jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Claudia Ortíz | Néstor Enrique Cerquera Peña | Jennifer Katiusca Castro Camacho | J. Gállego | Rodrigo Torres | Mauricio Herrera | Daniel Ricardo Delgado
Source: Revista Colombiana de Ciencias Químico Farmacéuticas (article, 2024-04-06)
Field: Materials Science — Materials Chemistry

Preferential solvation of triclocarban in N-methyl-2-pyrrolidone + water cosolvent mixtures according to the Inverse Kirkwood-Buff Integrals (IKBI) method and correlation of solubility by means of some mathematical models Introduction: Solubility is an important thermodynamic property due to its role in product development, as well as the understanding of biological processes. This research aims to evaluate the preferential solvation parameter (δx1,3) of the triclocarban (TCC) solubility in N-methyl-2-pyrrolidone + water cosolvent mixtures and to assess some correlational and predictive mathematical models of concern to the pharmaceutical industry. Calculations: δx1,3 was determined from experimental data following the Inverse Kirkwood-Buff Integrals model (IKBI). The mathematical models were developed using Python, and functions for each model were fitted by non-linear least squares using the libraries scipy.optimize. curve_fit, and sklearn.model_selection. Results: According to the δx1,3 heat, TCC has preferential solvation by water in water-rich mixtures, and preferential solvation by N-methyl-2-pyrrolidone in intermediate and N-methyl-2-pyrrolidone-rich mixtures. The models Yalkowsky–Roseman–van’t Hoff, Wilson, Modified Wilson, NRTL, van’t Hoff, Apelblat, and Buchowski–Ksiazaczak 𝜆ℎ were assessed, finding good correlations with all. Conclusions: The TCC solubility increase in N-methyl 2-pyrrolidone + water cosolvent mixtures with increasing N-methyl-2-pyrrolidone (NMP) concentration may be related to the rise in the local mole fraction (x1,3 L) of NMP in the TCC solvation sphere. Regarding the mathematical models, the Yalkowsky–Roseman–van’t Hoff model can be considered the most versatile due to its capability estimate solubility data as a function of both temperature and cosolvent composition, given a limited range of experimental data. Solvation ; Solubility ; Triclocarban ; Thermodynamics ; Inverse ; Chemistry ; Physical chemistry ; Organic chemistry ; Mathematics ; Physics ; Molecule ; Geometry Crystallization and Solubility Studies ; Chemical and Physical Properties in Aqueous Solutions ; Thermal and Kinetic Analysis
Authors: Daniela López-Betancur | Tonatiuh Saucedo-Anaya | Carlos Guerrero-Méndez | David Navarro-Solís | Luis Silva-Acosta | Antonio Robles-Guerrero | Salvador Gómez-Jiménez
Source: International Journal of Combinatorial Optimization Problems and Informatics. (article, 2024-06-12)
Field: Chemistry — Analytical Chemistry

Evaluating CNN Models and Optimization Techniques for Quality Classification of Dried Chili Peppers (Capsicum annuum L.) This paper analyzes Convolutional Neural Network (CNN) models for classifying dried chili pepper quality. The models categorize images into five categories: “Extra”, “First Class”, “Second Class”, “Trash”, and “Empty”, each representing different qualities and scenarios in a sorting machine. We compared architectures from the Torchvision library, including ResNet, ResNeXt, Wide_ResNet, and RegNet using Transfer Learning (TL) in a feature extraction approach. All models employ residual blocks, an innovative technique enhancing deep learning performance. The models were evaluated using crossvalidation and metrics such as Precision, Recall, Specificity, F1-score, Geometric_mean, Index of Balanced Accuracy, and the Matthews Correlation Coefficient. They were trained using SGD, Adagrad, and Adam optimizers. Our findings suggest that ResNet-152, trained with the Adagrad optimizer, achieved the highest mean validation accuracy of 96.62%. The selected model can assist agricultural producers in classifying their products according to international standards. Capsicum annuum ; Chili pepper ; Horticulture ; Botany ; Computer science ; Biology ; Pepper Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Milionis Anna | Kottaridi Klimentia | Vasilis Nikolaidis | Dimopoulos F. Ioannis | Demopoulos Vasilis
Source: Journal of Sensory Studies (article, 2024-12-01)
Field: Chemistry — Analytical Chemistry

Sensory Profiling and Classification of Greek Olive Oil Varieties Using Principal Component Analysis and Learning Vector Quantization ABSTRACT This study aimed to explore whether the three primary sensory attributes—fruitiness, bitterness, and pungency—could be used to discriminate among seven Greek olive oil varieties. Sensory data from 110 extra virgin olive oil samples, collected over 9 years by the Kalamata Olive Oil Taste Laboratory, were analyzed using principal component analysis (PCA) and learning vector quantization (LVQ). PCA revealed that varieties with overlapping sensory profiles formed three distinct sensory groups: Koroneiki‐Lianolia‐Chalkidikis, Makri‐Manaki‐Megaritiki, and Athenolia. Consequently, the research focus shifted to evaluating the ability of these sensory attributes to differentiate between groups of varieties rather than individual varieties. LVQ, validated through leave‐one‐out cross‐validation (LOOCV), classified the olive oil samples into these sensory groups with 80% accuracy. The results demonstrate that the intensity levels of fruitiness, bitterness, and pungency provide valuable information for distinguishing groups of varieties with similar sensory profiles. While the study benefits from a robust nine‐year dataset, limitations include a relatively small sample size, potentially limiting generalizability, and a selection criterion that excludes samples with lower fruitiness scores, possibly introducing a degree of bias. Practical Applications The study's findings provide practical applications in various areas. For panel training, the identified sensory profiles can serve as benchmarks, helping tasters recognize and classify olive oil varieties. Producers can utilize these sensory profiles for consistent quality control and strategic blending, ensuring their products meet desired sensory standards. Finally, these profiles can be used in culinary education, enabling chefs and food enthusiasts to select and pair olive oils more effectively with different dishes. Principal component analysis ; Olive oil ; Sensory system ; Learning vector quantization ; Profiling (computer programming) ; Pattern recognition (psychology) ; Artificial intelligence ; Mathematics ; Vector quantization ; Computer science ; Food science ; Psychology ; Cognitive psychology ; Biology Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Advanced Chemical Sensor Technologies
Authors: Tara Cohen | Chalom Zemmour | Ora T. Cohen | Ofra Benny
Source: Nanomaterials (article, 2024-11-25)
Field: Materials Science — Materials Chemistry

Elongated Particles Show a Preferential Uptake in Invasive Cancer Cells Mechanically driven cellular preference for drug carriers can enhance selectivity in cancer therapy, underscoring the importance of understanding the physical aspects of particle uptake. In this study, it was hypothesized that elongated particles might be preferentially taken up by deformable, aggressive cancer cells compared to normal cells. Two film-stretching methods were tested for 0.8-2.4 μm polystyrene (PS) particles: one based on solubility in organic solvents and the other on heat-induced softening. The heat-induced method produced more homogenous particle batches, with a standard deviation in the particle aspect ratio of 0.42 compared to 0.91 in the solvent-based method. The ability of cells to engulf elongated PS particles versus spherical particles was assessed in two subsets of human melanoma A375 cells. In the more aggressive cancer cell subset (A375+), uptake of elongated PS particles increased by 10% compared to spherical particles. In contrast, the less aggressive subset (A375-) showed a 25% decrease in uptake of elongated particles. This resulted in an uptake ratio between A375+ and A375- that was 1.5 times higher for elongated PS particles than for spherical ones. To further demonstrate relevance to drug delivery, elongated paclitaxel-loaded biodegradable, slow-releasing poly(lactic-co-glycolic) acid (PLGA) particles were synthesized. No significant difference in cytotoxic effect was observed between A375+ and A375- cells treated with spherical drug-loaded particles. However, treatment with ellipsoidal particles led to a significantly enhanced cytotoxic effect in aggressive cells compared to less aggressive cells. These findings present promising directions for tailored cancer drug delivery and demonstrate the importance of particle physical properties in cellular uptake and drug delivery mechanisms. Cancer cell ; Biophysics ; Cancer ; Nanotechnology ; Materials science ; Chemistry ; Cancer research ; Biology ; Genetics Nanoparticles: synthesis and applications ; Nanoparticle-Based Drug Delivery ; Air Quality and Health Impacts
Authors: Insung Seo | S. Tanaka | Mitsuru ENDO | Yoshihiro Gohda
Source: Applied Physics Express (article, 2024-07-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Prediction of NdFe<sub>16</sub>-based permanent-magnet compounds with high magnetization Abstract We find a candidate for new permanent-magnet materials with the 1–16 stoichiometry on the basis of first-principles calculations utilizing a materials database. An extremely iron-rich NdFe 16 compound shows exceptionally high saturation magnetization with an adequate magnetocrystalline anisotropy energy and Curie temperature. Although it has an excessive formation energy in its binary composition, introducing interstitial light elements into NdFe 16 successfully decreases the formation energy while maintaining its superior magnetic properties. The density of states explains the alteration of magnetization from incorporating light elements. Magnetocrystalline anisotropy ; Magnetization ; Magnet ; Materials science ; Condensed matter physics ; Stoichiometry ; Anisotropy ; Saturation (graph theory) ; Curie temperature ; Magnetic anisotropy ; Ferromagnetism ; Chemistry ; Magnetic field ; Physics ; Physical chemistry ; Optics ; Mathematics Magnetic Properties of Alloys ; Magnetic properties of thin films ; Magnetic and transport properties of perovskites and related materials
Authors: Μάρκος Πούλος | Konstantinos Papagelis | Emmanuel Ν. Koukaras | G. Kalosakas | Giorgia Fugallo | Konstantinos Termentzidis
Source: Physical review. B./Physical review. B (article, 2024-08-29)
Field: Materials Science — Materials Chemistry

Temperature dependence of phonon energies and lifetimes in single- and few-layered graphene In this work, we have studied the phonon properties of multilayered graphene with the use of molecular dynamics simulations and the $k$-space velocity autocorrelation sequence method, for which we provide a theoretical proof. We calculate the phonon dispersion curves, densities of states, and lifetimes $\ensuremath{\tau}$ of few-layered graphene consisting of 1--5 layers and graphite. $\mathrm{\ensuremath{\Gamma}}$-point phonon energies and lifetimes are investigated for different temperatures ranging from 80 to 1000 K. The study focuses on the impact of the interlayer interaction and temperature on the energies and lifetimes of the $\mathrm{\ensuremath{\Gamma}}$-point phonons, as well as the type of interlayer potential used. For the latter we used the Kolmogorov-Crespi and the Lennard-Jones potentials. We have found that the number of layers $N$ has little effect on the intralayer ZO and $G$ mode energies and greater effect on the interlayer shear and breathing modes, while $\ensuremath{\tau}$ is generally affected by $N$ for all modes, except for the layer shear mode. The influence of $N$ on the lifetimes was also found to be independent of the type of potential used. For the Raman-active $G$ phonon, our calculations show that the lifetime increases with $N$ and that this increase is directly connected to the strength of the interlayer coupling and how this is modeled. Phonon ; Graphene ; Condensed matter physics ; Graphite ; Raman spectroscopy ; Materials science ; Coupling (piping) ; Physics ; Nanotechnology ; Quantum mechanics Graphene research and applications ; Thermal properties of materials ; Boron and Carbon Nanomaterials Research
Authors: Milagros Amichetti | Bruno A. Franco | Marı́a M. Zanardi | Ariel M. Sarotti
Source: Magnetic Resonance in Chemistry (article, 2024-10-30)
Field: Chemistry — Spectroscopy

To Gibbs or Not to Gibbs Effect of Entropic Contribution in the NMR Calculations of Flexible and Polar Molecules—Updating the DP4+App The application of quantum-based NMR methods for the structural elucidation of natural and unnatural products has grown significantly. However, accurately calculating the conformational landscape of flexible molecules with intricate intramolecular hydrogen bonding (IHB) networks continues to be a major challenge. In this work, we thoroughly studied the effect of entropic contributions (trough Gibbs free energies calculations) in the DP4+ performance. Our results show that to solve biased systems with strong IHB interactions requires computing the Boltzmann contributions using Gibbs free energies computed with at least triple-ξ basis set and SMD solvation model. In response to this finding, we have updated our DP4+App, a user-friendly Python applet that automates the entire process of calculating DP4+ probabilities. In the new version, the program allows for calculating of conformational contributions at any selected theory level, using either SCF or Gibbs free energies. Chemistry ; Gibbs free energy ; Intramolecular force ; Solvation ; Hydrogen bond ; Molecule ; Computational chemistry ; Statistical physics ; Thermodynamics ; Stereochemistry ; Physics ; Organic chemistry Molecular spectroscopy and chirality ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Talía Tene | Gabriela Tubón-Usca | Camilo Haro-Barroso | Geoconda Marisela Velasco Castelo | Cristian Vacacela Gómez
Source: Coatings (article, 2024-09-29)
Field: Materials Science — Materials Chemistry

Two-Dimensional Materials-Based Thin Films and Coatings Here, we highlight the research presented in this Special Issue, focusing on the innovative use of graphene and other two-dimensional (2D) materials to develop advanced coating technologies. The contributions herein address critical challenges such as the scalable fabrication and stable dispersion of such materials and their compatibility with conventional coating systems, offering solutions that enhance their mechanical strength, chemical stability, and multifunctionality. The featured studies demonstrate the diverse applications of these materials, from protective anticorrosive barriers to high-performance optoelectronic devices and environmental remediation. Moving forward, future research is encouraged to explore novel 2D materials, hybrid coating strategies, and advanced computational modeling to overcome existing limitations and unlock new possibilities. Materials science ; Thin film ; Composite material ; Nanotechnology Graphene research and applications ; ZnO doping and properties ; Chemical and Physical Properties of Materials
Authors: Hubin Liu | Li Zhang | Guimin Cai | Zhixiang Zhang | Jiachen Li | Longlian Zhao | Junhui Li
Source: Research Square (preprint, 2024-04-19)
Field: Chemistry — Analytical Chemistry

Qualitative and quantitative analysis of three-dimensional fluorescence spectra by improved parallel factor analysis with internal standard sample embedding <title>Abstract</title> The strategy of parallel factor analysis, combined with the internal standard method, has been increasingly applied to the qualitative and quantitative analysis of three-dimensional fluorescence spectra of unknown mixed fluorophores. Nevertheless, the disparity in the number of fluorophores included in the internal standard sample set and the number included in test samples may impact the qualitative and quantitative outcomes of parallel factor analysis. In this work, we systematically established the framework of the parallel factor analysis with internal standard sample embedding (ISSE-PARAFAC) strategy. We applied this framework to six datasets representing two scenarios and conducted a detailed discussion on the effects of the disparity between the number of fluorophores in the internal standard sample set and the number in the test set on both qualitative and quantitative results. Additionally, we introduced an enhancement to PARAFAC by aggregating fluorophores with similar emission wavelengths, corresponding to the peaks of emission loadings (spectra) obtained from PARAFAC, as a single fluorophore. This aggregation aimed to mitigate the strong correlation between similar fluorophores. The results imply that the presence of irrelevant fluorophores in the internal standard sample set, whether increased or decreased, does not significantly affect the qualitative and quantitative analysis of target fluorophores in the test set. Moreover, we demonstrated that the improved parallel factor analysis with internal standard sample embedding not only fully decomposes the uncorrelated mixed fluorophores for qualitative analysis but also allows the established linear concentration model for fluorescent components to predict the corresponding fluorophore concentration of test samples, enabling quantitative analysis at the ppm level (µg/mL). Quantitative analysis (chemistry) ; Embedding ; Qualitative analysis ; Sample (material) ; Fluorescence ; Analytical Chemistry (journal) ; Chemistry ; Biological system ; Computer science ; Chromatography ; Physics ; Artificial intelligence ; Optics ; Biology ; Qualitative research Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Marie-Anne Batel | Jean‐jacques Letourneau | Cendrine Gatumel | Henri Berthiaux
Source: Particulate Science And Technology (article, 2024-10-22)
Field: Materials Science — Materials Chemistry

Determination of steady-state for continuous powder mixing: Analysis of mixture properties and signal processing International audience Mixing (physics) ; Steady state (chemistry) ; Materials science ; Signal processing ; SIGNAL (programming language) ; Process engineering ; Chemistry ; Computer science ; Physics ; Physical chemistry ; Engineering ; Digital signal processing ; Electronic engineering Crystallization and Solubility Studies ; Thermodynamic properties of mixtures ; Chemical Thermodynamics and Molecular Structure
Authors: М. Кеддам | Z. Nait Abdellah
Source: Zastita materijala (article, 2024-11-30)
Field: Materials Science — Materials Chemistry

Modelling the formation kinetics of Fe2B layers with a diffusionmodel using taylor expansion This work aimed to model the growth kinetics of Fe2B layers on the DIN 1.2738 steel by using a novel kinetic approach. The proposed model considered the transient diffusion regime of boron atoms through the surface of treated steel. The distribution of boron atoms across the Fe2B layer was expressed as a Taylor expansion of second order. Afterward, the boron activation energy in the Fe2B layers was assessed as equal to 214.48 kJmol-1 in the temperature range 1123-1223 K using the experimental results taken from the literature. Finally, the present model has been validated experimentally by using additional boriding conditions (1198 K for 4.5 h). The experimental Fe2B layer thickness obtained at 1198 K for 4.5 h aligned with the predicted value provided by the model. Furthermore, the mass gain per unit area resulting from the formation of Fe2B layer was also calculated as a function of processing parameters. Kinetics ; Taylor series ; Taylor rule ; Thermodynamics ; Econometrics ; Economics ; Keynesian economics ; Physics ; Mathematics ; Monetary policy ; Mathematical analysis ; Classical mechanics Solidification and crystal growth phenomena ; Microstructure and mechanical properties ; Advanced Materials Characterization Techniques
Authors: M. Manav | R. Molinaro | Siddhartha Mishra | Laura De Lorenzis
Source: arXiv (Cornell University) (preprint, 2024-04-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Phase-Field Modeling of Fracture with Physics-Informed Deep Learning We explore the potential of the deep Ritz method to learn complex fracture processes such as quasistatic crack nucleation, propagation, kinking, branching, and coalescence within the unified variational framework of phase-field modeling of brittle fracture. We elucidate the challenges related to the neural-network-based approximation of the energy landscape, and the ability of an optimization approach to reach the correct energy minimum, and we discuss the choices in the construction and training of the neural network which prove to be critical to accurately and efficiently capture all the relevant fracture phenomena. The developed method is applied to several benchmark problems and the results are shown to be in qualitative and quantitative agreement with the finite element solution. The robustness of the approach is tested by using neural networks with different initializations. Field (mathematics) ; Fracture (geology) ; Deep learning ; Phase (matter) ; Artificial intelligence ; Theoretical physics ; Physics ; Geology ; Computer science ; Geotechnical engineering ; Mathematics ; Quantum mechanics ; Pure mathematics Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Non-Destructive Testing Techniques