jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Collins Ekeowa | Jacob Muthu
Source: Polymers (article, 2024-04-26)
Field: Materials Science — Materials Chemistry

Multiscale Modeling and Characterization of Graphene Epoxy Nanocomposite This study aims to characterize graphene epoxy nanocomposite properties using multiscale modeling. Molecular dynamics was used to study the nanocomposite at the nanoscale and finite element analysis at the macroscale to complete the multiscale modeling. The coupling of these two scales was carried out using the Irving-Kirkwood averaging method. First, the functionalization of graphene was carried and 6% grafted graphene was selected based on Young's modulus and the tensile strength of the grafted graphene sheet. Functionalized graphene with weight fractions of 1.8, 3.7, and 5.6 wt.% were reinforced with epoxy polymer to form a graphene epoxy nanocomposite. The results showed that the graphene with 3.7 wt.% achieved the highest modulus. Subsequently, a functionalized graphene sheet with an epoxy matrix was developed to obtain the interphase properties using the MD modeling technique. The normal and shear forces at the interphase region of the graphene epoxy nanocomposite were investigated using a traction-separation test to analyze the mechanical properties including Young's modulus and traction forces. The mean stiffness of numerically tested samples with 1.8, 3.7, and 5.6 wt.% graphene and the stiffness obtained from experimental results from the literature were compared. The experimental results are lower than the multiscale model results because the experiments cannot replicate the molecular-scale behavior. However, a similar trend could be observed for the addition of up to 3.7 wt.% graphene. This demonstrated that the graphene with 3.7 wt.% shows improved interphase properties. The macroscale properties of the graphene epoxy nanocomposite models with 1.8 and 3.7 wt.% were comparatively higher. Epoxy ; Graphene ; Materials science ; Nanocomposite ; Characterization (materials science) ; Multiscale modeling ; Nanotechnology ; Composite material ; Chemistry Graphene research and applications ; Polymer Nanocomposites and Properties ; Graphene and Nanomaterials Applications
Authors: Idarh Matadamas | Erik Zamora | Teodulfo Aquino-Bolaños
Source: Agriculture (article, 2024-12-02)
Field: Chemistry — Analytical Chemistry

Detection and Classification of Agave angustifolia Haw Using Deep Learning Models In Oaxaca, Mexico, there are more than 30 species of the Agave genus, and its cultivation is of great economic and social importance. The incidence of pests, diseases, and environmental stress cause significant losses to the crop. The identification of damage through non-invasive tools based on visual information is important for reducing economic losses. The objective of this study was to evaluate and compare five deep learning models: YOLO versions 7, 7-tiny, and 8, and two from the Detectron2 library, Faster-RCNN and RetinaNet, for the detection and classification of Agave angustifolia plants in digital images. In the town of Santiago Matatlán, Oaxaca, 333 images were taken in an open-air plantation, and 1317 plants were labeled into five classes: sick, yellow, healthy, small, and spotted. Models were trained with a 70% random partition, validated with 10%, and tested with the remaining 20%. The results obtained from the models indicate that YOLOv7 is the best-performing model, in terms of the test set, with a mAP of 0.616, outperforming YOLOv7-tiny and YOLOv8, both with a mAP of 0.606 on the same set; demonstrating that artificial intelligence for the detection and classification of Agave angustifolia plants under planting conditions is feasible using digital images. Agave ; Biology ; Artificial intelligence ; Botany ; Computer science Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Yaoyao Mi | Lu Yu | Delong Wang | Yihui Zhao | Yuecheng Dong | Hui Chang | Igor Alexandrov
Source: Materials (article, 2024-03-10)
Field: Materials Science — Materials Chemistry

A Study of the Superplastic Deformation Behavior of Low-Cost Ti-2Fe-0.1B Alloys Titanium alloys have high specific strength and corrosion resistance, which have promising applications in industry. However, the machinability of titanium alloys is limited due to their crystal lattice and physical properties. Thus, in recent years, the superplastic forming of titanium alloys has been intensively developing, in particular, forming at low temperatures and/or high strain rates. In this work, a tensile test of low-cost Ti-2Fe-0.1B alloys was carried out at a temperature of 550~750 °C and a strain rate of 1 × 10<sup>-3</sup> s<sup>-1</sup>~1 × 10<sup>-2</sup> s<sup>-1</sup>. The results showed that the alloy exhibited good superplasticity even at a high strain rate (1 × 10<sup>-2</sup> s<sup>-1</sup>) and a low deformation temperature of 550 °C; the elongation of the alloy in this state reached 137.5%. The high strain rate sensitivity coefficient m (0.3) and the maximum elongation (452%) were obtained at a strain rate of 1 × 10<sup>-3</sup> s<sup>-1</sup> and a temperature of 750 °C. Characteristics of the microstructure showed that during superplastic deformation, the recrystallization and grain boundary sliding of the alloy phases were accelerated, which could be ascribed to the effect of the element Fe. At the same time, the TiB phase located around the primary elongated α grains could also induce dynamic recrystallization and dynamic globularization during deformation. Superplasticity ; Materials science ; Deformation (meteorology) ; Titanium alloy ; Metallurgy ; Composite material ; Alloy Titanium Alloys Microstructure and Properties ; Microstructure and mechanical properties ; Metallurgy and Material Forming
Authors: Mingli Liu | Shuai Liu | Jian Yao | Yu Teng | Lin Geng | Alei Li | Lin Wang | Yunfei Li | Qing Guo | Zongjie Shen | Lixing Kang | Mingsheng Long
Source: Small Science (article, 2024-09-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Space‐Confined Growth of Ultrathin 2D β‐Ga<sub>2</sub>O<sub>3</sub> Nanoflakes for Artificial Neuromorphic Application In recent years, wide-bandgap semiconductor β-Ga<sub>2</sub>O<sub>3</sub> material has been widely studied because of its excellent properties. Simultaneously, 2D metal oxides (2DMOs) have also become a focus of research owing to their superior stability and unique physical properties arising from quantum confinement effects. Therefore, the exploration of 2D β-Ga<sub>2</sub>O<sub>3</sub> is expected to reveal its novel electrical properties in electronic applications. However, the synthesis of high-quality 2D β-Ga<sub>2</sub>O<sub>3</sub> remains a formidable challenge. Herein, a confined space is constructed to synthesize high-quality 2D β-Ga<sub>2</sub>O<sub>3</sub> nanoflakes by enhancing the control of the kinetics of chemical vapor deposition process. In the device results, it is shown that the grown nanoflakes have excellent switching properties and potential artificial synaptic response characteristics. Based on this premise, an artificial recognition system for handwritten numerals is developed, achieving a peak recognition accuracy of approximately 96%. This system holds significant potential for application within an emerging neuromorphic recognition framework tailored for advanced driver-assistance systems. In this work, a new feasible pathway is provided for the synthesis of 2D non-layered oxides and the potential of 2D oxides in the field of neuroanalog electronics and recognition is shown, thereby advancing the fields of 2D β-Ga<sub>2</sub>O<sub>3</sub> electronics and 2DMOs electronics. Neuromorphic engineering ; Space (punctuation) ; Materials science ; Artificial intelligence ; Nanotechnology ; Computer science ; Artificial neural network Ga2O3 and related materials ; ZnO doping and properties ; Advanced Photocatalysis Techniques
Authors: Rajendhar Junjuri | Tobias Meyer‐Zedler | Jürgen Popp | Thomas Bocklitz
Source: Optics Continuum (article, 2024-10-11)
Field: Chemistry — Analytical Chemistry

Investigating the effect of non-resonant background variation on the CARS data analysis of bacteria samples and classification using machine learning Non-resonant background (NRB) plays a significant role in coherent anti-Stokes Raman scattering (CARS) spectroscopic applications. All the recent works primarily focused on removing the NRB using different deep learning methods, and only one study explored the effect of NRB. Hence, in this work, we systematically investigated the impact of NRB variation on Raman signal retrieval. The NRB is simulated as a linear function with different strengths relative to the resonant Raman signal, and the variance also changes for each NRB strength. The resonant part of nonlinear susceptibility is extracted from real experimental Raman data; hence, the simulated CARS data better approximate the experimental CARS spectra. Then, the corresponding Raman signal is retrieved by four different methods: maximum entropy method (MEM), Kramers-Kronig (KK), convolutional neural network (CNN), and long short-term memory (LSTM) network. Pearson correlation measurements and principal component analysis combined with linear discriminant analysis modeling revealed that MEM and KK methods have an edge over LSTM and CNN for higher NRB strengths. It is also demonstrated that normalizing the input data favors LSTM and CNN predictions. In contrast, background removal from the predictions significantly influenced Pearson correlation but not the classification accuracies for MEM and KK. Further, the LSTM performance is found to be limited and can only be applied for low NRB strengths. This comprehensive study has the potential to impact the CARS spectroscopy and microscopy applications in different areas. Variation (astronomy) ; Artificial intelligence ; Statistics ; Computer science ; Mathematics ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research
Source: Letters in Applied NanoBioScience (article, 2024-06-30)
Field: Chemistry — Organic Chemistry

Synthesis, Characterization, and Study of Anti-HPV Activity and Cell Cytotoxicity of Novel 1,3-Oxazole-4-Carbonitrile and 4-Sulfonylamide-5-Phenyl-1,3-Thiazole Derivatives in Vitro Five new 1,3-oxazole-4-carbonitrile and two 4-sulfonylamide-5-phenyl-1,3-thiazole derivatives were synthesized, and their activity against human papillomavirus (HPV) types 11, 16, and 18 was evaluated in C33-A cells in vitro. Bioassays showed that compounds 1 and 5-7 were inactive, while compounds 2 and 3 showed moderate activity against low-risk HPV11 and low cellular toxicity but were slightly selective. Only compound 4 (5-(4-benzenesulfonylpiperazine-1-sulfonyl)-2-phenyl-1,3-oxazole-4-carbonitrile) showed high activity against HPV11 similar to the reference drug (9-[2-phosphonomethoxy)ethyl]guanine) (EC50=3.68 and 1.77 μM, respectively), but had worse toxicity and selectivity However, the activity and selectivity of compound 4 exceeded that of the reference drug in concentration that caused 90% inhibition of virus replication, but was more toxic. Compound 4 also showed moderate activity against high-risk HPV16 and HPV18 in half maximal effective concentration. Thus, we have shown that compound 4 is a promising structure for in-depth study in order to develop a novel drug against HPV lesions. Oxazole ; Thiazole ; Chemistry ; Cytotoxicity ; Toxicity ; In vitro ; Stereochemistry ; Selectivity ; EC50 ; Sulfonyl ; Drug ; Structure–activity relationship ; Pharmacology ; Acute toxicity ; Combinatorial chemistry ; Biochemistry ; Biology ; Organic chemistry Synthesis and Characterization of Heterocyclic Compounds ; Fungal Plant Pathogen Control ; Quinazolinone synthesis and applications
Authors: Lange Yakubu Saleh | Soner Özdemir | Begüm Nurpelin Sağlık | H. Ali Döndaş | Cevher Altuğ
Source: Journal of Molecular Structure (article, 2024-05-17)
Field: Chemistry — Organic Chemistry

Synthesis and biological activities of pyridine N-oxide bearing 5-aminoisoxazoles as potential acetylcholinesterase and monoamine oxidase inhibitors for Alzheimerʼs disease A series of ten novel pyridine N-oxide-bearing 5-aminoisoxazoles was efficiently synthesized in moderate yield by reacting 2-(cyanomethyl)pyridine 1-oxide with α-chlorooximes, employing sodium ethoxide as a base. The synthesized compounds were verified with a variety of spectra. Subsequently, the inhibitory potency of compounds 4c, 4e, 4f, 4h, and 4i against AChE and BChE, primary targets in Alzheimer's disease, was assessed. In silico docking analyses were conducted to evaluate the interaction of compounds 4c, 4e, 4f, 4h, and 4i with AChE and MAO-B. Among the tested compounds, 4e and 4h demonstrated remarkable AChE inhibition, exhibiting IC50 values of (0.050 µM and 0.039 µM, respectively), comparable to the inhibition achieved by donepezil (IC50 = 0.020 µM). Additionally, compounds 4c, 4e, 4f, 4h, and 4i displayed potent MAO-A inhibition, with IC50 values of (0.203, 0.067, 0.083, 0.044, and 0.159 µM, respectively), surpassing the efficacy of moclobemide (IC50 = 6.061 µM). Compounds 4e, 4f, and 4h also inhibited MAO-B, with IC50 values of (0.076, 0.058, and 0.049 µM, respectively), close to the inhibitory effects of Selegiline (IC50 = 0.037 µM). Compound 4h emerged as a multi-target inhibitor, effectively inhibiting AChE, MAO-A, and MAO-B. These findings underscore the therapeutic potential of these novel compounds in the treatment of Alzheimer's disease, warranting further investigation into their clinical application. Chemistry ; Monoamine oxidase ; Acetylcholinesterase ; Pyridine-N-oxide ; Pyridine ; Bearing (navigation) ; Monoamine neurotransmitter ; Monoamine oxidase B ; Aché ; Pharmacology ; Enzyme ; Biochemistry ; Medicinal chemistry ; Serotonin ; Receptor Synthesis and biological activity ; Cholinesterase and Neurodegenerative Diseases ; Synthesis of heterocyclic compounds
Authors: George A. Kevrekidis | Daniel A. Serino | Mark Alexander Kaltenborn | J. Tinka Gammel | J. W. Burby | Marc Klasky
Source: Scientific Reports (article, 2024-12-05)
Field: Materials Science — Materials Chemistry

Neural network representations of multiphase Equations of State Equations of State model relations between thermodynamic variables and are ubiquitous in scientific modelling, appearing in modern day applications ranging from Astrophysics to Climate Science. The three desired properties of a general Equation of State model are adherence to the Laws of Thermodynamics, incorporation of phase transitions, and multiscale accuracy. Analytic models that adhere to all three are hard to develop and cumbersome to work with, often resulting in sacrificing one of these elements for the sake of efficiency. In this work, two deep-learning methods are proposed that provably satisfy the first and second conditions on a large-enough region of thermodynamic variable space. The first is based on learning the generating function (thermodynamic potential) while the second is based on structure-preserving, symplectic neural networks, respectively allowing modifications near or on phase transition regions. They can be used either "from scratch" to learn a full Equation of State, or in conjunction with a pre-existing consistent model, functioning as a modification that better adheres to experimental data. We formulate the theory and provide several computational examples to justify both approaches, highlighting their advantages and shortcomings. Computer science ; Artificial neural network ; Function (biology) ; Work (physics) ; Phase space ; State variable ; Variable (mathematics) ; Artificial intelligence ; Theoretical computer science ; Statistical physics ; Applied mathematics ; Mathematics ; Physics ; Thermodynamics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Protein Structure and Dynamics
Authors: Juan F. Avellaneda-Tamayo | Andrés Sánchez-Ruiz | Biljana Savic | José L. Medina‐Franco | Gonzalo Colmenarejo
Source: TIP Revista Especializada en Ciencias Químico-Biológicas (article, 2024-06-12)
Field: Chemistry — Physical and Theoretical Chemistry

Quimioinformática, Inteligencia Artificial y la Química de Alimentos La Quimioinformática y la Inteligencia Artificial (IA) son disciplinas con métodos que, actualmente, contribuyen al desarrollo de varias áreas del conocimiento de la Química, por medio del almacenamiento, la organización, la búsqueda de datos químicos, el procesamiento y el modelado, para generar información y conocimiento a nivel molecular de las relaciones estructura-propiedad de los compuestos químicos existentes y determinar las propiedades de los nuevos, a partir del diseño con base en un perfil deseado. Todas estas técnicas se han utilizado en la Química de Alimentos, por lo que el objetivo de este artículo es analizar las bases de los métodos Quimioinformáticos y de la IA en su aplicación al estudio de los compuestos químicos de los alimentos. Mica ; Materials science ; Composite material History and advancements in chemistry ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Yudi Purnomo | Andri Tilaqza
Source: Tropical Journal of Natural Product Research (article, 2024-09-01)
Field: Chemistry — Organic Chemistry

Inhibitory Potential of Pulutan (Urena lobata) Leaf Extract on Inducible Nitric Oxide Synthase as Anti-inflammatory Agent: In Vitro and In Silico Approaches Urena lobata is an herbal remedy traditionally used to cure certain ailments. Its flavonoid and phenol-rich content has been shown to have free radical scavenging activity. Nitric oxide (NO) is an active gaseous substance that is released by cells and plays a role in inflammation. This study examined the ability of U. lobata leaf extract to induce NO production and suppress inducible NO synthase (iNOS). The leaves of U. lobata were extracted with ethanol. The phytoconstituents of the extract were identified, their physicochemical and pharmacokinetic properties were predicted, and their anti-inflammatory activity was evaluated. The structures of the phytoconstituents were determined using the molecular docking approach. Griess technique was used to evaluate NO generation in IL-1β-treated hepatocyte culture media containing the extract. An in silico approach was employed to investigate the suppression of iNOS by phytoconstituents from U. lobata. The results showed that stigmasterol and gossypetin were identified in the extract and had a strong inhibitory effect on iNOS based on the inhibition constant. However, it has lower potency than β-sitosterol. The ethanol extract of U. lobata (IC50 = 163.03 µg/mL) had a lower potency than gossypin (IC50 = 100.31 µg/mL) but was comparable to gossypetin (IC50 = 164.18 µg/mL) for inhibiting NO generation. The ethanol extract of U. lobata demonstrated anti-inflammatory action by lowering NO level, but at a lower concentration than gossypin and gossypetin. The study revealed that U. lobata ethanol extract inhibited NO production through active substances, like gossypetin, mangiferin, β-sitosterol, and stigmasterol, thereby suppressing iNOS activity. In silico ; In vitro ; Nitric oxide synthase ; Nitric oxide ; Anti-inflammatory ; Inhibitory postsynaptic potential ; Chemistry ; Pharmacology ; Biochemistry ; Biology ; Gene Chemical Thermodynamics and Molecular Structure ; Phytochemical and Pharmacological Studies
Authors: Julio Cesar Estrada-Moreno | Eréndira Rendón | María de la Luz Jiménez-Núñez
Source: Applied Sciences (article, 2024-01-17)
Field: Chemistry — Analytical Chemistry

Combination of Artificial Neural Networks and Principal Component Analysis for the Simultaneous Quantification of Dyes in Multi-Component Aqueous Mixtures Dyes are organic compounds capable of transmitting their color to materials, which is why they are widely used, for example, in textile fibers, leather, paper, plastic, and the food industry. In the dying process, measuring the dye’s content is extremely important to evaluate the process efficiency and minimize the dye’s discharge in wastewater, but most of the time, dyes are present in multi-component mixtures; hence, quantification by spectrophotometric methods presents a great challenge because the signal obtained in the measurement overlaps the components in the mixture. In order to overcome this issue, the use of the high-performance liquid chromatography (HPLC) method is recommended; however, it has the disadvantage of being an expensive technique, complex, and requiring excessive sample preparation. In recent years, some direct spectrophotometric methods based on multivariate regression algorithms for the quantification of dyes in bicomponent mixtures have been reported. This study presents a new framework that uses a combined ANN and principal component analysis (PCA) model for the determination of the concentration of three dyes in aqueous mixtures: Tartrazine (TZ), Amaranth Red (AR), and Blue 1 CFC (B1) dyes. The PCA–ANN model was trained and validated with ternary mixture samples of TZ, AR, and B1, and with known different compositions, spectra absorbance samples were measured in a UV-Vis spectrophotometer at wavelengths between 350–700 nm with intervals of 1 nm. The PCA–ANN model showed a mean absolute prediction error and correlation coefficient (r2) of less than 1% and greater than 0.99, respectively. The results demonstrate that the PCA–ANN model is a quick and highly accurate alternative in the simultaneous determination of dyes in ternary aqueous mixtures. Tartrazine ; Principal component analysis ; Amaranth ; Absorbance ; Chromatography ; Chemometrics ; Aqueous solution ; Principal component regression ; Correlation coefficient ; Chemistry ; Analytical Chemistry (journal) ; Biological system ; Materials science ; Computer science ; Artificial intelligence ; Organic chemistry ; Machine learning Dye analysis and toxicity ; Biochemical Analysis and Sensing Techniques ; Electrochemical Analysis and Applications
Authors: Yixin Qiu | Yong Tan | Yingying Zhou | Zhipeng Li | Zhuang Miao | Changming Li | Xitian Mei | Chunyu Liu | Xing Teng
Source: Agriculture (article, 2024-10-06)
Field: Chemistry — Analytical Chemistry

Study on Rice Origin and Quality Identification Based on Fluorescence Spectral Features The origin of agricultural products significantly influences their quality and safety. Fluorescence spectroscopy was used to analyse Japonica rice 830, grown in different areas of Jilin Province, by examining rice seed, brown rice, and rice flour from 12 origins. Fluorescence spectra were pre-processed through normalisation and smoothing to remove noise. These processed spectra were input into decision trees, support vector machines (SVMs), K-nearest neighbour (KNN), and neural network models for classification. The analysis revealed that the combined four models achieved an average classification accuracy of 98.05% with a computation time of 180 s, while the reduced-scale models improved accuracy to 98.36% and reduced computation time to 11.25 s. Additionally, prediction models using standard rice starch content values across different states achieved R² values over 0.8. This method provides a rapid, precise approach for assessing rice quality and origin, demonstrating significant potential for application in rice analysis. Identification (biology) ; Fluorescence ; Quality (philosophy) ; Biology ; Botany ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use ; Identification and Quantification in Food