jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Filip Grgić | Tamara Jurina | Davor Valinger | Jasenka Gajdoš Kljusurić | Ana Jurinjak Tušek | Maja Benković
Source: Micromachines (article, 2022-10-31)
Field: Chemistry — Analytical Chemistry

Near-Infrared Spectroscopy Coupled with Chemometrics and Artificial Neural Network Modeling for Prediction of Emulsion Droplet Diameters There is increased interest in the food industry for emulsions as delivery systems to preserve the stability of sensitive biocompounds with the aim of improving their bioavailability, solubility, and stability; maintaining their texture; and controlling their release. Emulsification in continuously operated microscale devices enables the production of emulsions of controllable droplet sizes and reduces the amount of emulsifier and time consumption, while NIR, as a nondestructive, noninvasive, fast, and efficient technique, represents an interesting aspect for emulsion investigation. The aim of this work was to predict the average Feret droplet diameter of oil-in-water and oil-in-aqueous mint extract emulsions prepared in a continuously operated microfluidic device with different emulsifiers (PEG 1500, PEG 6000, and PEG 20,000) based on the combination of near-infrared (NIR) spectra with chemometrics (principal component analysis (PCA) and partial least-squares (PLS) regression) and artificial neural network (ANN) modeling. PCA score plots for average preprocessed NIR spectra show the specific grouping of the samples into three groups according to the emulsifier used, while the PCA analysis of the emulsion samples with different emulsifiers showed the specific grouping of the samples based on the amount of emulsifier used. The developed PLS models had higher <i>R</i><sup>2</sup> values for oil-in-water emulsions, ranging from 0.6863 to 0.9692 for calibration, 0.5617 to 0.8740 for validation, and 0.4618 to 0.8692 for prediction, than oil-in-aqueous mint extract emulsions, with <i>R</i><sup>2</sup> values that were in range of 0.8109-0.8934 for calibration, 0.5017-0.6620, for validation and 0.5587-0.7234 for prediction. Better results were obtained for the developed nonlinear ANN models, which showed <i>R</i><sup>2</sup> values in the range of 0.9428-0.9917 for training, 0.8515-0.9294 for testing, and 0.7377-0.8533 for the validation of oil-in-water emulsions, while for oil-in-aqueous mint extract emulsions <i>R</i><sup>2</sup> values were higher, in the range of 0.9516-0.9996 for training, 0.9311-0.9994 for testing, and 0.8113-0.9995 for validation. Chemometrics ; Artificial neural network ; Emulsion ; Spectroscopy ; Infrared spectroscopy ; Materials science ; Infrared ; Analytical Chemistry (journal) ; Biological system ; Artificial intelligence ; Computer science ; Optics ; Chemical engineering ; Engineering ; Chemistry ; Machine learning ; Chromatography ; Physics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Analytical Chemistry and Chromatography
Authors: Henrique Boschetti Pereira | Tarcisio Henrique Carvalhaes Pimentel | Carlos Alberto da Silva | Zehbour Panossian | C.R.F. Azevedo
Source: Materials Research (article, 2022-01-01)
Field: Materials Science — Metals and Alloys

Influence of Welding Energy on Intergranular and Pitting Corrosion Susceptibility of UNS S32205 Duplex Stainless-steel Joints This study analysed the integrity of welded joints of a UNS S32205 duplex stainless steel, using different welding energies: 0.5 kJ.mm-1, 1.0 kJ.mm-1, and 3.5 kJ.mm-1. Microstructural characterisation, tensile testing, intergranular attack susceptibility testing (ASTM A262 practice A) and pitting/crevice corrosion resistance tests (critical pitting temperature test, ASTM G48 practice C) were performed in these welded joints. The results showed that the tensile properties of the welded joints did not vary significantly with the welding energy. The microstructure's ferrite content in the molten zone (MZ) was proportional to the welding energy: the ferrite concentration in the 0.5 kJ.mm-1 weldings was approximately 75%. When the welding energy increased to 3.5 kJ.mm-1, the amount of ferrite dropped to 54%. The 3.5 kJ.mm-1 welded joint featured a comparatively higher proportion of coarser austenite grains. The HAZ of the 3.5 kJ.mm-1 welded joint was comparatively more susceptible to the intergranular attack along the α/γ interfaces, while in the base metal and the other two welded joints, the intergranular attack along the α/γ interfaces was not prominent. Additionally, the pitting corrosion took place preferentially in the ferrite phase of the HAZ for all welding conditions. Welding ; Materials science ; Intergranular corrosion ; Metallurgy ; Ferrite (magnet) ; Pitting corrosion ; Austenite ; Microstructure ; Base metal ; Heat-affected zone ; Ultimate tensile strength ; Corrosion ; Composite material Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Fatigue and fracture mechanics
Authors: Maharajan Sivasubramanian | Li-Jie Lin | Yu-Chao Wang | Chung‐Shi Yang | Leu‐Wei Lo
Source: Frontiers in Chemistry (review, 2022-08-19)
Field: Materials Science — Biomaterials

Industrialization’s eye view on theranostic nanomedicine The emergence of nanomedicines (NMs) in the healthcare industry will bring about groundbreaking improvements to the current therapeutic and diagnostic scenario. However, only a few NMs have been developed into clinical applications due to a lack of regulatory experience with them. In this article, we introduce the types of NM that have the potential for clinical translation, including theranostics, multistep NMs, multitherapy NMs, and nanoclusters. We then present the clinical translational challenges associated with NM from the pharmaceutical industry's perspective, such as NMs' intrinsic physiochemical properties, safety, scale-up, lack of regulatory experience and standard characterization methods, and cost-effectiveness compared with their traditional counterparts. Overall, NMs face a difficult task to overcome these challenges for their transition from bench to clinical use. Nanomedicine ; Industrialisation ; Nanotechnology ; Medicine ; Materials science ; Political science ; Nanoparticle Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Jalil Shafagh-Kolvanagh | Hassan Dehghanian | Adel Dabbagh Mohammadi-Nassab | Mohammad Moghaddam | Yaegoob Raei | سعید زهتاب سلماسی | Peyvand Samimifar | Soheila Abdoli | Behnam Gholizadeh-Khajeh
Source: Scientific Reports (article, 2022-11-10)
Field: Chemistry — Analytical Chemistry

Machine learning-assisted analysis for agronomic dataset of 49 Balangu (Lallemantia iberica L.) ecotypes from different regions of Iran The Balangu (Lallemantia iberica) species have a high gastronomical impact in the Middle East and Balkan region. It is widely used in the local food industry, such as confectionery, edible oil, and protein food. In this study, 49 ecotypes were collected from different regions of Iran. 37 agronomic traits were measured during the growing season and at harvest time. To find the correlation between the grain yield per unit area, grain yield per single plant (GYSP), oil percent (OP), and protein percent (PP) with other measured traits, which these were utilized as the labels of different machine learning (ML) procedures including Linear Regression (LR), Support Vector Regression (SVR), Random Forest Regression (RFR), and Gradient Boosting Decision Tree Regression (GBDTR). It was observed that there is a linear relationship between the measured agronomic traits and the considered labels. So, the LR, RFR, and GBDTR models showed the lowest mean absolute error, mean square error, and root mean square error than SVR models and good prediction ability of the test data. Although, the RFR and GBDTR have naturally lower bias than other methods in this study, but the GBDTR scheme is preferred because of the over-fitting shortcoming of the RFR technique. The GBDTR method showed better results rather than the other ML regression methods according to the RMSE 3.302, 0.040, 0.028, and 0.060 for GYUA, GYSP, OP, and PP, respectively. Ecotype ; Biology ; Computer science ; Botany Spectroscopy and Chemometric Analyses ; Nuts composition and effects ; Horticultural and Viticultural Research
Authors: Zhao Xiaoqing | Lei Pang | Lianming Wang | Sen Men | Lei Yan
Source: Mathematical and Computational Applications (article, 2022-12-14)
Field: Chemistry — Analytical Chemistry

Deep Convolutional Neural Network for Detection and Prediction of Waxy Corn Seed Viability Using Hyperspectral Reflectance Imaging This paper aimed to combine hyperspectral imaging (378–1042 nm) and a deep convolutional neural network (DCNN) to rapidly and non-destructively detect and predict the viability of waxy corn seeds. Different viability levels were set by artificial aging (aging: 0 d, 3 d, 6 d, and 9 d), and spectral data for the first 10 h of seed germination were continuously collected. Bands that were significantly correlated (SC) with moisture, protein, starch, and fat content in the seeds were selected, and another optimal combination was extracted using a successive projection algorithm (SPA). The support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), and deep convolutional neural network (DCNN) approaches were used to establish the viability detection and prediction models. During detection, with the addition of different levels, the recognition effect of the first three methods decreased, while the DCNN method remained relatively stable (always above 95%). When using the previous 2.5 h data, the prediction accuracy rate was generally higher than the detection model. Among them, SVM + full band increased the most, while DCNN + full band was the highest, reaching 98.83% accuracy. These results indicate that the combined use of hyperspectral imaging technology and the DCNN method is more conducive to the rapid detection and prediction of seed viability. Hyperspectral imaging ; Support vector machine ; Artificial intelligence ; Convolutional neural network ; Random forest ; Pattern recognition (psychology) ; Computer science ; Mathematics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Xuan Zhang
Source: Journal of Nuclear Materials (article, 2022-01-29)
Field: Materials Science — Materials Chemistry

Unveiling microstructure-property correlations in nuclear materials with high-energy synchrotron X-ray techniques Synchrotron ; Microstructure ; Nuclear engineering ; Materials science ; Property (philosophy) ; Synchrotron radiation ; Nuclear material ; Nuclear data ; Plutonium ; Engineering physics ; Computer science ; Nanotechnology ; Nuclear physics ; Physics ; Metallurgy ; Engineering ; Neutron Nuclear Materials and Properties ; Nuclear Physics and Applications ; X-ray Diffraction in Crystallography
Authors: İsmail Yıldız
Source: Open Chemistry (article, 2022-01-01)
Field: Materials Science — Biomaterials

Tribological properties and characterization of borided Co–Mg alloys Abstract In this study, tribological properties and characterization of borided Co–Mg alloys were investigated. Cobalt–magnesium (CM) alloys with 97% Co–3% Mg composition were borided at temperatures of 850–900°C and for 1.5–4.5 h with solid boriding. The properties of the resulting boride layers were measured and determined by microhardness, scanning electron microscopy (SEM), X-ray diffraction (XRD), density, and surface roughness testers. XRD analysis results after boriding found CoB, Co 2 B, and Co phases. Depending on the boriding time and temperature, the thickness of the boride layer for the CM alloy varied from 51 to 138 µm. The boride layer had a hardness varying between 1,674 and 1,956 HV 0.05 for the CM alloy, while the Vickers hardness value of untreated Cobalt was 52 HV 0.05 . The wear tests were carried out in a ball-disc arrangement under a dry friction condition at room temperature with an applied load of 10 N and with a sliding speed of 0.3 m/s at a sliding distance of 250 m. It was observed that the wear rate of borided and unborided CM alloy ranged from 25.89 × 10 −5 to 94.95 × 10 −5 mm 3 /N m. As a consequence of the findings, the author reported that boriding CM alloys in the given conditions can make a difference for different application areas. Boriding ; Boride ; Materials science ; Indentation hardness ; Metallurgy ; Tribology ; Alloy ; Scanning electron microscope ; Microstructure ; Composite material Magnesium Alloys: Properties and Applications ; Metal and Thin Film Mechanics ; Aluminum Alloys Composites Properties
Authors: Gosaitse Tubatsi | Lemme P. Kebaabetswe | Ndeke Musee
Source: PROTEOMICS (review, 2022-09-15)
Field: Materials Science — Materials Chemistry

Proteomic evaluation of nanotoxicity in aquatic organisms: A review The alteration of organisms protein functions by engineered nanoparticles (ENPs) is dependent on the complex interplay between their inherent physicochemical properties (e.g., size, surface coating, shape) and environmental conditions (e.g., pH, organic matter). To date, there is increasing interest on the use of 'omics' approaches, such as proteomics, genomics, and others, to study ENPs-biomolecules interactions in aquatic organisms. However, although proteomics has recently been applied to investigate effects of ENPs and associated mechanisms in aquatic organisms, its use remain limited. Herein, proteomics techniques widely applied to investigate ENPs-protein interactions in aquatic organisms are reviewed. Data demonstrates that 2DE and mass spectrometry and/or their combination, thereof, are the most suitable techniques to elucidate ENPs-protein interactions. Furthermore, current status on ENPs and protein interactions, and possible mechanisms of nanotoxicity with emphasis on those that exert influence at protein expression levels, and key influencing factors on ENPs-proteins interactions are outlined. Most reported studies were done using synthetic media and essay protocols and had wide variability (not standardized); this may consequently limit data application in actual environmental systems. Therefore, there is a need for studies using realistic environmental concentrations of ENPs, and actual environmental matrixes (e.g., surface water) to aid better model development of ENPs-proteins interactions in aquatic systems. Proteomics ; Nanotoxicology ; Aquatic ecosystem ; Biochemical engineering ; Metaproteomics ; Biology ; Computational biology ; Environmental chemistry ; Chemistry ; Nanotechnology ; Biochemistry ; Nanoparticle Nanoparticles: synthesis and applications ; Microplastics and Plastic Pollution ; Graphene and Nanomaterials Applications
Authors: Bruno Zêzere | Inês Portugal | Carlos M. Silva | José R. B. Gomes
Source: Journal of Molecular Liquids (article, 2022-12-15)
Field: Chemistry — Spectroscopy

Diffusivities of ketones and aldehydes in liquid ethanol by molecular dynamics simulations The tracer diffusion coefficients of six ketones (propanone, butanone, pentan-2-one, pentan-3-one, hexan-2-one, hexan-3-one) and six aldehydes (methanal, ethanal, propanal, butanal, pentanal and hexanal) in liquid ethanol were computed by classical molecular dynamics (MD) simulations over 303.15–333.15 K and 1–150 bar. The calculated tracer diffusion coefficients, D12MD, compared very satisfactorily with experimental data from the literature, with average absolute relative deviations (AARD) between 9.48 % and 12.18 % for ketones, and between 6.30 % and 9.11 % for aldehydes. The trends of D12MD with solute size and temperature were accurately simulated in all cases, while the weaker influence of pressure was not rigorously reached in all cases when jumping from 1 to 75 bar and then to 150 bar. Furthermore, a temperature-based correction to D12MD was introduced, which decreased the AARD values of ketones to the range 1.52–5.16 % and aldehydes to 2.94–3.45 %. The analyses of the spatial distribution functions and coordination numbers show that ethanol has more affinity with ketones than with aldehydes, though such affinity difference is not always translated to the computed D12MD of ketone-aldehyde isomers. Nevertheless, the experimental diffusivities of both families of compounds are only ca. 7 % different, hence within the uncertainties associated with the calculated results. Chemistry ; Ketone ; Hexanal ; Bar (unit) ; Aldehyde ; Molecular dynamics ; Ethanol ; Diffusion ; Computational chemistry ; Thermodynamics ; Organic chemistry Diffusion Coefficients in Liquids ; Thermodynamic properties of mixtures ; Phase Equilibria and Thermodynamics
Authors: Haichun Zuo
Source: International Journal of Advanced Computer Science and Applications (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Analysis and Detection of Tomatoes Quality using Machine Learning Algorithm and Image Processing Grading of agricultural products methods based on artificial intelligence is more important. Because these methods have the ability to learn and thus increase the flexibility of the system. In this paper, image processing systems, detection analysis methods, and artificial intelligence are used to grade tomatoes, and the success rate of grading these methods is compared with each other. However, the purpose of this study is to obtain a solution to detect appearance defects and grade and sort the tomato crop and provide an efficient system in this field. A visual dataset is created, to investigate the approach of image processing and machine learning based on a tomato image. Tomato models are placed individually under the camera and samples are classified in a lighting box away from the effects of ambient light. Data sets have been used in three types of first, second, and third quality categories. It should be noted that quality category one has the best quality and quality category two has the medium quality and category three has the worst quality, Also, each data class contains 80 samples. Using tomato appearance such as size, texture, color, shape, etc. Image processing is performed for extract features. Tomato images are pre-processed for optimization. Then, to prepare for classification, the dimensions of the images are reduced by principal component analysis (PCA). Three categories of an artificial neural network, a support vector machine, and a decision tree are compared to show the most efficient support machine. The analysis is examined in two classes and three classes. The support vector machine has the best accuracy compared to other methods so this rate is 99.9% for two classes and 99.79% for three classes. Computer science ; Artificial intelligence ; sort ; Artificial neural network ; Support vector machine ; Image processing ; Principal component analysis ; Decision tree ; Machine learning ; Pattern recognition (psychology) ; Machine vision ; Image (mathematics) ; Database Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Hongqiang Wu | Yuqi Miao | Yong Li | Huashan Yan | Jinbiao Tan | Sen Qiu | Hao Wu | Tingsheng Qiu
Source: Processes (article, 2022-12-24)
Field: Materials Science — Biomaterials

Density Functional Theory Study on the Adsorption of Fe(OH)2+ on Kaolinite Surface in Water Environment Fe impurity is abundant in rare earth leaching solutions. The optimal hydrate structure of Fe(OH)2+ was calculated based on the quantum chemical in the water environment to investigate the microscopic occurrence of Fe impurity on kaolinite surfaces. The adsorption structure and bonding mechanism (including outer and inner layer) of hydrate Fe(OH)2+ on the kaolinite (001) surface were investigated. According to the results, the stable hydrate form of Fe(OH)2+ is [Fe(OH)(H2O)5]2+. Hydrated Fe(OH)2+ has a tendency to adhere to the Si-O surface in the form of outer layer adsorption. Adsorbate tends to adsorb to the Ou (deprotonated upright hydroxyl) site, where it generates a monodentate adsorption compound, and to the Ol and Ou (deprotonated lying and upright hydroxyl) sites, where it generates a bidentate adsorption compound if inner layer adsorption occurs. The Mulliken population and density of state analysis demonstrate that the ionic properties of Fe-Os in the inner layer adsorption compound are reduced and Fe-Os bonds are filled with strong bonding and weak antibonding orbitals. More chemical bonds are formed in the bidentate adsorption compound, and the bidentate adsorption has lower adsorption energy. Therefore, in the process of inner layer adsorption, bidentate adsorption is more likely to occur. Adsorption ; Kaolinite ; Chemistry ; Inorganic chemistry ; Density functional theory ; Deprotonation ; Hydrate ; Mulliken population analysis ; Denticity ; Antibonding molecular orbital ; Ionic bonding ; Hydrogen bond ; Molecular orbital ; Physical chemistry ; Crystallography ; Molecule ; Computational chemistry ; Ion ; Crystal structure ; Atomic orbital ; Organic chemistry ; Mineralogy Clay minerals and soil interactions ; Iron oxide chemistry and applications ; Geochemistry and Elemental Analysis
Authors: Maxime Sueur | Julien Maillard | Oscar Lacroix‐Andrivet | Christopher P. Rüger | Pierre Giusti | Hélène Lavanant | Carlos Afonso
Source: ChemRxiv (preprint, 2022-11-17)
Field: Chemistry — Spectroscopy

PyC2MC: an open-source software solution for visualization and treatment of high-resolution mass spectrometry data Complex molecular mixtures are encountered in almost all research disciplines, such as biomedical ‘omics, petroleomics, and environmental sciences. State-of-the-art characterization of sample materials related to these fields, deploying high-end instrumentation, allow for gathering humongous quantity of molecular composition data. One established technological platform is ultrahigh-resolution mass spectrometry, e.g., Fourier-transform mass spectrometry (FT-MS). However, the huge amounts of data acquired in FT-MS often result in tedious data treatment and visualization. FT-MS analysis of complex matrices can easily lead to single mass spectra with more than 10,000 attributed unique molecular formulae. Sophisticated software solutions to conduct these treatment and visualization attempts from commercial and non-commercial origins exist. However, existing applications have distinct drawbacks, such as focusing on only one type of graphic representation, being unable to handle large datasets, or not being publicly available. In this respect, we developed a software, within the international complex matrices molecular characterization joint lab (IC2MC), named “python tools for complex matrices molecular characterization” (PyC2MC). This piece of software will be open-source and free to use. PyC2MC is written under python 3.9.7 and relies on well-known libraries such as pandas, NumPy, or SciPy. It is provided with a graphical user interface developed under PyQt5. The two options for execution, 1) user-friendly route with pre-packed executable file or 2) running the main python script through a Python interpreter, ensure a high applicability but also an open characteristic for further development by the community. Both are available on the GitHub platform (https://github.com/iC2MC/PyC2MC_viewer). Python (programming language) ; Visualization ; Computer science ; Software ; Executable ; Graphical user interface ; Mass spectrometry ; Computational science ; Open source ; Software engineering ; Data mining ; Programming language ; Chemistry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography