jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ammar Armghan | Meshari Alsharari | Khaled Aliqab | Osamah Alsalman | Juveriya Parmar | Shobhit K. Patel
Source: Mathematics (article, 2023-03-24)
Field: Materials Science — Materials Chemistry

Graphene Twistronics: Tuning the Absorption Spectrum and Achieving Metamaterial Properties Graphene twistronics using multilayer graphene is presented in such a way that it provides a metamaterial effect. This manuscript also analyzes the prediction of behavior using machine learning. The metamaterial effect is achieved by twisting the graphene layers. Graphene twistronics is a new concept for changing the electrical and optical properties of bilayer graphene by applying a small angle twist between the layers. The angle twists of 5°, 10°, and 15° are analyzed for the proposed graphene twistronics design. Tuning in the absorption spectrum is achieved by applying small twists to the angles of the bilayer graphene. Results in the form of absorption, conductivity, permeability, permittivity, and impedance are presented for different twist angles. The twisted graphene layers also demonstrate negative permittivity and negative permeability, similar to metamaterials. These negative refraction properties of graphene twistronics provide flexibility and transparency, which can be applied in photovoltaic applications. Machine-learning-based regression models are used to reduce the simulation time and resources. The results show that a regression model can reliably estimate intermediate wavelength absorption values with an R2 of 0.9999. Graphene ; Materials science ; Metamaterial ; Permittivity ; Bilayer graphene ; Optoelectronics ; Absorption (acoustics) ; Optics ; Condensed matter physics ; Composite material ; Nanotechnology ; Physics ; Dielectric Graphene research and applications ; Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research
Authors: Chia-Ming Lin | Yu‐Shiang Lin
Source: Bioengineering (article, 2023-11-04)
Field: Chemistry — Analytical Chemistry

Utilizing a Two-Stage Taguchi Method and Artificial Neural Network for the Precise Forecasting of Cardiovascular Disease Risk The complexity of cardiovascular disease onset emphasizes the vital role of early detection in prevention. This study aims to enhance disease prediction accuracy using personal devices, aligning with point-of-care testing (POCT) objectives. This study introduces a two-stage Taguchi optimization (TSTO) method to boost predictive accuracy in an artificial neural network (ANN) model while minimizing computational costs. In the first stage, optimal hyperparameter levels and trends were identified. The second stage determined the best settings for the ANN model's hyperparameters. In this study, we applied the proposed TSTO method with a personal computer to the Kaggle Cardiovascular Disease dataset. Subsequently, we identified the best setting for the hyperparameters of the ANN model, setting the hidden layer to 4, activation function to tanh, optimizer to SGD, learning rate to 0.25, momentum rate to 0.85, and hidden nodes to 10. This setting led to a state-of-the-art accuracy of 74.14% in predicting the risk of cardiovascular disease. Moreover, the proposed TSTO method significantly reduced the number of experiments by a factor of 40.5 compared to the traditional grid search method. The TSTO method accurately predicts cardiovascular risk and conserves computational resources. It is adaptable for low-power devices, aiding the goal of POCT. Hyperparameter ; Hyperparameter optimization ; Taguchi methods ; Artificial neural network ; Computer science ; Artificial intelligence ; Machine learning ; Data mining ; Support vector machine Spectroscopy and Chemometric Analyses
Authors: Mona Jani | Paulina Czarnecka | Zuzanna Orzechowska | Mariusz Mrózek | Wojciech Gawlik | Adam M. Wojciechowski
Source: ACS Applied Nano Materials (article, 2023-06-14)
Field: Materials Science — Materials Chemistry

Sensing of Magnetic-Field Gradients with Nanodiamonds on Optical Glass-Fiber Facets We demonstrate a photonic sensor of the magnetic field and its gradients with remote readout. The sensor is based on optically detected magnetic resonance (ODMR) in nanodiamonds with nitrogen-vacancy color centers that are covalently attached as a thin film on one facet of an optical fiber bundle. By measuring ODMR signals from a group of individual fibers in an ∼0.5-mm-wide imaging bundle, differences of local magnetic field strengths and magnetic field gradients are determined across the plane of the bundle facet. The measured gradients are created by direct electric currents flowing in a wire placed near the nanodiamond film. The measurement enabled the determination of the net magnetic field corresponding to various current directions and their corresponding magnetic field gradients. This demonstration opens up a perspective for compact fiber-based endoscopy, with additional avenues for remote and sensitive magnetic field detection with submicrometer spatial resolution under ambient conditions. Magnetic field ; Materials science ; Optical fiber ; Fiber ; Photonics ; Optics ; Optoelectronics ; Physics Diamond and Carbon-based Materials Research ; Advanced Fiber Laser Technologies ; Force Microscopy Techniques and Applications
Authors: J. Muñoz-Postigo | Eva M. Valero | Miguel Ángel Martínez‐Domingo | Francisco Lara | Juan L. Nieves | José Trinidad Álvarez Romero | Javier Hernández‐Andrés
Source: Journal of Food Engineering (article, 2023-11-10)
Field: Chemistry — Analytical Chemistry

Band selection pipeline for maturity stage classification in bell peppers: From full spectrum to simulated camera data This paper describes a workflow for classifying the maturity of bell peppers using hyperspectral imaging and machine learning. The approach involves using spectral reflectance to determine the number of maturity stages, followed by a classification task using the optimal bands for accurate classification. The study explores a realistic scenario using simulated camera responses and investigates the use of real sensors with their spectral sensitivities and noise. Four classifier algorithms (RBFNN, PLS-DA, SVM, and LDA) were employed to predict the maturity stage based on spectral reflectance. The best results were achieved with the LDA algorithm, which was used in the optimization process for band selection. The optimized bands in the VISNIR range (400–1000 nm) were found to be [783.5, 844.1, and 905.4] nm, with an accuracy of 90.67% for spectral data. For camera responses with intermediate-level noise, the best bands were [760, 820, and 900 nm], achieving an accuracy of 81%. Overall, using three bands yielded satisfactory and practical results for real-world implementation. Hyperspectral imaging ; Artificial intelligence ; Classifier (UML) ; Support vector machine ; Computer science ; Reflectivity ; Pattern recognition (psychology) ; Pipeline (software) ; Spectral bands ; Spectral imaging ; Workflow ; Remote sensing ; Optics ; Physics ; Geography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Essential Oils and Antimicrobial Activity
Authors: Ahmed M. Sayed | Alyaa Hatem Ibrahim | Nasir Tajuddeen | Jürgen Seibel | Jochen Bodem | Nina Geiger | Kathrin Striffler | Gerhard Bringmann | Usama Ramadan Abdelmohsen
Source: European Journal of Medicinal Chemistry (article, 2023-02-28)
Field: Chemistry — Organic Chemistry

Korupensamine A, but not its atropisomer, korupensamine B, inhibits SARS-CoV-2 in vitro by targeting its main protease (Mpro) Chemistry ; Docking (animal) ; Atropisomer ; Stereochemistry ; Protease ; In vitro ; Molecular dynamics ; Active site ; Enzyme ; Biochemistry ; Computational chemistry Axial and Atropisomeric Chirality Synthesis ; Sphingolipid Metabolism and Signaling ; Mathematical and Theoretical Epidemiology and Ecology Models
Authors: Zhongzhi Sun | Zhibin Ning | Kai Cheng | Haonan Duan | Qing Wu | Janice Mayne | Daniel Figeys
Source: Computational and Structural Biotechnology Journal (article, 2023-01-01)
Field: Chemistry — Spectroscopy

MetaPep: A core peptide database for faster human gut metaproteomics database searches Metaproteomics has increasingly been applied to study functional changes in the human gut microbiome. Peptide identification is an important step in metaproteomics research, with sequence database search (SDS) and spectral library search (SLS) as the two main methods to identify peptides. However, the large search space in metaproteomics studies causes significant challenges for both identification methods. Moreover, with the development of mass spectrometry, it is now feasible to perform metaproteomic projects involving 100-1000 individual microbiomes. These large-scale projects create a conundrum for searching large databases. In this study, we constructed MetaPep, a core peptide database (including both collections of peptide sequences and tandem MS spectra) greatly accelerating the peptide identifications. Raw files from fifteen metaproteomics projects were re-analyzed and the identified peptide-spectrum matches (PSMs) were used to construct the MetaPep database. The constructed MetaPep database achieved rapid and accurate identification of peptides for human gut metaproteomics. MetaPep has a large collection of peptides and spectra that have been identified in published human gut metaproteomics datasets. MetaPep database can be used as an important resource in the current stage of human gut metaproteomics research. This study showed the possibility of applying a core peptide database as a generic metaproteomics workflow. MetaPep could also be an important resource for future human gut metaproteomics research, such as DIA (data-independent acquisition) analysis. Metaproteomics ; Database search engine ; Database ; Sequence database ; Computer science ; Computational biology ; Metagenomics ; Biology ; Search engine ; Information retrieval Advanced Proteomics Techniques and Applications ; Gut microbiota and health ; Metabolomics and Mass Spectrometry Studies
Authors: J.-G. Castel | S. Delaux | Oscar Hernandez‐Alba | Sarah Cianférani
Source: Journal of Pharmaceutical and Biomedical Analysis (review, 2023-09-09)
Field: Chemistry — Spectroscopy

Recent advances in structural mass spectrometry methods in the context of biosimilarity assessment: from sequence heterogeneities to higher order structures Chemistry ; Biosimilar ; Context (archaeology) ; Mass spectrometry ; Biopharmaceutical ; Characterization (materials science) ; Biochemical engineering ; Sequence (biology) ; Nanotechnology ; Chromatography ; Engineering Analytical Chemistry and Chromatography ; Mass Spectrometry Techniques and Applications ; Protein purification and stability
Authors: Qingxu Li | Wanhuai Zhou | Qiaohua Wang | Dandan Fu
Source: Foods (article, 2023-05-06)
Field: Chemistry — Analytical Chemistry

Research on Online Nondestructive Detection Technology of Duck Egg Origin Based on Visible/Near-Infrared Spectroscopy As living standards rise, people have higher requirements for the quality of duck eggs. The quality of duck eggs is related to their origin. Thus, the origin traceability and identification of duck eggs are crucial for protecting the rights and interests of consumers and preserving food safety. As the world’s largest producer and consumer of duck eggs, China’s duck egg market suffers from a severe lack of duck egg traceability and rapid origin identification technology. As a result, a large number of duck eggs from other regions are sold as products from well-known brands, which seriously undermines the rights and interests of consumers and is not conducive to the sound development of the duck egg industry. To address the above issues, this study collected visible/near-infrared spectral data online from duck eggs of three distinct origins. To reduce noise in the spectral data, various pre-processing algorithms, including MSC, SNV, and SG, were employed to process the spectral data of duck eggs in the range of 400–1100 nm. Meanwhile, CARS and SPA were used to select feature variables that reflect the origin of duck eggs. Finally, classification models of duck egg origin were developed based on RF, SVM, and CNN, achieving the highest accuracy of 97.47%, 98.73%, and 100.00%, respectively. To promote the technology’s implementation in the duck egg industry, an online sorting device was built for duck eggs, which mainly consists of a mechanical drive device, spectral software, and a control system. The online detection performance of the machine was validated using 90 duck eggs, and the final detection accuracy of the RF, SVM, and CNN models was 90%, 91.11%, and 94.44%, with a detection speed of 0.1 s, 0.3 s, and 0.5 s, respectively. These results indicate that visible/near-infrared spectroscopy can be exploited to realize rapid online detection of the origin of duck eggs, and the methodologies used in this study can be immediately implemented in production practice. Traceability ; Identification (biology) ; Quality (philosophy) ; Range (aeronautics) ; Biology ; Engineering ; Ecology ; Mathematics ; Statistics ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Ziwen Zhao | Arunava Naha | Sagar Ganguli | Alina Sekretareva
Source: Advanced Intelligent Systems (article, 2023-11-21)
Field: Chemistry — Electrochemistry

Automated Analysis of Nano‐Impact Single‐Entity Electrochemistry Signals Using Unsupervised Machine Learning and Template Matching Nano‐impact (NIE) (also referred to as collision) single‐entity electrochemistry is an emerging technique that enables electrochemical investigation of individual entities, ranging from metal nanoparticles to single cells and biomolecules. To obtain meaningful information from NIE experiments, analysis and feature extraction on large datasets are necessary. Herein, a method is developed for the automated analysis of NIE data based on unsupervised machine learning and template matching approaches. Template matching not only facilitates downstream processing of the NIE data but also provides a more accurate analysis of the NIE signal characteristics and variations that are difficult to discern with conventional data analysis techniques, such as the height threshold method. The developed algorithm enables fast automated processing of large experimental datasets recorded with different systems, requiring minimal human intervention and thereby eliminating human bias in data analysis. As a result, it improves the standardization of data processing and NIE signal interpretation across various experiments and applications. Computer science ; Template matching ; Matching (statistics) ; Pattern recognition (psychology) ; Artificial intelligence ; Signal processing ; Standardization ; Feature extraction ; Data mining ; SIGNAL (programming language) ; Data extraction ; Unsupervised learning ; Machine learning ; Digital signal processing ; Image (mathematics) ; Computer hardware Electrochemical Analysis and Applications ; Conducting polymers and applications ; Electrochemical sensors and biosensors
Authors: Renan Falcioni | Gláucio Leboso Alemparte Abrantes dos Santos | Luís Guilherme Teixeira Crusiol | Werner Camargos Antunes | Marcelo Luiz Chicati | Roney Berti de Oliveira | José Alexandre Melo Demattê | Marcos Rafael Nanni
Source: Plants (article, 2023-07-02)
Field: Chemistry — Analytical Chemistry

Non−Invasive Assessment, Classification, and Prediction of Biophysical Parameters Using Reflectance Hyperspectroscopy Hyperspectral technology offers significant potential for non-invasive monitoring and prediction of morphological parameters in plants. In this study, UV-VIS-NIR-SWIR reflectance hyperspectral data were collected from <i>Nicotiana tabacum</i> L. plants using a spectroradiometer. These plants were grown under different light and gibberellic acid (GA<sub>3</sub>) concentrations. Through spectroscopy and multivariate analyses, key growth parameters, such as height, leaf area, energy yield, and biomass, were effectively evaluated based on the interaction of light with leaf structures. The shortwave infrared (SWIR) bands, specifically SWIR1 and SWIR2, showed the strongest correlations with these growth parameters. When classifying tobacco plants grown under different GA<sub>3</sub> concentrations in greenhouses, artificial intelligence (AI) and machine learning (ML) algorithms were employed, achieving an average accuracy of over 99.1% using neural network (NN) and gradient boosting (GB) algorithms. Among the 34 tested vegetation indices, the photochemical reflectance index (PRI) demonstrated the strongest correlations with all evaluated plant phenotypes. Partial least squares regression (PLSR) models effectively predicted morphological attributes, with R<sup>2</sup><sub>CV</sub> values ranging from 0.81 to 0.87 and RPD<sub>P</sub> values exceeding 2.09 for all parameters. Based on Pearson's coefficient XYZ interpolations and HVI algorithms, the NIR-SWIR band combination proved the most effective for predicting height and leaf area, while VIS-NIR was optimal for optimal energy yield, and VIS-VIS was best for predicting biomass. To further corroborate these findings, the SWIR bands for certain morphological characteristic wavelengths selected with <i>s</i>-PLS were most significant for SWIR1 and SWIR2, while <i>i</i>-PLS showed a more uniform distribution in VIS-NIR-SWIR bands. Therefore, SWIR hyperspectral bands provide valuable insights into developing alternative bands for remote sensing measurements to estimate plant morphological parameters. These findings underscore the potential of remote sensing technology for rapid, accurate, and non-invasive monitoring within stationary high-throughput phenotyping systems in greenhouses. These insights align with advancements in digital and precision technology, indicating a promising future for research and innovation in this field. Reflectivity ; Remote sensing ; Environmental science ; Artificial intelligence ; Geography ; Computer science ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Thị Chinh Ngo | Sonia Taamalli | Zainab Srour | Valérie Fèvre-Nollet | Abderrahman El Bakali | Florent Louis | Ivan Černušák | Duy Quang Dao
Source: Journal of environmental chemical engineering (article, 2023-04-17)
Field: Chemistry — Physical and Theoretical Chemistry

Theoretical insights into the oxidation of quinmerac herbicide initiated by HO• radical in aqueous media: Mechanism, kinetics, and ecotoxicity Chemistry ; Radical ; Kinetics ; Hydrogen atom abstraction ; Aqueous solution ; Ecotoxicity ; Adduct ; Reaction rate constant ; Electron transfer ; Reaction mechanism ; Photochemistry ; Medicinal chemistry ; Organic chemistry ; Toxicity ; Catalysis Photochemistry and Electron Transfer Studies ; Atmospheric chemistry and aerosols ; Advanced oxidation water treatment
Authors: Pablo Romero-Marimon | Juan José Gutiérrez‐Sevillano | Sofı́a Calero
Source: Chemistry of Materials (article, 2023-06-27)
Field: Chemistry — Inorganic Chemistry

Adsorption of Carbon Dioxide in Non-Löwenstein Zeolites We investigated the effect of the aluminum distribution in the adsorption properties of carbon dioxide in the MFI, MOR, and ITW zeolites. Because of its lack of experimental evidence and theoretical validation, Löwenstein’s rule was not generally imposed, and special attention was paid to the effect of the Al–O–Al linkages. To this end, we first generalized an existing transferable force field for CO2 adsorption in non-Löwenstein zeolites. By means of molecular simulations based on this force field, we showed that the carbon dioxide adsorption efficiency in MFI is determined by the number of Al atoms, rather than by their distribution in the framework. This was attributed to the small size of the CO2 molecules compared to the 3D wide-channel topology of the structure. Conversely, we found that the Al distribution has a higher impact on the heat of adsorption in MOR. Although structures with a very high and very low number of non-Löwenstein bonds presented significant differences, the bonds themselves do not affect the heat of adsorption directly. Instead, we found that an homogeneous distribution of the Al atoms in the sites forming the C-channel is more favorable. Finally, the small-pore distribution of the ITW zeolite led to high values of the heat of adsorption and wide error bars, which made the study feasible just for low aluminum concentrations. In that case, we report a small dependency of the heat of adsorption on the Al distribution. Adsorption ; Zeolite ; Carbon dioxide ; Molecule ; Materials science ; Carbon fibers ; Aluminium ; Chemical physics ; Thermodynamics ; Chemical engineering ; Computational chemistry ; Chemistry ; Inorganic chemistry ; Physical chemistry ; Organic chemistry ; Catalysis ; Composite material ; Physics Zeolite Catalysis and Synthesis ; Carbon Dioxide Capture Technologies ; Membrane Separation and Gas Transport
Authors: Xianqiang Fan | N. Shevchenko | Catherine Tonry | Samuel J. Clark | Robert Atwood | Sven Eckert | K. Pericleous | Peter Lee | Andrew Kao
Source: Acta Materialia (article, 2023-06-25)
Field: Materials Science — Materials Chemistry

Controlling solute channel formation using magnetic fields Solute channel formation introduces compositional and microstructural variations in a range of processes, from metallic alloy solidification, to salt fingers in ocean and water reservoir flows. Applying an external magnetic field interacts with thermoelectric currents at solid/liquid interfaces generating additional flow fields. This thermoelectric (TE) magnetohydrodynamic (TEMHD) effect can impact on solute channel formation, via a mechanism recently drawing increasing attention. To investigate this phenomenon, we combined in situ synchrotron X-ray imaging and Parallel-Cellular-Automata-Lattice-Boltzmann based numerical simulations to study the characteristics of flow and solute transport under TEMHD. Observations suggest the macroscopic TEMHD flow appearing ahead of the solidification front, coupled with the microscopic TEMHD flow arising within the mushy zone are the primary mechanisms controlling plume migration and channel bias. Two TE regimes were revealed, each with distinctive mechanisms that dominate the flow. Further, we show that grain orientation modifies solute flow through anisotropic permeability. These insights led to a proposed strategy for producing solute channel-free solidification using a time-modulated magnetic field. Materials science ; Magnetohydrodynamic drive ; Thermoelectric effect ; Magnetic field ; Lattice Boltzmann methods ; Plume ; Flow (mathematics) ; Anisotropy ; Condensed matter physics ; Mechanics ; Synchrotron ; Directional solidification ; Magnetohydrodynamics ; Fluid dynamics ; Liquid metal ; Chemical physics ; Alloy ; Thermodynamics ; Physics ; Metallurgy ; Optics Block Copolymer Self-Assembly ; Lattice Boltzmann Simulation Studies ; Solidification and crystal growth phenomena