ; Martensite ; Alloy ; Manganese ; Austenite ; Microstructure ; Shape-memory alloy ; Quenching (fluorescence) ; Metallurgy ; Differential scanning calorimetry ; Scanning electron microscope ; Bainite ; Diffusionless transformation ; Composite material ; Thermodynamics ; Fluorescence Shape Memory Alloy Transformations ; Microstructure and Mechanical Properties of Steels ; Titanium Alloys Microstructure and Properties
Authors: Jing Cao | Yan Xu
Source: FEBS Open Bio (article, 2023-11-15)
Field: Chemistry — Spectroscopy

Predicting cysteine reactivity changes upon phosphorylation using <scp>XGBoost</scp> Cysteine reactivity serves as a significant indicator of protein function and can be affected by phosphorylation events. Experimental approaches have been developed to investigate this effect, but the scale is still relatively limited. Machine-learning approaches promise to accelerate the investigation of these phenomena. In this study, protein sequence information, distances to the closest phosphorylation sites, and the membership score of the intrinsically disordered region were used to represent the cysteine. Following the feature selection using an elastic net model, two groups of binary classifiers based on XGBoost were built to predict the occurrence and the direction of the reactivity change as a response to phosphorylation events, respectively. In addition, function enrichment analysis was performed on proteins/genes predicted to have reactivity changes. XGBoost performed the best in the independent test with AUC of 0.8192 and 0.9203 for the prediction of the change's occurrence and direction, respectively. The use of two binary classifiers successively resulted in an accuracy of 0.7568 in predicting whether reactivity would be unchanged, increased, or decreased. The enrichment analysis revealed the association of proteins carrying reactivity-changed cysteine residues with various disease-related pathways, particularly cancer, autosomal dominant diseases, and viral infections. Changes in cysteine reactivity influenced by phosphorylation are site-specific and can be predicted by XGBoost algorithms. Our model provides an efficient alternative way to explore the cysteine reactivity upon phosphorylation at the proteome-wide level, facilitating the investigation of protein functions and their clinical insights. Our code is available on GitHub (https://github.com/DarinaOsamu/predictors-of-cysteine-reactivity-changes). Phosphorylation ; Cysteine ; Reactivity (psychology) ; Proteome ; Function (biology) ; Computational biology ; Protein phosphorylation ; Protein function ; Biochemistry ; Biology ; Chemistry ; Gene ; Genetics ; Medicine ; Protein kinase A ; Enzyme ; Pathology Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Protein Structure and Dynamics
Authors: Bogdan Dziadak
Source: Energies (article, 2023-10-13)
Field: Materials Science — Materials Chemistry

Hybrid Optical and Thermal Energy Conversion System to Power Internet of Things Nodes This article presents research about a hybrid power system dedicated to Internet of Things (IoT) nodes. As an introduction, performance tests of the harvesters, that is, a 40 × 40 mm Peltier cell based on Bi2Te3 and three solar cells, monocrystalline, polycrystalline, and amorphous, are presented. The study established the dependence of the effect of generated power on the load resistance. Thus, it states how the internal resistance of the harvesters changes. Following the above tests, a complete power unit with a single harvester and an LTC3108 conversion circuit, as well as an energy buffer in the form of a 1 mF supercapacitor, were built and tested. The unit with a thermoelectric generator generated power from 14 to 409 µW. The unit with a 65 × 65 mm polycrystalline cell generated power from 150 to 409 µW. Next, a hybrid system was built and tested with both of the aforementioned harvesters, which generated power from 205 to 450 µW at 2000 lx illumination and a temperature difference of 20 °C for the thermoelectric generator claddings. Thermoelectric generator ; Electrical engineering ; Materials science ; Internal resistance ; Monocrystalline silicon ; Optoelectronics ; Power (physics) ; Seebeck coefficient ; Energy conversion efficiency ; Internet of Things ; Crystallite ; Thermoelectric effect ; Engineering ; Embedded system ; Physics ; Silicon ; Electrical resistivity and conductivity Advanced Thermoelectric Materials and Devices ; solar cell performance optimization ; Energy Harvesting in Wireless Networks
Authors: Md. Delwar Hossain | Swapan Saha | Md Kamrul Hassan | Anthony Chun Yin Yuen | Cheng Wang
Source: Case Studies in Construction Materials (article, 2023-10-04)
Field: Materials Science — Polymers and Plastics

Establishing pyrolysis kinetics for fire modelling and thermal analysis of polymeric cladding materials used in high-rise buildings This research paper presents a comprehensive analysis of kinetics data concerning with the core materials of Aluminium Composite Panels (ACPs), with a specific focus on composite materials comprising mineral fillers and organic polymers. The study addresses the current scarcity of kinetics data for cladding panel core materials, particularly those with complex compositions involving mineral fillers and organic polymers, which poses a significant challenge for accurate fire modelling. The study aims to extract essential parameters, including activation energy, pre-exponential factors, and reaction orders, essential for precise fire modelling. This study involves Thermogravimetric Analysis (TGA) on three distinct cladding core materials, namely, ACP-1, ACP-2, and ACP-3, featuring varying proportions of organic polymer content (100%, 30%, and 7%) and exposure to different heating rates (5, 10, 15, and 20 K/min). By employing both model-free and model-based techniques in conjunction with TGA analysis, the study successfully derives kinetics data aligned with specific pyrolysis reaction stages. To validate the extracted kinetics data, the study conducted experimental tests utilising a Cone calorimeter. The results from these experiments showcased a strong correlation between fire performance parameters such as Total Heat Release (THR), Effective Heat of Combustion (EHC), and Mass Loss Rate (MLR) and the proportion of polymer in the cladding composites. Furthermore, the study examined the implications of the derived kinetics data within the Fire Dynamics Simulator (FDS). The insights gained from this research substantially contribute to our comprehension of core material kinetics in ACPs, while also offering valuable input for fire dynamics simulations, thereby enhancing fire safety modelling and practices knowledge in the field of construction materials and design. Thermogravimetric analysis ; Cone calorimeter ; Kinetics ; Pyrolysis ; Materials science ; Cladding (metalworking) ; Polymer ; Composite material ; Combustion ; Chemical engineering ; Char ; Chemistry ; Organic chemistry ; Engineering Flame retardant materials and properties ; Thermal and Kinetic Analysis ; Fire dynamics and safety research
Authors: Kaili Xie | Marc Léonetti
Source: Comptes Rendus Mécanique (article, 2023-04-07)
Field: Materials Science — Materials Chemistry

Mechanical characterization of core-shell microcapsules Core-shell configurations are ubiquitous in nature such as in the form of bacterial and cells. Inspired by this, microcapsules are designed with actives as the cores surrounded by thin shells. They not only play an increasing role as artificial models for understanding dynamic behaviors of biological cells in flows, but are also becoming a fundamental class of artificial vehicles at the heart of drug delivery and release in applications. The mechanical properties of the shells are of great importance in this context. Here, we review recent experimental and theoretical characterizations of microcapsules, focusing on the soft and deformable particles with liquid cores. We begin by exploring the concept and fabrication of artificial microcapsules, followed by a discussion of different methods on the mechanical characterization of the shell. Shell (structure) ; Characterization (materials science) ; Context (archaeology) ; Nanotechnology ; Core (optical fiber) ; Fabrication ; Materials science ; Drug delivery ; Composite material ; Geology Pickering emulsions and particle stabilization ; Blood properties and coagulation ; Proteins in Food Systems
Authors: Pushpendra Kumar Verma | Paresh Pathak | Bhupendra Kumar | Himani Himani | Preety Preety
Source: International Journal on Recent and Innovation Trends in Computing and Communication (article, 2023-05-17)
Field: Chemistry — Analytical Chemistry

Automatic Optical Imaging System for Mango Fruit using Hyperspectral Camera and Deep Learning Algorithm This research paper explores focused on developing an automatic mango fruit quality detection system using a combination of artificial intelligence and the Internet of Things technologies. The system utilizes a hyperspectral camera to capture images of the mango fruit and image processing techniques to analyze the images. Deep learning algorithms are employed to classify the mango fruit based on quality parameters such as ripeness, size, and color. The proposed system aims to automate the mango fruit quality inspection process, improve the accuracy of quality assessment, and reduce human error. The results of this research could have applications in the food industry, specifically in the field of fruit quality inspection and sorting. Mango Fruit, Hyperspectral Camera, Image Processing, Deep Learning algorithms, Quality Recognition. Hyperspectral imaging ; Ripeness ; Computer science ; Artificial intelligence ; Sorting ; Process (computing) ; Deep learning ; Image processing ; Computer vision ; Field (mathematics) ; Quality (philosophy) ; Pattern recognition (psychology) ; Algorithm ; Image (mathematics) ; Mathematics ; Food science ; Ripening Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Zhenlong Zhang | Zhe Wang | Ying Luo | Jiaqian Zhang | Duan Tian | Yongde Zhang
Source: Sensors (article, 2023-09-06)
Field: Chemistry — Analytical Chemistry

Rapid Estimation of Soil Pb Concentration Based on Spectral Feature Screening and Multi-Strategy Spectral Fusion Traditional methods for obtaining soil heavy metal content are expensive, inefficient, and limited in monitoring range. In order to meet the needs of soil environmental quality evaluation and health status assessment, visible near-infrared spectroscopy and XRF spectroscopy for monitoring heavy metal content in soil have attracted much attention, because of their rapid, nondestructive, economical, and environmentally friendly features. The use of either of these spectra alone cannot meet the accuracy requirements of traditional measurements, while the synergistic use of the two spectra can further improve the accuracy of monitoring heavy metal lead content in soil. Therefore, this study applied various spectral transformations and preprocessing to vis-NIR and XRF spectra; used the whale optimization algorithm (WOA) and competitive adaptive re-weighted sampling (CARS) algorithms to identify feature spectra; designed a combination variable model (CVM) based on multi-layer spectral data fusion, which improved the spectral preprocessing and spectral feature screening process to increase the efficiency of spectral fusion; and established a quantitative model for soil Pb concentration using partial least squares regression (PLSR). The estimation performance of three spectral fusion strategies, CVM, outer-product analysis (OPA), and Granger-Ramanathan averaging (GRA), was discussed. The results showed that the accuracy and efficiency of the CARS algorithm in the fused spectra estimation model were superior to those of the WOA algorithm, with an average coefficient of determination (R<sup>2</sup>) value of 0.9226 and an average root mean square error (RMSE) of 0.1984. The accuracy of the estimation models established, based on the different spectral types, to predict the Pb content of the soil was ranked as follows: the CVM model > the XRF spectral model > the vis-NIR spectral model. Within the CVM fusion strategy, the estimation model based on CARS and PLSR (CARS_D1+D2) performed the best, with R<sup>2</sup> and RMSE values of 0.9546 and 0.2035, respectively. Among the three spectral fusion strategies, CVM had the highest accuracy, OPA had the smallest errors, and GRA showed a more balanced performance. This study provides technical means for on-site rapid estimation of Pb content based on multi-source spectral fusion and lays the foundation for subsequent research on dynamic, real-time, and large-scale quantitative monitoring of soil heavy metal pollution using high-spectral remote sensing images. Fusion ; Feature (linguistics) ; Sensor fusion ; Computer science ; Environmental science ; Pattern recognition (psychology) ; Remote sensing ; Artificial intelligence ; Geology Spectroscopy and Chemometric Analyses ; Geochemistry and Geologic Mapping ; Remote-Sensing Image Classification
Authors: Stanislav K. Ignatov | Artëm E. Masunov
Source: RSC Advances (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Unexpected polarization properties of sub-nanosized magnesium clusters The isotropic electrostatic polarizability (IEP) of sub-nanosized magnesium clusters Mg<sub>2</sub>-Mg<sub>32</sub> was studied in an extensive set comprising 1237 structurally unique isomers. These isomers were found in the course of the global search for the potential energy surface minima of the magnesium clusters at the BP86/6-31G(d) level. The calculation of the polarizability at the same DFT level reveals an unexpected property of the IEP: the linear correlation between the polarizability of the most favorable isomers (and only them) and the cluster nuclearity <i>n</i>. Moreover, for each <i>n</i>, the most stable cluster isomer demonstrates nearly minimal IEP value among all found isomers of a given nuclearity. Surprisingly, these observed features are independent of the cluster structures which are quite different. We hypothesize that the energetic favorability of a cluster structure is related to their low polarizability. Apparently, the atoms forming the cluster tend to arrange themselves in such a way as to provide the most compact distribution of the cluster electron density. A possible explanation of the observed trends, their significance for cluster structure prediction, and the practical applications are discussed. Polarizability ; Isotropy ; Magnesium ; Polarization (electrochemistry) ; Chemical physics ; Materials science ; Crystallography ; Computational chemistry ; Chemistry ; Physical chemistry ; Physics ; Organic chemistry ; Molecule ; Optics Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Inorganic Chemistry and Materials