jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ungku Nur Ainaa Ungku Mohd Aziz | Nor Dalila Nor Affandi | Ahmad Mukifza Harun | Mohd Azizi Abdul Rahman | Liliana Indrie | Noor Najmi Bonnia
Source: Autex Research Journal (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Facemask comfort enhancement with graphene oxide from recovered carbon waste tyres Abstract Commercial disposable facemasks have lower breathability and may cause discomfort after hours of wearing them. Graphene oxide (GO) nanoparticles offer a significant opportunity to improve the breathability of facemask materials. Hence, the current study aimed to investigate the feasibility of incorporating GO in facemask materials. The GO was synthesized from recovered carbon of waste carbon tyre. In this study, two concentrations of GO (0.01 and 0.02%) were used to enhance the comfort properties of the polypropylene (PP) facemask fabric. The GO-coated PP facemask fabrics were characterized for scanning electron microscopy, energy dispersive X-ray, and Raman spectroscopy. The comfort properties were determined using air permeability, water vapour permeability, and moisture management test. Raman analysis revealed distinctive peaks corresponding to GO at approximately 1,300 and 1,500 cm −1 . The GO displayed bumping pieces of particles and a textured surface, with a diameter ranging from 30 to 80 nm. The result of mercury porosimetry shows that the PP fabric coated with 0.02% of GO provided a higher pore diameter and porosity at approximately 21.31 µm and 82.79%, respectively. Due to its high pore diameter and porosity, the PP filter facemask fabric coated with 0.02% GO demonstrated enhanced air permeability, water vapour permeability, and moisture management. These results suggested that the sample possesses favourable breathability properties as compared to the sample without GO. By undertaking this study, GO synthesized from the waste carbon tyre was developed, which can enhance the breathability of fabric materials. Materials science ; Air permeability specific surface ; Graphene ; Composite material ; Porosity ; Porosimetry ; Raman spectroscopy ; Oxide ; Scanning electron microscope ; Polypropylene ; Porous medium ; Nanotechnology Textile materials and evaluations ; Advanced Sensor and Energy Harvesting Materials ; Natural Fiber Reinforced Composites
Authors: Guanglai Wang | Congcong Wang | Dayang Liu
Source: Journal of Agricultural Engineering (article, 2024-07-09)
Field: Chemistry — Analytical Chemistry

Detection of early collision and compression bruises for pears based on hyperspectral imaging technology Early detection of bruising is one of the major challenges in postharvest quality sorting processes for pears. In this study, visible/near infrared (VIS/NIR) hyperspectral imaging (400–1000 nm) was utilized for early detection of pear bruise type and timing (1, 12, and 24 h post-bruise). Spectral images of nonbruised and mechanically bruised pears (collision and compression) were captured at these intervals for modeling. Spectral data was processed using principal component analysis (PCA) and uninformative variable elimination (UVE) to select optimum wavelengths. Classification models were then built using an extreme learning machine (ELM) and support vector machine (SVM), and compared with a model combining genetic algorithm, sooty tern optimization algorithm, and SVM (STOA-GA-SVM). For PCA-ELM, UVE-ELM, PCA-SVM, and UVE-SVM models, the calibration set accuracies were 98.99%, 98.98%, 96.94%, and 99.23% respectively. And the validation set accuracies were 89.29%, 87.97%, 88.78%, and 88.78% respectively. The STOA-GA-SVM model shows the best performance, and the accuracy of the calibration set and validation set is determined to be 97.19% and 92.86%, respectively. This study shows that the use of the VIS/NIR hyperspectral imaging technique combined with the STOA-GA-SVM algorithm is feasible for the rapid and nondestructive identification of the bruise type and time for pears. Hyperspectral imaging ; Collision ; Compression (physics) ; Engineering ; Medicine ; Artificial intelligence ; Computer vision ; Veterinary medicine ; Biomedical engineering ; Computer science ; Medical physics ; Computer security ; Materials science Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Bee Products Chemical Analysis
Authors: Rajkamal Balu | Swati Sharma | Rachael Roberts | Jitraporn Vongsvivut | Namita Roy Choudhury
Source: Polymers (article, 2024-09-18)
Field: Materials Science — Polymers and Plastics

Upcycling Post-Consumer Paint Pail Plastic Waste The need for ending plastic waste and creating a circular economy has prompted significant interest in developing a new family of composite materials through recycling and recovery of waste resources (including bio-sourced materials). In this work, a family of natural fiber-reinforced plastic composites has been developed from paint pail waste recycled polypropylene (rPP) and waste wool fibers of different diameter and aspect ratio. Composites were fabricated by melt processing using polypropylene-graft-maleic anhydride as a compatibilizer. The internal morphology, interfacial and thermal characteristics, viscoelastic behavior, water sorption/wettability, and mechanical properties of composites were studied using electron microscopy, high-resolution synchrotron Fourier transform infrared microspectroscopy, thermal analysis, rheology, immersion test, contact angle measurement, tensile test and flexural test. The composite matrix exhibited an internal morphology of coalescent micro-droplets due to the presence of polyethylene and dry paint in the rPP phase. In general, the rheological and mechanical properties of the composites comprising higher-aspect-ratio (lower diameter) fibers exhibited relatively superior performance. About an 18% increase in tensile strength and a 39% increase in flexural strength were measured for composites with an optimal fiber loading of 10 wt.%. Interfacial debonding and fiber pull-out were observed as the main failure mechanism of the composites. The developed composites have potential for applications in automotive, decking, and building industries. Materials science ; Composite material ; Flexural strength ; Polypropylene ; Ultimate tensile strength ; Composite number ; Fiber Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Polymer crystallization and properties
Authors: Loran Cheplanov | Shai Avidan | David J. Bonfil | Iftach Klapp
Source: Machine Vision and Applications (article, 2024-03-22)
Field: Chemistry — Analytical Chemistry

Hyperspectral image dynamic range reconstruction using deep neural network-based denoising methods Abstract Hyperspectral (HS) measurement is among the most useful tools in agriculture for early disease detection. However, the cost of HS cameras that can perform the desired detection tasks is prohibitive-typically fifty thousand to hundreds of thousands of dollars. In a previous study at the Agricultural Research Organization’s Volcani Institute (Israel), a low-cost, high-performing HS system was developed which included a point spectrometer and optical components. Its main disadvantage was long shooting time for each image. Shooting time strongly depends on the predetermined integration time of the point spectrometer. While essential for performing monitoring tasks in a reasonable time, shortening integration time from a typical value in the range of 200 ms to the 10 ms range results in deterioration of the dynamic range of the captured scene. In this work, we suggest correcting this by learning the transformation from data measured with short integration time to that measured with long integration time. Reduction of the dynamic range and consequent low SNR were successfully overcome using three developed deep neural networks models based on a denoising auto-encoder, DnCNN and LambdaNetworks architectures as a backbone. The best model was based on DnCNN using a combined loss function of $$\ell _{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:math> and Kullback–Leibler divergence on images with 20 consecutive channels. The full spectrum of the model achieved a mean PSNR of 30.61 and mean SSIM of 0.9, showing total improvement relatively to the 10 ms measurements’ mean PSNR and mean SSIM values by 60.43% and 94.51%, respectively. Hyperspectral imaging ; Computer science ; Artificial intelligence ; Artificial neural network ; Noise reduction ; Range (aeronautics) ; Time delay and integration ; Deep learning ; Dynamic range ; Reduction (mathematics) ; Encoder ; Algorithm ; Computer vision ; Pattern recognition (psychology) ; Mathematics ; Materials science Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Image and Signal Denoising Methods
Authors: Łukasz Łach | Dmytro Svyetlichnyy
Source: Materials (article, 2024-01-30)
Field: Materials Science — Materials Chemistry

3D Model of Carbon Diffusion during Diffusional Phase Transformations The microstructure plays a crucial role in determining the properties of metallic materials, in terms of both their strength and functionality in various conditions. In the context of the formation of microstructure, phase transformations that occur in materials are highly significant. These are processes during which the structure of a material undergoes changes, most commonly as a result of variations in temperature, pressure, or chemical composition. The study of phase transformations is a broad and rapidly evolving research area that encompasses both experimental investigations and modeling studies. A foundational understanding of carbon diffusion and phase transformations in materials science is essential for comprehending the behavior of materials under different conditions. This understanding forms the basis for the development and optimization of materials with desired properties. The aim of this paper is to create a three-dimensional model for carbon diffusion in the context of modeling diffusional phase transformations occurring in carbon steels. The proposed model relies on the utilization of the LBM (Lattice Boltzmann Method) and CUDA architecture. The resultant carbon diffusion model is intricately linked with a microstructure evolution model grounded in FCA (Frontal Cellular Automata). This manuscript provides a concise overview of the LBM and the FCA method. It outlines the structure of the developed three-dimensional model for carbon diffusion, details its correlation with the microstructure evolution model, and presents the developed algorithm for simulating carbon diffusion. Demonstrative examples of simulation results, illustrating the growth of the emerging phase and affected by various model parameters within particular planes of the 3D calculation domain, are also presented. Microstructure ; Diffusion ; Context (archaeology) ; Materials science ; Carbon fibers ; Phase (matter) ; Lattice Boltzmann methods ; Cellular automaton ; Statistical physics ; Computer science ; Thermodynamics ; Algorithm ; Chemistry ; Metallurgy ; Physics ; Composite material Solidification and crystal growth phenomena ; Microstructure and mechanical properties ; Aluminum Alloy Microstructure Properties
Authors: Xinyue Jiang | Yongzhong Song | Zhibin Sun | Kuang–Chao Fan | Xuehai Tang
Source: Forests (article, 2024-11-04)
Field: Chemistry — Analytical Chemistry

Using a Two-Stage Hybrid Dimensionality Reduction Method on Hyperspectral Data to Predict Chlorophyll Content of Camellia oleifera Camellia oleifera is an oilseed crop that holds significant economic, ecological, and social value. In the realm of Camellia oleifera cultivation, utilizing hyperspectral analysis techniques to estimate chlorophyll content can enhance our understanding of its physiological parameters and response characteristics. However, hyperspectral datasets contain information from many wavelengths, resulting in high-dimensional data. Therefore, selecting effective wavelengths is crucial for processing hyperspectral data and modeling in retrieval studies. In this study, by using hyperspectral data and chlorophyll content from Camellia oleifera samples, three different dimensionality reduction methods (Taylor-CC, NCC, and PCC) are used in the first round of dimensionality reduction. Combined with these methods, various thresholds and dimensionality reduction methods (with/without further dimensionality reduction) are used in the second round of dimensionality reduction; different sets of core wavelengths with equal size are identified respectively. Using hyperspectral reflectance data at different sets of core wavelengths, multiple machine learning models (Lasso, ANN, and RF) are constructed to predict the chlorophyll content of Camellia oleifera. The purpose of this study is to compare the performance of various dimensionality reduction methods in conjunction with machine learning models for predicting the chlorophyll content of Camellia oleifera. Results show that (1) the Taylor-CC method can effectively select core wavelengths with high sensitivity to chlorophyll variation; (2) the two-stage hybrid dimensionality reduction methods demonstrate superiority in three models; (3) the Taylor-CC + NCC method combined with an ANN achieves the best predictive performance of chlorophyll content. The new two-stage dimensionality reduction method proposed in this study not only improves both the efficiency of hyperspectral data processing and the predictive accuracy of models, but can serve as a complement to the study of Camellia oleifera properties using the Taylor-CC method. Camellia oleifera ; Hyperspectral imaging ; Stage (stratigraphy) ; Dimensionality reduction ; Reduction (mathematics) ; Chlorophyll ; Mathematics ; Botany ; Computer science ; Artificial intelligence ; Environmental science ; Biology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Remote Sensing and Land Use
Authors: Jose Gutierrez-Cardenas | Benjamin D. Gibbas | K. P. K. Whitaker | Martina Kaledin | Alexey L. Kaledin
Source: The Journal of Physical Chemistry A (article, 2024-08-29)
Field: Materials Science — Materials Chemistry

A Low-Order Permutationally Invariant Polynomial Approach to Learning Potential Energy Surfaces Using the Bond-Order Charge-Density Matrix: Application to C<sub><i>n</i></sub> Clusters for <i>n</i> = 3–10, 20 A representation for learning potential energy surfaces (PESs) in terms of permutationally invariant polynomials (PIPs) using the Hartree-Fock expression for electronic energy is proposed. Our approach is based on the one-electron core Hamiltonian weighted by the configuration-dependent elements of the bond-order charge density matrix (CDM). While the previously reported model used an <i>s</i>-function Gaussian basis for the CDM, the present formulation is expanded with <i>p</i>-functions, which are crucial for describing chemical bonding. Detailed results are demonstrated on linear and cyclic C<sub><i>n</i></sub> clusters (<i>n</i> = 3-10) trained on extensive B3LYP/aug-cc-pVTZ data. The described method facilitates PES learning by reducing the root mean squared error (RMSE) by a factor of 5 relative to the <i>s</i>-function formulation and by a factor of 20 relative to the conventional PIP approach. This is equivalent to using CDM and an <i>sp</i> basis with a PIP of order <i>M</i> to achieve the same RMSE as with the conventional method with a PIP of order <i>M</i> + 2. Implications for large-scale problems are discussed using the case of the PES of the C<sub>20</sub> fullerene in full permutational symmetry. Invariant (physics) ; Hamiltonian (control theory) ; Charge (physics) ; Density matrix ; Charge density ; Representation (politics) ; Physics ; Matrix representation ; Bond order ; Invariant polynomial ; Quantum mechanics ; Statistical physics ; Mathematics ; Polynomial ; Matrix polynomial ; Bond length ; Mathematical analysis ; Molecule ; Mathematical optimization Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; X-ray Diffraction in Crystallography
Authors: Yuanfan Yang | Hui Pan | Yunlin Xiao | Lijun Li | Yang Hu | Lufang Chen | Wang Ying | Hui Ni | Feng Chen | Juzhong Tan | Fan He
Source: Quality Assurance and Safety of Crops & Foods (article, 2024-09-22)
Field: Chemistry — Analytical Chemistry

Modelling and nondestructive quality prediction of Guanxi Honey Red Pomelo (&lt;i&gt;Citrus grandis&lt;/i&gt; (L.) Osbeck) based on shape characteristics and bitterness Currently, it is difficult to distinguish the inner quality of pachycarpous fruits using nondestructive instruments, and trace nutrients and flavor compounds are rarely considered in fruit sorting. In this study, the external features and internal quality indices of an extensively farmed citrus fruit, the Guanxi honey red pomelo, were measured. The correlation between the inner and external quality was analyzed using Pearson correlation, canonical correlation, and grey correlation analyses. This research innovatively introduced bitter substances and revealed that the height-diameter ratio could serve as a predictor for ultra-size fruits, which partially reflects inner quality indices. Several grey formulas were developed to express these correlations and were fitted to a matrix, which demonstrated accuracies at all level 1 (C &lt; 0.35, P &gt; 0.95). Based on the matrix, fruit quality analysis of white and red pomelo showed high prediction accuracy in TSS and bitter substance indices (R2 = 0.6 – 0.8) and indicated that mature and symmetrical pomelo (height-diameter ratio of 0.9 – 1.1) exhibit a similar quality pattern. This study achieved rapid quality assessment using easy measurable shape characteristics and explored the application of characteristic bitter substances in the quality evaluation model, providing theoretical references and potential application guidance for pachycarpous fruit sorting, as well as introducing a new idea to incorporating characteristic quality into modeling index. Guanxi ; Quality (philosophy) ; Food science ; Horticulture ; Mathematics ; Materials science ; Chemistry ; Physics ; Biology ; Geography Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality
Authors: Javier Cruz-Salgado | Sergio Alonso‐Romero | EDGAR AUGUSTO RUELAS SANTOYO | José Alfredo Jiménez García | Roxana Zaricell Bautista López
Source: Journal of Engineering (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Inclusion of Inverse Terms in Design of Experiments for Mixtures Applied to Wood Plastic Composites Wood–plastic composites (WPCs) are materials that combine a polymeric phase with a filler, typically wood powder or fiber. This common practice significantly enhances certain mechanical properties of WPCs, such as tensile, compressive, and flexural strength, especially when coupling agents are added. Polyethylene terephthalate (PET) is a thermoplastic commonly used due to its high stiffness, mechanical strength, and chemical resistance. However, the increasing demand for PET has led to a rise in plastic waste accumulation, posing an environmental challenge. Therefore, recycling and promoting a circular economy are considered key strategies to reduce environmental impact and achieve sustainable development. The main challenge in WPC production lies in its formulation—specifically, determining the appropriate proportion of thermoplastic polymer, wood filler, and coupling agent. To optimize the mechanical properties of WPCs, it is necessary to vary these component proportions and understand how these changes affect the composite properties. This article describes a statistical approach for optimizing the mechanical properties of a WPC with PET as the polymer matrix. It is assumed that the mechanical properties of the WPC are a function of the proportions of the components that make up the mixture, so the optimization consists of determining these proportions in a way that maximizes the mechanical properties. With the experimental design, three models with inverse terms were fitted, which modeled the relationship between the component proportions and the flexural, compression, and tensile strength. The optimal formulation for maximizing flexural strength was found to be 58% PET, 39% wood, and 3% E‐GMA. The optimal formulation for maximizing compressive strength was found to be 58% PET, 40% wood, and 2% E‐GMA. The optimal formulation for maximizing tensile strength was found to be 86% PET, 11% wood, and 3% E‐GMA. The results are validated through analysis of variance of the mixture experimental design for mixtures. Finally, three response surface plots are presented for each mechanical property, which graphically describe the relationship between the different proportions of the mixture components and the mechanical properties. Materials science ; Composite material ; Flexural strength ; Ultimate tensile strength ; Wood flour ; Thermoplastic ; Izod impact strength test ; Composite number ; Wood-plastic composite ; Polyethylene terephthalate Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Innovations in Concrete and Construction Materials
Authors: Xiaotong Liu | Udit Pant | Natasha Logan | Christopher T. Elliott | Cuong Cao
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Non-Linear Responses Via Agglomeration and Aggregation of Gold Nanoparticles for Surface-Enhanced Raman Spectroscopy (Sers) Coupled with Chemometric Analysis for Chlorpyrifos Detection Economies of agglomeration ; Surface-enhanced Raman spectroscopy ; Raman spectroscopy ; Colloidal gold ; Nanoparticle ; Nanotechnology ; Materials science ; Spectroscopy ; Analytical Chemistry (journal) ; Chemistry ; Chemical engineering ; Chromatography ; Raman scattering ; Optics ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced biosensing and bioanalysis techniques
Authors: Francisco Javier Esquivel | José L. Romero‐Béjar | José Antonio Esquivel Guerrero
Source: Analytical Science Advances (article, 2024-10-01)
Field: Chemistry — Analytical Chemistry

Preprocessing of spectroscopic data to highlight spectral features of materials The study of the extensive data sets generated by spectrometers, which are of the type commonly referred to as big data, plays a crucial role in extracting valuable information on mineral composition in various fields, such as chemistry, geology, archaeology, pharmacy and anthropology. The analysis of these spectroscopic data falls into the category of big data, which requires the application of advanced statistical methods such as principal component analysis and cluster analysis. However, the large amount of data (big data) recorded by spectrometers makes it very difficult to obtain reliable results from raw data. The usual method is to carry out different mathematical transformations of the raw data. Here, we propose to use the affine transformation for highlight the underlying features for each sample. Finally, an application to spectroscopic data collected from minerals or rocks recorded by NASA's Jet Propulsion Laboratory is performed. An illustrative example has been included by analysing three mineral samples, which have different diageneses and parageneses and belong to different mineralogical groups. Raw data ; Preprocessor ; Principal component analysis ; Computer science ; Compositional data ; Affine transformation ; Big data ; Sample (material) ; Data processing ; Data pre-processing ; Data mining ; Mineralogy ; Geology ; Chemistry ; Mathematics ; Artificial intelligence ; Database Spectroscopy and Chemometric Analyses ; Geochemistry and Geologic Mapping ; Mineral Processing and Grinding