jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shilpi Shrivastava | Sanyogita Shahi
Source: MIGRATION LETTERS (article, 2023-12-20)
Field: Materials Science — Materials Chemistry

Advancing Energy Storage and Conversion with Ti3C2Tx MXene-Based Flexible Two-Dimensional Hybrid Nanomaterials Enhancing efficiency in current energy systems necessitates the development of novel materials for energy storage and conversion. The MXene, known as Ti3C2Tx, has gained recognition for its exceptional characteristics, which include elevated conductivity, flexibility, and a substantial surface area. It has garnered considerable attention as a prospective contender for various energy-related applications, including supercapacitors, lithium-ion batteries, photocatalysis, and sensors. The synthesis, scalability, and process integration of Ti3C2Tx MXene present notable obstacles within conventional approaches. This study shows a Ti3C2Tx MXene-based flexible Two-Dimensional Hybrid Nanomaterials (TMF2DHNM) as a solution to address the challenges. This innovative technique aims to enhance energy storage and conversion applications significantly. The TMF2DHNM has remarkable characteristics, such as a specific capacitance of 62.51 F/g, a capacity of 268.53 mAh/g, lithium (Li) element concentration of 216.65 parts per million (ppm), and an average particle size distribution of 15.30 μm. The results demonstrate the exceptional performance of TMF2DHNM compared to currently available techniques, hence establishing a new standard for energy materials. The TMF2DHNM shows a progression within the discipline, establishing a foundation for developing future energy technologies that exhibit enhanced efficiency and sustainability. Supercapacitor ; Energy storage ; Nanomaterials ; Nanotechnology ; Materials science ; Flexibility (engineering) ; Lithium (medication) ; Scalability ; Efficient energy use ; Energy transformation ; Capacitance ; MXenes ; Computer science ; Electrical engineering ; Engineering ; Chemistry MXene and MAX Phase Materials ; Supercapacitor Materials and Fabrication ; Advanced Memory and Neural Computing
Authors: Malvika Viswanathan | Leqi Yin | Yashwant Kurmi | Zhongliang Zu
Source: PubMed (preprint, 2023-11-03)
Field: Materials Science — Materials Chemistry

Amide Proton Transfer (APT) imaging in tumor with a machine learning approach using partially synthetic data Partially synthetic CEST data can address the challenges in conventional ML methods. Synthetic data ; Computer science ; Ground truth ; Experimental data ; Artificial intelligence ; Training set ; Fidelity ; Machine learning ; Flexibility (engineering) ; Mathematics Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Electron Spin Resonance Studies
Authors: Elkenany Brens Elkenany | O. A. Alfrnwani
Source: Research Square (preprint, 2023-01-05)
Field: Materials Science — Materials Chemistry

Insight into pressure effect on the structural, electronic, optical, mechanical, phonon frequencies, and acoustic speed of nanostructure Ga x In 1-x P y Sb z As 1-y-z alloy lattice matched to GaSb substrate Abstract The electronic, optical, and elastic characteristics of the GaxIn1-xPySbzAs1-y-z alloy lattice matched to the GaSb substrate using a pseudo-potential formalism (EPM) based on the virtual crystal approximation (VCA) are performed. The mechanical features, acoustic velocity, and phonon frequencies of the GaxIn1-xPySbzAs1-y-z/GaSb system are calculated. The sensitivity of these properties to pressure is considered. Our findings are reasonably consistent with the experimental evidence that is currently available. The studied properties of this alloy under the influence of pressure are a new achievement. Novel device applications would be made possible by the pentanary GaxIn1-xPySbzAs1-y-z alloy under high pressure. Phonon ; Alloy ; Materials science ; Nanostructure ; Lattice (music) ; Condensed matter physics ; Formalism (music) ; Crystal structure ; Lattice constant ; Optics ; Nanotechnology ; Crystallography ; Physics ; Composite material ; Diffraction ; Chemistry ; Acoustics Boron and Carbon Nanomaterials Research ; Thermal properties of materials ; Semiconductor Quantum Structures and Devices
Authors: Yansheng Zhai | Xinyu Zhang | Dingyuan Yan | Xianghe Wang | Kailu Tian | Zhe Zhang | Yan Huang | Wen Sun | Xi Yang | Dong Wang | Yu‐Hsuan Tsai | Gang Li
Source: Research Square (preprint, 2023-06-21)
Field: Chemistry — Organic Chemistry

Global profiling of functional histidines in live cells using small molecule photosensitizer and chemical probe relay labeling Abstract Recent advances in chemical proteomics have focused on developing chemical probes reacting with nucleophilic amino acid residues throughout the proteome. Among the nucleophilic amino acids, histidine is an attractive candidate due to its presence in enzyme active sites, metal-binding sites, and protein-protein interaction interfaces. However, histidine has moderate nucleophilicity, and its modification is easily influenced by cysteine and lysine, resulting in poor selectivity and narrow proteome coverage. Here, we report a singlet oxygen and chemical probe relay labeling method that achieves exquisite selectivity towards histidine. A small molecular photosensitizer library is screened together with a chemical probe library to optimize the histidine labeling, enabling histidine profiling in live cells with over 7500 unique sites. We applied this method to discover unannotated histidine sites for metalloproteins and characterize the key histidine residues H309 for metabolic enzyme IDH1 and H73 for CRIP1. Finally, we used this method to probe the accessibility of histidine residues in mitophagy, revealing H138 of PARK7 as the key residue influencing the protein subcellular localization and stability. These findings demonstrate the applicability of our method in discovering functional histidine sites in the human proteome. Photosensitizer ; Relay ; Profiling (computer programming) ; Chemistry ; Small molecule ; Molecule ; Biophysics ; Combinatorial chemistry ; Nanotechnology ; Photochemistry ; Biochemistry ; Computer science ; Materials science ; Biology ; Physics ; Organic chemistry Click Chemistry and Applications ; Chemical Synthesis and Analysis ; Nanoplatforms for cancer theranostics
Authors: Joel Davidsson | William Stenlund | Abhijith S. Parackal | Rickard Armiento | Igor A. Abrikosov
Source: arXiv (Cornell University) (preprint, 2023-06-19)
Field: Materials Science — Materials Chemistry

Na in Diamond: High Spin Defects Revealed by the ADAQ High-Throughput Computational Database Color centers in diamond are at the forefront of the second quantum revolution. A handful of defects are in use, and finding ones with all the desired properties for quantum applications is arduous. By using high-throughput calculations, we screen 21607 defects in diamond and collect the results in the ADAQ database. Upon exploring this database, we find not only the known defects but also several unexplored defects. Specifically, defects containing sodium stand out as particularly relevant because of their high spins and predicted improved optical properties compared to the NV center. Hence, we studied these in detail, employing high-accuracy theoretical calculations. The single sodium substitutional (Na$\mathrm{_C}$) has various charge states with spin ranging from 0.5 to 1.5, ZPL in the near-infrared, and a high Debye-Waller factor, making it ideal for biological quantum applications. The sodium vacancy (NaV) has a ZPL in the visible region and a potential rare spin-2 ground state. Our results show sodium implantation yields many interesting spin defects that are valuable additions to the arsenal of point defects in diamond studied for quantum applications. Diamond ; Spins ; Spin (aerodynamics) ; Vacancy defect ; Spin states ; Materials science ; Condensed matter physics ; Physics Diamond and Carbon-based Materials Research ; Semiconductor materials and devices ; Electronic and Structural Properties of Oxides
Authors: Tammo van der Heide | Bálint Aradi | B. Hourahine | Thomas Frauenheim | Thomas A. Niehaus
Source: arXiv (Cornell University) (preprint, 2023-02-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hybrid functionals for periodic systems in the density functional tight-binding method Screened range-separated hybrid (SRSH) functionals within generalized Kohn-Sham density functional theory (GKS-DFT) have been shown to restore a general $1/(r\varepsilon)$ asymptotic decay of the electrostatic interaction in dielectric environments. Major achievements of SRSH include an improved description of optical properties of solids and correct prediction of polarization-induced fundamental gap renormalization in molecular crystals. The density functional tight-binding method (DFTB) is an approximate DFT that bridges the gap between first principles methods and empirical electronic structure schemes. While purely long-range corrected RSH are already accessible within DFTB for molecular systems, this work generalizes the theoretical foundation to also include screened range-separated hybrids, with conventional pure hybrid functionals as a special case. The presented formulation and implementation is also valid for periodic boundary conditions (PBC) beyond the $Γ$-point. To treat periodic Fock exchange and its integrable singularity in reciprocal space, we resort to techniques successfully employed by DFT, in particular a truncated Coulomb operator and the minimum image convention. Starting from the first principles Hartree-Fock operator, we derive suitable expressions for the DFTB method, using standard integral approximations and their efficient implementation in the DFTB+ software package. Convergence behavior is investigated and demonstrated for the polyacene series as well as two- and three-dimensional materials. Benzene and pentacene molecular and crystalline systems show the correct polarization-induced gap renormalization by SRSH-DFTB at heavily reduced computational cost compared to first principles methods. Periodic boundary conditions ; Tight binding ; Hybrid functional ; Coulomb ; Renormalization ; Density functional theory ; Operator (biology) ; Electronic structure ; Fock space ; Singularity ; Physics ; Quantum mechanics ; Boundary value problem ; Mathematics ; Mathematical analysis ; Chemistry ; Electron Nonlinear Optical Materials Research ; Solid-state spectroscopy and crystallography ; Advanced Chemical Physics Studies
Authors: Kaihu Hou | Jin Zhang | Jie Long | Xiaolei Gai | Xiaowei Zhang | Yuchen Liu | Jiwu Zhang | Haowei Sun | Ke Zhang
Source: Research Square (preprint, 2023-07-21)
Field: Chemistry — Analytical Chemistry

A WOA-Stacking Tobacco Leaf Grading Method Based on Multi-Heterogeneous Classifiers Ensemble Abstract Tobacco leaf grading is an essential step for the rational use of tobacco resources and enhancing tobacco benefits. However, the existing tobacco leaf grading technology suffers from the problem of limited grade discrimination and low identification accuracy of single classifiers. To address these issues, we proposed a tobacco leaf grading method based on Whale Optimization Algorithm and multi-heterogeneous classifier Stacking ensemble (WOA-Stacking). In this paper, we utilized the differences in data observation and training principles of heterogeneous classifiers and integrate them using the Whale Optimization Algorithm (WOA) to build a stacked ensemble model. The fitness function is calculated based on the classification performance matrix and the discriminant function. The optimal combination of base classifier and meta classifier is selected from the classifier pool composed of 9 heterogeneous classifiers, and a data-driven classification model is constructed. Subsequently, hyperparameter tuning and hierarchical coupling are performed by Grid Search (GS) and K-fold Cross-Validation (K-CV). Finally, the model is evaluated and validated by applying the tobacco leaf grade dataset. The experimental results demonstrate that the WOA-Stacking model outperforms traditional single classification models, and achieves high accuracy in tobacco leaf grade classification. The model constructed in this paper can be applied as a data-driven general model for tobacco leaf grade classification in different regions, providing technical support towards industrialization of tobacco leaf grading. Tobacco leaf ; Classifier (UML) ; Computer science ; Stacking ; Hyperparameter optimization ; Artificial intelligence ; Grading (engineering) ; Pattern recognition (psychology) ; Machine learning ; Data mining ; Support vector machine ; Biology ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Yan He | Kai Kang | Yin Qiwei | Peng Yang | Wei Zhang
Source: Engenharia Agrícola (article, 2023-12-01)
Field: Chemistry — Analytical Chemistry

CNN CLASSIFICATION OF SOYBEANS WITH STORAGE TIME BASED ON NEAR INFRARED SPECTROSCOPY In soybeans from different years, the infrared spectroscopy waveforms exhibit similarities, yet variations in aging time lead to significant differences in their fatty acid content. To rapidly discern the year of soybean production, this study employed ten feature extraction algorithms in conjunction with Convolutional Neural Networks (CNN) to establish models for classifying soybeans from different years (2019, 2020, and 2021). The research findings reveal the following: (1) The CNN models, after feature extraction, all demonstrated improved accuracy. Notably, the optimal models for both powdered and granulated states were the Kpca-CNN models, achieving an accuracy of 100%. The corresponding loss function values were 0.0002 and 0.0007, with processing times of 0.19 seconds and 0.20 seconds, respectively. (2) The modeling results of all models suggest that the classification accuracy of soybean powder spectral data is higher compared to soybean particle spectral data. (3) Validation of the optimal Kpca-CNN model confirmed its consistent accuracy of 100% when introducing new data or reducing the size of the training dataset. In conclusion, the fusion of near-infrared spectroscopy analysis and CNN technology is considered an effective method for classifying soybeans from different years. This method provides a practical solution for the rapid and precise determination of seed production years. Spectroscopy ; Near-infrared spectroscopy ; Environmental science ; Remote sensing ; Materials science ; Chemistry ; Pattern recognition (psychology) ; Biological system ; Artificial intelligence ; Computer science ; Optics ; Geology ; Physics ; Biology Spectroscopy and Chemometric Analyses ; Proteins in Food Systems ; Food composition and properties
Authors: Lauren Nicole Core
Source: American Water Works Association (article, 2023-08-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advancing Water Treatment Technology Through Materials Science and Supercomputing M uch of the conversation about the resilience, sustain- ability, and security of water revolves around reducing its commercial and domestic use, but many of the most innovative and promising ideas for managing water supply relate to the technologies used to treat it.These technologies in turn often rely on the development of new materials with novel capabilities and potential applications.However, discovering new materials tends to be laborious.New materials must be conceived and fabricated, then evaluated, and finally tested in a wide range of applications.Research at the Lawrence Berkeley National Laboratory (Berkeley Lab) aims to disrupt this inefficient process by accelerating materials discovery with high-throughput computation. Supercomputer ; Citation ; Library science ; Computer science ; Core (optical fiber) ; World Wide Web ; Telecommunications ; Operating system Ga2O3 and related materials ; Advanced Photocatalysis Techniques ; Water Quality Monitoring Technologies
Authors: Muhammad Rizwan Khan | Harshan Reddy Gopidi | Oleksandr I. Malyi
Source: arXiv (Cornell University) (preprint, 2023-04-27)
Field: Materials Science — Materials Chemistry

Optical Properties and Electronic Structures of Intrinsic Gapped Metals: Inverse Materials Design Principles for Transparent Conductors Traditional solid-state physics has long correlated the optical properties of materials with their electronic structures. However, recent discoveries of intrinsic gapped metals have challenged this classical view. Gapped metals possess electronic properties distinct from both metals and insulators, with a large concentration of free carriers without any intentional doping and an internal band gap. This unique electronic structure makes gapped metals potentially superior to materials designed by intentional doping of the wide band gap insulators. Despite their promising applications, such as transparent conductors, designing gapped metals for specific purposes remains challenging due to the lack of understanding of the correlation between their electronic band structures and optical properties. This study focuses on representative examples of gapped metals and demonstrates the cases of (i) gapped metals (e.g., CaN2) with strong intraband absorption in the visible range, (ii) gapped metals (e.g., SrNbO3) with strong interband absorption in the visible range, (iii) gapped metals (e.g., Sr5Nb5O17) that are potential transparent conductors. We explore the complexity of identifying potential gapped metals for transparent conductors and propose inverse materials design principles for discovering new-generation transparent conductors. Electrical conductor ; Electronic band ; Doping ; Materials science ; Band gap ; Absorption (acoustics) ; Inverse ; Nanotechnology ; Optoelectronics Electronic and Structural Properties of Oxides ; Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials
Authors: Rajiv K. Kalia | Nitish Baradwaj | Anikeya Aditya | Ankit Mishra | Kory Burns | Eric Lang | Jordan A. Hachtel | Khalid Hattar | Assel Aitkaliyeva | Aiichiro Nakano | Priya Vashishta
Source: Research Square (preprint, 2023-12-19)
Field: Materials Science — Materials Chemistry

Probing Phonon Focusing, Thermomechanical Behavior, and Moiré Patterns in van der Waals Architectures Using Surface Acoustic Waves <title>Abstract</title> Surface acoustic waves (SAWs) propagate along solid-air, solid-liquid and solid-solid interfaces. Their characteristics depend on the elastic properties of the solid. Combining transmission electron microscopy (TEM) experiments with molecular dynamics (MD) simulations, we probe atomic environments around intrinsic defects that generate SAWs in vertically stacked two-dimensional (2D) bilayers of MoS<sub>2</sub>. Our joint experimental-simulation study provides new insights into SAW-induced structural and dynamical changes and thermomechanical responses of MoS<sub>2</sub> bilayers. Using MD simulations, we compute mechanical properties from the SAW velocity and thermal conductivity from thermal diffusion of SAWs. The results for Young’s modulus and thermal conductivity of an MoS<sub>2</sub> monolayer are in good agreement with experiments. The presence of defects, such as nanopores which generate SAWs, reduces the thermal conductivity of 2D-MoS<sub>2</sub> by an order of magnitude. We also observe dramatic changes in moiré patterns, phonon focusing, and cuspidal structures on 2D-MoS<sub>2</sub> layers. Materials science ; Thermal conductivity ; Phonon ; Condensed matter physics ; van der Waals force ; Molecular dynamics ; Thermal ; Acoustic wave ; Molecular physics ; Optics ; Composite material ; Physics ; Thermodynamics Thermal properties of materials ; 2D Materials and Applications ; Graphene research and applications
Authors: Luca Foppiano | Tomoya MATO | Kensei Terashima | Pedro Ortiz Suárez | Taku Tou | Chikako Sakai | Weisheng Wang | Toshiyuki Amagasa | Yoshihiko Takano | Masashi Ishii
Source: arXiv (Cornell University) (preprint, 2023-09-19)
Field: Materials Science — Materials Chemistry

Semi-automatic staging area for high-quality structured data extraction from scientific literature We propose a semi-automatic staging area for efficiently building an accurate database of experimental physical properties of superconductors from literature, called SuperCon2, to enrich the existing manually-built superconductor database SuperCon. Here we report our curation interface (SuperCon2 Interface) and a workflow managing the state transitions of each examined record, to validate the dataset of superconductors from PDF documents collected using Grobid-superconductors in a previous work. This curation workflow allows both automatic and manual operations, the former contains ``anomaly detection'' that scans new data identifying outliers, and a ``training data collector'' mechanism that collects training data examples based on manual corrections. Such training data collection policy is effective in improving the machine-learning models with a reduced number of examples. For manual operations, the interface (SuperCon2 interface) is developed to increase efficiency during manual correction by providing a smart interface and an enhanced PDF document viewer. We show that our interface significantly improves the curation quality by boosting precision and recall as compared with the traditional ``manual correction''. Our semi-automatic approach would provide a solution for achieving a reliable database with text-data mining of scientific documents. Workflow ; Computer science ; Interface (matter) ; Database ; Data curation ; Precision and recall ; Data extraction ; Outlier ; Data mining ; Information retrieval ; Boosting (machine learning) ; Data quality ; Artificial intelligence Machine Learning in Materials Science ; Topic Modeling ; Scientific Computing and Data Management
Authors: Ally K. Smith | Daniel Deredge
Source: Biophysical Journal (article, 2023-02-01)
Field: Chemistry — Spectroscopy

Optimization of hydrogen-deuterium exchange ensemble reweighting (HDXer) to aid the biophysical characterization of proteins Hydrogen–deuterium exchange ; Deuterium ; Characterization (materials science) ; Chemistry ; Nanotechnology ; Physics ; Materials science ; Nuclear physics Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Advanced Proteomics Techniques and Applications
Authors: Şadiye Peral Eyduran | Zeynebi Kübra AZİTİ
Source: Journal of Agriculture (article, 2023-12-31)
Field: Chemistry — Inorganic Chemistry

Polyphenol Oxidase Enzyme Activity of Mulberries Grown in Iğdir Ecological Conditions The presence of polyphenol oxidase enzyme (PFO) in black and white mulberry varieties grown in Iğdır province were determined. In the study, no polyphenol oxidase enzyme was detected in the white mulberry varietie, therefore the study was conducted only with black mulberry varieties. The mulberry samples were collected from Iğdır Center, Melekli and Karakoyunlu districts during June and July, both in 2015 and 2016, when the fruits were fully ripe. Mulberries were stored at -20 °C, and the crude enzyme extract was stabilized with pH 5.0 sodium-acetate buffer containing 1% Polyethylene glycol (PEG). To weigh with a precision scale, 20 grams of frozen mulberries were taken into mortars. After the mulberries were thoroughly crushed in these mortars, 40 ml of crude enzyme extract preparation solution was added to each variety and mixed homogeneously. These mixtures were filtered through a cheesecloth and rotated at 4 °C, 10,000 rpm for 30 minutes. The obtained supernatants were used for enzyme solubilization, and the enzyme activity of the solution was determined spectrophotometrically. The PFO content depended on the genotype. There was a significant difference among the tested mulberry genotypes. Polyphenol oxidase ; Polyethylene glycol ; Chemistry ; Polyphenol ; Enzyme ; Enzyme assay ; PEG ratio ; Food science ; Biochemistry ; Antioxidant Coconut Research and Applications ; Silkworms and Sericulture Research ; Insect Utilization and Effects