jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yan Wang | Zeyu Wu | Wenming Yu | Zhengqi Liu
Source: Journal of Physics D Applied Physics (review, 2024-08-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent progresses and applications on chiroptical metamaterials: a review Abstract Chiroptical metamaterials have attracted considerable attention owing to their exciting opportunities for fundamental research and practical applications over the past 20 years. Through practical designs, the chiroptical response of chiral metamaterials can be several orders of magnitude higher than that of natural chiral materials. Chiroptical metamaterials therefore represent a special type of artificial structures for unique chiroptical activities. In this review, we present a comprehensive overview of the progresses in the development of chiroptical metamaterials. Chiroptical metamaterial progress enables applications, including asymmetric transmission, polarization conversion, chiral absorber, chiral imaging, chiral sensor and chiral emission. We also review fabrication techniques and design of chiroptical metamaterials based on deep learning. In the conclusion, we present possible further research directions in this field. Metamaterial ; Design elements and principles ; Nanotechnology ; Chirality (physics) ; Materials science ; Physics ; Systems engineering ; Optoelectronics ; Engineering ; Chiral symmetry ; Quantum mechanics Metamaterials and Metasurfaces Applications
Authors: Ashish Kumar Gupta | Eva Zarkadoula | Maxim Ziatdinov | Sergei V. Kalinin | Vikas Reddy Paduri | Jordan A. Hachtel | Yanwen Zhang | C. Trautmann | William J. Weber | Ritesh Sachan
Source: Nanoscale (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Nanoscale core–shell structure and recrystallization of swift heavy ion tracks in SrTiO<sub>3</sub> It is widely accepted that the interaction of swift heavy ions with many complex oxides is predominantly governed by the electronic energy loss that gives rise to nanoscale amorphous ion tracks along the penetration direction. The question of how electronic excitation and electron-phonon coupling affect the atomic system through defect production, recrystallization, and strain effects has not yet been fully clarified. To advance the knowledge of the atomic structure of ion tracks, we irradiated single crystalline SrTiO<sub>3</sub> with 629 MeV Xe ions and performed comprehensive electron microscopy investigations complemented by molecular dynamics simulations. This study shows discontinuous ion-track formation along the ion penetration path, comprising an amorphous core and a surrounding few monolayer thick shell of strained/defective crystalline SrTiO<sub>3</sub>. Using machine-learning-aided analysis of atomic-scale images, we demonstrate the presence of 4-8% strain in the disordered region interfacing with the amorphous core in the initially formed ion tracks. Under constant exposure of the electron beam during imaging, the amorphous part of the ion tracks readily recrystallizes radially inwards from the crystalline-amorphous interface under the constant electron-beam irradiation during the imaging. Cation strain in the amorphous region is observed to be significantly recovered, while the oxygen sublattice remains strained even under the electron irradiation due to the present oxygen vacancies. The molecular dynamics simulations support this observation and suggest that local transient heating and annealing facilitate recrystallization process of the amorphous phase and drive Sr and Ti sublattices to rearrange. In contrast, the annealing of O atoms is difficult, thus leaving a remnant of oxygen vacancies and strain even after recrystallization. This work provides insights for creating and transforming novel interfaces and nanostructures for future functional applications. Swift heavy ion ; Nanoscopic scale ; Recrystallization (geology) ; Materials science ; Ion ; Amorphous solid ; Chemical physics ; Ion track ; Swift ; Penetration (warfare) ; Nanotechnology ; Atomic physics ; Fluence ; Physics ; Crystallography ; Chemistry Electronic and Structural Properties of Oxides ; Ion-surface interactions and analysis ; Nuclear materials and radiation effects
Authors: İbrahim Dağcı | Kübra Solak | Nazlı Öncer | Şeyda Yıldız Arslan | Yağmur Ünver | Mehmet Yılmaz | Ahmet Mavi
Source: ACS Applied Materials & Interfaces (article, 2024-12-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning-Assisted SERS Reveals the Biochemical Signature of Enhanced Protein Secretion from Surface-Modified Magnetic Nanoparticles This study introduces a novel investigation of the interaction between <i>Komagataella phaffii</i> cells and iron oxide-based magnetic nanoparticles (Fe<sub>3</sub>O<sub>4</sub> MNPs) via protein secretion and machine learning (ML)-assisted surface-enhanced Raman scattering (SERS). For the first time, we produced Fe<sub>3</sub>O<sub>4</sub>, Fe<sub>3</sub>O<sub>4</sub>@PEG, Fe<sub>3</sub>O<sub>4</sub>@PEI<sub>10kDa</sub>, and Fe<sub>3</sub>O<sub>4</sub>@PEI<sub>25kDa</sub> MNPs by a one-pot coprecipitation reaction. The addition of polymers to the reaction conditions significantly affected the shape, surface charge, size, and size distribution of the MNPs. The surface modification of MNPs is effectively accomplished using polyethylenimine (PEI), and the ζ-potential values of the MNPs exceed +25 mV under the NH<sub>4</sub>OH control. The homogeneity of MNPs synthesized with NH<sub>4</sub>OH is more pronounced according to transmission electron microscopy (TEM) pictures. All MNPs exhibited excellent immobilization efficiency (>92%) when we used 250 ppm Fe-containing MNP solutions. Smaller MNPs uniformly encapsulated the surface of <i>K. phaffii</i> cells, whereas larger MNPs exhibited irregular accumulation. <i>K. phaffii</i> cells exhibited excellent viability in all MNP solutions at up to 1000 ppm of Fe concentrations. Finally, the highest recombinant azurin protein secretion rate was obtained in Fe<sub>3</sub>O<sub>4</sub>@PEI<sub>10kDa</sub> MNP-immobilized cells (about 1.3 times). The ML-assisted SERS analysis revealed that MNP interactions with <i>K. phaffii</i> cells were mediated by proteins such as mannoproteins and membrane transporter proteins as well as N-acetylglucosamine (i.e., chitin). These findings revealed the effect of the size and surface properties of MNPs on the immobilization of <i>K. phaffii</i> cells and the enormous potential of magnetic immobilization for protein secretion. Materials science ; Polyethylenimine ; Surface modification ; Magnetic nanoparticles ; Nanoparticle ; Chemical engineering ; Surface charge ; Nanotechnology ; Biochemistry ; Chemistry ; Transfection Gold and Silver Nanoparticles Synthesis and Applications ; Nanoparticle-Based Drug Delivery ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Apoorv Kushwaha | Pooja Chahal | T. J. Dhilip Kumar
Source: The Journal of Chemical Physics (article, 2024-08-09)
Field: Chemistry — Spectroscopy

Rotational dynamics of CNCN by <i>p-</i>H2 and <i>o-</i>H2 collision at interstellar temperatures The rotational dynamics of isocyanogen (CNCN) is studied for its collision with para (p-) and ortho (o-) hydrogen (H2) in the temperature range of 1-100 K. These temperatures correspond to the cold dense molecular clouds in the interstellar medium where molecular hydrogen is the primary collider. An ab initio 4D potential energy surface (PES) is constructed keeping the two molecules under rigid rotor approximation. The PES is generated using the CCSD(T)-F12b/AVTZ level of theory. The 4D PES is further fitted into a neural network (NN) model, which can augment the surface and account for missing data points within spectroscopic accuracy. This NN-fitted PES is then expanded over a bispherical harmonics function to get radial terms, which are expressed into analytic functions. Thereafter, the cross sections (σ) are computed for rotational transitions of CNCN (j → j') using the close-coupling and centrifugal sudden methods for both p-H2 (jc = 0) and o-H2 (jc = 1) collision till 194 cm-1. In addition, p-H2 (jc = 0, 2) cross sections are also computed using the centrifugal sudden approximation method. The collisional rates are achieved by taking the Boltzmann distribution of σ over the translational energy of H2 till 100 K. Finally, the CNCN-H2 rates are compared to CNCN-He and NCCN-H2 collisional rates. Comparing even and odd transitions for the CNCN-H2 rates show a propensity toward higher rates for even transitions especially for o-H2 collisions considering low-order transitions. Atomic physics ; Physics ; Potential energy surface ; Ab initio ; Collision ; Rigid rotor ; Boltzmann constant ; Rotational transition ; Interstellar medium ; Hydrogen ; Rotational energy ; Rotor (electric) ; Angular momentum ; Astrophysics ; Classical mechanics ; Thermodynamics ; Quantum mechanics Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Advanced Chemical Physics Studies
Authors: Changhyo Sun | Panithan Sriboriboon | Junghun Han | Sang-Jin Ko | Seung-Yong Lee | Yooun Heo | Jae-Hyeok Shim | Sejung Yang | Jung‐Gu Kim | Yunseok Kim
Source: Corrosion Science (article, 2024-09-03)
Field: Materials Science — Metals and Alloys

Prediction of quantitative in-situ local corrosion via deep learning Corrosion ; In situ ; Materials science ; Environmental science ; Artificial intelligence ; Metallurgy ; Computer science ; Physics ; Meteorology Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques
Authors: Abhishek Chandra | Bram Daniels | Mitrofan Curti | Koen Tiels | E.A. Lomonova
Source: IEEE Transactions on Magnetics (article, 2024-11-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetic Hysteresis Modeling With Neural Operators Hysteresis modeling is crucial to comprehend the behavior of magnetic devices, facilitating optimal designs. Hitherto, deep learning-based methods employed to model hysteresis face challenges in generalizing to novel input magnetic fields. This article addresses the generalization challenge by proposing neural operators for modeling constitutive laws that exhibit magnetic hysteresis by learning a mapping between magnetic fields. In particular, three neural operators—deep operator network (DeepONet), Fourier neural operator (FNO), and wavelet neural operator (WNO)—are employed to predict novel first-order reversal curves and minor loops, where novel means that they are not used to train the model. In addition, a rate-independent FNO is proposed to predict material responses at sampling rates different from those used during training to incorporate the rate-independent characteristics of magnetic hysteresis. The presented numerical experiments demonstrate that neural operators efficiently model magnetic hysteresis, outperforming the traditional neural recurrent methods on various metrics and generalizing to novel magnetic fields. The findings emphasize the advantages of using neural operators for modeling hysteresis under varying magnetic conditions, underscoring their importance in characterizing magnetic material-based devices. The codes related to this article are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/chandratue/magnetic_hysteresis_neural_operator</uri>. Magnetic hysteresis ; Hysteresis ; Computer science ; Magnetic domain ; Materials science ; Condensed matter physics ; Nuclear magnetic resonance ; Magnetization ; Magnetic field ; Physics Magnetic Properties and Applications ; Neural Networks and Applications
Authors: Taizhong Yao | Lanping Chen | Nan Hu | Lei Xu | Sichao Sun
Source: AIP Advances (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Discovering ABO3-type perovskite with different dielectric constants via intelligent optimization algorithm Nowadays, dielectric materials are playing an increasingly important role in various fields. A high dielectric constant (D) can store more charge per unit volume, improving performance, reducing device size, lowering D limit cross communication, and enabling better packaging of devices. Differentiating high D and low D has been recognized as a significant concern in electronics. However, calculating the dielectric constant from first principles is notoriously difficult. Therefore, it is essential to find important descriptors for predicting the dielectric constant (D) of different dielectric materials. In this work, a novel intelligence optimization approach was proposed based on data-driven methods to predict the dielectric constant (D) of ABO3-type perovskites. By applying the machine learning (ML) algorithm, key features strongly correlated with D were identified. To reduce feature dimension, Random Forest Regression-Gradient Boosting Regressor (RFR-GBR) feature screening, sure independence screening, and the sparsifying operator approach were employed to compress the feature set for creating valid descriptors. Furthermore, the Shapley additive explanation technique was used to reveal the scaling relation between the dielectric constant and the identified descriptors for predicting the D of ABO3-type perovskites. In addition, a hybrid artificial rabbits optimization algorithm and random forest regression were developed for predicting D, achieving remarkable predictive performance with an R2 score of 0.95, MAE of 0.23, and RMSE of 0.108 using five-fold cross-validation. Ultimately, from a pool of 300 candidate materials, we screened and identified two potential dielectric perovskites with different D values. The proposed framework will facilitate D prediction technology for the discovery of dielectric perovskite materials with favorable performance. Dielectric ; Perovskite (structure) ; Materials science ; Permittivity ; Computer science ; Algorithm ; Condensed matter physics ; Physics ; Chemistry ; Optoelectronics ; Organic chemistry Ferroelectric and Piezoelectric Materials ; Microwave Dielectric Ceramics Synthesis ; Multiferroics and related materials
Authors: Qin Wang | Pan Dai | Ao Yang | Weifeng Shen | Jun Zhang
Source: Separation and Purification Technology (article, 2024-12-16)
Field: Chemistry — Spectroscopy

Deep learning-driven green solvent design and process intensification towards isopropyl alcohol-water azeotrope system Azeotrope ; Isopropyl alcohol ; Isopropyl ; Process (computing) ; Solvent ; Alcohol ; Chemistry ; Process engineering ; Environmental science ; Organic chemistry ; Waste management ; Engineering ; Computer science Analytical Chemistry and Chromatography ; Process Optimization and Integration ; Computational Drug Discovery Methods
Authors: Lina Zhou | Leijinyu Zhou | Hongbo Wu | Tingting Jing | Tianhao Li | Jinsheng Li | Lijuan Kong | Limei Chen
Source: Agronomy (article, 2024-03-22)
Field: Chemistry — Analytical Chemistry

Estimation of Cadmium Content in Lactuca sativa L. Leaves Using Visible–Near-Infrared Spectroscopy Technology In order to monitor cadmium contamination in lettuce quickly, non-invasively, and accurately, and to understand the growth status of lettuce under cadmium pollution, lettuce was used as the test material to detect and analyze the visible–near-infrared reflectance spectra and leaf cadmium content under different concentrations of cadmium stress. A model for estimating lettuce leaf cadmium content was established. For model establishment, firstly, the original spectra were preprocessed using smoothing (Savitzky–Golay, SG), SG combined with multiplicative scatter correction (MSC), SG combined with standard normal variable transformation (SNV), SG combined with mean normalization (MN), SG combined with the first derivative (FD), SG combined with the second derivative (SD), SG combined with the baseline offset (B), and SG combined with de-trending (D). Then, the principal component analysis (PCA) was applied to perform dimensionality reduction on the data. Finally, the reduced dataset was divided into training and testing sets in a 2:1 ratio, and separate models for estimating the lettuce leaf cadmium content were built using partial least squares regression (PLSR), the backpropagation neural network (BP-NN), and support vector regression (SVR) in combination. The results showed that the accumulated cadmium content in lettuce leaves increased with an increase in the soil cadmium concentration. In the visible light range, the spectral reflectance of lettuce leaves increased with an increase in the cadmium concentration. In the near-infrared range, the spectral reflectance of the lettuce leaves under 10 mg/kg and 20 mg/kg cadmium stress was lower than that of the control group. The PLSR models established using the SG + MSC and SG + SNV preprocessing methods exhibited the strongest estimation capability for lettuce leaf cadmium content, with Rp2 and RMSEp values of 0.92 and 1.53 mg/kg, respectively, for the testing dataset. This study demonstrated that visible–near-infrared spectroscopy has great potential in monitoring cadmium contamination in lettuce. Lactuca ; Near-infrared spectroscopy ; Cadmium ; Infrared ; Spectroscopy ; Visible spectrum ; Near infrared reflectance spectroscopy ; Remote sensing ; Chemistry ; Environmental science ; Analytical Chemistry (journal) ; Materials science ; Horticulture ; Environmental chemistry ; Optics ; Biology ; Optoelectronics ; Physics ; Geography ; Metallurgy Spectroscopy and Chemometric Analyses ; Medicinal Plants and Neuroprotection ; Heavy Metals in Plants
Authors: Ebenezer O. Olaniyi | Christopher Kucha | Priyanka Dahiya | Allison Niu
Source: Infrared Physics & Technology (article, 2024-09-30)
Field: Chemistry — Analytical Chemistry

Precision variety identification of shelled and in-shell pecans using hyperspectral imaging with machine learning Hyperspectral imaging ; Identification (biology) ; Shell (structure) ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Materials science ; Biology ; Composite material Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Cultural Heritage Materials Analysis
Authors: Lili Qian | Xu Zhang | Xianguang Ma | Peng Xue | Xingying Tang | Xiang Li | Shuang Wang
Source: Energies (review, 2024-10-22)
Field: Chemistry — Analytical Chemistry

A Review on Machine Learning-Aided Hydrothermal Liquefaction Based on Bibliometric Analysis Hydrothermal liquefaction (HTL) is an effective biomass thermochemical conversion technology that can convert organic waste into energy products. However, the HTL process is influenced by various complex factors such as operating conditions, feedstock properties, and reaction pathways. Machine learning (ML) methods can utilize existing HTL data to develop accurate models for predicting product yields and properties, which can be used to optimize HTL operation conditions. This paper presents a bibliometric review on ML applications in HTL from 2020 to 2024. CiteSpace, VOSviewer, and Bibexcel were used to analyze seven key bibliometric attributes: annual publication output, author co-authorship networks, country co-authorship networks, co-citation of references, co-citation of journals, collaborating institutions, and keyword co-occurrence networks, as well as time zone maps and timelines, to identify the development of ML in HTL research. Through the detailed analysis of co-occurring keywords, this study aims to identify frontiers, research gaps, and development trends in the field of ML-aided HTL. Liquefaction ; Hydrothermal circulation ; Hydrothermal liquefaction ; Computer science ; Geology ; Engineering ; Geotechnical engineering ; Chemical engineering ; Waste management ; Biofuel Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis
Authors: Dalia Morcillo | Alexander Winckelmann | Daniel A. Frick | Lars Henrik Jacobsen | T. Seger | Stefan Florek | Silke Richter | Jochen Vogl | Sebastian Recknagel | Ulrich Panne | Carlos Abad
Source: Spectrochimica Acta Part B Atomic Spectroscopy (article, 2024-08-03)
Field: Chemistry — Analytical Chemistry

Lithium isotope ratio analysis of geological samples using atomic absorption spectrometry with improved spectral resolution This study introduces an improved spectrometric method with enhanced precision to determine isotope ratios in geological samples without chromatographic separation. Firstly, the improvement is achieved by increasing the spectral resolution of the spectrometer applied in well-known high-resolution continuum source atomic absorption spectrometry (HR-CS-AAS). The resulting resolving power and linear dispersion of the upgraded setup, which is denoted in the following as HR + CS-AAS, is well adapted to the line widths of the Li isotope components we investigated. Secondly, our proposed method combines optical absorption spectrometry with machine learning data analysis using an extreme gradient boosting algorithm (XGBoost). This method was applied to analyze certified geological reference materials with δLSVEC(7Li/6Li) (hereafter δ7Li) values ranging from −0.5 ‰ to 4.5 ‰. With a pixel related optical resolving power of λ/∆λ ≈ 780,000, we obtain precisions in δ7Li measurements from 1.0 ‰ to 2.5 ‰. The method is validated by comparing the results with multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS), confirming its metrological compatibility. This work presents a fast, robust, and reliable method for δ7Li measurement in geological samples. Analytical Chemistry (journal) ; Mass spectrometry ; Certified reference materials ; Chemistry ; Isotope ; Analyte ; Atomic absorption spectroscopy ; Isotopes of lithium ; Resolution (logic) ; Inductively coupled plasma mass spectrometry ; Detection limit ; Chromatography ; Physics ; Ion ; Computer science Analytical chemistry methods development ; Geochemistry and Geologic Mapping ; Radioactive element chemistry and processing
Authors: Wei Pei | Zi Wang | Weizhi Xia | Zhijing Huang | Pengju Wang | Yongfeng Liu | Si Zhou | Yusong Tu | Jijun Zhao
Source: The Journal of Physical Chemistry Letters (article, 2024-01-25)
Field: Materials Science — Materials Chemistry

Rational Design of Full-Color Fluorescent C<sub>3</sub>N Quantum Dots Carbon-based quantum dots (QDs) exhibit unique photoluminescence due to size-dependent quantum confinement, giving rise to fascinating full-color emission properties. Accurate emission calculations using time-dependent density functional theory are a time-costing and expensive process. Herein, we employed an artificial neural network (ANN) combined with statistical learning to establish the relationship between geometrical/electronic structures of ground states and emission wavelength for C<sub>3</sub>N QDs. The emission energy of these QDs can be doubly modulated by size and edge effects, which are governed by the number of C<sub>4</sub>N<sub>2</sub> rings and the CH group, respectively. Moreover, these two structural characteristics also determine the phonon vibration mode of C<sub>3</sub>N QDs to harmonize the emission intensity and lifetime of hot electrons in the electron-hole recombination process, as indicated by nonadiabatic molecular dynamics simulation. These computational results provide a general approach to atomically precise design the full-color fluorescent carbon-based QDs with targeted functions and high performance. Quantum dot ; Photoluminescence ; Fluorescence ; Density functional theory ; Phonon ; Electron ; Materials science ; Optoelectronics ; Molecular physics ; Physics ; Optics ; Quantum mechanics Carbon and Quantum Dots Applications ; Luminescence and Fluorescent Materials ; ZnO doping and properties