jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiaojiang Wang | Junying Han | Chengzhong Liu | Feng Tong
Source: Frontiers in Plant Science (article, 2024-11-06)
Field: Chemistry — Analytical Chemistry

Non-destructive assessment of apple internal quality using rotational hyperspectral imaging This work aims to predict the starch, vitamin C, soluble solids, and titratable acid contents of apple fruits using hyperspectral imaging combined with machine learning approaches. First, a hyperspectral camera by rotating samples was used to obtain hyperspectral images of the apple fruit surface in the spectral range of 380~1018 nm, and its region of interest (ROI) was extracted; then, the optimal preprocessing method was preferred through experimental comparisons; on this basis, genetic algorithms (GA), successive projection algorithms (SPA), and competitive adaptive reweighting adoption algorithms (CARS) were used to extract feature variables; subsequently, multiple machine learning models (support vector regression SVR, principal component regression PCR, partial least squares regression PLSR, and multiple linear regression MLR) were used to model the inversion between hyperspectral images and internal nutrient quality physicochemical indexes of fruits, respectively. Through the comparative analysis of all the model prediction results, it was found that among them, for starch, vitamin C, soluble solids, and titratable acid content, 2<sup>nd</sup> Der-CARS-MLR were the optimal prediction models with superior performance (the prediction coefficients of determination R<sub>p</sub> <sup>2</sup> exceeded 90% in all of them). In addition, potential relationships among four nutritional qualities were explored based on t-values and p-values, and a significant conclusion was drew that starch and vitamin C was highly correlated. Hyperspectral imaging ; Titratable acid ; Partial least squares regression ; Principal component analysis ; Artificial intelligence ; Mathematics ; Support vector machine ; Linear regression ; Regression ; Principal component regression ; Pattern recognition (psychology) ; Preprocessor ; Computer science ; Biological system ; Chemistry ; Statistics ; Food science ; Biology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Phytochemicals and Antioxidant Activities
Authors: Francisco Carlos Paes | Romain Privat | Jean‐Noël Jaubert | Baptiste Sirjean
Source: Journal of Molecular Liquids (article, 2024-04-03)
Field: Chemistry — Organic Chemistry

Predicting solvation energies of free radicals and their mixtures: A robust approach coupling the Peng-Robinson and COSMO-RS models Detailed kinetic models are valuable tools for a clear understanding of the dynamics of complex reacting systems based on radical-chain mechanisms, such as combustion and oxidation. Gas-phase automatic kinetic generators, such as EXGAS, RMG, MAMOX, NetGen, REACTION, and GENESYS can be used to establish a detailed network of reaction pathways and the rates associated with each reaction. The range of applications of these gas-phase kinetic generators can be extended to liquids through diffusional and solvation corrections directly applied to the gas-phase thermo-kinetic data. In this work, a flexible framework is proposed for the calculation of the solvation correction involving closed-shell (i.e., non-free radical) and open-shell (i.e., free radicals) molecules. This novel model relies on the Peng-Robinson cubic equation of state (EoS) combined with a quantum-based continuum solvation model (COSMO-RS) through an advanced mixing rule. Unlike most predictive equations of state, the proposed model requires only pure compound inputs: critical temperature (Tc,i), critical pressure (Pc,i), acentric factor (ωi), and the screening charge distribution (σ-profile). These inputs were obtained for C/H/O free radicals using group corrections applied to the known values of the associated closed-shell molecules (parent molecules). The resulting EoS is able to provide fast predictions of solvation quantities of closed-shell molecules and C/H/O free radicals with a mean unsigned error of around 0.30 kcal/mol. The great advantage of this method is that it allows high-throughput computation of solvation quantities in pure solvents and mixtures at any temperature (including the supercritical domain), which could enable the simulation of complex oxidation kinetics in a wide range of applications. Solvation ; Chemistry ; Radical ; Solvation shell ; Implicit solvation ; Thermodynamics ; Kinetic energy ; Molecule ; Computational chemistry ; Physical chemistry ; Physics ; Organic chemistry ; Quantum mechanics Free Radicals and Antioxidants ; Chemical Thermodynamics and Molecular Structure ; Phase Equilibria and Thermodynamics
Authors: Eliise Tammekivi | Hugo Lilti | Magali Batteau | C. Lorentz | Christophe Geantet | D. Laurenti | Karine Faure
Source: Journal of Chromatography A (article, 2024-09-26)
Field: Chemistry — Spectroscopy

Complementarity of two-dimensional gas chromatography and two-dimensional liquid chromatography for the analysis of depolymerised lignin Chemistry ; Supercritical fluid chromatography ; Chromatography ; Gas chromatography ; Gas chromatography–mass spectrometry ; Mass spectrometry Analytical Chemistry and Chromatography ; Chromatography in Natural Products ; Fermentation and Sensory Analysis
Authors: Mohammed Shahadat Hossain | Mashiur Rahman | Nazim Çiçek
Source: Polymers (article, 2024-09-24)
Field: Materials Science — Polymers and Plastics

Tensile Properties of Cattail Fibres at Various Phenological Development Stages Cattails (<i>Typha latifolia</i> L.) are naturally occurring aquatic macrophytes with significant industrial potential because of their abundance, high-quality fibers, and high fiber yields. This study is the first attempt to investigate how phenological development and plant maturity impact the quality of cattail fibers as they relate to composite applications. It was observed that fibers from all five growth stages exhibited a Weibull shape parameter greater than 1.0, with a goodness-of-fit exceeding 0.8. These calculations were performed using both the Least Square Regression (LSR) and Maximum Likelihood Estimation (MLE) methods. Among the estimators, the MLE method provided the most conservative estimation of Weibull parameters. Based on the Weibull parameters obtained with all estimators, cattail fibers from all five growth stages appear suitable for composite applications. The consistency of shape parameters across all five growth stages can be attributed to the morphological and molecular developments of cattail fiber during the vegetative period. These developments were confirmed through the presence of calcium oxalate (CaOx) plates, elemental composition, and specific infrared peaks at 2360 cm<sup>-1</sup> contributing to the strength, cellulose peaks at 1635 cm<sup>-1</sup>, 2920 cm<sup>-1</sup>, and 3430 cm<sup>-1</sup>. In conclusion, it was found that the mechanical properties of cattail fiber remain similar when harvested multiple times in a single growing season. Weibull distribution ; Fiber ; Materials science ; Phenology ; Composite material ; Typha angustifolia ; Mathematics ; Statistics ; Botany ; Biology ; Ecology Natural Fiber Reinforced Composites ; Advanced Cellulose Research Studies ; Textile materials and evaluations
Authors: Meng‐Hsin Chen | Yan-Liang Liu | Vin‐Cent Su
Source: Heliyon (article, 2024-01-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Gallium nitride-based geometric and propagation metasurfaces for vortex beam emissions This work experimentally demonstrates the highly-efficient geometric and propagation metasurfaces for vortex beam emissions. These metasurfaces are respectively composed of high-aspect-ratio fin-like and cylindrical gallium nitride (GaN) meta-atoms. Remarkably, the optimized configuration of the fin-like GaN meta-atoms achieves a cross-polarization transmission efficiency of up to 99 %. Similarly, the cylindrical GaN meta-atoms exhibit an average co-polarization transmission efficiency of 97 %. Both metasurfaces, designed for vortex beam emission, exhibit annular intensity converging capabilities at distinct wavelengths in the visible. Notably, the geometric metasurface shows achromatic annular intensity distributions over a continuous wavelength range up to 100 nm, in sharp contrast to the propagation metasurface, which is subject to inherent wavelength dispersion limitations. Achromatic lens ; Wavelength ; Materials science ; Gallium nitride ; Optics ; Polarization (electrochemistry) ; Vortex ; Optoelectronics ; Gallium ; Beam (structure) ; Physics ; Nanotechnology ; Chemistry Metamaterials and Metasurfaces Applications ; Orbital Angular Momentum in Optics ; Plasmonic and Surface Plasmon Research
Authors: Lukas Meier | Asif Iqbal Bhatti | Stefaan Cottenier
Source: Critical reviews in solid state and materials sciences/CRC critical reviews in solid state and materials sciences (review, 2024-12-17)
Field: Materials Science — Metals and Alloys

Surface-to-bulk hydrogen transport in BCC iron: a computational review of adsorption and diffusion mechanisms Hydrogen embrittlement poses a significant challenge in various engineering applications. In this context, the iron/hydrogen system serves as a crucial prototype for examining the interaction between hydrogen and steel. Despite the critical importance of this topic, there is a notable lack of knowledge regarding the influence of surface characteristics on the penetration of hydrogen into materials. To address this gap, we have conducted an extensive review of the existing literature, particularly focusing on how different surface orientations of body-centered cubic (bcc) iron affect the adsorption, diffusion, and subsequent penetration of hydrogen into an iron crystal. The review primarily focuses on computational methods, incorporating experimental data for comparative analysis wherever feasible. This comprehensive synthesis of scattered information leads to several key conclusions. First, there is a systematic relationship between surface geometry and adsorption energy that has previously been overlooked. Second, bulk diffusion characteristics are recovered just a few atomic layers beneath the surface, emphasizing the importance of the initial surface layers in determining initial penetration. Third, penetrating through the surface layers is generally more challenging than further diffusion through bulk iron, with the specifics heavily dependent on the surface orientation. Studies on high-index surfaces are limited. We identify this as an area needing further research, and this review provides a solid foundation for such studies. Adsorption ; Hydrogen ; Materials science ; Hydrogen embrittlement ; Penetration (warfare) ; Surface energy ; Context (archaeology) ; Nanotechnology ; Chemical physics ; Diffusion ; Metallurgy ; Chemistry ; Physical chemistry ; Thermodynamics ; Composite material ; Physics ; Geology ; Engineering ; Corrosion Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; Corrosion Behavior and Inhibition
Authors: Shuping Guo | Ryan Morrow | Jeroen van den Brink | Oleg Janson
Source: Journal of Materials Chemistry A (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine learning facilitated by microscopic features for discovery of novel magnetic double perovskites Using microscopic parameters obtained from first-principles electronic structure calculations, we developed a machine learning model for predicting ordering transition temperatures in magnetic double perovskites. Condensed matter physics ; Materials science ; Computer science ; Statistical physics ; Physics Machine Learning in Materials Science ; Magnetic and transport properties of perovskites and related materials ; Electronic and Structural Properties of Oxides
Authors: Umesh Chandra Garnaik | Anshuman Chandra | Vijay K. Goel | Balázs Gulyás | Parasuraman Padmanabhan | Shilpi Agarwal
Source: International Journal of Nanomedicine (article, 2024-08-01)
Field: Materials Science — Materials Chemistry

Development of SERS Active Nanoprobe for Selective Adsorption and Detection of Alzheimer’s Disease Biomarkers Based on Molecular Docking This peptide-probe interaction provides a general enhancement factor and the molecular structure of the misfolded peptides. Secondary structural information may be obtained at the molecular level for specific residues owing to isotope shifts in the Raman spectra. Conjugation of the nanoprobe with Aβ42 selectively detected AD in bodily fluids. The proposed nanoprobes can be easily applied to the detection of Aβ plaques in blood, saliva, and sweat samples. Nanoprobe ; Alzheimer's disease ; Docking (animal) ; Nanotechnology ; Disease ; Chemistry ; Computational biology ; Materials science ; Medicine ; Biology ; Pathology ; Nanoparticle Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques
Authors: Adam Wawerski | B. Siemiątkowska | Michał Jóźwik | Bartłomiej Fajdek | Małgorzata Partyka
Source: Sensors (article, 2024-02-18)
Field: Chemistry — Analytical Chemistry

Machine Learning Method and Hyperspectral Imaging for Precise Determination of Glucose and Silicon Levels This article introduces an algorithm for detecting glucose and silicon levels in solution. The research focuses on addressing the critical need for accurate and efficient glucose monitoring, particularly in the context of diabetic management. Understanding and monitoring silicon levels in the body is crucial due to its significant role in various physiological processes. Silicon, while often overshadowed by other minerals, plays a vital role in bone health, collagen formation, and connective tissue integrity. Moreover, recent research suggests its potential involvement in neurological health and the prevention of certain degenerative diseases. Investigating silicon levels becomes essential for a comprehensive understanding of its impact on overall health and well-being and paves the way for targeted interventions and personalized healthcare strategies. The approach presented in this paper is based on the integration of hyperspectral data and artificial intelligence techniques. The algorithm investigates the effectiveness of two distinct models utilizing SVMR and a perceptron independently. SVMR is employed to establish a robust regression model that maps input features to continuous glucose and silicon values. The study outlines the methodology, including feature selection, model training, and evaluation metrics. Experimental results demonstrate the algorithm's effectiveness at accurately predicting glucose and silicon concentrations and showcases its potential for real-world application in continuous glucose and silicon monitoring systems. Context (archaeology) ; Computer science ; Artificial intelligence ; Machine learning ; Silicon ; Data mining ; Materials science ; Biology Spectroscopy and Chemometric Analyses ; Optical Imaging and Spectroscopy Techniques ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Ebony Shire | André A. B. Coimbra | Carlos Barba‐Ostria | Leonardo Rios‐Solis | Diego López Barreiro
Source: Molecular Systems Design & Engineering (article, 2024-01-01)
Field: Materials Science — Biomaterials

Molecular design of protein-based materials – state of the art, opportunities and challenges at the interface between materials engineering and synthetic biology Recombinant DNA technology allows us to generate new nature-inspired structural proteins with monomer-level control. These proteins can be used for the manufacture of materials with applications in healthcare, textiles or adhesives, to name a few. Synthetic biology ; Interface (matter) ; Protein engineering ; Nanotechnology ; State (computer science) ; Engineering ; Materials science ; Computational biology ; Engineering ethics ; Computer science ; Biology ; Chemical engineering ; Biochemistry Silk-based biomaterials and applications ; Biochemical and Structural Characterization ; Supramolecular Self-Assembly in Materials
Authors: Jin Liu | Tatsuaki Tagami | Koki Ogawa | Tetsuya Ozeki
Source: Biological and Pharmaceutical Bulletin (article, 2024-01-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Development of Hollow Gold Nanoparticles for Photothermal Therapy and Their Cytotoxic Effect on a Glioma Cell Line When Combined with Copper Diethyldithiocarbamate Gold-based nanoparticles hold promise as functional nanomedicines, including in combination with a photothermal effect for cancer therapy in conjunction with chemotherapy. Here, we synthesized hollow gold nanoparticles (HGNPs) exhibiting efficient light absorption in the near-IR (NIR) region. Several synthesis conditions were explored and provided monodisperse HGNPs approximately 95-135 nm in diameter with a light absorbance range of approximately 600-720 nm. The HGNPs were hollow and the surface had protruding structures when prepared using high concentrations of HAuCl<sub>4</sub>. The simultaneous nucleation of a sacrificial AgCl template and Au nanoparticles may affect the resulting HGNPs. Diethyldithiocarbamate (DDTC) is metabolized from disulfiram and is a repurposed drug currently attracting attention. The chelation of DDTC with copper ion (DDTC-Cu) has been investigated for treating glioma, and here we confirmed the cytotoxic effect of DDTC-Cu towards rat C6 glioma cells in vitro. HGNPs alone were biocompatible and showed little cytotoxicity, whereas a mixture of DDTC-Cu and HGNPs was cytotoxic in a dose dependent manner. The temperature of HGNPs was increased by NIR-laser irradiation. The photothermal effect on HGNPs under NIR-laser irradiation resulted in cytotoxicity towards C6 cells and was dependent on the irradiation time. Photothermal therapy by HGNPs combined and DDTC-Cu was highly effective, suggesting that this combination approach hold promise as a future glioma therapy. Photothermal therapy ; Cytotoxicity ; Nanoparticle ; Colloidal gold ; Chemistry ; Glioma ; Irradiation ; Cytotoxic T cell ; Chelation ; Nuclear chemistry ; Absorbance ; Nanotechnology ; In vitro ; Biophysics ; Materials science ; Cancer research ; Organic chemistry ; Biochemistry ; Chromatography Gold and Silver Nanoparticles Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Nanocluster Synthesis and Applications
Authors: Yang Xu | Kejia Ma | Dejia Zhang | Shaozhong Song | Xiaofeng An
Source: PLoS ONE (article, 2024-09-04)
Field: Chemistry — Analytical Chemistry

Classification of soybean seeds based on RGB reconstruction of hyperspectral images Soyabean is an incredibly significant component of Chinese agricultural product, and categorizing soyabean seeds allows for a better understanding of the features, attributes, and applications of many species of soyabean. This enables farmers to choose appropriate seeds for sowing in order to increase production and quality. As a result, this thesis provides a method for classifying soybean seeds that uses hyperspectral RGB picture reconstruction. Firstly, hyperspectral images of seven varieties of soybean, H1, H2, H3, H4, H5, H6 and H7, were collected by hyperspectral imager, and by using the principle of the three base colours, the R, G and B bands which have more characteristic information are selected to reconstruct the images with different texture and colour characteristics to generate a new dataset for seed segmentation, and finally, a comparison is made with the classification effect of the seven models. The experimental results in ResNet34 show that the classification accuracy of the dataset before and after RGB reconstruction increases from 88.87% to 91.75%, demonstrating that RGB image reconstruction can strengthen image features; ResNet18, ResNet34, ResNet50, ResNet101, CBAM-ResNet34, SENet-ResNet34, and SENet-ResNet34-DCN models have classification accuracies of 72.25%, 91.75%, 89%, 88.48%, 92.28%, 92.80%, and 94.24%, respectively.SENet-ResNet34-DCN achieves the greatest classification accuracy results, with a model loss of roughly 0.3. The proposed SENet-ResNet34-DCN model is the most effective at classifying soybean seeds. By classifying and optimally selecting seed varieties, agricultural production can become more scientific, efficient, and sustainable, resulting in higher returns for farmers and contributing to global food security and sustainable development. Hyperspectral imaging ; RGB color model ; Artificial intelligence ; Pattern recognition (psychology) ; Sowing ; Computer science ; Mathematics ; Segmentation ; Agronomy ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing and Land Use
Authors: Hang Xue | Xiping Xu | Xiang Meng
Source: Sensors (article, 2024-09-21)
Field: Chemistry — Analytical Chemistry

Estimation Model for Maize Multi-Components Based on Hyperspectral Data Assessing the quality of corn seeds necessitates evaluating their water, fat, protein, and starch content. This study integrates hyperspectral imaging technology with chemometric analysis techniques to achieve non-invasive and rapid detection of multiple key components in corn seeds. Hyperspectral images of the embryo surface of maize seeds were collected within the wavelength range of 1100~2498 nm. Subsequently, image segmentation techniques were applied to extract the germ structure of the corn seeds as the region of interest. Seven spectral data preprocessing algorithms were employed, and the Detrending Transformation (DT) algorithm was identified as the optimal preprocessing method through comparative analysis using the Partial Least Squares Regression (PLSR) model. To reduce spectral redundancy and streamline the prediction model, three algorithms were employed for characteristic wavelength extraction: Successive Projections Algorithm (SPA), Competitive Adaptive Reweighted Sampling (CARS), and Uninformative Variable Elimination (UVE). Using the original spectra and extracted characteristic wavelengths, PLSR, BP, RBF, and LSSVM models were constructed to detect the content of four components. The analysis indicated that the CARS-LSSVM algorithm had the best prediction performance. The PSO algorithm was employed to further optimize the parameters of the LSSVM model, thereby improving the model's prediction performance. The R values for the four components in the test set were 0.9884, 0.9490, 0.9864, and 0.9687, respectively. This indicates that hyperspectral technology combined with the DT-CARS-PSO-LSSVM algorithm can effectively detect the main component content of corn seeds. This study not only provides a scientific basis for the evaluation of corn seed quality but also opens up new avenues for the development of non-destructive testing technology in related fields. Hyperspectral imaging ; Partial least squares regression ; Principal component analysis ; Preprocessor ; Variable elimination ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis