jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Züleyha Değırmencı
Source: Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering (article, 2024-12-24)
Field: Materials Science — Polymers and Plastics

SELECTING THE OPTIMUM AMOUNT OF RECYCLED POLYPROPYLENE NONWOVEN WASTE IN SPUNBOND PRODUCTION FOR SUSTAINABILITY Structural Abstract During the Covid-19 pandemic, there was a significant increase in the use of polypropylene-based masks, leading to challenges related to raw material waste and supply limitations. This study aims to identify the optimal ratio of recycled and standard polypropylene fibers to produce a nonwoven spun-bond fabric with the best possible strength and elongation properties. In the rapidly expanding industry, polypropylene (PP)-based nonwoven fabrics (spunbond and meltblown) are increasingly being recycled by converting fabric waste into granules, which are then blended with virgin PP in specific proportions for reuse in production. This research focuses on analyzing the tensile strength and elongation at break of spunbond fabrics. First, fabric wastes from Spunbond-Meltblown processes were converted into granules, and their melt flow index (MFI) values were measured. These granules were then blended with virgin polypropylene fibers in varying proportions, resulting in five different spunbond fabric samples. The mechanical properties of these samples were compared with those of a fabric produced solely from virgin PP. The optimal mixing ratio of recycled to virgin fibers was then determined based on the results. The MFI of the recycled PP1 waste was measured at 38, while the MFI of a 75%-25% Spunbond-Meltblown waste blend (Recycle-PP5) was 104. Spunbond fabrics were produced by blending Recycle-PP1 granules with virgin raw material at ratios ranging from 0% to 20%. It was found that a 10% blend of recycled granules yielded the best results without compromising fabric quality. Higher proportions of recycled granules led to defects in the fabric. For instance, the tensile strength of spunbond fabrics containing 20% Recycle-PP5 granules decreased by approximately 26.9% compared to the fabric produced with 100% virgin PP. This study demonstrates the potential for using recycled granules in spunbond fabric production for specific applications, based on the observed strength properties. A key distinction of this research from existing literature is the identification of the most effective blending ratio of recycled and virgin polypropylene in a conventional production setting. Polypropylene ; Materials science ; Composite material ; Elongation ; Raw material ; Ultimate tensile strength ; Pulp and paper industry ; Chemistry Textile materials and evaluations ; Additive Manufacturing and 3D Printing Technologies ; Natural Fiber Reinforced Composites
Authors: Eric Lewis | Mehrdad Shahnam | MaryAnn Clarke | Omer Dogan | Dushyant Shekhawat | Eric J. Popczun | Pranjali Muley | Ping Wang | Ranjani Siriwardane | Robert Stevens | Gavin Pickenpaugh | Matthew Jamieson | Benjamin Chorpening | Samuel Bayham
Source:  (article, 2024-04-24)
Field: Chemistry — Inorganic Chemistry

Advanced Reaction Systems Portfolio Objective Value Proposition: Gasification technologies offer promising opportunities to generate value from biomass and waste materials with minimal carbon emissionsThe objective of the Advanced Reaction Systems Portfolio is to design, develop, and analyze technologies to support the mission of the Gasification Program to enable the use of diverse feedstocks to produce hydrogen and other value-added products with net-zero greenhouse gas emissions Current Computer science Zeolite Catalysis and Synthesis
Authors: Cyrill Bösch | Marc Serra‐Garcia | Christian Böhm | Andreas Fichtner
Source: arXiv (Cornell University) (preprint, 2024-10-10)
Field: Materials Science — Biomaterials

Adjoint computation of Berry phase gradients Berry phases offer a geometric perspective on wave propagation and are key to designing materials with topological wave transport. However, controlling Berry phases is challenging due to their dependence on global integrals over the Brillouin zone, making differentiation difficult. We present an adjoint-based method for efficiently computing the gradient of the Berry phase with respect to system parameters, involving only one forward and one adjoint calculation. This approach enables the use of advanced optimization techniques, such as topology optimization, to design new materials with tailored topological wave properties. Berry ; Computation ; Phase (matter) ; Geometric phase ; Mathematics ; Physics ; Mathematical physics ; Algorithm ; Condensed matter physics ; Quantum mechanics ; Biology ; Botany Supramolecular Self-Assembly in Materials ; Fullerene Chemistry and Applications
Authors: Émilie Bertrand | Valérie Gabelica
Source: ChemRxiv (preprint, 2024-07-08)
Field: Chemistry — Spectroscopy

THERMOMETER IONS, INTERNAL ENERGIES, AND AMBIENT IONIZATION IN-SOURCE FRAGMENTATION Ionization and fragmentation are at the core of mass spectrometry. But they are not separated in space, as in-source fragmentation can also occur. Here we survey the literature published since our 2005 review on the internal energy and fragmentation in electrospray ionization sources (Gabelica & De Pauw, Mass Spectrom. Rev. 2005, 24, 566). We present new thermometer molecules to diagnose and quantify source heating, provide tables of recommended threshold (E0) and appearance energies (Eapp) for the survival yield method, and systematically compare the softness of a variety of ambient pressure ionization methods. The droplet size distribution and desolvation dynamics play a major role: lower average internal energies are obtained when the ions remain protected by a solvation shell and spend less time nakedly exposed to activating conditions in the transfer interface. Methods based on small droplet formation without electrophoretic charging can thus be softer than electrospray. The tuning of the entire source interface often has an even greater influence on ion internal energies and fragmentation than on the ionization process itself. We also highlight new dielectric barrier discharge sources that can gas-phase ionize small molecules while conferring barely more internal energy than electrospray ionization. We hope that this review will facilitate the control and standardization of in-source ion activation conditions, to ensure the transferability of data and research results in mass spectrometry. Fragmentation (computing) ; Ionization ; Ion ; Thermometer ; Atomic physics ; Materials science ; Physics ; Computer science ; Thermodynamics Mass Spectrometry Techniques and Applications ; Nuclear Physics and Applications ; Ion-surface interactions and analysis
Authors: Nisha Nisha | Renu Mavi | Manisha Chaudhary | Preeti Singh
Source: International Journal of Agricultural Invention (article, 2024-12-06)
Field: Chemistry — Analytical Chemistry

Adsorption of malachite green and Congo red dyes onto Maize husk: Optimization of process parameters The objective of this study is to determine whether environment friendly maize husk (MH), a lignocellulosic agricultural by-product, can effectively remove Congo Red (CR) and Malachite Green (MG) dyes from aqueous solutions. Several experimental factors, such as adsorbent dosage, contact time, stirring speed, and pH, were examined in order to optimize the adsorption process. Adsorption efficiency raised within ideal dosage of 0.7 g for MG and 0.9 g for CR. For MG and CR, the contact times were found to be 50 and 60minutes respectively. It was discovered that the adsorption process was pH sensitive with the best removal of MG occurring at pH 5 and for CR above pH 8. Its potential as a low cost and sustainable adsorbent was validated by FTIR spectroscopy, which verified theefficient adsorption of dye molecules onto the MH surface through distinctive interactions with functional groups. Husk ; Malachite green ; Congo red ; Adsorption ; Zea mays ; Process (computing) ; Chemistry ; Pulp and paper industry ; Materials science ; Agronomy ; Botany ; Biology ; Organic chemistry ; Computer science ; Engineering Dye analysis and toxicity
Authors: David Moss
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2024-07-01)
Field: Materials Science — Materials Chemistry

Maximizing the Kerr nonlinear optical performance of silicon nanowires integrated with graphene oxide films The Kerr nonlinear optical performance of silicon nanowire waveguides integrated with 2D layered graphene oxide (GO) films is theoretically studied and optimized based on experimentally measured linear and nonlinear optical parameters of the GO films. The strong mode overlap between the silicon nanowires and highly nonlinear GO films yields a significantly enhanced Kerr nonlinearity for the hybrid waveguides. A detailed analysis for the influence of waveguide geometry and GO film thickness on the propagation loss, nonlinear parameter, and nonlinear figure of merit (FOM) is performed. The results show that the effective nonlinear parameter and nonlinear FOM can be increased by up to ~52 and ~79 times relative to bare silicon nanowires, respectively. Self-phase modulation (SPM)-induced spectral broadening of optical pulses is used as a benchmark to evaluate the nonlinear performance, examining the trade-off between enhancing Kerr nonlinearity and minimizing loss. By optimizing the device parameters to balance this, a high spectral broadening factor of 27.8 can be achieved ‒ more than 6 times that achieved in previous experiments. Finally, the influence of pulse chirp, material anisotropy, and the interplay between saturable absorption and SPM is also discussed, together with the comparison between the spectral broadening after going through GO-coated and graphene-coated silicon waveguides. These results provide useful guidance for optimizing the Kerr nonlinear optical performance of silicon waveguides integrated with 2D layered GO films. Graphene ; Oxide ; Materials science ; Kerr effect ; Nanowire ; Optoelectronics ; Nonlinear system ; Nanotechnology ; Silicon nanowires ; Silicon oxide ; Silicon ; Physics ; Quantum mechanics ; Silicon nitride ; Metallurgy Silicon Nanostructures and Photoluminescence ; Photonic and Optical Devices ; Nonlinear Optical Materials Studies
Authors: H. Li | Rui Li | Xiaoyu Tian | L. CHEN | Hongyang Wang | S. FAHAD | Zhijun Qiao | J.J. WANG
Source: Applied Ecology and Environmental Research (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

HYPERSPECTRAL ESTIMATION OF FOXTAIL MILLET (SETARIA ITALICA) GRAIN PROTEIN CONTENTS BY USING PHOTOSYNTHETIC RATE PARAMETERS UNDER DIFFERENT PHOTOPERIODS To predict the grain protein content of foxtail millet in a timely, effective, and rapid manner, the study used the net photosynthetic rate of leaves as an intermediate parameter.By using hyperspectral reflectance-net photosynthetic rate of leaves-grain protein content and using the 2020 potted plant experiment data, hyperspectral data and its first derivative (1ST) data with different photoperiod treatments, the models were constructed using machine learning algorithms (SVM, PLS and BP) based on net photosynthetic rate.The results revealed that the accuracy of the SVM model was better than that of the PLS model and BP model, and the SVM model based on the first derivative (1ST) spectral reflectivity was better than the original spectral (R) reflectivity, the R 2 , RMSE, RPD of the modeling set and validation set were 0.988, 0.732, 0.823, 4.061, 8.697, 1.810, respectively The correlation coefficient between net photosynthetic rate and grain protein content was 0.872 and R 2 was 0.719 at 10 days after anthesis.The SVM model can be used to accurately monitor the grain protein content of foxtail millet, and provide technical support for hyperspectral techniques in high-yield cultivation and Intensive farming of foxtail millet. Foxtail ; Setaria ; Hyperspectral imaging ; Biology ; Photosynthesis ; Agronomy ; Estimation ; Botany ; Geography ; Remote sensing ; Engineering Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement
Authors: Mohammed Nedjar | Fatima Kerkarine
Source: Mathematical Modelling and Engineering Problems (article, 2024-10-30)
Field: Materials Science — Materials Chemistry

Weibull Statistic in Hydrolytic Aging of Polyesterimide Used in Rotating Electrical Machine Windings Weibull distribution ; Statistic ; Electromagnetic coil ; Automotive engineering ; Electrical engineering ; Materials science ; Computer science ; Statistics ; Mathematics ; Engineering High voltage insulation and dielectric phenomena ; Tribology and Wear Analysis ; Power Transformer Diagnostics and Insulation
Authors: Shannon Ryan | Hung Lê | Julian Berk | AV Arun Kumar | Svetha Venkatesh
Source:  (article, 2024-09-13)
Field: Materials Science — Materials Chemistry

Physics-Informed Machine Learning for Predicting the Ballistic Limit of Whipple Shields Abstract Introduction Ballistic limit equations (BLEs) define the performance of a spacecraft structure or component impacted by a space debris particle, typically in terms of a critical projectile diameter for a given impact velocity and obliquity. BLEs are typically based on analytical considerations, e.g., kinetic energy scaling, etc., but utilise varying degrees of empiricism to improve agreement with experimental data. When applied to predict the binary pass/fail outcome of hypervelocity impact experiments, including those on which they were empirically fit, they are accurate approximately 70-75% of the time (according to [1]). In [2] several machine learning (ML) classification models were developed for predicting the ballistic limit of Whipple shields, achieving improved test accuracies of 77-85%. However, data-driven ML models are not generalisable, i.e., they are only valid only for application on conditions encompassed in their training data. This is a major limitation. Ideally, we desire a model that can achieve the improved accuracy of data-driven ML models, when we have experimental data for training, and that is generalisable to any hypervelocity impact conditions that a shield may be expected to encounter in orbit when we don’t. Shields ; Limit (mathematics) ; Physics ; Ballistic limit ; Statistical physics ; Quantum mechanics ; Mathematical analysis ; Mathematics ; Electromagnetic shielding High-Velocity Impact and Material Behavior ; Structural Response to Dynamic Loads ; Automotive and Human Injury Biomechanics
Authors: Tal Raviv | Zeev Kalyuzhner | Zeev Zalevsky
Source: ACS Omega (article, 2024-12-11)
Field: Materials Science — Materials Chemistry

Machine Learning Models for a Novel Optical Memory Approach In recent years, there has been growing interest in optical data processing, driven by the demand for high-speed and high-bandwidth data handling in data centers. One of the key milestones for enabling effective all-optical data processing systems is the development of efficient optical memory. Previously, we introduced a novel approach for establishing nonvolatile optical memory, based on the classification of scattering fields generated by gold nanoparticles. In this ongoing research, we apply advanced machine learning techniques to enhance the performance of the proposed nonvolatile memory element. By utilizing Random Forest and t-SNE algorithms, we successfully classified and analyzed the scattered images obtained from the optical memory device. The classification model presented in this study achieved an accuracy and average <i>F</i>1-score of 0.81 across nine distinct classes. Computer science ; Artificial intelligence ; Machine learning ; Cognitive science ; Psychology Phase-change materials and chalcogenides ; Optical Network Technologies ; Neural Networks and Reservoir Computing
Authors: Khabab Abbasher Hussien Mohamed Ahmed | Hamza Ahmed | Aashaq Shah
Source: The Journal of Animal and Plant Sciences (article, 2024-06-13)
Field: Chemistry — Analytical Chemistry

ON-SITE IDENTIFICATION OF RAPESEED VARIETIES WITH HANDHELD NEAR-INFRARED SPECTROPHOTOMETER AND MACHINE LEARNING ALGORITHMS Rapeseed is one of the most important oil crops in the world, but its oil quality and seed yield are affected by the genetic purity of the varieties. Plant variety identification plays a vital role in maintaining genetic purity leading to improve seed business. Current methods for rapeseed variety identification include visual inspection and modern techniques such as DNA profiling. The former which are based on phenotypic character may be prone to error while the later may be expensive and cannot be performed on-site. NIR spectroscopy offers a rapid and non-destructive approach that could overcome these limitations. This study is aimed to evaluate the potential of portable/handheld NIR to make a supervised classification model for the rapeseed varieties. The seed samples (N=225) of three (03) rapeseed varieties were scanned with handheld SCiO NIR sensor and the average of the three scans were used for classification of varieties. The classification model developed by the combination of different pre-processing and classification algorithms were tested on unknown samples (n=75). It was found that all classifiers exhibited good results except Partial Least Square–Discrimination Analysis (Rc2=0.8). SIMCA classification was tested which correctly identified 96.4% and 93.3% samples from training and test sets respectively followed by Random Forest classifier (F1=0.97) with a success rate of 93.3% on test set. However, Support vector machine (C-SVM type) with a polynomial kernel (3rd degree) gave accurate results after a combination of Standard Normal Variate (SNV) and first order Savitzky-Golay derivative (polynomial degree of 2) with number of smoothing points (window size) of 5. It classified 100% samples of training set and 97.3% samples of test set into their correct classes. Based on initial evaluation of four classification algorithm, it was found that SVM can be better utilized for varietal classification. This study reveals that handheld NIR can be a reliable and useful tool for rapeseed variety identification, which can benefit both the seed industry as well as the farmers. Keywords: NIR spectroscopy, support vector machine, SIMCA, standard normal variate, Savitzky–Golay, classification Mobile device ; Rapeseed ; Identification (biology) ; Computer science ; Infrared ; Algorithm ; Chemistry ; Food science ; World Wide Web ; Optics ; Physics ; Biology Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Ali Ibrahim Ahmed | Salah Yehia Hussain | Basim Ghalib Mejbel | Nataliia Bodnar | Mustafa Mohammed Ameen
Source: Radioelectronics Nanosystems Information Technologies (article, 2024-09-17)
Field: Materials Science — Materials Chemistry

Improving Energy Conversion Devices' Thermoelectric Efficiency by the Use of Nanostructured Materials Background: Thermoelectric materials have received much interest because of their capacity to transform thermal energy into electrical power, providing a sustainable method for energy collecting. Despite substantial study, the actual use and effectiveness of these materials require more investigation as they move from laboratory to industrial settings. Objective: This study's objective is to provide a thorough analysis of the evolution of thermoelectric materials, from their fundamental theoretical underpinnings to their integration into devices, highlighting material science advances that improve their efficiency and applicability. Methods: We did a thorough review of current research that focused on new materials, improved synthesis procedures, and innovative design strategies to improve the thermoelectric figure of merit. Furthermore, we investigated the procedures of integrating these materials into devices, taking into account both performance measurements and scalability. Results: The findings show substantial advances in the fabrication of nanostructured thermoelectric materials, which significantly boost the Seebeck coefficient while preserving electrical conductivity. We also found essential solutions for optimising device architecture that simultaneously boost heat management and efficiency. Conclusion: Breakthroughs in material science and engineering are driving the fast integration of improved thermoelectric materials into practical systems. Future research should concentrate on the scalability of these technologies and their integration into a larger range of renewable energy systems, therefore improving their economic feasibility and environmental effect. Thermoelectric materials ; Scalability ; Thermoelectric effect ; Renewable energy ; Computer science ; Thermoelectric generator ; Efficient energy use ; Process engineering ; Figure of merit ; Seebeck coefficient ; Energy transformation ; Materials science ; Engineering physics ; Systems engineering ; Nanotechnology ; Engineering ; Electrical engineering Advanced Thermoelectric Materials and Devices
Authors: Luis Enrique Coronas | Giancarlo Franzese
Source: arXiv (Cornell University) (preprint, 2024-05-16)
Field: Materials Science — Materials Chemistry

Phase behavior of metastable water from large-scale simulations of a quantitative accurate model: The liquid-liquid critical point Water's unique anomalies are vital in various applications and biological processes, yet the molecular mechanisms behind these anomalies remain debated, particularly in the metastable liquid phase under supercooling and stretching conditions. Experimental challenges in these conditions have led to simulations suggesting a liquid-liquid phase transition between low-density and high-density water phases, culminating in a liquid-liquid critical point (LLCP). However, these simulations are limited by computational expense, small system sizes, and reliability of water models. Using the FS model, we improve accuracy in predicting water's density and response functions across a broad range of temperatures and pressures. The FS model avoid by design first-order phase transitions towards crystalline phases, allowing thorough exploration of the metastable phase diagram. We employ advanced numerical techniques to bypass dynamical slowing down and perform finite-size scaling on systems significantly larger than those used in previous analyses. Our study extrapolates thermodynamic behavior in the infinite-system limit, accurately demonstrating the existence of the LLCP in the 3D Ising universality class at TC = 186 +/- 4 K and PC = 174 +/- 14 MPa, following a liquid-liquid phase separation below 200 MPa. These predictions align with recent experimental data and more sophisticated models, highlighting that hydrogen bond cooperativity governs the LLCP and the origin of water anomalies. Moreover, we observe that the hydrogen bond network exhibits substantial cooperative fluctuations at scales larger than 10 nm, even at temperatures relevant to biopreservation. These findings have significant implications for fields such as nanotechnology and biophysics, offering new insights into water's behavior under varied conditions. Metastability ; Liquid water ; Liquid liquid ; Critical point (mathematics) ; Scale (ratio) ; Liquid phase ; Point (geometry) ; Phase (matter) ; Statistical physics ; Thermodynamics ; Materials science ; Environmental science ; Physics ; Chemistry ; Mathematics ; Chromatography ; Geometry Material Dynamics and Properties ; Theoretical and Computational Physics ; Phase Equilibria and Thermodynamics