jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: M.-S. Jemeli Sang | Nan Pan | Jingna Wu | Xiaoting Chen | Shuilin Cai | Huan Fang | Meitian Xiao | Xiaoming Jiang | Zhiyu Liu
Source: Marine Drugs (article, 2024-06-19)
Field: Chemistry — Spectroscopy

Reversed-Phase Medium-Pressure Liquid Chromatography Purification of Omega-3 Fatty Acid Ethyl Esters Using AQ-C18 Omega-3 fatty acids are in high demand due to their efficacy in treating hypertriglyceridemia and preventing cardiovascular diseases. However, the growth of the industry is hampered by low purity and insufficient productivity. This study aims to develop an efficient RP-MPLC purification method for omega-3 fatty acid ethyl esters with high purity and capacity. The results indicate that the AQ-C18 featuring polar end-capped silanol groups outperformed C18 and others in retention time and impurity separation. By injecting pure fish oil esters with a volume equivalent to a 1.25% bed volume on an AQ-C18 MPLC column using a binary isocratic methanol-water (90:10, <i>v</i>:<i>v</i>) mobile phase at 30 mL/min, optimal omega-3 fatty acid ethyl esters were obtained, with the notable purity of 90.34% and a recovery rate of 74.30%. The total content of EPA and DHA produced increased from 67.91% to 85.27%, meeting the acceptance criteria of no less than 84% set by the 2020 edition of the Pharmacopoeia of the People's Republic of China. In contrast, RP-MPLC significantly enhanced the production efficiency per unit output compared to RP-HPLC. This study demonstrates a pioneering approach to producing omega-3 fatty acid ethyl esters with high purity and of greater quantity using AQ-C18 RP-MPLC, showing this method's significant potential for use in industrial-scale manufacturing. Chromatography ; Chemistry ; Fatty acid ; High-performance liquid chromatography ; Organic chemistry Analytical Chemistry and Chromatography ; Eicosanoids and Hypertension Pharmacology ; Fatty Acid Research and Health
Authors: Jun Hu | Sijie Xu | Zhenying Huang | Wennan Liu | Jiahao Zheng | Yuxi Liao
Source: Agriculture (article, 2024-12-26)
Field: Chemistry — Analytical Chemistry

Rapid Non-Destructive Detection of Rice Seed Vigor via Terahertz Spectroscopy Rice seed vigor significantly impacts yield, making the selection of high-vigor seeds crucial for agricultural production. Traditional methods for assessing seed vigor are time-consuming and destructive. This study aimed to develop a rapid, non-destructive method for evaluating rice seed vigor using terahertz spectroscopy. Rice seeds with varying vigor levels were prepared through high-temperature and high-humidity aging and classified into high-, low-, and non-vigorous groups based on germination performance. Terahertz transmission imaging (0.1–3 THz) was conducted on 420 seeds, and spectral data were preprocessed using several advanced data processing techniques, including competitive adaptive reweighting (CARS), uninformative variable elimination (UVE), and principal component analysis (PCA). Three chemometric models, namely random forest (RF), K-nearest neighbors (KNN), and partial least squares–discriminant analysis (PLS-DA), were established. The model based on CARS-KNN after band selection achieved the highest prediction accuracy of 97.14%. The results indicate that terahertz spectroscopy combined with band selection methods provides a reliable, non-destructive approach for rice seed vigor assessment, offering significant potential for agricultural quality control. Spectroscopy ; Agronomy ; Terahertz spectroscopy and technology ; Terahertz radiation ; Materials science ; Biology ; Botany ; Horticulture ; Physics ; Optoelectronics ; Astronomy Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Plant and animal studies
Authors: Cécile Le Sueur | Magnus Rattray | Mikhail M. Savitski
Source: PLoS Computational Biology (article, 2024-09-27)
Field: Chemistry — Spectroscopy

GPMelt: A hierarchical Gaussian process framework to explore the dark meltome of thermal proteome profiling experiments Thermal proteome profiling (TPP) is a proteome wide technology that enables unbiased detection of protein drug interactions as well as changes in post-translational state of proteins between different biological conditions. Statistical analysis of temperature range TPP (TPP-TR) datasets relies on comparing protein melting curves, describing the amount of non-denatured proteins as a function of temperature, between different conditions (e.g. presence or absence of a drug). However, state-of-the-art models are restricted to sigmoidal melting behaviours while unconventional melting curves, representing up to 50% of TPP-TR datasets, have recently been shown to carry important biological information. We present a novel statistical framework, based on hierarchical Gaussian process models and named GPMelt, to make TPP-TR datasets analysis unbiased with respect to the melting profiles of proteins. GPMelt scales to multiple conditions, and extension of the model to deeper hierarchies (i.e. with additional sub-levels) allows to deal with complex TPP-TR protocols. Collectively, our statistical framework extends the analysis of TPP-TR datasets for both protein and peptide level melting curves, offering access to thousands of previously excluded melting curves and thus substantially increasing the coverage and the ability of TPP to uncover new biology. Proteome ; Computational biology ; Human proteome project ; Gaussian ; Protein function ; Computer science ; Melting temperature ; Biological system ; Bioinformatics ; Statistical physics ; Data mining ; Chemistry ; Biology ; Proteomics ; Physics ; Materials science ; Computational chemistry ; Genetics Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics
Authors: Tiantian Wen | Juan Liu | Yuanyuan Fu | Jibo Yue | Yuheng Li | Wei Guo
Source: Horticulturae (article, 2024-01-30)
Field: Chemistry — Analytical Chemistry

Development and Evaluation of a New Spectral Index to Detect Peanut Southern Blight Disease Using Canopy Hyperspectral Reflectance Peanut southern blight is a soil-borne fungal disease caused by Agroathelia rolfsii (syn. Sclerotium rolfsii) Sacc, which seriously affects peanut yield. The disease mainly affects the stem, pod, and root of the plant, and it is difficult to detect the disease by visual interpretation. Detecting peanut southern blight using existing technology is an urgent problem that needs to be solved. To address this issue, field experiments were conducted in September 2022 to determine whether hyperspectral techniques could be used to assess the severity of peanut southern blight. In this study, we obtained 610 canopy-scale spectral data through field experiments. Firstly, 18 traditional spectral features were calculated. Then, wavelengths of 544 nm, 678 nm, and 769 nm were selected as sensitive by the Relief-F algorithm, and the NDSISB and NSISB were constructed using normalization and ratio calculation methods. Finally, Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), and ANN were used to evaluate the diagnostic ability of all spectral features to assess disease severity levels. The results showed that the NSISB had the highest association with peanut southern blight (R2 = 0.817), exceeding the other spectral features. Compared to the other three models, CatBoost demonstrated superior accuracy, with an overall accuracy (OA) and Kappa coefficient of 84.18% and 78.31%, respectively. The findings of this study can serve as a reference for estimating the severity levels of peanut southern blight using ground-based hyperspectral data. Hyperspectral imaging ; Blight ; Canopy ; Support vector machine ; Sclerotium ; Multispectral image ; Coefficient of determination ; Boosting (machine learning) ; Remote sensing ; Agronomy ; Mathematics ; Horticulture ; Artificial intelligence ; Computer science ; Biology ; Statistics ; Botany ; Geography Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Essential Oils and Antimicrobial Activity
Authors: Fabian Neumann
Source: Journal of Composites Science (review, 2024-04-03)
Field: Materials Science — Polymers and Plastics

Variable Shape Tooling for Composite Manufacturing: A Systematic Review The choice of material, manufacturing process, and molding tool significantly affects the quality, environmental impact, and cost efficiency of composite components. Producing one-piece hollow profiles with smooth inner surfaces and undercuts presents major challenges for conventional mold concepts. There is yet no thorough review of shape-variable mandrels in composite manufacturing to be found in the literature. This paper provides an overview of research on shape memory polymers and other shape-variable materials used in tooling applications for composite manufacturing. This work covers shape memory, heat shrink, and other deformable tooling concepts that enable the production of one-piece Type V pressure vessels, air intake ducts, or curved struts and tubes. A systematic literature review in combination with a state-of-the-art open-source active learning tool ASReview is conducted. Fifteen relevant studies were identified. Research on shape-variable tooling is mainly conducted by three research groups in the USA and the PRC. The tooling is mostly made of unreinforced thermosets, especially styrene-based ones. Thermoplastic resins are less common, and reinforcements limit the usable elongation in the temporary shape. The shape variability is either a shape memory and/or a softening process, which, in all studies, is activated by heating. Release agents are widely used to ease demolding. No ecological or economical assessment of the manufacturing methods was conducted in the reviewed studies. Three fields for further research that could be identified are as follows: (1) thorough ecological end economical assessment of shape-variable mandrels in comparison with conventional tooling; (2) thermoplastic shape memory polymer mandrels; and (3) further investigation of simulation capabilities for shape memory mandrels. Composite number ; Variable (mathematics) ; Computer science ; Manufacturing engineering ; Materials science ; Engineering ; Mathematics ; Composite material ; Mathematical analysis Polymer composites and self-healing ; Photopolymerization techniques and applications ; Additive Manufacturing and 3D Printing Technologies
Authors: Angela Vacaro de Souza | Vitória Ferreira da Silva Fávaro | Jéssica Marques de Mello | Vinícius Canato | Diogo de Lucca Sartori | Fernando Ferrari Putti | Yasmin S. Tadayozzi | Douglas D’Alessandro Salgado
Source: Foods (article, 2024-07-20)
Field: Chemistry — Analytical Chemistry

Prediction of Bioactive Compounds and Antioxidant Activity in Bananas during Ripening Using Non-Destructive Parameters as Input Data Vegetable quality parameters are established according to standards primarily based on visual characteristics. Although knowledge of biochemical changes in the secondary metabolism of plants throughout development is essential to guide decision-making about consumption, harvesting and processing, these determinations involve the use of reagents, specific equipment and sophisticated techniques, making them slow and costly. However, when non-destructive methods are employed to predict such determinations, a greater number of samples can be tested with adequate precision. Therefore, the aim of this work was to establish an association capable of modeling between non-destructive-physical and colorimetric aspects (predictive variables)-and destructive determinations-bioactive compounds and antioxidant activity (variables to be predicted), quantified spectrophotometrically and by HPLC in 'Nanicão' bananas during ripening. It was verified that to predict some parameters such as flavonoids, a regression equation using predictive parameters indicated the importance of R<sup>2</sup>, which varied from 83.43 to 98.25%, showing that some non-destructive parameters can be highly efficient as predictors. Ripening ; Antioxidant ; Chemistry ; Food science ; Antioxidant capacity ; Biological system ; Biology ; Biochemistry Spectroscopy and Chemometric Analyses ; Banana Cultivation and Research ; Leaf Properties and Growth Measurement
Authors: Carlos Augusto Alves Cardoso Silva | Rodnei Rizzo | Marcelo A. da Silva | M. Caron | Peterson Ricardo Fiorio
Source: Remote Sensing (article, 2024-11-14)
Field: Chemistry — Analytical Chemistry

Spatio-Temporal Generalization of VIS-NIR-SWIR Spectral Models for Nitrogen Prediction in Sugarcane Leaves Nitrogen fertilization is a challenging task that usually requires intensive use of resources, such as fertilizers, management and water. This study explored the potential of VIS-NIR-SWIR remote sensing for quantifying leaf nitrogen content (LNC) in sugarcane from different regions and vegetative stages. Conducted in three regions of São Paulo, Brazil (Jaú, Piracicaba and Santa Maria), the research involved three experiments, one per location. The spectral data were obtained at 140, 170, 200, 230 and 260 days after cutting (DAC). From the hyperspectral data, clustering analysis was performed to identify the patterns between the spectral bands for each region where the spectral readings were made, using the Partitioning Around Medoids (PAM) algorithm. Then, the LNC values were used to generate spectral models using Partial Least Squares Regression (PLSR). Subsequently, the generalization of the models was tested with the leave-one-date-out cross-validation (LOOCV) technique. The results showed that although the variation in leaf N was small, the sensor demonstrated the ability to detect these variations. Furthermore, it was possible to determine the influence of N concentrations on the leaf spectra and how this impacted cluster formation. It was observed that the greater the average variation in N content in each cluster, the better defined and denser the groups formed were. The best time to quantify N concentrations was at 140 DAC (R2 = 0.90 and RMSE = 0.74 g kg−1). From LOOCV, the areas with sandier soil texture presented a lower model performance compared to areas with clayey soil, with R2 &lt; 0.54. The spatial generalization of the models recorded the best performance at 140 DAC (R2 = 0.69, RMSE = 1.18 g kg−1 and dr = 0.61), decreasing in accuracy at the crop-maturation stage (260 DAC), R2 of 0.05, RMSE of 1.73 g kg−1 and dr of 0.38. Although the technique needs further studies to be improved, our results demonstrated potential, which tends to provide support and benefits for the quantification of nutrients in sugarcane in the long term. Environmental science ; Nitrogen ; Generalization ; Remote sensing ; Mathematics ; Geology ; Chemistry Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Weiqi Chen | Tianbao Ma
Source: Science China Technological Sciences (article, 2024-06-26)
Field: Materials Science — Materials Chemistry

Interfacial tribochemical kinetics: A new perspective on superlubricity of diamond-like carbon films Materials science ; Kinetics ; Diamond ; Carbon fibers ; Perspective (graphical) ; Diamond-like carbon ; Chemical engineering ; Nanotechnology ; Composite material ; Thin film ; Computer science ; Engineering ; Physics ; Composite number Diamond and Carbon-based Materials Research ; Force Microscopy Techniques and Applications ; Lubricants and Their Additives
Authors: Joe D. Morrow | Chinonso Ugwumadu | D. A. Drabold | Stephen R. Elliott | Andrew L. Goodwin | Volker L. Deringer
Source: Angewandte Chemie (article, 2024-03-22)
Field: Materials Science — Materials Chemistry

Understanding Defects in Amorphous Silicon with Million‐Atom Simulations and Machine Learning Abstract The structure of amorphous silicon (a‐Si) is widely thought of as a fourfold‐connected random network, and yet it is defective atoms, with fewer or more than four bonds, that make it particularly interesting. Despite many attempts to explain such “dangling‐bond” and “floating‐bond” defects, respectively, a unified understanding is still missing. Here, we use advanced computational chemistry methods to reveal the complex structural and energetic landscape of defects in a‐Si. We study an ultra‐large‐scale, quantum‐accurate structural model containing a million atoms, and thousands of individual defects, allowing reliable defect‐related statistics to be obtained. We combine structural descriptors and machine‐learned atomic energies to develop a classification of the different types of defects in a‐Si. The results suggest a revision of the established floating‐bond model by showing that fivefold‐bonded atoms in a‐Si exhibit a wide range of local environments–analogous to fivefold centers in coordination chemistry. Furthermore, it is shown that fivefold (but not threefold) coordination defects tend to cluster together. Our study provides new insights into one of the most widely studied amorphous solids, and has general implications for understanding defects in disordered materials beyond silicon alone. Dangling bond ; Atom (system on chip) ; Silicon ; Amorphous solid ; Chemical physics ; Amorphous silicon ; Cluster (spacecraft) ; Materials science ; Atomic units ; Nanotechnology ; Crystallography ; Computer science ; Chemistry ; Crystalline silicon ; Physics ; Optoelectronics ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Suci Ramadhani | B Muslimin | Ida Maratul Khamidah
Source: Jurnal Info Sains Informatika dan Sains (article, 2024-06-03)
Field: Chemistry — Analytical Chemistry

Random Forest Analysis In Classifying Orange Quality Data The quality of oranges is important to determine selling value. However, citrus quality assessments are often subjective and inconsistent, which can impact consumer satisfaction and market efficiency. In the agricultural industry, especially in citrus commodities, there are difficulties in classifying fruit quality accurately and efficiently, which has an impact on the assessment and determination of market prices. Given the importance of citrus quality in the agricultural and food industries, there is an urgent need for objective and efficient methods for classifying citrus quality. Inappropriate classification can cause economic losses for farmers and distributors, as well as reduce consumer satisfaction with product quality. As a solution, this research proposes the use of the Random Forest method to classify orange quality data. The method used in this research involved collecting orange quality data, including characteristics such as color, texture, and size. This data is then analyzed using the Random Forest algorithm. The Random Forest method is used to process orange quality data, by utilizing features such as color, size and skin texture. This model is trained using historical data to predict fruit quality. The research results show that the Random Forest method successfully classifies citrus quality data with high accuracy, demonstrating its potential as an effective tool for future citrus quality assessment by proving its effectiveness in supporting decisions in the agricultural sector. Random forest ; Orange (colour) ; Computer science ; Quality (philosophy) ; Data quality ; Data mining ; Data science ; Artificial intelligence ; Chemistry ; Business Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Smart Agriculture and AI
Authors: Alexander Malik | Geralf Hütter | Martin Abendroth | Bjöern Kiefer
Source: International Journal of Mechanical Sciences (preprint, 2024-07-25)
Field: Materials Science — Polymers and Plastics

Micromorphic FE2 simulation of plastic deformations of foam structures Capturing and predicting the effective mechanical properties of highly porous cellular media still represents a significant challenge for the research community, due to their complex structural interdependencies and known size effects. Micromorphic theories are often applied in this context to model the inelastic deformation behavior of foam-like structures, in particular to incorporate such size effect into the investigation of structure–property correlations. This raises the problems of formulating appropriate constitutive relations for the numerous non-classical stress measures and determining the corresponding material parameters, which are usually difficult to assess experimentally. The present contribution therefore alternatively employs a hierarchical micromorphic multi-scale approach within the direct FE2 framework to simulate the complex irreversible behavior of foam-like porous solids. The predictions of Cosserat (micropolar) and a fully-micromorphic theory are compared with conventional FE2 results and direct numerical simulations (DNS) for complex loading scenarios with elastic, elastic–plastic, and creep deformations. Therein, non-classical deformation modes of the microstructure resulting from the introduced micromorphic kinematics are visualized, as are the macroscopic hyperstresses and deformations. Materials science ; Composite material Polymer Foaming and Composites ; Rheology and Fluid Dynamics Studies ; Elasticity and Material Modeling
Authors: Emanuel A. Lazar | Jiayin Lü | Chris H. Rycroft | Deborah Schwarcz
Source: Modelling and Simulation in Materials Science and Engineering (article, 2024-10-24)
Field: Materials Science — Materials Chemistry

Characterizing structural features of two-dimensional particle systems through Voronoi topology Abstract This paper introduces a new approach toward characterizing local structural features of two-dimensional particle systems. The approach can accurately identify and characterize defects in high-temperature crystals, distinguish a wide range of nominally disordered systems, and robustly describe complex structures such as grain boundaries. This paper also introduces two-dimensional functionality into the open-source software program VoroTop which automates this analysis. This software package is built on a recently-introduced multithreaded version of Voro++ , enabling the analysis of systems with billions of particles on high-performance computer architectures. Particle system ; Voronoi diagram ; Range (aeronautics) ; Software ; Materials science ; Complex system ; Particle (ecology) ; Software system ; Open source software ; Topology (electrical circuits) ; Computational science ; Computer science ; Artificial intelligence ; Computer graphics (images) ; Geometry ; Engineering ; Programming language Graphene research and applications ; Ion-surface interactions and analysis ; nanoparticles nucleation surface interactions