jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wanlong Zhang | Julian Schneider | Maksym F. Prodanov | Valerii V. Vashchenko | Andrey L. Rogach | Xiaocong Yuan | Abhishek Kumar Srivastava
Source: Journal of Semiconductors (article, 2023-09-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Photo-induced flexible semiconductor CdSe/CdS quantum rods alignment Abstract The anisotropic absorption and emission from semiconductor CdSe/CdS quantum rods (QRs) provide extra benefits among other photoluminescence nanocrystals. Using photo-induced alignment technique, the QRs can be oriented in liquid crystal polymer matrix at a large scale. In this article, a 2D Dammann grating pattern, within “SKL” characters domains aligned QRs in composite film, was fabricated by multi-step photo exposure using several photo masks, and a continuous geometric lens profile pattern aligned QRs was realized by the single step polarization converting holographic irradiation method. Both polarized optical microscope and fluorescence microscope are employed to determine the liquid crystal director profiles and QRs anisotropic excitation properties. We have been able to orient the QRs in fine binary and continuous patterns that confirms the strong quantum rod aligning ability of the proposed method. Thus, the proposed approach paves a way for photo-induced flexible QRs alignments to provide a highly specific and difficult-to-replicate security application at a large scale. Materials science ; Optoelectronics ; Rod ; Grating ; Photoluminescence ; Anisotropy ; Optics ; Microscope ; Quantum ; Physics Liquid Crystal Research Advancements ; Quantum Dots Synthesis And Properties ; Photonic Crystals and Applications
Authors: Keith Bryce | Kun Yang | Tiankai Yao | Jie Lian
Source: Ceramics International (article, 2023-09-27)
Field: Materials Science — Materials Chemistry

Chemical durability of multicomponent lanthanide zirconate solid solutions Materials science ; Lanthanide ; Zirconium ; Zirconate ; Dissolution ; Durability ; Solid solution ; Elemental analysis ; Phosphoric acid ; Spark plasma sintering ; Inorganic chemistry ; Nuclear chemistry ; Analytical Chemistry (journal) ; Sintering ; Ion ; Physical chemistry ; Ceramic ; Metallurgy ; Chemistry ; Composite material ; Chromatography ; Organic chemistry Nuclear materials and radiation effects ; Ferroelectric and Piezoelectric Materials ; Inorganic Chemistry and Materials
Authors: Seyed Iman Saedi | Mehdi Rezaei
Source: Inventions (article, 2023-12-31)
Field: Chemistry — Analytical Chemistry

A Modified Xception Deep Learning Model for Automatic Sorting of Olives Based on Ripening Stages Olive fruits at different ripening stages give rise to various table olive products and oil qualities. Therefore, developing an efficient method for recognizing and sorting olive fruits based on their ripening stages can greatly facilitate post-harvest processing. This study introduces an automatic computer vision system that utilizes deep learning technology to classify the ‘Roghani’ Iranian olive cultivar into five ripening stages using color images. The developed model employs convolutional neural networks (CNN) and transfer learning based on the Xception architecture and ImageNet weights as the base network. The model was modified by adding some well-known CNN layers to the last layer. To minimize overfitting and enhance model generality, data augmentation techniques were employed. By considering different optimizers and two image sizes, four final candidate models were generated. These models were then compared in terms of loss and accuracy on the test dataset, classification performance (classification report and confusion matrix), and generality. All four candidates exhibited high accuracies ranging from 86.93% to 93.46% and comparable classification performance. In all models, at least one class was recognized with 100% accuracy. However, by taking into account the risk of overfitting in addition to the network stability, two models were discarded. Finally, a model with an image size of 224 × 224 and an SGD optimizer, which had a loss of 1.23 and an accuracy of 86.93%, was selected as the preferred option. The results of this study offer robust tools for automatic olive sorting systems, simplifying the differentiation of olives at various ripening levels for different post-harvest products. Overfitting ; Computer science ; Convolutional neural network ; Artificial intelligence ; Ripening ; Sorting ; Deep learning ; Confusion matrix ; Contextual image classification ; Pattern recognition (psychology) ; Transfer of learning ; Machine learning ; Image (mathematics) ; Artificial neural network ; Algorithm Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Elena Zamaraeva | Christopher M. Collins | Dmytro Antypov | Vladimir V. Gusev | Rahul Savani | Matthew S. Dyer | George R. Darling | Igor Potapov | Matthew J. Rosseinsky | Paul G. Spirakis
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Reinforcement learning in crystal structure prediction Reinforcement learning accelerates crystal structure prediction by learning a dynamic policy to maximise the reward for exploring new crystal structures. Reinforcement ; Reinforcement learning ; Artificial intelligence ; Computer science ; Machine learning ; Materials science ; Composite material Machine Learning in Materials Science ; Crystallization and Solubility Studies ; X-ray Diffraction in Crystallography
Authors: Qinchao Zhang | Liangde Liu | Jiangyun Zhang | Guoqing Zhang | You‐Peng Chen | Sizhi Liu | Hongwei Wu | Hongni Huang
Source: Journal of Energy Storage (article, 2023-01-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Experimental investigation of starting-up, energy-saving, and emission-reducing performances of hybrid supercapacitor energy storage systems for automobiles Supercapacitor ; Energy storage ; Process engineering ; Electrical engineering ; Automotive engineering ; Engineering ; Materials science ; Environmental science ; Electrochemistry ; Chemistry ; Power (physics) ; Electrode ; Physics Supercapacitor Materials and Fabrication ; Advanced battery technologies research ; Advanced Battery Technologies Research
Authors: Vladislava V. Matveevskaya | Dmitry I. Pavlov | Anastasia R. Kovrizhina | Taisiya S. Sukhikh | Evgeniy H. Sadykov | Павел В. Дороватовский | Vladimir A. Lazarenko | Аndrei I. Khlebnikov | Аndrei S. Potapov
Source: Pharmaceutics (article, 2023-06-23)
Field: Chemistry — Organic Chemistry

Experimental and Computational Investigation of the Oxime Bond Stereochemistry in c-Jun N-terminal Kinase 3 Inhibitors 11H-Indeno[1,2-b]quinoxalin-11-one Oxime and Tryptanthrin-6-oxime 11<i>H</i>-Indeno[1,2-<i>b</i>]quinoxalin-11-one oxime (<b>IQ-1</b>) and tryptanthrin-6-oxime are potent c-Jun N-terminal kinase 3 (JNK-3) inhibitors demonstrating neuroprotective, anti-inflammatory and anti-arthritic activity. However, the stereochemical configuration of the oxime carbon-nitrogen double bond (<i>E</i>- or <i>Z</i>-) in these compounds was so far unknown. In this contribution, we report the results of the determination of the double bond configuration in the solid state by single crystal X-ray diffraction and in solution by 1D and 2D NMR techniques and DFT calculations. It was found that both in the solid state and in solution, <b>IQ-1</b> adopts the <i>E</i>-configuration stabilized by intermolecular hydrogen bonds, in contrast to previously assumed <i>Z</i>-configuration that could be stabilized only by an intramolecular hydrogen bond. Oxime ; Chemistry ; Stereochemistry ; Terminal (telecommunication) ; Medicinal chemistry ; Computer science ; Computer network Synthesis and Biological Evaluation ; Quinazolinone synthesis and applications ; Cancer therapeutics and mechanisms
Authors: Ganpati Ramanath | Collin Rowe | Geetu Sharma | Venkat Venkataramani | Johan G. Alauzun | Ravishankar Sundararaman | Pawel Keblinski | Davide G. Sangiovanni | Per Eklund | Henrik Pedersen
Source: Applied Physics Letters (article, 2023-06-26)
Field: Materials Science — Materials Chemistry

Engineering inorganic interfaces using molecular nanolayers Advances in interface science over the last 20 years have demonstrated the use of molecular nanolayers (MNLs) at inorganic interfaces to access emergent phenomena and enhance a variety of interfacial properties. Here, we capture important aspects of how a MNL can induce multifold enhancements and tune multiple interfacial properties, including chemical stability, fracture energy, thermal and electrical transport, and electronic structure. Key challenges that need to be addressed for the maturation of this emerging field are described and discussed. MNL-induced interfacial engineering has opened up attractive opportunities for designing organic–inorganic hybrid nanomaterials with high interface fractions, where properties are determined predominantly by MNL-induced interfacial effects for applications. Materials science ; Nanotechnology ; Nanomaterials ; Thermal stability ; Characterization (materials science) ; Interface (matter) ; Chemical engineering ; Composite material ; Engineering Electronic and Structural Properties of Oxides ; Surface and Thin Film Phenomena ; Semiconductor materials and devices
Authors: Margarita Rekhtina | Aram L. Bugaev | Matthew T. Dunstan | Alessandro Dal Pozzo | Manouchehr Nadjafi | Camelia N. Borca | Thomas Huthwelker | Paula M. Abdala | Christoph R. Müller
Source: Chemistry of Materials (article, 2023-12-01)
Field: Materials Science — Materials Chemistry

Probing the Local Structure of Na in NaNO<sub>3</sub>-Promoted, MgO-Based CO<sub>2</sub> Sorbents via X-ray Absorption Spectroscopy This work provides insight into the local structure of Na in MgO-based CO<sub>2</sub> sorbents that are promoted with NaNO<sub>3</sub>. To this end, we use X-ray absorption spectroscopy (XAS) at the Na K-edge to interrogate the local structure of Na during the CO<sub>2</sub> capture (MgO + CO<sub>2</sub> ↔ MgCO<sub>3</sub>). The analysis of Na K-edge XAS data shows that the local environment of Na is altered upon MgO carbonation when compared to that of NaNO<sub>3</sub> in the as-prepared sorbent. We attribute the changes observed in the carbonated sorbent to an alteration in the local structure of Na at the NaNO<sub>3</sub>/MgCO<sub>3</sub> interfaces and/or in the vicinity of [Mg<sup>2+</sup>···CO<sub>3</sub><sup>2-</sup>] ionic pairs that are trapped in the cooled NaNO<sub>3</sub> melt. The changes observed are reversible, i.e., the local environment of NaNO<sub>3</sub> was restored after a regeneration treatment to decompose MgCO<sub>3</sub> to MgO. The ex situ Na K-edge XAS experiments were complemented by ex situ magic-angle spinning <sup>23</sup>Na nuclear magnetic resonance (MAS <sup>23</sup>Na NMR), Mg K-edge XAS and X-ray powder diffraction (XRD). These additional experiments support our interpretation of the Na K-edge XAS data. Furthermore, we develop in situ Na (and Mg) K-edge XAS experiments during the carbonation of the sorbent (NaNO<sub>3</sub> is molten under the conditions of the in situ experiments). These in situ Na K-edge XANES spectra of molten NaNO<sub>3</sub> open new opportunities to investigate the atomic scale structure of CO<sub>2</sub> sorbents modified with Na-based molten salts by using XAS. X-ray absorption spectroscopy ; XANES ; Carbonation ; Sorbent ; K-edge ; Materials science ; Absorption spectroscopy ; Analytical Chemistry (journal) ; Magic angle spinning ; X-ray absorption fine structure ; Extended X-ray absorption fine structure ; Absorption (acoustics) ; Ionic bonding ; Spectroscopy ; Crystallography ; Chemistry ; Ion ; Physical chemistry ; Nuclear magnetic resonance spectroscopy ; Adsorption ; Stereochemistry X-ray Diffraction in Crystallography ; High-pressure geophysics and materials ; Zeolite Catalysis and Synthesis
Authors: Marwa F. Abed | Ayad A.H. Faisal
Source: Adsorption Science & Technology (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Green Synthesis of Calcium/Iron-Layered Double Hydroxides-Sodium Alginate Nanoadsorbent as Reactive Barrier for Antibiotic Amoxicillin Removal from Groundwater This work uses a new nanoadsorbent after chemically synthesis from chicken eggshell wastes for removing amoxicillin (AMX) from aqueous solution. This removal was examined as a time function, initial concentration of AMX, pH, agitation speed, and adsorbent dosage. The study achieved the optimum time for equilibration in (90) min, at [Formula: see text] with an adsorbent dosage of 1.2 g. We applied many kinetic models to the sorption kinetic data where the pseudo-second-order model ([Formula: see text]) was used to interpret the gained data at a rate constant K 2 of (0.0077) g/(mg. min) at 200 rpm. Moreover, the adsorption calculated amount reached the experimentally required value and isotherm data best fitted the Langmuir model with [Formula: see text] (≥0.9486) than the Freundlich model. The intraparticle diffusion model revealed a diffusion dependent process. The different functional sets on the calcium/iron-surface as a layered double hydroxide (Ca/Fe)-LDH were important in sorpting the selected antibiotic. Forming (Ca/Fe)-LDH nanoparticles in the manufactured beads interacted with polluted water confirming that the nanoparticles own the prospective for acting as a latent sorbent to remove contaminants from aquatic media. Chemistry ; Adsorption ; Freundlich equation ; Sorption ; Aqueous solution ; Nuclear chemistry ; Langmuir ; Sodium hydroxide ; Hydroxide ; Inorganic chemistry ; Organic chemistry Layered Double Hydroxides Synthesis and Applications ; Advanced Photocatalysis Techniques ; Covalent Organic Framework Applications
Authors: Amit Kumar Choudhary | Tvrtko Grubesa | Andreas Jansche | Timo Bernthaler | D. Goll | Gerhard Schneider
Source: Acta Materialia (article, 2023-11-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep learning and correlative microscopy for quantification of grain orientation in sintered FeNdB-type permanent magnets by domain pattern analysis Based on a data-driven approach, a computer-assisted workflow for the quantitative analysis of optical Kerr microscopy images of sintered FeNdB-type permanent magnets was developed. By analyzing the domain patterns visible in the Kerr image with data-driven approaches such as traditional machine learning and advanced deep learning, we can quantify grain orientation and size with a better trade-off between accuracy and higher throughput than electron backscatter diffraction (EBSD). The key distinction between traditional machine learning and advanced deep learning lies in feature extraction. Traditional methods require manual, user-dependent feature extraction from input data, while advanced deep learning achieves this automatically. The predictions from the trained models were compared to the measurements from EBSD for performance evaluation. The proposed data-driven model is trained on the dataset created from the correlative microscopy technique, which requires the images of grains extracted from the Kerr microscopy and corresponding EBSD grain orientation data (Euler angles). The fine-tuned deep learning model shows better generalization ability than the traditional machine learning models trained on the manually extracted features and resulted in a mean absolute error of less than 5° for grain orientation of the anisotropic magnet samples when evaluated against the measured EBSD values. The developed approach has reduced the measurement effort for grain orientation by 5 times and have sufficient accuracy when compared to the EBSD. Further, the application of the proposed approach to determine the quality of the alignment or texture in anisotropic sintered magnets and its relationship with the magnetic remanence based on reliable statistical grain orientation data has been discussed. This approach could emerge as a tool for rapidly analyzing large-scale samples to discover and quantify heterogeneities in grain size and grain orientation. Electron backscatter diffraction ; Materials science ; Artificial intelligence ; Orientation (vector space) ; Texture (cosmology) ; Anisotropy ; Magnet ; Remanence ; Machine learning ; Computer science ; Pattern recognition (psychology) ; Optics ; Geometry ; Mechanical engineering ; Magnetization ; Microstructure ; Composite material ; Physics ; Magnetic field ; Image (mathematics) ; Mathematics Magnetic Properties and Applications ; Magnetic properties of thin films ; Advanced Electron Microscopy Techniques and Applications
Authors: Dustin Eby | Mikolaj Jakowski | Valeria Lauter | Mathieu Doucet | Panchapakesan Ganesh | Miguel Fuentes‐Cabrera | Rajeev Kumar
Source: Nanoscale (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Extraction of interaction parameters from specular neutron reflectivity in thin films of diblock copolymers: an “inverse problem” Diblock copolymers have been shown to undergo microphase separation due to an interplay of repulsive interactions between dissimilar monomers, which leads to the stretching of chains and entropic loss due to the stretching. In thin films, additional effects due to confinement and monomer-surface interactions make microphase separation much more complicated than in that in bulks (<i>i.e.</i>, without substrates). Previously, physics-based models have been used to interpret and extract various interaction parameters from the specular neutron reflectivities of annealed thin films containing diblock copolymers (J. P. Mahalik, J. W. Dugger, S. W. Sides, B. G. Sumpter, V. Lauter and R. Kumar, Interpreting neutron reflectivity profiles of diblock copolymer nanocomposite thin films using hybrid particle-field simulations, <i>Macromolecules</i>, 2018, <b>51</b>(8), 3116; J. P. Mahalik, W. Li, A. T. Savici, S. Hahn, H. Lauter, H. Ambaye, B. G. Sumpter, V. Lauter and R. Kumar, Dispersity-driven stabilization of coexisting morphologies in asymmetric diblock copolymer thin films, <i>Macromolecules</i>, 2021, <b>54</b>(1), 450). However, extracting Flory-Huggins <i>χ</i> parameters characterizing monomer-monomer, monomer-substrate, and monomer-air interactions has been labor-intensive and prone to errors, requiring the use of alternative methods for practical purposes. In this work, we have developed such an alternative method by employing a multi-layer perceptron, an autoencoder, and a variational autoencoder. These neural networks are used to extract interaction parameters not only from neutron scattering length density profiles constructed using self-consistent field theory-based simulations, but also from a noisy <i>ad hoc</i> model constructed previously. In particular, the variational autoencoder is shown to be the most promising tool when it comes to the reconstruction and extraction of parameters from an <i>ad hoc</i> neutron scattering length density profile of a thin film containing a symmetric di-block copolymer (poly(deuterated styrene-<i>b-n</i>-butyl methacrylate)). This work paves the way for automated analysis of specular neutron reflectivities from thin films of copolymers using machine learning tools. Specular reflection ; Reflectivity ; Materials science ; Neutron scattering ; Neutron reflectometry ; Neutron ; Inverse ; Artificial neural network ; Optics ; Extraction (chemistry) ; Small-angle neutron scattering ; Copolymer ; Inverse problem ; Scattering ; X-ray reflectivity ; Physics ; Computer science ; Chromatography ; Nuclear physics ; Chemistry ; Artificial intelligence ; Mathematical analysis ; Mathematics ; Geometry ; Polymer ; Composite material Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Electron and X-Ray Spectroscopy Techniques
Authors: Marco Gambini | Federica Guarnaccia | Michele Manno | Michela Vellini
Source: Journal of Energy Storage (article, 2023-11-22)
Field: Materials Science — Materials Chemistry

Ragone plots of material-based hydrogen storage systems This paper presents an analytical assessment of the energy–power relationship for different material-based hydrogen storage systems, namely Metal Hydrides (MHs) and Liquid Organic Hydrogen Carriers (LOHCs). Storage systems are subjected to continuous flow discharge processes through suitable control systems to meet constant specific power demands of end users. By means of reasonable assumptions, analytical expressions of the time-dependent degree of hydrogenation (representing the state of charge) are obtained to find the amount of hydrogen discharged (delivered energy) as a function of flow rate (required power). The results are first presented in the form of dimensionless Ragone plots to highlight the dependence of the amount of discharged hydrogen on the required mass flow rate. Numerical examples are presented for a couple of illustrative systems. Moreover, these analytical expressions are shown to produce very similar results to those obtained with the solution of a dynamic model, including, beyond the kinetic equation, mass and energy conservation applied to the reactor. The results show a significant impact of power demand on the released hydrogen for most systems, similar to that of capacitors, due to the dependence of the rate of reaction on the degree of hydrogenation: as a consequence, the amount of energy that can be delivered to an end user decreases substantially with an increase in the required power, resulting in a poor utilisation factor. Based on these results, MHs exhibit almost first-order kinetics and can sustain efficient discharge with theoretical specific power up to 2 kW/kg (rate of chemical energy delivered per unit mass of active substance), corresponding to a discharge duration of the order of 0.25 h; some LOHCs are limited by second-order kinetics and the specific power should be lower than 1 kW/kg, with discharge durations that must be above 2 hour, to ensure effective utilisation of stored hydrogen. Hydrogen storage ; Hydrogen ; Energy storage ; Dimensionless quantity ; Mass flow rate ; Thermodynamics ; Power (physics) ; Degree (music) ; Process engineering ; Electric power system ; Chemistry ; Mechanics ; Materials science ; Physics ; Engineering Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Advanced Battery Materials and Technologies
Authors: Hiroto Tachikawa | Yoshiki Izumi | Tetsuji Iyama | Shigeaki Abe | Ikuya Watanabe
Source: Nanomaterials (article, 2023-07-11)
Field: Materials Science — Materials Chemistry

Aluminum-Doping Effects on the Electronic States of Graphene Nanoflake: Diffusion and Hydrogen Storage Mechanism Graphene nanoflakes are widely utilized as high-performance molecular devices due to their chemical stability and light weight. In the present study, the interaction of aluminum species with graphene nanoflake (denoted as GR-Al) has been investigated using the density functional theory (DFT) method to elucidate the doping effects of Al metal on the electronic states of GR. The mechanisms of the diffusion of Al on GR surface and the hydrogen storage of GR-Al were also investigated in detail. The neutral, mono-, di-, and trivalent Al ions (expressed as Al, Al<sup>+</sup>, Al<sup>2+</sup>, and Al<sup>3+</sup>, respectively) were examined as the Al species. The DFT calculations showed that the charge transfer interaction between Al and GR plays an important role in the binding of Al species to GR. The diffusion path of Al on GR surface was determined: the barrier heights of Al diffusion were calculated to be 2.1-2.8 kcal mol<sup>-1</sup>, which are lower than Li<sup>+</sup> on GR (7.2 kcal/mol). The possibility of using GR-Al for hydrogen storage was also discussed on the basis of the theoretical results. Materials science ; Hydrogen storage ; Doping ; Graphene ; Mechanism (biology) ; Diffusion ; Hydrogen ; Nanotechnology ; Aluminium ; Chemical engineering ; Chemical physics ; Metallurgy ; Chemistry ; Optoelectronics ; Thermodynamics ; Physics ; Engineering Graphene research and applications ; Chemical and Physical Properties of Materials ; Molecular Junctions and Nanostructures