jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Olja Šovljanski | Biljana Lončar | Lato Pezo | Anja Saveljić | Ana Tomić | S. Brunet | Vladimir Filipović | Jelena Filipović | Jasna Čanadanović‐Brunet | Gordana Ćetković | Vanja Travičić
Source: Foods (article, 2023-12-29)
Field: Chemistry — Analytical Chemistry

Unlocking the Potential of the ANN Optimization in Sweet Potato Varieties Drying Processes This study explores the unexploited potential of Artificial Neural Network (ANN) optimization techniques in enhancing different drying methods and their influence on the characteristics of various sweet potato varieties. Focusing on the intricate interplay between drying methods and the unique characteristics of white, pink, orange, and purple sweet potatoes, the presented experimental study indicates the impact of ANN-driven optimization on food-related characteristics such as color, phenols content, biological activities (antioxidant, antimicrobial, anti-hyperglycemic, and anti-inflammatory), chemical, and mineral contents. The results unveil significant variations in drying method efficacy across different sweet potato types, underscoring the need for tailored optimization strategies. Specifically, purple sweet potatoes emerge as robust carriers of phenolic compounds, showcasing superior antioxidant activities. Furthermore, this study reveals the optimized parameters of dried sweet potato, such as total phenols content of 1677.76 mg/100 g and anti-inflammatory activity of 8.93%, anti-hyperglycemic activity of 24.42%. The upgraded antioxidant capability is presented through DPPH●, ABTS●+, RP, and SoA assays with values of 1500.56, 10,083.37, 3130.81, and 22,753.97 μg TE/100 g, respectively. Additionally, the moisture content in the lyophilized sample reached a minimum of 2.97%, holding favorable chemical and mineral contents. The utilization of ANN optimization proves instrumental in interpreting complex interactions and unlocking efficiencies in sweet potato drying processes, thereby contributing valuable insights to food science and technology. ABTS ; Food science ; DPPH ; Phenols ; Chemistry ; Antioxidant ; Orange (colour) ; Water content ; Antioxidant capacity ; Biochemistry ; Engineering Spectroscopy and Chemometric Analyses ; Food Drying and Modeling
Authors: Yisong Lv | Shujie Liu | Jinping Tian | Chongrong Mou
Source: Results in Physics (article, 2023-09-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep neural network-enabled bifunctional terahertz metasurface design for absorption and polarization conversion With the assistance of deep neural networks, a bifunctional metasurface (MS) was designed and optimized, i.e., a broadband absorber and a broadband polarization converter. The MS acts as a wide absorber when the vanadium dioxide (VO2) is in the metallic state and has an absorption bandwidth of 5.21 THz with an absorption rate ≥ 90%. In contrast, the MS acts as a linear–linear polarization converter when the top VO2 is in the insulating state and has a bandwidth of 3.7 THz with a conversion efficiency ≥ 90%. The bandwidth in both states is maximum compared to other bifunctional counterparts, while this bifunctional MS has good parametric and angular tolerance characteristics and low material cost. The proposed structure and design method of the bifunctional MS can provide a useful reference for the research of new multifunctional terahertz devices. Bifunctional ; Terahertz radiation ; Broadband ; Polarization (electrochemistry) ; Microwave ; Bandwidth (computing) ; Materials science ; Optoelectronics ; Optics ; Computer science ; Chemistry ; Physics ; Telecommunications Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Plasmonic and Surface Plasmon Research
Authors: Dicho Stratiev | Sotir Sotirov | Evdokia Sotirova | Svetoslav Nenov | Rosen Dinkov | Ivelina Shishkova | Iliyan Kolev | Dobromir Yordanov | Svetlin Vasilev | Krassimir Atanassov | Stanislav Simeonov | Georgi Nikolov Palichev
Source: Processes (article, 2023-01-31)
Field: Chemistry — Analytical Chemistry

Prediction of Molecular Weight of Petroleum Fluids by Empirical Correlations and Artificial Neuron Networks The exactitude of petroleum fluid molecular weight correlations affects significantly the precision of petroleum engineering calculations and can make process design and trouble-shooting inaccurate. Some of the methods in the literature to predict petroleum fluid molecular weight are used in commercial software process simulators. According to statements made in the literature, the correlations of Lee–Kesler and Twu are the most used in petroleum engineering, and the other methods do not exhibit any significant advantages over the Lee–Kesler and Twu correlations. In order to verify which of the proposed in the literature correlations are the most appropriate for petroleum fluids with molecular weight variation between 70 and 1685 g/mol, 430 data points for boiling point, specific gravity, and molecular weight of petroleum fluids and individual hydrocarbons were extracted from 17 literature sources. Besides the existing correlations in the literature, two different techniques, nonlinear regression and artificial neural network (ANN), were employed to model the molecular weight of the 430 petroleum fluid samples. It was found that the ANN model demonstrated the best accuracy of prediction with a relative standard error (RSE) of 7.2%, followed by the newly developed nonlinear regression correlation with an RSE of 10.9%. The best available molecular weight correlations in the literature were those of API (RSE = 12.4%), Goosens (RSE = 13.9%); and Riazi and Daubert (RSE = 15.2%). The well known molecular weight correlations of Lee–Kesler, and Twu, for the data set of 430 data points, exhibited RSEs of 26.5, and 30.3% respectively. Artificial neural network ; Weight estimation ; Petroleum ; Correlation coefficient ; Thermodynamics ; Chemistry ; Mathematics ; Biological system ; Computer science ; Statistics ; Artificial intelligence ; Organic chemistry ; Physics ; Biology Petroleum Processing and Analysis ; Spectroscopy and Chemometric Analyses ; Heat transfer and supercritical fluids
Authors: Xiaoyang Wang | Yinan Wang | Ke Xu | Fu‐Zhi Dai | Haifeng Liu | Guang-Hong Lü | Handong Wang
Source: Physical Review Materials (article, 2023-09-06)
Field: Materials Science — Materials Chemistry

Deep neural network potential for simulating hydrogen blistering in tungsten Tungsten is a promising candidate for the plasma-facing material in fusion energy facilities, however, the low-energy, high-flux hydrogen plasma causes severe blistering in tungsten, which gives rise to safety concerns. By far, the formation mechanism of intragranular hydrogen blisters is still unclear. Large-scale atomistic simulations are crucial for improving the understanding, however, the available empirical interatomic potentials are mostly defective in predicting the formation of hydrogen self-clusters in tungsten, thus may lead to wrong blister formation mechanisms. In this work, we develop a machine-learning potential, DP-WH, for the tungsten-hydrogen binary system based on the Deep Potential method. We demonstrate that the DP-WH potential is able to describe, as accurately as ab initio calculations, the basic properties of bcc tungsten, the solute hydrogen properties in tungsten, adsorption and migration of hydrogen on tungsten free surfaces, interactions between hydrogen atoms and vacancy, dislocations, the interaction between neighboring interstitial hydrogen atoms, and the formation energy of H self-clusters. By using DP-WH, we perform nanosecond-long molecular dynamics simulations and report the formation of planar self-cluster of tetrahedral-interstitial-site hydrogen atoms normal to ${001}$ tungsten planes at a hydrogen concentration of $\ensuremath{\approx}10$ at.%. This form of the H self-cluster is highly possible to be the early nucleates of the crack-shaped H blisters observed in recent experiments. The DP-WH is thus proven as a good candidate potential for the atomistic simulations to unveil the formation mechanisms of the intragranular hydrogen blisters in tungsten under the relevant working conditions. Tungsten ; Blisters ; Materials science ; Hydrogen ; Molecular dynamics ; Chemical physics ; Cluster (spacecraft) ; Metallurgy ; Computational chemistry ; Composite material ; Chemistry Fusion materials and technologies ; Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Rajat Srivastava | Vinay Avasthi | Krishna Priya R.
Source: Advances in Engineering Software (article, 2023-03-01)
Field: Materials Science — Materials Chemistry

Deep convolutional neural network for partial discharge monitoring system Convolutional neural network ; Dimensionality reduction ; Principal component analysis ; Computer science ; Pattern recognition (psychology) ; Power (physics) ; Feature extraction ; Artificial neural network ; Artificial intelligence ; Partial discharge ; Reduction (mathematics) ; Curse of dimensionality ; Support vector machine ; Dimension (graph theory) ; Engineering ; Mathematics ; Voltage High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Elevator Systems and Control
Authors: Ni Wang | Jianli Zhou | Guanghao Guo | Yixu Zhang | Wenjie Gao | Jin Wang | Liang Tang | Yue‐Fei Zhang | Ze Zhang
Source: Materials Characterization (article, 2023-08-07)
Field: Materials Science — Materials Chemistry

Prediction and characterization of microstructure evolution based on deep learning method and in-situ scanning electron microscope Materials science ; Microstructure ; Characterization (materials science) ; Ultimate tensile strength ; Scanning electron microscope ; Artificial intelligence ; Composite material ; Nanotechnology ; Computer science Machine Learning in Materials Science ; Solidification and crystal growth phenomena ; Microstructure and Mechanical Properties of Steels
Authors: Yanyan Chen | Chenxin Zhang | Jie Sun | Dongyuan Ni | Cunzhi Zhang | Qian Wang
Source: Applied Surface Science (article, 2023-06-07)
Field: Materials Science — Materials Chemistry

Tuning the lattice thermal conductivity of bilayer penta-graphene by interlayer twisting Phonon ; Thermal conductivity ; Condensed matter physics ; Bilayer ; Bilayer graphene ; Materials science ; Boltzmann equation ; Anharmonicity ; Graphene ; Phonon scattering ; Scattering ; Density functional theory ; Nanotechnology ; Chemistry ; Physics ; Computational chemistry ; Thermodynamics ; Membrane ; Optics Thermal properties of materials ; Graphene research and applications ; Advanced Thermoelectric Materials and Devices
Authors: Duygu Kaya | Dilara Koroglu | Erdal Aydın | Betül Uralcan
Source: Journal of Power Sources (article, 2023-03-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimization of capacitance in supercapacitors by constructing an experimentally validated hybrid artificial neural networks-genetic algorithm framework Supercapacitor ; Capacitance ; Robustness (evolution) ; Artificial neural network ; Energy storage ; Genetic algorithm ; Computer science ; Generalizability theory ; Power (physics) ; Electronic engineering ; Engineering ; Artificial intelligence ; Machine learning ; Electrode ; Mathematics Supercapacitor Materials and Fabrication ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Sanket Thakre | Vir Karan | Anand K. Kanjarla
Source: Computational Materials Science (article, 2023-02-14)
Field: Materials Science — Materials Chemistry

Quantification of similarity and physical awareness of microstructures generated via generative models Microstructure ; Similarity (geometry) ; Artificial intelligence ; Computer science ; Materials science ; Pattern recognition (psychology) ; Biological system ; Composite material ; Image (mathematics) Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Microstructure and Mechanical Properties of Steels
Authors: Devianti Devianti | Sufardi Sufardi | Siti Hafsah | Sariadi Sariadi | Fachraniah Ahmad | Nunik Destria Arianti | Edo Saputra | Sri Hartuti
Source: Case Studies in Chemical and Environmental Engineering (article, 2023-07-31)
Field: Chemistry — Analytical Chemistry

Rapid and non-destructive determination of vitamin C and antioxidant activity of intact red chilies using visible near-infrared spectroscopy and machine learning tools Red chili peppers are extensively used in the culinary industry due to their high vitamin C, antioxidant content, spice, and natural colorant properties. Traditional methods for determining these parameters are time-consuming and unstable. This study investigates the viability of visible near-infrared spectroscopy as a rapid and nondestructive method for determining vitamin C content and antioxidant activity in intact red chilies. Visible near-infrared spectroscopy measures the sample's light absorption, proportional to its chemical composition. Collecting intact red chili samples from various sources and determining their vitamin C content and antioxidant activity using conventional methods were required for the study. The visible near-infrared spectra of the pieces were collected using a spectrometer, and chemometric models were devised to correlate the spectra with the vitamin C content and antioxidant activity. Traditional methods were compared to visible near-infrared spectroscopy regarding their performance of prediction. In conjunction with chemometric models and machine learning algorithms, visible near-infrared spectroscopy could accurately predict red chilies' vitamin C content and antioxidant activity. The developed models exhibited high calibration and prediction coefficients, low root mean square errors, and high prediction-to-deviation ratios. The identified absorption peaks in the visible near-infrared spectra were related to the samples' color pigments and water content. This study demonstrates the feasibility of visible near-infrared spectroscopy as a rapid and nondestructive method for determining the vitamin C content and antioxidant activity of intact red chilies. The findings may have significant ramifications for the food industry, providing a more efficient and secure quality control and nutritional labeling method. It is recommended that additional research and validation be conducted to ensure the applicability of the developed models to various red chili peppers varieties and growing conditions. Antioxidant ; Chemistry ; Near-infrared spectroscopy ; Infrared spectroscopy ; Spectroscopy ; Vitamin ; Analytical Chemistry (journal) ; Chromatography ; Biochemistry ; Optics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Essential Oils and Antimicrobial Activity
Authors: Shulin Chen | Tzu-Li Liu | Yizhen Jia | Jinghua Li
Source: Fundamental Research (review, 2023-12-19)
Field: Materials Science — Polymers and Plastics

Recent advances in bio-integrated electrochemical sensors for neuroengineering Detecting and diagnosing neurological diseases in modern healthcare presents substantial challenges that directly impact patient outcomes. The complex nature of these conditions demands precise and quantitative monitoring of disease-associated biomarkers in a continuous, real-time manner. Current chemical sensing strategies exhibit restricted clinical effectiveness due to labor-intensive laboratory analysis prerequisites, dependence on clinician expertise, and prolonged and recurrent interventions. Bio-integrated electronics for chemical sensing is an emerging, multidisciplinary field enabled by rapid advances in electrical engineering, biosensing, materials science, analytical chemistry, and biomedical engineering. This review presents an overview of recent progress in bio-integrated electrochemical sensors, with an emphasis on their relevance to neuroengineering and neuromodulation. It traverses vital neurological biomarkers and explores bio-recognition elements, sensing strategies, transducer designs, and wireless signal transmission methods. The integration of <i>in vivo</i> biochemical sensors is showcased through applications. The review concludes by outlining future trends and advancements in <i>in vivo</i> electrochemical sensing, and highlighting ongoing research and technological innovation, which aims to provide inspiring and practical instructions for future research. Computer science ; Neural engineering ; Relevance (law) ; Neuromodulation ; Multidisciplinary approach ; Systems engineering ; Data science ; Biochemical engineering ; Nanotechnology ; Risk analysis (engineering) ; Engineering ; Artificial intelligence ; Neuroscience ; Medicine ; Materials science ; Biology Conducting polymers and applications ; Neuroscience and Neural Engineering ; Advanced Sensor and Energy Harvesting Materials
Authors: Jing Hu | Yuge Zhang | Mingdong Dong
Source: Advanced Functional Materials (article, 2023-12-15)
Field: Materials Science — Materials Chemistry

Recent Advances in Layered Double Hydroxides Membranes: Insights into Multiple Mass Transport Abstract Layered double hydroxides (LDHs) consist of metal cations with positive charges and anions located between their layers, resulting in a structured gallery height and a wide array of surface functionalities. In recent years, LDHs have garnered substantial attention due to their expanded application range through the development of LDHs membranes, bringing exciting possibilities across various fields. A deep understanding of mass transport mechanisms is essential for designing membrane materials effectively and enhancing separation processes. In this review, In this review, the strategies employed for creating continuous and well‐intergrown LDHs membranes are first elaborated. Subsequently, the study delves into a detailed discussion of the selective transport processes involving gas molecules, liquid molecules, and ions through LDHs membranes. Based on the interactions between permeated molecules and LDHs membranes, the mass transport mechanisms can be concluded in molecular sieving mechanism, solution‐diffusion mechanism and facilitated transport mechanism. Finally, the current challenges and future perspectives are outlined. Materials science ; Layered double hydroxides ; Membrane ; Mass transport ; Nanotechnology ; Chemical engineering ; Engineering physics ; Hydroxide ; Engineering Layered Double Hydroxides Synthesis and Applications ; Covalent Organic Framework Applications ; Advanced Battery Materials and Technologies
Authors: Lizhi Liu | X. Yu | Shaoxia Wang | Lili Zhang | Xu-Cai Zhao | Bocheng Lei | Hongmei Yin | Yineng Huang
Source: Molecules (article, 2023-08-18)
Field: Materials Science — Materials Chemistry

First Principles Study of the Photoelectric Properties of Alkaline Earth Metal (Be/Mg/Ca/Sr/Ba)-Doped Monolayers of MoS2 The energy band structure, density of states, and optical properties of monolayers of MoS<sub>2</sub> doped with alkaline earth metals (Be/Mg/Ca/Sr/Ba) are systematically studied based on first principles. The results indicate that all the doped systems have a great potential to be formed and structurally stable. In comparison to monolayer MoS<sub>2</sub>, doping alkaline earth metals results in lattice distortions in the doped system. Therefore, the recombination of photogenerated hole-electron pairs is suppressed effectively. Simultaneously, the introduction of dopants reduces the band gap of the systems while creating impurity levels. Hence, the likelihood of electron transfer from the valence to the conduction band is enhanced, which means a reduction in the energy required for such a transfer. Moreover, doping monolayer MoS<sub>2</sub> with alkaline earth metals increases the static dielectric constant and enhances its polarizability. Notably, the Sr-MoS<sub>2</sub> system exhibits the highest value of static permittivity, demonstrating the strongest polarization capability. The doped systems exhibit a red-shifted absorption spectrum in the low-energy region. Consequently, the Be/Mg/Ca-MoS<sub>2</sub> systems demonstrate superior visible absorption properties and a favorable band gap, indicating their potential as photo-catalysts for water splitting. Monolayer ; Band gap ; Doping ; Polarizability ; Dielectric ; Alkaline earth metal ; Materials science ; Dopant ; Permittivity ; Chemical physics ; Chemistry ; Metal ; Optoelectronics ; Nanotechnology ; Molecule 2D Materials and Applications ; MXene and MAX Phase Materials ; Chalcogenide Semiconductor Thin Films
Authors: Xiaodong Zhao | YiXuan Luo | Juejing Liu | Wenjun Liu | Kevin M. Rosso | Xiaofeng Guo | Tong Geng | Ang Li | Xin Zhang
Source: The Journal of Physical Chemistry C (article, 2023-07-24)
Field: Materials Science — Materials Chemistry

Machine Learning Automated Analysis of Enormous Synchrotron X-ray Diffraction Datasets X-ray diffraction (XRD) data analysis can be a time-consuming and laborious task. Deep neural network (DNN) based models trained with synthetic XRD patterns have been proven to be a highly efficient, accurate, and automated method for analyzing common XRD data collected from solid samples in ambient environments. However, it remains unclear whether synthetic XRD-based models can be effective in solving micro(μ)-XRD mapping data for in situ experiments involving liquid phases, which always have lower quality and significant artifacts. In this study, we collected μ-XRD mapping data from a LaCl3-calcite hydrothermal fluid system and trained two categories of models to analyze the experimental XRD patterns. The models trained solely with synthetic XRD patterns showed low accuracy (as low as 64%) when solving experimental μ-XRD mapping data. However, the accuracy of the DNN models significantly improved (90% or above) when we trained them with a data set containing both synthetic and a small number of labeled experimental μ-XRD patterns. This study highlights the importance of labeled experimental patterns in training DNN models to solve μ-XRD mapping data from in situ experiments involving liquid phases. Artificial neural network ; Diffraction ; Synchrotron ; Materials science ; Artificial intelligence ; Experimental data ; Calcite ; In situ ; X-ray crystallography ; Computer science ; Synthetic data ; Phase (matter) ; Sample (material) ; Pattern recognition (psychology) ; Analytical Chemistry (journal) ; Machine learning ; Mineralogy ; Physics ; Optics ; Mathematics ; Chemistry ; Statistics ; Chromatography X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Nuclear Physics and Applications