jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kaiyao Hou | Tiecheng Bai | Xu Li | Ziyan Shi | Senwei Li
Source: Forests (article, 2024-01-30)
Field: Chemistry — Analytical Chemistry

Inversion Study of Nitrogen Content of Hyperspectral Apple Canopy Leaves Using Optimized Least Squares Support Vector Machine Approach The rapid and accurate estimation of the nitrogen content of fruit trees helps to achieve a precise management of orchards. Hyperspectral data were collected from leaves of apple tree canopies at different fertility stages through field experiments to investigate the relationship between the nitrogen content and spectral reflectance of apple canopy leaves. Two different preprocessing methods, Savitzky–Golay (SG) smoothing and multiple scattering correction (MSC), were used to extract the feature bands by combining the successive projection method (SPA) and the competitive adaptive weighting algorithm–partial least squares (CARS-PLS). The reflectance values of the feature bands screened via these two methods were used as inputs to construct the multi-factor inversion models of apple canopy leaf nitrogen content based on the long- and short-term memory (LSTM) network, the support vector regression (SVR) and the Least Squares Support Vector Machine Regression (RIME-LSSVM). The study compared the ability of three algorithmic models to estimate leaf nitrogen content, and the results showed that the model constructed with the reflectance values of the characteristic bands screened by the CARS-PLS algorithm as inputs was more effective in predicting the nitrogen content of leaves. Furthermore, the accuracy of the model constructed using RIME-LSSVM was significantly higher than that of the model constructed using the long- and short-term memory network and support vector regression, in which the coefficient of determination of the test set (R-squared) is 0.964 and the root-mean-squared error (RMSE) is 0.052. Finally, the CARS-PLS algorithm combined with the RIME-LSSVM model has a higher prediction accuracy. The study demonstrated the feasibility and reliability of hyperspectral techniques for the estimation of nitrogen content of apple leaves in the Aksu region. Hyperspectral imaging ; Canopy ; Least squares support vector machine ; Inversion (geology) ; Nitrogen ; Support vector machine ; Mathematics ; Botany ; Agronomy ; Environmental science ; Computer science ; Biology ; Artificial intelligence ; Chemistry Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement
Authors: Jyoti Bonlawar | Aseem Setia | Ranadheer Reddy Challa | Bhaskar Vallamkonda | Abhishesh Kumar Mehata | Vaishali | Matte Kasi Viswanadh | Madaswamy S. Muthu
Source: Nanotheranostics (review, 2024-01-01)
Field: Materials Science — Biomaterials

Targeted Nanotheranostics: Integration of Preclinical MRI and CT in the Molecular Imaging and Therapy of Advanced Diseases The integration of preclinical magnetic resonance imaging (MRI) and computed tomography (CT) methods has significantly enhanced the area of therapy and imaging of targeted nanomedicine. Nanotheranostics, which make use of nanoparticles, are a significant advancement in MRI and CT imaging. In addition to giving high-resolution anatomical features and functional information simultaneously, these multifunctional agents improve contrast when used. In addition to enabling early disease detection, precise localization, and personalised therapy monitoring, they also enable early disease detection. Fusion of MRI and CT enables precise in vivo tracking of drug-loaded nanoparticles. MRI, which provides real-time monitoring of nanoparticle distribution, accumulation, and release at the cellular and tissue levels, can be used to assess the efficacy of drug delivery systems. The precise localization of nanoparticles within the body is achievable through the use of CT imaging. This technique enhances the capabilities of MRI by providing high-resolution anatomical information. CT also allows for quantitative measurements of nanoparticle concentration, which is essential for evaluating the pharmacokinetics and biodistribution of nanomedicine. In this article, we emphasize the integration of preclinical MRI and CT into molecular imaging and therapy for advanced diseases. Nanomedicine ; Biodistribution ; Magnetic resonance imaging ; Molecular imaging ; Medicine ; Drug delivery ; Biomedical engineering ; In vivo ; Radiology ; Nanoparticle ; Materials science ; Nanotechnology Nanoparticle-Based Drug Delivery ; Ultrasound and Hyperthermia Applications ; Nanoplatforms for cancer theranostics
Authors: A. N. M. Tanvir | Md. Omarsany Bappy | Minxiang Zeng | Wenjie Shang | Ke Wang | K Song | Yukun Liu | Eleonora Isotta | Mercouri G. Kanatzidis | G. Jeffrey Snyder | Alexander W. Dowling | Tengfei Luo | Yanliang Zhang
Source: Energy & Environmental Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

High-performance thermoelectric composites <i>via</i> scalable and low-cost ink processing This work demonstrates a remarkable room-temperature figure of merit zT of 1.3 for BiSbTe-based composites with excellent reproducibility using a scalable, low-cost ink processing technique. Materials science ; Inkwell ; Composite material ; Thermoelectric effect ; Scalability ; Computer science ; Database Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Transition Metal Oxide Nanomaterials
Authors: Zhiyu Du | Rongjian Shi | Xingyu Peng | Kewei Gao | Xiaolu Pang
Source: International Journal of Minerals Metallurgy and Materials (article, 2024-05-28)
Field: Materials Science — Metals and Alloys

Review on the design of high-strength and hydrogen-embrittlement-resistant steels Hydrogen embrittlement ; Embrittlement ; Materials science ; Metallurgy ; High strength steel ; Corrosion Hydrogen embrittlement and corrosion behaviors in metals ; Material Properties and Failure Mechanisms ; Microstructure and Mechanical Properties of Steels
Authors: Wenting Shao | Dan C. Sorescu | Zhengru Liu | Alexander Star
Source: Small (article, 2024-04-28)
Field: Materials Science — Materials Chemistry

Machine Learning Discrimination and Ultrasensitive Detection of Fentanyl Using Gold Nanoparticle‐Decorated Carbon Nanotube‐Based Field‐Effect Transistor Sensors The opioid overdose crisis is a global health challenge. Fentanyl, an exceedingly potent synthetic opioid, has emerged as a leading contributor to the surge in opioid-related overdose deaths. The surge in overdose fatalities, particularly due to illicitly manufactured fentanyl and its contamination of street drugs, emphasizes the urgency for drug-testing technologies that can quickly and accurately identify fentanyl from other drugs and quantify trace amounts of fentanyl. In this paper, gold nanoparticle (AuNP)-decorated single-walled carbon nanotube (SWCNT)-based field-effect transistors (FETs) are utilized for machine learning-assisted identification of fentanyl from codeine, hydrocodone, and morphine. The unique sensing performance of fentanyl led to use machine learning approaches for accurate identification of fentanyl. Employing linear discriminant analysis (LDA) with a leave-one-out cross-validation approach, a validation accuracy of 91.2% is achieved. Meanwhile, density functional theory (DFT) calculations reveal the factors that contributed to the enhanced sensitivity of the Au-SWCNT FET sensor toward fentanyl as well as the underlying sensing mechanism. Finally, fentanyl antibodies are introduced to the Au-SWCNT FET sensor as specific receptors, expanding the linear range of the sensor in the lower concentration range, and enabling ultrasensitive detection of fentanyl with a limit of detection at 10.8 fg mL<sup>-1</sup>. Fentanyl ; Carbon nanotube ; Materials science ; Opioid ; Nanotechnology ; Pharmacology ; Medicine ; Receptor Diamond and Carbon-based Materials Research ; Neuroscience and Neural Engineering ; Forensic Toxicology and Drug Analysis
Authors: Jamil Ur Rahman | Shuping Guo | Nicolás Pérez | Kyuseon Jang | Chanwon Jung | Pingjun Ying | Christina Scheu | Duncan Zavanelli | Siyuan Zhang | A. V. Sotnikov | G. Jeffrey Snyder | Jeroen van den Brink | Kornelius Nielsch | Ran He
Source: Advanced Energy Materials (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

Grain Boundary Engineering Enhances the Thermoelectric Properties of Y<sub>2</sub>Te<sub>3</sub> Abstract The performance of thermoelectric materials is typically assessed using the dimensionless figure of merit, zT . Increasing zT is challenging due to the intricate relationships between electrical and thermal transport properties. This study focuses on Y 2 Te 3 ‐based thermoelectric materials, which are predicted to be promising for high‐temperature applications due to their inherently low lattice thermal conductivity. A series of Y 2+ x Te 3 compositions with excess Y is synthesized to explore the effects on electronic and structural characteristics. Density functional theory calculations suggest that additional Y atoms increase charge carriers, thereby enhancing electrical conductivity and boosting thermoelectric performance. X‐ray diffraction analysis reveals that the presence of excess Y reduces lattice volume and alters bonding structures. Furthermore, the addition of Bi significantly enhances the power factor by promoting the segregation of elemental Bi particles and the formation of Y‐Bi‐rich grain boundaries, which improve weighted mobility. This microstructural optimization leads to a fourfold increase in the Seebeck coefficient, resulting in a peak zT of 1.23 at 973 K and a predicted maximum conversion efficiency of 10.3% under a temperature difference of 673 K. These findings highlight the potential of Y 2 Te 3 for high‐temperature thermoelectric applications and demonstrate the effectiveness of grain boundary engineering in enhancing thermoelectric performance. Materials science ; Thermoelectric effect ; Grain boundary ; Engineering physics ; Thermoelectric materials ; Condensed matter physics ; Thermodynamics ; Metallurgy ; Composite material ; Thermal conductivity ; Microstructure ; Physics Advanced Thermoelectric Materials and Devices ; 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Lilla Virág | Attila Egedy | Csilla Varga | Gergely Erdős | Szabolcs Berezvai | László Kovács | Zsolt Ulbert
Source: Heliyon (article, 2024-01-24)
Field: Materials Science — Polymers and Plastics

Determination of the most significant rubber components influencing the hardness of natural rubber (NR) using various statistical methods Manufacturers use a large number of components in the production of modern rubber products. The selection of the constituents of the rubber recipe is primarily determined by the purpose of use. The different fields of applications of rubbers require the presence of appropriate mechanical properties. In this respect, it can be useful to know which substances forming the rubber recipe have significant influence on the different mechanical properties. In this study, the statistical analysis of the influence of rubber components on the hardness of natural rubber (NR) is proposed based on literature review. Based on the literature data, various statistical analyses, like <i>linear regression</i>, <i>constrained linear regression, Ridge</i> regression, <i>Ridge sparse</i> regression and <i>binary classification decision trees</i> were performed to determine which rubber components have the most significant effect on the hardness. In the statistical analyses, the effect of a total of 42 constituents of rubber compound on hardness was investigated. Most of the applied statistical methods confirmed that the traditional frequently used rubber components, such as carbon black and sulfur, have a primary effect on the hardness. However, the substances forming the rubber compound that are not widely used in practice or newly developed components appear differently in the lists of significant additives obtained by the different statistical methods. Natural rubber ; Statistical analysis ; Materials science ; Composite material ; Polymer science ; Mathematics ; Statistics Polymer Nanocomposites and Properties ; Asphalt Pavement Performance Evaluation ; Lubricants and Their Additives
Authors: Parsa Farzin | Amir Saman Nooramin | Mohammad Soleimani
Source: Scientific Reports (article, 2024-05-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Reprogrammable reflection-transmission integrated coding metasurface for real-time terahertz wavefront manipulations in full-space In recent years, there has been notable advancement in programmable metasurfaces, primarily attributed to their cost-effectiveness and capacity to manipulate electromagnetic (EM) waves. Nevertheless, a significant limitation of numerous available metasurfaces is their capability to influence wavefronts only in reflection mode or transmission mode, thus catering to only half of the spatial coverage. To the best of our knowledge and for the first time, a novel graphene-assisted reprogrammable metasurface that offers the unprecedented capability to independently and concurrently manipulate EM waves within both half-spaces has been introduced in the THz frequency band. This intelligent programmable metasurface achieves wavefront control in reflection mode, transmission mode, and the concurrent reflection-transmission mode, all within the same polarization and frequency channel. The meta-atom is constructed with two graphene sections, enabling straightforward modification of wave behavior by adjusting the chemical potential distribution within each graphene segment via an external electronic source. The proposed functionalities encompass various programmable modes, including single and dual beam control in reflection mode, dual beam control in transmission mode, simultaneous control of dual beams in reflection mode-direct transmission, and vice versa, and control of beam steering in reflection mode-dual beams in transmission mode simultaneously. The proposed metasurface is expected to be reprogrammable due to wavefront manipulation in both half-spaces separately and continuously for various applications such as imaging systems, encryption, miniaturized systems, and next-generation wireless intelligent communications. Terahertz radiation ; Wavefront ; Reflection (computer programming) ; Transmission (telecommunications) ; Coding (social sciences) ; Computer science ; Metamaterial ; Optics ; Physics ; Telecommunications ; Mathematics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Dalia G. Aseel | Mona Rabie | Ali H. El‐Far | Ahmed Abdelkhalek
Source: BMC Plant Biology (article, 2024-11-18)
Field: Materials Science — Materials Chemistry

Antiviral properties and molecular docking studies of eco-friendly biosynthesized copper oxide nanoparticles against alfalfa mosaic virus The overall analysis results indicate that the curative treatment is more influential and successful than the protective treatment in combating AMV infection. Consequentially, CuO-NPs could potentially be employed in foliar sprays for the effective and environmentally friendly management of plant virus infections. Biology ; Cucumber mosaic virus ; Alfalfa mosaic virus ; Pathogen ; Tobacco mosaic virus ; Plant virus ; Virus ; Virology ; Biotechnology ; Coat protein ; Microbiology ; Biochemistry Nanoparticles: synthesis and applications ; Medicinal Plant Research ; Plant-Microbe Interactions and Immunity
Authors: Ahmed S. A. Mohammed | Dmytro Soloviov | Cy M. Jeffries
Source: Physical Chemistry Chemical Physics (review, 2024-01-01)
Field: Materials Science — Materials Chemistry

Perspectives on solution-based small angle X-ray scattering for protein and biological macromolecule structural biology Small-angle X-ray scattering (SAXS) is used to extract structural information from a wide variety of non-crystalline samples in different fields (<i>e.g.</i>, materials science, physics, chemistry, and biology). This review provides an overview of SAXS as applied to structural biology, specifically for proteins and other biomacromolecules in solution with an emphasis on extracting key structural parameters and the interpretation of SAXS data using a diverse array of techniques. These techniques cover aspects of building and assessing models to describe data measured from monodispersed and ideal dilute samples through to more complicated structurally polydisperse systems. <i>Ab initio</i> modelling, rigid body modelling as well as normal-mode analysis, molecular dynamics, mixed component and structural ensemble modelling are discussed. Dealing with polydispersity both physically in terms of component separation as well as approaching the analysis and modelling of data of mixtures and evolving systems are described, including methods for data decomposition such as single value decomposition/principle component analysis and evolving factor analysis. This review aims to highlight that solution SAXS, with the cohort of developments in data analysis and modelling, is well positioned to build upon the traditional 'single particle view' foundation of structural biology to take the field into new areas for interpreting the structures of proteins and biomacromolecules as population-states and dynamic structural systems. Small-angle X-ray scattering ; Scattering ; Macromolecule ; Small-angle scattering ; Variety (cybernetics) ; Structural biology ; Physics ; Crystallography ; Materials science ; Chemical physics ; Chemistry ; Optics ; Computer science ; Biochemistry Enzyme Structure and Function ; Protein Structure and Dynamics ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Morakot Worachairungreung | Nayot Kulpanich | Pornperm SAE-NGOW | Kunyaphat Thanakunwutthirot | Kawinphop Anurak | Phonpat Hemwan
Source: Journal of Human Earth and Future (article, 2024-12-01)
Field: Chemistry — Inorganic Chemistry

Classification of Coconut Trees Within Plantations from UAV Images Using Deep Learning with Faster R-CNN and Mask R-CNN Agriculture currently serves as a crucial food source for the global population. However, coconut farming, in particular, demands extensive care and maintenance. This research aims to classify coconut trees across various plantation areas utilizing deep learning techniques, specifically through Faster R-CNN and Mask R-CNN models, based on unmanned aerial vehicle (UAV) imagery. The data collected by both types of RGB UAVs was used for the classification of coconut trees in experimental plots. For the analysis process, aerial photographs obtained from unmanned aerial vehicles, merged with the principles of aerial photography measurement, were analyzed. The research findings revealed that both Faster R-CNN and Mask R-CNN were capable of effectively classifying image data. Nevertheless, to achieve higher accuracy in results, it is essential that the characteristics of the test plots closely align with each other. This study points towards the adoption of a high-resolution tool, ensuring clearer images that facilitate more accurate classification of coconut trees across extensive areas. Consequently, this could lead to more efficient management and maintenance of coconut plantations. Thus, this approach can substantially enhance the efficiency of managing coconut plantations. Doi: 10.28991/HEF-2024-05-04-02 Full Text: PDF Aerial photography ; RGB color model ; Artificial intelligence ; Computer science ; Deep learning ; Process (computing) ; Population ; Convolutional neural network ; Remote sensing ; Pattern recognition (psychology) ; Geography Coconut Research and Applications ; Date Palm Research Studies ; Identification and Quantification in Food
Authors: Sebastian A. Ramos | Leonard J. Mueller | Gregory J. O. Beran
Source: Faraday Discussions (article, 2024-05-17)
Field: Chemistry — Spectroscopy

The interplay of density functional selection and crystal structure for accurate NMR chemical shift predictions <i>Ab initio</i> chemical shift prediction plays a central role in nuclear magnetic resonance (NMR) crystallography, and the accuracy with which chemical shifts can be predicted relative to experiment impacts the confidence with which structures can be assigned. For organic crystals, periodic density functional theory calculations with the gauge-including projector augmented wave (GIPAW) approximation and the PBE functional are widely used at present. Many previous studies have examined how using more advanced density functionals can increase the accuracy of predicted chemical shifts relative to experiment, but nearly all of those studies employed crystal structures that were optimized with generalized-gradient approximation (GGA) functionals. Here, we investigate how the accuracy of the predicted chemical shifts in organic crystals is affected by replacing GGA-level PBE-D3(BJ) crystal geometries with more accurate hybrid functional PBE0-D3(BJ) ones. Based on benchmark data sets containing 132 <sup>13</sup>C and 35 <sup>15</sup>N chemical shifts, plus case studies on testosterone, acetaminophen, and phenobarbital, we find that switching from GGA-level geometries and chemical shifts to hybrid-functional ones reduces <sup>13</sup>C and <sup>15</sup>N chemical shift errors by ∼40-60% <i>versus</i> experiment. However, most of the improvement stems from the use of the hybrid functional for the chemical shift calculations, rather than from the refined geometries. In addition, even with the improved geometries, we find that double-hybrid functionals still do not systematically increase chemical shift agreement with experiment beyond what hybrid functionals provide. In the end, these results suggest that the combination of GGA-level crystal structures and hybrid-functional chemical shifts represents a particularly cost-effective combination for NMR crystallography in organic systems. Chemical shift ; Crystal (programming language) ; Density functional theory ; Crystal structure prediction ; Selection (genetic algorithm) ; Materials science ; Chemical physics ; Quality (philosophy) ; Computational chemistry ; Crystal structure ; Crystallography ; Chemistry ; Physical chemistry ; Physics ; Computer science ; Machine learning Molecular spectroscopy and chirality ; Advanced NMR Techniques and Applications ; Crystallography and molecular interactions