jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: CI Chemistry International
Source:  (preprint, 2021-06-20)
Field: Chemistry — Analytical Chemistry

Adsorption of crystal violet dye onto olive leaves powder: Equilibrium and kinetic studies Adsorption of crystal violet dye from aqueous solutions applying olive leaves powder (OLP) as a biosorbent has been examined under various experimental circumstances. The influence of contact time, pH, initial concentration of studied dye and adsorbent dose on the adsorption process has been investigated applying batch experiments. The concentration of remaining dye has been determined using molecular absorption spectrometry at wave length of 580 nm. The maximum removal of studied dye has been realized at pH 7.5 with a percent removal of 99.2% after 20 min of agitation time. Langmuir, Freundlich, and Temkin isotherm models exemplify the best fit for the experimental data; while the elevated adsorption capacity was 181.1 mg.g1. Adsorption kinetics of crystal violet was expected sufficiently with the empirical pseudo-second-order model. Corresponding to the adsorption capacity, olive leaves powder thought as a low cost, effective, and environmentally friendly biosorbent for the removal of crystal violet dye from aqueous solutions. Crystal violet ; Adsorption ; Aqueous solution ; Freundlich equation ; Chemistry ; Kinetics ; Langmuir ; Absorption (acoustics) ; Nuclear chemistry ; Crystal (programming language) ; Chromatography ; Materials science ; Organic chemistry ; Composite material Dye analysis and toxicity
Authors: Zihang Liu | Wenhao Zhang | Weihong Gao | Takao Mori
Source: Energy & Environmental Science (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

A material catalogue with glass-like thermal conductivity mediated by crystallographic occupancy for thermoelectric application We proposed a new crystallographic parameter, namely the site occupancy factor, as an effective indicator to discover a material catalogue with low lattice thermal conductivity for thermoelectric application. Thermoelectric effect ; Occupancy ; Thermoelectric materials ; Thermal conductivity ; Materials science ; Lattice (music) ; Thermal ; Crystallography ; Thermodynamics ; Chemistry ; Physics ; Composite material ; Engineering Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Transition Metal Oxide Nanomaterials
Authors: Brian A. Collins | Eliot Gann
Source: Journal of Polymer Science (article, 2021-09-25)
Field: Materials Science — Materials Chemistry

Resonant soft X‐ray scattering in polymer science Abstract Resonant soft X‐ray scattering (RSoXS) is an emerging, powerful technique to probe the nano‐to‐mesoscale structure of polymers and other molecules. It joins together small‐angle X‐ray scattering (a statistical nanoprobe) with X‐ray spectroscopy that brings with it unique chemical and bond‐orientation sensitivity. Through over a decade of discovery and development, RSoXS is moving from a niche technique applied to organic electronic thin films to a mature tool applicable to a plethora of polymeric and molecular systems, encompassing new modalities, analyses, and simulation methods. This development promises to deliver increasingly quantitative answers to challenging questions in polymer science as well as expand its usefulness to complementary fields. This review presents a full synopsis of the technique, including background on the theoretical underpinnings, measurement best practices, and examples of recent RSoXS applications and discoveries provided here to accelerate the transition to a broader range of soft matter and polymeric fields. Scattering ; Polymer ; Nanoprobe ; Joins ; Nanotechnology ; Soft matter ; Materials science ; Polymer physics ; Emulation ; Physics ; Computer science ; Chemistry ; Optics ; Nanoparticle ; Physical chemistry ; Colloid Block Copolymer Self-Assembly ; X-ray Spectroscopy and Fluorescence Analysis ; Ion-surface interactions and analysis
Authors: Tianying Yan | Wei Xu | Lin Jiao | Long Duan | Pan Gao | Chu Zhang | Xin Lv
Source: Frontiers in Plant Science (article, 2021-02-15)
Field: Chemistry — Analytical Chemistry

Combining Multi-Dimensional Convolutional Neural Network (CNN) With Visualization Method for Detection of Aphis gossypii Glover Infection in Cotton Leaves Using Hyperspectral Imaging Cotton is a significant economic crop. It is vulnerable to aphids (<i>Aphis gossypii</i> Glovers) during the growth period. Rapid and early detection has become an important means to deal with aphids in cotton. In this study, the visible/near-infrared (Vis/NIR) hyperspectral imaging system (376-1044 nm) and machine learning methods were used to identify aphid infection in cotton leaves. Both tall and short cotton plants (Lumianyan 24) were inoculated with aphids, and the corresponding plants without aphids were used as control. The hyperspectral images (HSIs) were acquired five times at an interval of 5 days. The healthy and infected leaves were used to establish the datasets, with each leaf as a sample. The spectra and RGB images of each cotton leaf were extracted from the hyperspectral images for one-dimensional (1D) and two-dimensional (2D) analysis. The hyperspectral images of each leaf were used for three-dimensional (3D) analysis. Convolutional Neural Networks (CNNs) were used for identification and compared with conventional machine learning methods. For the extracted spectra, 1D CNN had a fine classification performance, and the classification accuracy could reach 98%. For RGB images, 2D CNN had a better classification performance. For HSIs, 3D CNN performed moderately and performed better than 2D CNN. On the whole, CNN performed relatively better than conventional machine learning methods. In the process of 1D, 2D, and 3D CNN visualization, the important wavelength ranges were analyzed in 1D and 3D CNN visualization, and the importance of wavelength ranges and spatial regions were analyzed in 2D and 3D CNN visualization. The overall results in this study illustrated the feasibility of using hyperspectral imaging combined with multi-dimensional CNN to detect aphid infection in cotton leaves, providing a new alternative for pest infection detection in plants. Hyperspectral imaging ; Convolutional neural network ; Aphis gossypii ; RGB color model ; Artificial intelligence ; Pattern recognition (psychology) ; Visualization ; Computer science ; Biology ; Botany ; Aphididae ; PEST analysis ; Homoptera Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Vineetha Joy | Alka Dileep | P. V. Abhilash | Raveendranath U. Nair | Hema Singh
Source: Journal of Electronic Materials (review, 2021-04-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metasurfaces for Stealth Applications: A Comprehensive Review Metamaterial ; Holography ; Broadband ; Polarization (electrochemistry) ; Photonics ; Computer science ; Multiplexing ; Interfacing ; Aerospace ; Planar ; Radar ; Optics ; Physics ; Engineering ; Telecommunications ; Aerospace engineering Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: A. Shcherbakov | Grant S. Hisao | Venkata S. Mandala | Nathan E. Thomas | M. Soltani | E. Alan Salter | James H. Davis | Katherine A. Henzler‐Wildman | Mei Hong
Source: Nature Communications (article, 2021-01-08)
Field: Chemistry — Spectroscopy

Structure and dynamics of the drug-bound bacterial transporter EmrE in lipid bilayers The dimeric transporter, EmrE, effluxes polyaromatic cationic drugs in a proton-coupled manner to confer multidrug resistance in bacteria. Although the protein is known to adopt an antiparallel asymmetric topology, its high-resolution drug-bound structure is so far unknown, limiting our understanding of the molecular basis of promiscuous transport. Here we report an experimental structure of drug-bound EmrE in phospholipid bilayers, determined using <sup>19</sup>F and <sup>1</sup>H solid-state NMR and a fluorinated substrate, tetra(4-fluorophenyl) phosphonium (F<sub>4</sub>-TPP<sup>+</sup>). The drug-binding site, constrained by 214 protein-substrate distances, is dominated by aromatic residues such as W63 and Y60, but is sufficiently spacious for the tetrahedral drug to reorient at physiological temperature. F<sub>4</sub>-TPP<sup>+</sup> lies closer to the proton-binding residue E14 in subunit A than in subunit B, explaining the asymmetric protonation of the protein. The structure gives insight into the molecular mechanism of multidrug recognition by EmrE and establishes the basis for future design of substrate inhibitors to combat antibiotic resistance. Antiparallel (mathematics) ; Antiporters ; Chemistry ; Protonation ; Stereochemistry ; Protein subunit ; Lipid bilayer ; Protein structure ; Molecular dynamics ; Transporter ; Crystallography ; Biophysics ; Biochemistry ; Biology ; Membrane ; Computational chemistry Advanced NMR Techniques and Applications ; Cancer therapeutics and mechanisms ; Antibiotic Resistance in Bacteria
Authors: Guangyu Guo | Michael Papanicolaou | Nicholas J. Demarais | Z. Wang | Kevin L. Schey | Paul Timpson | Thomas R. Cox | Angus C. Grey
Source: Nature Communications (article, 2021-05-28)
Field: Chemistry — Spectroscopy

Automated annotation and visualisation of high-resolution spatial proteomic mass spectrometry imaging data using HIT-MAP Spatial proteomics has the potential to significantly advance our understanding of biology, physiology and medicine. Matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) is a powerful tool in the spatial proteomics field, enabling direct detection and registration of protein abundance and distribution across tissues. MALDI-MSI preserves spatial distribution and histology allowing unbiased analysis of complex, heterogeneous tissues. However, MALDI-MSI faces the challenge of simultaneous peptide quantification and identification. To overcome this, we develop and validate HIT-MAP (High-resolution Informatics Toolbox in MALDI-MSI Proteomics), an open-source bioinformatics workflow using peptide mass fingerprint analysis and a dual scoring system to computationally assign peptide and protein annotations to high mass resolution MSI datasets and generate customisable spatial distribution maps. HIT-MAP will be a valuable resource for the spatial proteomics community for analysing newly generated and retrospective datasets, enabling robust peptide and protein annotation and visualisation in a wide array of normal and disease contexts. Proteomics ; Mass spectrometry imaging ; Computer science ; Computational biology ; Visualization ; Mass spectrometry ; Workflow ; Annotation ; Quantitative proteomics ; Data mining ; Artificial intelligence ; Biology ; Database ; Chemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Younes Raadi | Andrea Alù
Source: IEEE photonics journal (article, 2021-12-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metagratings for Efficient Wavefront Manipulation Recently, it was revealed that conventional gradient metasurfaces are fundamentally limited on their overall efficiency to reroute the impinging waves towards arbitrary directions in reflection and transmission. Their efficiency is particularly limited for extreme wavefront transformations. In addition, due to the fastly varying impedance profiles that these surfaces require, they usually need high-resolution fabrication processes, limiting their applicability and overall bandwidth of operation. To address these issues, the concept of metagrating was recently introduced, enabling engineered surfaces capable of manipulating light with unitary efficiency even in the limit of extreme wavefront manipulation. Metagratings are periodic or locally periodic arrays operated in the few-diffraction order regime. Their unit cell contains carefully designed scatterers that, in contrast with conventional metasurfaces, do not need to support a continuous gradient of surface impedance, and consequently are far simpler to fabricate and can afford broader bandwidths because of the larger footprint of the constituent elements. Tailoring the coupling towards the few available diffraction channels, metagratings enable wavefront transformations with high efficiency. In this paper, we review the physical mechanisms behind the functionality of metagratings and provide an overview and outlook of the recent progress in this field of science and technology. Wavefront ; Computer science ; Bandwidth (computing) ; Diffraction ; Electrical impedance ; Reflection (computer programming) ; Limiting ; Optics ; Physics ; Telecommunications ; Electrical engineering ; Engineering ; Mechanical engineering Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Jesús Gándara-Loe | Laura Pastor‐Pérez | Luis F. Bobadilla | J.A. Odriozola | Tomás Ramı́rez Reina
Source: Reaction Chemistry & Engineering (article, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

Understanding the opportunities of metal–organic frameworks (MOFs) for CO<sub>2</sub>capture and gas-phase CO<sub>2</sub>conversion processes: a comprehensive overview The rapid increase in the concentration of atmospheric carbon dioxide is one of the most pressing problems facing our planet. Carbon dioxide ; Metal-organic framework ; Environmental science ; Gas phase ; Phase (matter) ; Planet ; Astrobiology ; Materials science ; Earth science ; Environmental chemistry ; Chemistry ; Geology ; Physics ; Astrophysics ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Catalytic Processes in Materials Science
Authors: Ershad Mohammadi | Andreas Tittl | Kosmas L. Tsakmakidis | T. V. Raziman | Alberto G. Curto
Source: ACS Photonics (article, 2021-05-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dual Nanoresonators for Ultrasensitive Chiral Detection The discrimination of enantiomers is crucial in biochemistry. However, chiral sensing faces significant limitations due to inherently weak chiroptical signals. Nanophotonics is a promising solution to enhance sensitivity thanks to increased optical chirality maximized by strong electric and magnetic fields. Metallic and dielectric nanoparticles can separately provide electric and magnetic resonances. Here we propose their synergistic combination in hybrid metal-dielectric nanostructures to exploit their dual character for superchiral fields beyond the limits of single particles. For optimal optical chirality, in addition to maximization of the resonance strength, the resonances must spectrally coincide. Simultaneously, their electric and magnetic fields must be parallel and π/2 out of phase and spatially overlap. We demonstrate that the interplay between the strength of the resonances and these optimal conditions constrains the attainable optical chirality in resonant systems. Starting from a simple symmetric nanodimer, we derive closed-form expressions elucidating its fundamental limits of optical chirality. Building on the trade-offs of different classes of dimers, we then suggest an asymmetric dual dimer based on realistic materials. These dual nanoresonators provide strong and decoupled electric and magnetic resonances together with optimal conditions for chiral fields. Finally, we introduce more complex dual building blocks for a metasurface with a record 300-fold enhancement of local optical chirality in nanoscale gaps, enabling circular dichroism enhancement by a factor of 20. By combining analytical insight and practical designs, our results put forward hybrid resonators to increase chiral sensitivity, particularly for small molecular quantities. Chirality (physics) ; Metamaterial ; Nanophotonics ; Dielectric ; Physics ; Resonance (particle physics) ; Electric field ; Circular dichroism ; Materials science ; Topology (electrical circuits) ; Nanotechnology ; Optoelectronics ; Quantum mechanics ; Symmetry breaking ; Chemistry ; Chiral symmetry breaking Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Orbital Angular Momentum in Optics
Authors: T. Boominathan | Akella Sivaramakrishna
Source: Topics in Current Chemistry (review, 2021-04-08)
Field: Materials Science — Biomaterials

Recent Advances in the Synthesis, Properties, and Applications of Modified Chitosan Derivatives: Challenges and Opportunities Chitosan ; Biopolymer ; Chemistry ; Chitin ; Adsorption ; Sorption ; Nanocomposite ; Drug delivery ; Biocompatible material ; Nanotechnology ; Polymer ; Chemical engineering ; Organic chemistry ; Materials science Nanocomposite Films for Food Packaging ; Adsorption and biosorption for pollutant removal ; Advanced Drug Delivery Systems
Authors: Luke W. Giles | Charl F. J. Faul | Rico F. Tabor
Source: Materials Advances (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Azobenzene isomerization in condensed matter: lessons for the design of efficient light-responsive soft-matter systems This work reviews photoswitching of azobenzenes in soft and self-assembled materials, providing an outlook for smart soft matter design. Soft matter ; Azobenzene ; Soft materials ; Isomerization ; Work (physics) ; Living matter ; Smart material ; Nanotechnology ; Materials science ; Chemistry ; Physics ; Computer science ; Composite material ; Thermodynamics ; Organic chemistry ; Artificial intelligence ; Catalysis Photochromic and Fluorescence Chemistry ; Supramolecular Self-Assembly in Materials ; Porphyrin and Phthalocyanine Chemistry
Authors: Haoyue Guo | Qian Wang | Annika Stuke | Alexander Urban | Nongnuch Artrith
Source: Frontiers in Energy Research (article, 2021-06-04)
Field: Materials Science — Materials Chemistry

Accelerated Atomistic Modeling of Solid-State Battery Materials With Machine Learning Materials for solid-state batteries often exhibit complex chemical compositions, defects, and disorder, making both experimental characterization and direct modeling with first principles methods challenging. Machine learning (ML) has proven versatile for accelerating or circumventing first-principles calculations, thereby facilitating the modeling of materials properties that are otherwise hard to access. ML potentials trained on accurate first principles data enable computationally efficient linear-scaling atomistic simulations with an accuracy close to the reference method. ML-based property-prediction and inverse design techniques are powerful for the computational search for new materials. Here, we give an overview of recent methodological advancements of ML techniques for atomic-scale modeling and materials design. We review applications to materials for solid-state batteries, including electrodes, solid electrolytes, coatings, and the complex interfaces involved. Multiscale modeling ; Battery (electricity) ; Characterization (materials science) ; Computer science ; Scaling ; Solid-state ; Linear scale ; Property (philosophy) ; Nanotechnology ; Materials science ; Computational science ; Engineering physics ; Engineering ; Chemistry ; Physics ; Computational chemistry ; Thermodynamics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; X-ray Diffraction in Crystallography
Authors: Jiaqi Ding | Nan Xu | Manh Tien Nguyen | Qi Qiao | Yao Shi | Yi He | Qing Shao
Source: Chinese Journal of Chemical Engineering (article, 2021-01-12)
Field: Materials Science — Materials Chemistry

Machine learning for molecular thermodynamics Molecular machine ; Computer science ; Artificial intelligence ; Machine learning ; Field (mathematics) ; Nanotechnology ; Mathematics ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Daniel R. Cassar | Saulo Martiello Mastelini | Tiago Botari | Edesio Alcobaça | André C. P. L. F. de Carvalho | Edgar Dutra Zanotto
Source: Ceramics International (article, 2021-05-17)
Field: Materials Science — Ceramics and Composites

Predicting and interpreting oxide glass properties by machine learning using large datasets Machine learning ; Liquidus ; Hyperparameter ; Computer science ; Artificial intelligence ; Inverse ; Algorithm ; Thermal expansion ; Decision tree ; Materials science ; Mathematics Glass properties and applications ; Phase-change materials and chalcogenides ; Cultural Heritage Materials Analysis
Authors: Antonio A. Gentile | Brian Flynn | Sebastian Knauer | Nathan Wiebe | Stefano Paesani | Christopher Granade | John Rarity | Raffaele Santagati | Anthony Laing
Source: Nature Physics (article, 2021-04-29)
Field: Materials Science — Materials Chemistry

Learning models of quantum systems from experiments Hamiltonian (control theory) ; Exploit ; Quantum ; Protocol (science) ; Experimental data ; Computer science ; Physics ; Set (abstract data type) ; Quantum system ; Artificial intelligence ; Theoretical computer science ; Machine learning ; Statistical physics ; Quantum mechanics ; Mathematical optimization ; Mathematics Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Catalysis and Oxidation Reactions
Authors: İlkay Çınar | Murat Köklü
Source: Tarım Bilimleri Dergisi (article, 2021-05-27)
Field: Chemistry — Analytical Chemistry

IDENTIFICATION OF RICE VARIETIES USING MACHINE LEARNING ALGORITHMS Rice, which has the highest production and consumption rates worldwide, is among the main nutrients in terms of being economical and nutritious in our country as well. Rice goes through some stages of production from the field to the dinner tables. The cleaning phase is the separation of rice from unwanted materials. During the classification phase, solid ones and broken ones are separated and calibration operations are performed. Finally, in the process of extraction based on color features, the striped and stained ones other than the whiteness on the surface of the rice grain are separated. In this paper, five different varieties of rice belonging to the same trademark were selected to carry out classification operations using morphological, shape and color features. A total of 75,000 rice grain images, including 15,000 for each varieties, were obtained. The images were pre-processed using MATLAB software and prepared for feature extraction. Using a combination of 12 morphological, 4 shape features and 90 color features obtained from five different color spaces, a total of 106 features were extracted from the images. For classification, models were created with algorithms using machine learning techniques of k-nearest neighbor, decision tree, logistic regression, multilayer perceptron, random forest and support vector machines. With these models, performance measurement values were obtained for feature sets of 12, 16, 90 and 106. Among the models, the success of the algorithms with the highest average classification accuracy was achieved 97.99% with random forest for morphological features. 98.04% were obtained with random forest for morphological and shape features. It was achieved with logistic regression as 99.25% for color features. Finally, 99.91% was obtained with multilayer perceptron for morphological, shape and color features. When the results are examined, it is observed that with the addition of each new feature, the success of classification increases. Based on the performance measurement values obtained, it is possible to say that the study achieved success in classifying rice varieties. Artificial intelligence ; Random forest ; Support vector machine ; Computer science ; Perceptron ; Algorithm ; Feature extraction ; Identification (biology) ; Multilayer perceptron ; Feature (linguistics) ; Decision tree ; Machine learning ; Pattern recognition (psychology) ; Mathematics ; Artificial neural network ; Botany Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; GABA and Rice Research