jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ilaria Grigoletto | Enrico Casadei | Filippo Panni | Enrico Valli | Chiara Cevoli | Alessandra Bendini | Diego L. García‐González | F. Focante | Angela Felicita Savino | Stefania Carpino | Tullia Gallina Toschi
Source: European Journal of Lipid Science and Technology (article, 2024-02-13)
Field: Chemistry — Organic Chemistry

Screening tools combined with multivariate data analysis to predict or confirm virgin olive oil classification by the Panel test Abstract A particular aspect of quality control of virgin olive oil (VOO) is the mandatory application, together with chemical and instrumental determinations, of a standardized and official method for sensory assessment. The latter, known as Panel test, is carried out by trained assessors and contributes to the classification of VOOs into three commercial categories (extra virgin, virgin, and lampante). One drawback of this method is related to the large number of samples to be analyzed, compared to the work capacity of a sensory panel, especially during the selection for purchase by companies that blend and market virgin oils and the quality control conducted by the authorities to verify the declared commercial category. For this reason, it is helpful to develop and validate robust and rapid screening methods, based on volatile fingerprints, to preclassify each sample into one of the three commercial categories. Considering the strict relation between volatile compounds and the main sensory attributes (fruity and defects), a gas‐chromatographic volatile fingerprint can be the right choice. In this paper, the comparison of two emerging techniques, namely, headspace‐gas chromatography‐ion mobility spectrometry (HS‐GC‐IMS) and flash‐gas chromatography (FGC), applied on a sample set of 49 VOOs, using calibrations previously built with a larger number of samples, is presented. The number of correctly classified samples, with respect to the commercial category determined by the Panel test, was satisfactory and comparable (92% for HS‐GC‐IMS, and 94% for FGC), confirming the effectiveness of both methods and the robustness of the predictive models. Practical Applications : The demand for rapid screening tools to reduce the number of samples to be assessed by the Panel test has increased in recent years. The validation of robust models and their joint adoption by companies that market VOOs as well as official control bodies could reduce nonconformities and increase the batches of VOO being controlled, thus better protecting the consumer. Therefore, it is desirable to have different tools available to analyze volatile compounds, together with the associated calibration models, along with detailed instructions for their application, to have different alternatives that suit the equipment of individual laboratories. Multivariate statistics ; Olive oil ; Multivariate analysis ; Statistics ; Test (biology) ; Edible oil ; Mathematics ; Food science ; Chemistry ; Botany ; Biology Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Isotope Analysis in Ecology
Authors: Alexander D. Sapp | Carlos Eduardo Díaz-Cano | Jozef Lengyel | Lucía Abarca‐Cabrera | Paula Fraga‐García
Source: ChemNanoMat (article, 2024-09-13)
Field: Chemistry — Organic Chemistry

Amino Acid Adsorption Onto Magnetic Nanoparticles Reveals Correlations With Physicochemical Parameters Abstract We analyze the adsorption of the proteinogenic amino acids (AAs) glutamine, glutamic acid, lysine, tyrosine, proline, and valine onto bare iron oxide nanoparticles (approx. 10 nm). Aiming to identify the governing principles of low molecular weight coronae, which remain underinvestigated, our study covers broad concentration ranges up to the solubility limit of the AAs. Isothermal experiments reveal that the highly soluble AAs valine, proline, and lysine form extensive multilayers on the nanoparticle surface, and infrared measurements indicate intermolecular interactions, particularly with valine and lysine, for higher AA contents. Conversely, the low solubility of tyrosine and glutamic acid restricts their adsorption capacity, despite their higher partitioning on the solid surface. Parameters derived from fitting a classic saturation model seem to align with well‐documented physicochemical properties such as the hydrophobicity and the complexity indices – a promising first step towards formulating design principles. Scaling these parameters by the AA solubility reveals a clear correlation with the adsorption behavior. In adsorption experiments with AA model mixtures, sequential incubation increases the adsorption capacity for valine and proline, whereas simultaneous incubation with these AAs reduces tyrosine's capacity. Future studies should seek to elucidate adsorption patterns to advance our understanding of corona growth and evolution mechanisms. Adsorption ; Nanoparticle ; Magnetic nanoparticles ; Chemistry ; Chemical engineering ; Nanotechnology ; Materials science ; Physical chemistry ; Engineering Nanomaterials for catalytic reactions ; Nanoparticle-Based Drug Delivery ; Supramolecular Self-Assembly in Materials
Authors: Saumita Kar | Bipan Tudu | Rajib Bandyopadhyay
Source: Journal of Food Science and Technology (article, 2024-03-19)
Field: Chemistry — Analytical Chemistry

Statistical machine learning techniques applied to NIR spectral data for rapid detection of sudan dye-I in turmeric powders with optimized pre-processing and wavelength selection Selection (genetic algorithm) ; Computer science ; Wavelength ; Artificial intelligence ; Pattern recognition (psychology) ; Materials science ; Machine learning ; Optoelectronics Dye analysis and toxicity ; Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity
Authors: Samare Rostami | Xavier Gonze
Source: Physical review. B./Physical review. B (article, 2024-07-09)
Field: Materials Science — Materials Chemistry

Approximations in first-principles volumetric thermal expansion determination In the realm of thermal expansion determination, the quasiharmonic approximation (QHA) stands as a widely embraced method for discerning minima of free energies across diverse temperatures such that the temperature dependence of lattice parameters as well as internal atomic positions can be determined. However, this methodology often imposes substantial computational demand, necessitating numerous costly calculations of full phonon spectra in a possibly many-dimensional geometry parameter space. Focusing on the volumetric thermal expansion only, the volume-constrained zero static internal stress approximation (v-ZSISA) within QHA allows one to limit significantly the number of phonon spectra determinations to typically less than 10. The linear Gr\"uneisen approach goes even further with only two phonon spectra determinations to find the volumetric thermal expansion, but a deterioration of the accuracy of the computed thermal expansion is observed, except at low temperatures. We streamline this process by introducing further intermediate approximations between the linear Gr\"uneisen and the v-ZSISA-QHA, corresponding to different orders of the Taylor expansion. The minimal number of phonon spectra calculations that is needed to maintain precise outcomes is investigated. The different approximations are tested on a representative set of 12 materials. For the majority of materials, three full phonon spectra, corresponding to quadratic order, is enough to determine the thermal expansion in reasonable agreement with the v-ZSISA-QHA method up to 800 K. Near perfect agreement is obtained with five phonon spectra. This study paves the way to multidimensional generalizations, beyond the volumetric case, with the expectation of much bigger benefits. Thermal expansion ; Materials science ; Thermal ; Thermodynamics ; Physics ; Composite material Thermal Expansion and Ionic Conductivity ; High-pressure geophysics and materials ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Swati Singh | Sunil Kumar Jakhar | R. Kavitha | Kuldeep Singh Kulhar
Source: E3S Web of Conferences (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Harnessing Nanotechnology and Artificial Intelligence for Precision Agriculture in Smart Cities This short review article, titled “Harnessing Nanotechnology and Artificial Intelligence for Precision Agriculture in Smart Cities,” delves into the fusion of nanotechnology, artificial intelligence (AI), and precision farming to drive sustainable agriculture in alignment with the United Nations’ 2030 Sustainable Development Goals. It spotlights the transformative potential of nanotechnology, encompassing both natural and man-made nanoparticles, to enhance crop growth and mitigate environmental impacts. Nano-fertilizers and nano-pesticides are unveiled as promising strategies for optimizing nutrient availability while minimizing harm to ecosystems. The integration of AI into precision farming, supported by cutting-edge nanoinformatics, emerges as a linchpin for the establishment of safe and sustainable agricultural practices, enabling smart and resilient agriculture. However, as this integrated approach accelerates progress and provides vital insights for addressing contemporary agricultural challenges, it also underscores the paramount importance of scrutinizing nanotechnology’s effects on soil microbial communities and plant health. The phytotoxicity of nanoparticles, contingent upon size, concentration, and plant species, necessitates further examination. In conclusion, this comprehensive article calls for interdisciplinary collaboration to fully exploit the potential of nanotechnology and AI in transforming agriculture, all the while ensuring the preservation of environmental and human health and advancing the global sustainability agenda for agriculture in smart cities by 2030. Nanotechnology ; Agriculture ; Precision agriculture ; Engineering ; Artificial intelligence ; Computer science ; Data science ; Geography ; Materials science Nanoparticles: synthesis and applications
Authors: Sahar S. Alghamdi | Allulu Yousef Alturki | Rizwan Ali | Rasha Saad Suliman | Afrah E. Mohammed | Atheer Al Dairem | Zeyad Alehaideb | Raghad Abdullah Alshafi | Sara Abdulaziz Alghashem | Ishrat Rahman
Source: Journal of Cancer (article, 2024-12-11)
Field: Materials Science — Biomaterials

Pharmacological Profiling of <i>Calotropis Procera</i> and <i>Rhazya Stricta</i>: Unraveling the Antibacterial and Anti-Cancer Potential of Chemically Active Metabolites <b>Background:</b> The increasing prevalence of cancer and bacterial resistance necessitates more effective anti-cancer and anti-bacterial treatments. This study explores the potential of medicinal plants, specifically <i>Calotropis procera</i> (<i>C. procera</i>) and <i>Rhazya stricta</i> (<i>R. stricta</i>), in addressing this need, aiming to uncover new therapeutic interventions. <b>Methods:</b> Various extraction methods for the leaves of <i>C. procera</i> and <i>R. stricta</i> were employed to investigate the anti-bacterial and anti-cancer properties of these herbs. For anti-bacterial testing, extracts were prepared using water, chloroform, and ethyl acetate, and their activity against methicillin-resistant <i>Staphylococcus aureus</i> (<i>S. aureus</i>) (MRSA) and <i>Escherichia coli</i> (<i>E. coli</i>) was assessed. The anti-cancer potential was evaluated through MTT cytotoxicity assays on various cancer cell lines and further testing using high-content imaging (HCI)-Apoptosis Assay and the ApoTox-GloTM Triplex Assay. Liquid chromatography-mass spectrometry (LC-MS) was used to identify the secondary metabolites of <i>C. procera</i>, and computational predictions were performed to assess the activity of these metabolites. <b>Results:</b> The leaf extracts of both <i>C. procera</i> and <i>R. stricta</i> demonstrated antibacterial activity against <i>S. aureus</i> and <i>E. coli</i>. The <i>C. procera</i> ethyl acetate extract exhibited potent anti-cancer effects on several cancer cell lines. The research also revealed a dose-dependent induction of apoptosis and a decline in cell viability. Computational predictions suggested the identified metabolites were active as nuclear receptor ligands and enzyme inhibitors, with good oral bioavailability. Most metabolites were found to be immunologic and cytotoxic, except for proceragenin and calotropone, which were determined to be non-cardiotoxic. <b>Conclusion:</b> The study's findings demonstrate the remarkable cytotoxic and antibacterial effects of <i>C. procera</i> extracts prepared using ethyl acetate. These results pave the way for further <i>in vitro</i> studies to explore the full potential of these extracts and highlight the presence of chemically active metabolites in <i>C. procera</i>, which hold promise as lead molecules for the development of novel therapies targeting bacterial infections and cancer while minimizing potential side effects. Calotropis procera ; Apocynaceae ; Pharmacology ; Traditional medicine ; Biology ; Computational biology ; Medicine Phytochemistry and Bioactive Compounds ; Natural product bioactivities and synthesis ; Phytochemistry and Biological Activities
Authors: Lei Yan | Cen Liu | Muhammad Zain | Minghan Cheng | Zhonhyang Huo | Chengming Sun
Source: Agronomy (article, 2024-10-24)
Field: Chemistry — Analytical Chemistry

Estimation of Rice Protein Content Based on Unmanned Aerial Vehicle Hyperspectral Imaging Identification of nutritious rice varieties through non-destructive detection technology is important for high-quality seed production. With the development of technology, rapid and non-destructive identification methods based on unmanned aerial vehicle (UAV) remote sensing technology are increasingly gaining attention in the scientific community. This study utilized hyperspectral imaging technology to acquire spectral reflectance data from the rice canopy during the grain filling stage. Different models (stepwise multiple linear regression (SMLR) and the Back Propagation Neural Network (BPNN)) for estimating rice protein content based on canopy spectral information were constructed using both multiple stepwise regression and BP neural networks. The results showed that the model based on BPNN estimation performed best for predicting grain protein content, with an R2 = 0.9516 and RMSE = 0.3492, indicating high accuracy and stability in the model. Overall, hyperspectral imaging technology combined with various models could significantly help to identify rice varieties. Further, the current findings provide a technical reference for the selection of high-quality rice varieties in a non-destructive manner. Hyperspectral imaging ; Remote sensing ; Content (measure theory) ; Environmental science ; Computer vision ; Computer science ; Artificial intelligence ; Geography ; Mathematics Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use ; Identification and Quantification in Food
Authors: Chun‐Chun Hung | Menghui Wang | Shiue‐Der Lu | Cheng‐Chien Kuo
Source: IET Science Measurement & Technology (article, 2024-08-23)
Field: Materials Science — Materials Chemistry

Detection of failures in HV surge arrester using chaos pattern with deep learning neural network Abstract As a protective component of HV equipment, the primary function of a surge arrester is to mitigate the impact of surge voltages. When a surge arrester fails, the equipment it protects becomes vulnerable to damage. This study integrates chaotic systems with Convolutional Neural Networks (CNN) to diagnose faults in HV surge arresters. The Partial Discharge (PD) test was initially performed on six HV surge arrester fault models. The Discrete Wavelet Transform (DWT) was performed for filtering the PD signals. Subsequently, the Chen‐Lee chaotic system converted the filtered PD signals into a dynamic error scatter diagram, creating a feature map of various fault states. This feature map was then used as the input layer to train the CNN model. The results demonstrate that the proposed CNN achieved an accuracy of 97.0%, outperforming AlexNet and traditional methods using Histograms of Oriented Gradients (HOG) combined with Support Vector Machine (SVM), Decision Tree (DT), Backpropagation Neural Network (BPNN), and K‐Nearest Neighbor (KNN). This study also incorporates the LabVIEW graphic control software with a fault diagnosis system for HV surge arresters. The PD data can identify the fault type in real‐time, enhancing power equipment maintenance efficiency. Surge arrester ; Artificial neural network ; Backpropagation ; Fault (geology) ; Computer science ; Feature (linguistics) ; Pattern recognition (psychology) ; Lightning arrester ; Artificial intelligence ; Convolutional neural network ; Chaotic ; Wavelet ; Engineering ; Electronic engineering ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Lightning and Electromagnetic Phenomena
Authors: Sharon Berkowicz | Iason Andronis | Anita Girelli | Mariia Filianina | Maddalena Bin | Kyeongmin Nam | Myoungyoup Shin | Markus Kowalewski | Tetsuo Katayama | Nicolás Giovambattista | Kyung Hwan Kim | Fivos Perakis
Source: Nature Communications (article, 2024-12-05)
Field: Materials Science — Materials Chemistry

Supercritical density fluctuations and structural heterogeneity in supercooled water-glycerol microdroplets Recent experiments and theoretical studies strongly indicate that water exhibits a liquid-liquid phase transition (LLPT) in the supercooled domain. An open question is how the LLPT of water can affect the properties of aqueous solutions. Here, we study the structural and thermodynamic properties of supercooled glycerol-water microdroplets at dilute conditions (χ<sub>g</sub> = 3.2% glycerol mole fraction). The combination of rapid evaporative cooling with femtosecond X-ray scattering allows us to outrun crystallization and gain access to the deeply supercooled regime down to T = 229.3 K. We find that the density fluctuations of the glycerol-water solution or, equivalently, its isothermal compressibility, κ<sub>T</sub>, increases upon cooling. This is confirmed by molecular dynamics simulations, which indicate that the presence of glycerol shifts the temperature of maximum κ<sub>T</sub> from T = 230 K in pure water down to T = 223 K in the solution. Our findings elucidate the interplay between the complex behavior of water, including its LLPT, and the properties of aqueous solutions at low temperatures, which can have practical consequences in cryogenic biological applications and cryopreservation techniques. Supercooling ; Aqueous solution ; Thermodynamics ; Crystallization ; Supercritical fluid ; Chemical physics ; Glycerol ; Materials science ; Isothermal process ; Phase transition ; Molecular dynamics ; Chemistry ; Physical chemistry ; Physics ; Organic chemistry ; Computational chemistry Material Dynamics and Properties ; Phase Equilibria and Thermodynamics ; nanoparticles nucleation surface interactions
Authors: Mohammed Alqarni | Amal Adnan Ashour | Alaa Shafie | Ali Alqarni | Mohammed Fareed Felemban | Bandar Saud Shukr | Mohammed Abdullah Alzubaidi | Fahad Saeed Algahtani
Source: Scientific Reports (article, 2024-09-17)
Field: Chemistry — Spectroscopy

Intelligence computational analysis of letrozole solubility in supercritical solvent via machine learning models Supercritical fluids (SCFs) can be used to prepare drugs nanoparticles with improved solubility. SCFs have shown superior advantages in pharmaceutical industry as an environmentally friendly alternative to toxic/harmful organic solvents. They possess gas-like transport characteristics and liquid-like solvation power for solutes. Evaluation of chemotherapeutic drugs' solubility in supercritical carbon dioxide (SCCO<sub>2</sub>) has been recently an attractive subject for developing this method in pharmaceutical sector. To reach this purpose, the utilization of accurate models is of great necessity to estimate experimental-based solubility data. In this paper, the authors tried to employ machine learning (ML) approaches to estimate the solubility of Letrozole (LET) drug as chemotherapeutic agent and correlate its values in wide ranges of temperature and pressure. To do this, PAR (Passive Aggressive Regression), RF (Random Forest), and RBF-SVM are the models used (Support Vector Machine with RBF kernel). These models optimized in terms of their hyper-parameters using GA algorithm. The optimized PAR, RF, RBF-SVM models obtained coefficients of determination (R-squared) of 0.8277, 0.9534, and 0.9947. Also, the MSE error rate of the models are 0.1342, 0.0305, and 0.0045, in the same order. The final result of the evaluations shows the optimized RBF-SVM model as the most appropriate model in this research. The model exhibits a maximum prediction error of 0.1289. Solubility ; Supercritical fluid ; Computer science ; Letrozole ; Artificial intelligence ; Machine learning ; Chemistry ; Organic chemistry ; Biology ; Genetics Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Crystallization and Solubility Studies
Authors: Sthanley R. de Lima | Thiago Vial Costa | Tácio T. S. Santos | Dora G. Felipe | Teófanes B. Serna | Acácio A. Andrade | Viviane Pilla
Source: Colorants (article, 2024-10-21)
Field: Materials Science — Materials Chemistry

Optical Characterization of Fluorescent Chitosan-Based Carbon Dots Embedded in Aqueous Natural Dye (1) Background: This work evaluated the optical characterization of aqueous fluorescent chitosan-based carbon dots (or carbon nanoparticles CNPs) embedded in natural dye for potential functional packaging applications. Chitosan-based materials are nontoxic, biodegradable, biocompatible, bactericidal, and produced from renewable polymer sources. Anthocyanins are pigments of different colors with a large range of potential applications, such as in bioindicators and biomonitoring; (2) Methods: The CNPs were synthetized in aqueous solutions using chitosan as a carbon source. The natural dye was extracted from the leaves of Tradescantia pallida Purpurea in aqueous solutions. The fluorescence quantum efficiency (η) and fluorescence lifetime (τ) were determined using the mode-mismatched pump–probe thermal lens (TL) technique and time-resolved fluorescence lifetimes (TRFL) measurements, respectively; (3) Results: The η and τ were measured for CNPs embedded in natural dye solution at different concentrations (5.2, 12.09, and 21.57 mass percentage composition). The η and τ photophysical parameters obtained for CNPs embedded in natural dye were compared with those of other CNPs synthesized using different carbon sources, such as leaves, seeds, and protein; (4) Conclusions: Fluorescence spectra and time-resolved fluorescence measurements corroborate the TL results, and relatively high values of η were obtained for the CNP synthesized and embedded in natural dye. Fluorescence ; Characterization (materials science) ; Chitosan ; Aqueous solution ; Carbon fibers ; Materials science ; Nanotechnology ; Chemical engineering ; Chemistry ; Organic chemistry ; Composite material ; Optics Carbon and Quantum Dots Applications ; Quantum Dots Synthesis And Properties ; Nanocluster Synthesis and Applications