jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ian W. Hamley | Valeria Castelletto
Source: Colloids and Surfaces A Physicochemical and Engineering Aspects (article, 2024-05-28)
Field: Chemistry — Organic Chemistry

Sodium dodecyl sulfate micelles: Accurate analysis of small-angle X-ray scattering data through form factor and atomistic molecular dynamics modelling The structure of micelles of sodium dodecyl sulfate (SDS) is probed via analysis of small-angle x-ray scattering (SAXS) data with the aim to fit the data over an extended wavenumber q range. This provides detailed information on the micelle shape, which can be described as a polydisperse triaxial ellipsoidal core-shell structure according to model form factor fitting. This model was necessary to fit the data over a wide q range, which is not accurately represented by simpler models such as biaxial ellipsoidal core-shell structures. Data for SDS (at fixed concentration) in a NaCl concentration series revealed increasing structure factor effects with decreasing salt concentration. This reflects decreased charge screening on the headgroups. The structure factor could be modelled using a simple hard sphere structure factor. The analysis of form factor was complemented by atomistic molecular dynamics (MD) simulations, starting from an unbiased initial configuration of a defined number of molecules in a box. The MD configurations were used to calculate the form factor using the software CRYSOL (for SAS analysis of solution scattering, traditionally for proteins here for micelles) accounting for the boundary layer hydration effects. Good agreement with experimental data was found for systems with association numbers close to p = 60. This association number is consistent with that obtained from analysis of the form factor (in the case where structure factor effects could be neglected) and from model-free analysis of the forward scattering intensity. It is also in agreement with prior literature and our findings in regard to form factor parameters are also compared to previous reports. Sodium dodecyl sulfate ; Micelle ; Small-angle X-ray scattering ; Scattering ; Structure factor ; Molecular dynamics ; Chemical physics ; Sulfate ; Small-angle scattering ; Dynamics (music) ; Chemistry ; Materials science ; Chemical engineering ; Physical chemistry ; Physics ; Crystallography ; Computational chemistry ; Chromatography ; Optics ; Organic chemistry ; Aqueous solution ; Engineering Surfactants and Colloidal Systems ; Electrostatics and Colloid Interactions ; Material Dynamics and Properties
Authors: Tancredi Testasecca | Manfredi Picciotto Maniscalco | Giovanni Brunaccini | Girolama Airò Farulla | Giuseppina Ciulla | Marco Beccali | Marco Ferraro
Source: Energies (article, 2024-08-20)
Field: Materials Science — Materials Chemistry

Toward a Digital Twin of a Solid Oxide Fuel Cell Microcogenerator: Data-Driven Modelling Solid oxide fuel cells (SOFC) could facilitate the green energy transition as they can produce high-temperature heat and electricity while emitting only water when supplied with hydrogen. Additionally, when operated with natural gas, these systems demonstrate higher thermoelectric efficiency compared to traditional microturbines or alternative engines. Within this context, although digitalisation has facilitated the acquisition of extensive data for precise modelling and optimal management of fuel cells, there remains a significant gap in developing digital twins that effectively achieve these objectives in real-world applications. Existing research predominantly focuses on the use of machine learning algorithms to predict the degradation of fuel cell components and to optimally design and theoretically operate these systems. In light of this, the presented study focuses on developing digital twin-oriented models that predict the efficiency of a commercial gas-fed solid oxide fuel cell under various operational conditions. This study uses data gathered from an experimental setup, which was employed to train various machine learning models, including artificial neural networks, random forests, and gradient boosting regressors. Preliminary findings demonstrate that the random forest model excels, achieving an R2 score exceeding 0.98 and a mean squared error of 0.14 in estimating electric efficiency. These outcomes could validate the potential of machine learning algorithms to support fuel cell integration into energy management systems capable of improving efficiency, pushing the transition towards sustainable energy solutions. Fuel cells ; Solid oxide fuel cell ; Oxide ; Materials science ; Computer science ; Process engineering ; Chemistry ; Chemical engineering ; Engineering ; Metallurgy ; Physical chemistry ; Anode Advancements in Solid Oxide Fuel Cells ; Machine Learning in Materials Science
Authors: Stella Green | Emily Fanning | Joy Sim | Graham T. Eyres | Russell Frew | Biniam Kebede
Source: Molecules (article, 2024-01-09)
Field: Chemistry — Analytical Chemistry

The Potential of NIR Spectroscopy and Chemometrics to Discriminate Roast Degrees and Predict Volatiles in Coffee This study aimed to establish a rapid and practical method for monitoring and predicting volatile compounds during coffee roasting using near-infrared (NIR) spectroscopy coupled with chemometrics. Washed Arabica coffee beans from Ethiopia and Congo were roasted to industry-validated light, medium, and dark degrees. Concurrent analysis of the samples was performed using gas chromatography-mass spectrometry (GC-MS) and NIR spectroscopy, generating datasets for partial least squares (PLS) regression analysis. The results showed that NIR spectroscopy successfully differentiated the differently roasted samples, similar to the discrimination achieved by GC-MS. This finding highlights the potential of NIR spectroscopy as a rapid tool for monitoring and standardizing the degree of coffee roasting in the industry. A PLS regression model was developed using Ethiopian samples to explore the feasibility of NIR spectroscopy to indirectly measure the volatiles that are important in classifying the roast degree. For PLSR, the data underwent autoscaling as a preprocessing step, and the optimal number of latent variables (LVs) was determined through cross-validation, utilizing the root mean squared error (RMSE). The model was further validated using Congo samples and successfully predicted (with R<sup>2</sup> values > 0.75 and low error) over 20 volatile compounds, including furans, ketones, phenols, and pyridines. Overall, this study demonstrates the potential of NIR spectroscopy as a practical and rapid method to complement current techniques for monitoring and predicting volatile compounds during the coffee roasting process. Chemometrics ; Partial least squares regression ; Roasting ; Near-infrared spectroscopy ; Chemistry ; Spectroscopy ; Process analytical technology ; Chromatography ; Analytical Chemistry (journal) ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Coffee research and impacts
Authors: Agata Hajda | Rweetuparna Guha | Stacy M. Copp | Joanna Olesiak‐Bańska
Source: Chemical Science (article, 2024-12-16)
Field: Materials Science — Materials Chemistry

Two-photon brightness of NIR-emitting, atomically precise DNA-stabilized silver nanoclusters Near-infrared (NIR) emitters with high two-photon absorption (2PA) cross-sections are of interest to enable <i>in vivo</i> imaging in the tissue transparency windows. This study explores the potential of DNA-stabilized silver nanoclusters (Ag <sub><i>N</i></sub> -DNAs) as water-soluble two-photon absorbers. We investigate 2PA of four different atomically precise Ag <sub><i>N</i></sub> -DNA species with far-red to NIR emission and varying nanocluster and ligand compositions. 2PA cross-sections, <i>σ</i> <sub>2</sub>, were determined by two-photon excited luminescence (2PEL) technique for a wide wavelength range from 810 to 1400 nm. The Ag <sub><i>N</i></sub> -DNAs exhibited reversed strength of corresponding transitions in the two-photon regime, as compared to one-photon, which further demonstrates the complex photophysics of these emitters. Maximal 2PA cross-section value (∼582 GM) was observed for (DNA)<sub>3</sub>[Ag<sub>21</sub>]<sup>15+</sup>, which is stabilized by 3 DNA oligomers. (DNA)<sub>2</sub>[Ag<sub>16</sub>Cl<sub>2</sub>]<sup>8+</sup> presented distinct 2PA behavior from the Ag <sub><i>N</i></sub> -DNAs without chlorido ligands, with a high 2PA of 176 GM at 1050 nm. Our findings support the potential of Ag <sub><i>N</i></sub> -DNAs as NIR-to-NIR two-photon probes that are both excited and emit in the NIR. Their high <i>σ</i> <sub>2</sub> and fluorescence quantum yield values result in superior two-photon brightness on the order of ∼10<sup>2</sup> GM, significantly higher than water-soluble organic fluorophores. Nanoclusters ; Brightness ; Photon ; Materials science ; Optoelectronics ; Nanotechnology ; Physics ; Optics Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Min Liang | Zhiqiang Wang | Yu Lin | Caixia Li | Liang Zhang | Yaxi Liu
Source: Frontiers in Plant Science (article, 2024-09-30)
Field: Chemistry — Analytical Chemistry

Study on detection of pesticide residues in tobacco based on hyperspectral imaging technology The results show that combining spectral preprocessing and feature extraction algorithms with machine learning models can significantly enhance the performance of pesticide residue detection models and provide robust, efficient, and accurate solutions for food safety monitoring. This study provides a new technical means for the detection of pesticide residues in tobacco, which is of great significance for improving the efficiency and accuracy of food safety detection. Hyperspectral imaging ; Support vector machine ; Preprocessor ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Pesticide Residue Analysis and Safety
Authors: Gharam A. Alharshan | Ammar M. Mahmoud | Nasra M. Ebrahem | R. A. Elsad | Shaaban M. Shaaban | Mohamed Elsafi | Shimaa Ali Said
Source: Scientific Reports (article, 2024-10-02)
Field: Materials Science — Ceramics and Composites

The manufacture, optical properties, and mechanical aspects of europium-doped borate glasses An investigation into the optical and mechanical properties of a novel borate glasses with the chemical composition of 70 B<sub>2</sub>O<sub>3</sub>-10 Li<sub>2</sub>O-10ZnO-5Bi<sub>2</sub>O<sub>3</sub>-5CaO-xEu<sub>2</sub>O<sub>3</sub> was conducted. The glassy specimens of Eu<sup>3+</sup>-doped borate were prepared by the melting-quenching technique. An enhanced density from 3.0860 to 3.2176 g cm<sup>-3</sup> and reduced molar volume from 29.27819 to 29.17447 (cm<sup>3</sup> mol<sup>-1</sup>) are the outcome of increasing the concentration of Eu<sup>3+</sup> in glasses. Plotting the extinction coefficient, dielectric constant (ε<sub>1</sub>, ε<sub>2</sub>), and refractive index (n) against wavelength reveals that they all rise as level of Eu<sup>3+</sup> elements in the glass lattice increases. An increase in Eu<sup>3+</sup> concentration results in a decrease in both the volume (VELF) and surface (SELF) energy loss functions. Also, all elastic-mechanical moduli (such as Young's, Bulk, Shear, and Elongation) increase with increasing the quantity of Eu<sup>3+</sup> ions in the glass lattice. The Young's modulus (Y, GPa) of the glassy specimens was 34.512, 36.089, 36.504, 36.730 and 37.114 GPa for x equal 0, 0.25, 0.5, 0.75 and 1 mol ratio in the glass system, and coded by Eu-0.0, Eu-0.25, Eu-0.5, Eu-0.75 and Eu-1.0, respectively. Growing Eu<sub>2</sub>O<sub>3</sub> levels resulted in an increase in Micro-Hardness from 2.050 to 2.146 GPa. Poisson's ratio values for Eu-0.0, Eu-0.250, Eu-0.5, Eu-0.75 and Eu-1.0 were 0.273, 0.275, 0.277, 0.277 and 0.278, respectively. Europium ; Doping ; Boron ; Materials science ; Chemistry ; Optoelectronics ; Luminescence ; Organic chemistry Glass properties and applications ; Luminescence Properties of Advanced Materials ; Material Science and Thermodynamics
Authors: Abraham Rojas Zuniga | Sam Bakhtiari | Chris Aldrich | Victor M. Calo | Mariano Iannuzzi
Source: npj Materials Degradation (article, 2024-12-18)
Field: Materials Science — Metals and Alloys

XGBoost model for the quantitative assessment of stress corrosion cracking This study employs a data-driven methodology to assess the susceptibility of Fe-Cr-Ni alloys to stress corrosion cracking (SCC) in chloride-containing environments. Historical data from constant-load SCC testing in boiling magnesium chloride were used to train an XGBoost regression model. This model overcomes limitations related to multicollinearity and insufficient sample sizes seen in previous studies. The XGBoost model captures complex interactions between alloy compositions and stresses, explaining 94.9% (R² = 0.949) of SCC susceptibility of the specimens. Shapley additive explanations (SHAP) were employed to interpret the model, offering new metallurgical insights, such as the critical role of nickel content. The SHAP analysis identified an optimal nickel range between 14.5 and 45 wt%, which markedly enhances SCC resistance. The XGBoost-SHAP framework in this work comprehensively isolates the contributions of chemical constituents and stress, offering a path toward more systematic alloy design—departing from the traditional reliance on trial and error or serendipity. Stress corrosion cracking ; Corrosion ; Cracking ; Stress (linguistics) ; Forensic engineering ; Materials science ; Environmental science ; Metallurgy ; Engineering ; Composite material ; Philosophy Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Fatigue and fracture mechanics
Authors: Qiaoling Xiao | Yaozhou Xie | Feng Hu | Chengyang Hu
Source: Materials (review, 2024-06-25)
Field: Materials Science — Materials Chemistry

Current Status and Trends of Low-Temperature Steel Used in Polar Regions The desire to explore the natural resources and geopolitical patterns of the North and South Poles has significantly increased the interest of experts and researchers in the development and utilization of the polar regions. In this article, we comprehensively analyzed the current state of the development of polar low-temperature steel around the world. We highlighted the challenges that must be addressed in the ongoing development efforts and summarized the expected future trends in this field. The main theme of this article involves the challenges encountered in polar environments primarily caused by the low-temperature toughness and seawater corrosion of marine steel. Polar ; Geopolitics ; Theme (computing) ; Current (fluid) ; Materials science ; Environmental science ; Engineering ; Political science ; Computer science ; Physics ; Politics ; Electrical engineering Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Material Properties and Failure Mechanisms
Authors: Sai Xu | Yinghua Guo | Xin Liang | Huazhong Lu
Source: Foods (review, 2024-04-06)
Field: Chemistry — Analytical Chemistry

Intelligent Rapid Detection Techniques for Low-Content Components in Fruits and Vegetables: A Comprehensive Review Fruits and vegetables are an important part of our daily diet and contain low-content components that are crucial for our health. Detecting these components accurately is of paramount significance. However, traditional detection methods face challenges such as complex sample processing, slow detection speed, and the need for highly skilled operators. These limitations fail to meet the growing demand for intelligent and rapid detection of low-content components in fruits and vegetables. In recent years, significant progress has been made in intelligent rapid detection technology, particularly in detecting high-content components in fruits and vegetables. However, the accurate detection of low-content components remains a challenge and has gained considerable attention in current research. This review paper aims to explore and analyze several intelligent rapid detection techniques that have been extensively studied for this purpose. These techniques include near-infrared spectroscopy, Raman spectroscopy, laser-induced breakdown spectroscopy, and terahertz spectroscopy, among others. This paper provides detailed reports and analyses of the application of these methods in detecting low-content components. Furthermore, it offers a prospective exploration of their future development in this field. The goal is to contribute to the enhancement and widespread adoption of technology for detecting low-content components in fruits and vegetables. It is expected that this review will serve as a valuable reference for researchers and practitioners in this area. Computer science ; Field (mathematics) ; Mathematics Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: H. Rahmani | Faı̈çal Larachi | Seyed Mohammad Taghavi
Source: ACS Engineering Au (article, 2024-01-04)
Field: Materials Science — Surfaces, Coatings and Films

Modeling of Shear Flows over Superhydrophobic Surfaces: From Newtonian to Non-Newtonian Fluids The design and use of superhydrophobic surfaces have gained special attentions due to their superior performances and advantages in many flow systems, e.g., in achieving specific goals including drag reduction and flow/droplet handling and manipulation. In this work, we conduct a brief review of shear flows over superhydrophobic surfaces, covering the classic and recent studies/trends for both Newtonian and non-Newtonian fluids. The aim is to mainly review the relevant mathematical and numerical modeling approaches developed during the past 20 years. Considering the wide ranges of applications of superhydrophobic surfaces in Newtonian fluid flows, we attempt to show how the developed studies for the Newtonian shear flows over superhydrophobic surfaces have been evolved, through highlighting the major breakthroughs. Despite the fact that, in many practical applications, flows over superhydrophobic surfaces may show complex non-Newtonian rheology, interactions between the non-Newtonian rheology and superhydrophobicity have not yet been well understood. Therefore, in this Review, we also highlight emerging recent studies addressing the shear flows of shear-thinning and yield stress fluids in superhydrophobic channels. We focus on reviewing the models developed to handle the intricate interaction between the formed liquid/air interface on superhydrophobic surfaces and the overlying flow. Such an intricate interaction will be more complex when the overlying flow shows nonlinear non-Newtonian rheology. We conclude that, although our understanding on the Newtonian shear flows over superhydrophobic surfaces has been well expanded via analyzing various aspects of such flows, the non-Newtonian counterpart is in its early stages. This could be associated with either the early applications mainly concerning Newtonian fluids or new complexities added to an already complex problem by the nonlinear non-Newtonian rheology. Finally, we discuss the possible directions for development of models that can address complex non-Newtonian shear flows over superhydrophobic surfaces. Non-Newtonian fluid ; Newtonian fluid ; Rheology ; Mechanics ; Shear thinning ; Drag ; Materials science ; Shear stress ; Shear (geology) ; Flow (mathematics) ; Nanotechnology ; Physics ; Composite material Surface Modification and Superhydrophobicity ; Fluid Dynamics and Heat Transfer ; Fluid Dynamics and Turbulent Flows
Authors: Yalin Guo | Lina Zhang | Yakai He | Chengxu Lv | Yijun Liu | Haiyun Song | Huangzhen Lv | Zhilong Du
Source: Frontiers in Plant Science (article, 2024-06-07)
Field: Chemistry — Analytical Chemistry

Online inspection of blackheart in potatoes using visible-near infrared spectroscopy and interpretable spectrogram-based modified ResNet modeling The prediction accuracy for blackheart and healthy potatoes test sets reached 0.971 using the original spectrum combined with a modified ResNet model. Moreover, the modified ResNet model significantly reduced the number of parameters to 1434052, achieving a substantial 62.71% reduction in model complexity. Meanwhile, its performance was evidenced by a 4.18% improvement in accuracy. The Grad-CAM++ visualizations provided a qualitative assessment of the model's focus across different severity grades of blackheart condition, highlighting the importance of different wavelengths in the analysis. In these visualizations, the most significant features were predominantly found in the 650-750 nm range, with a notable peak near 700 nm. This peak was speculated to be associated with the vibrational activities of the C-H bond, specifically the fourth overtone of the C-H functional group, within the molecular structure of the potato components. This research demonstrated that the modified ResNet model combined with Vis/NIR could assist in the detection of different degrees of black in potatoes. Spectrogram ; Residual neural network ; Spectroscopy ; Near-infrared spectroscopy ; Infrared ; Infrared spectroscopy ; Artificial intelligence ; Computer science ; Remote sensing ; Pattern recognition (psychology) ; Environmental science ; Optics ; Chemistry ; Geology ; Physics ; Deep learning ; Astronomy Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Carsten Hinzmann | Nicolai Frost‐Jensen Johansen | Charlotte Bay Hasager | Bodil Holst
Source: Composites Part B Engineering (article, 2024-05-24)
Field: Materials Science — Polymers and Plastics

Failsafe layer for wind turbine blades: Erosion protection of glass fiber composite through nanodiamond-treated flax composite top layer Wind turbine blades are mainly made from E-glass fiber (GF) epoxy composites, because of their good ratio of strength to weight and costs. With the increase in blade length and tip speed, the problem of leading edge erosion is becoming more severe, reducing annual energy production and raising maintenance cost. It was recently shown that nanodiamond-treated flax fiber (FFND) composites have significantly less erosion than GF composites and could be an alternative for GF in the turbine blade aeroshells. However, FFND alone might not be suitable for manufacturing turbine blades at the large scale of modern wind turbines. Here, we show that a hybrid composite with a thin layer of only 1.5 mm of FFND on a GF base, can achieve the same superior results as bulk material FFND composite. In addition, we show and explain why aramid fibers, that are known for impact resistance, do not perform well as erosion protection. Our research shows the great potential of this technology to be implemented as a low-cost, lightweight skin layer on the leading edge. Acting as damage-tolerant failsafe layer, negligible ∼0.04% extra weight of the FFND could increase the blade’s base erosion resistance by a factor of 60±20 compared to plain GF, expanding the repair window, reducing costs, and enhancing reliability. Materials science ; Composite number ; Composite material ; Layer (electronics) ; Nanodiamond ; Glass fiber ; Turbine blade ; Turbine ; Fiber ; Erosion ; Diamond ; Mechanical engineering ; Engineering Natural Fiber Reinforced Composites ; Electrospun Nanofibers in Biomedical Applications ; Cellular and Composite Structures