jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ranjan Kumar Barik | Abhishek K. Singh
Source: Chemistry of Materials (article, 2021-08-03)
Field: Materials Science — Materials Chemistry

Accelerated Discovery of the Valley-Polarized Quantum Anomalous Hall Effect in MXenes The topological phase preserved by spontaneous magnetization in non-centrosymmetric two-dimensional systems leads to valley-polarized quantum anomalous Hall (VP-QAH) insulators. Because of these strict criteria, the VP-QAH effect has been observed in a very few materials. We applied a high-throughput first-principles approach, wherein the magnetic and topological properties of inversion symmetry-broken 2D MXenes are screened, systematically. We find 14 MXenes having the out-of-plane ferromagnetic ground state in the presence of spin–orbit coupling. The nontrivial Berry curvature and chiral edge states confirm the VP-QAH effect in these MXenes. Furthermore, using basic elemental features, we have developed a machine-learning-based magnetic nodal line semimetal classification model and a regression model, which accurately predict both the nodal positions. Our study provides a robust platform to incorporate valley and topological physics, which would accelerate the search for promising VP-QAH materials. MXenes ; Berry connection and curvature ; Topological insulator ; Topology (electrical circuits) ; Condensed matter physics ; Physics ; Point reflection ; Ferromagnetism ; Quantum ; Geometric phase ; Quantum mechanics ; Materials science ; Nanotechnology ; Mathematics Graphene research and applications ; 2D Materials and Applications ; Topological Materials and Phenomena
Authors: Pierre-Paul De Breuck | Matthew L Evans | Gian-Marco Rignanese
Source: Journal of Physics Condensed Matter (article, 2021-07-08)
Field: Materials Science — Materials Chemistry

Robust model benchmarking and bias-imbalance in data-driven materials science: a case study on MODNet As the number of novel data-driven approaches to material science continues to grow, it is crucial to perform consistent quality, reliability and applicability assessments of model performance. In this paper, we benchmark the Materials Optimal Descriptor Network (MODNet) method and architecture against the recently released MatBench v0.1, a curated test suite of materials datasets. MODNet is shown to outperform current leaders on 6 of the 13 tasks, while closely matching the current leaders on a further 2 tasks; MODNet performs particularly well when the number of samples is below 10 000. Attention is paid to two topics of concern when benchmarking models. First, we encourage the reporting of a more diverse set of metrics as it leads to a more comprehensive and holistic comparison of model performance. Second, an equally important task is the uncertainty assessment of a model towards a target domain. Significant variations in validation errors can be observed, depending on the imbalance and bias in the training set (i.e., similarity between training and application space). By using an ensemble MODNet model, confidence intervals can be built and the uncertainty on individual predictions can be quantified. Imbalance and bias issues are often overlooked, and yet are important for successful real-world applications of machine learning in materials science and condensed matter. Benchmarking ; Benchmark (surveying) ; Suite ; Task (project management) ; Reliability (semiconductor) ; Set (abstract data type) ; Matching (statistics) ; Similarity (geometry) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Rhett L. Martineau | Alexandra V. Bayles | Chia‐Suei Hung | Kristofer G. Reyes | Matthew E. Helgeson | Maneesh K. Gupta
Source: Advanced Biology (article, 2021-11-23)
Field: Materials Science — Biomaterials

Engineering Gelation Kinetics in Living Silk Hydrogels by Differential Dynamic Microscopy Microrheology and Machine Learning Microbes embedded in hydrogels comprise one form of living material. Discovering formulations that balance potentially competing for mechanical and biological properties in living hydrogels-for example, gel time of the hydrogel formulation and viability of the embedded organisms-can be challenging. In this study, a pipeline is developed to automate the characterization of the gel time of hydrogel formulations. Using this pipeline, living materials comprised of enzymatically crosslinked silk and embedded E. coli-formulated from within a 4D parameter space-are engineered to gel within a pre-selected timeframe. Gelation time is estimated using a novel adaptation of microrheology analysis using differential dynamic microscopy (DDM). In order to expedite the discovery of gelation regime boundaries, Bayesian machine learning models are deployed with optimal decision-making under uncertainty. The rate of learning is observed to vary between artificial intelligence (AI)-assisted planning and human planning, with the fastest rate occurring during AI-assisted planning following a round of human planning. For a subset of formulations gelling within a targeted timeframe of 5-15 min, fluorophore production within the embedded cells is substantially similar across treatments, evidencing that gel time can be tuned independent of other material properties-at least over a finite range-while maintaining biological activity. Self-healing hydrogels ; Microrheology ; Biological system ; Artificial intelligence ; Computer science ; Characterization (materials science) ; Materials science ; SILK ; Rheology ; Nanotechnology ; Composite material ; Polymer chemistry ; Biology Silk-based biomaterials and applications ; Cellular Mechanics and Interactions ; Force Microscopy Techniques and Applications
Authors: Hemanta Chutia | Charu Lata Mahanta
Source: Journal of Food Processing and Preservation (article, 2021-02-15)
Field: Chemistry — Inorganic Chemistry

Influence of cold plasma voltage and time on quality attributes of tender coconut water ( <i>Cocos</i> <i>nucifera</i> L.) and degradation kinetics of its blended beverage In this study, the influence of cold plasma treatment on quality parameters of tender coconut water (TCW) and its beverage and the kinetics of degradation were investigated. In TCW, the kinetics of ascorbic acid, antioxidant activity, and transmittance followed the zero-order model whereas TA, total fatty acid (TFA) content, and microbial load followed first-order reaction. TCW stored well for 2 days at 6°C. Process parameters for nonthermal cold plasma treatment were optimized by response surface methodology (RSM) as well as hybrid artificial neural network (ANN)/genetic algorithm (GA) for maximum DPPH scavenging activity and minimum peroxidase activity. Treated TCW stored at 6°C in a glass bottle was microbiologically acceptable till 8 days, whereas treated TCW that was blended with orange juice and ascorbic acid showed a shelf life of 35 and 18 days on storage at 6°C and 27°C, respectively. Degradation kinetics of the quality parameters of cold plasma treated blended beverage were similar to untreated TCW. Novelty impact statement The application of cold plasma to food materials is a novel nonthermal way of extending the shelf life of food. Optimized conditions of cold plasma treatment were predicted with the minimum error of prediction to obtain longer shelf life and microbiologically safe tender coconut water (TCW)-based beverage with minimal degradation of bioactive compounds. Also, the kinetics parameters related to quality that were studied will help in designing the process parameters of a robust cold plasma unit. The effect of hurdles like orange juice and ascorbic acid will help to understand their synergistic effect along with cold plasma on the quality parameters of TCW for the improved storage period. Degradation kinetics of the treated beverage at accelerated temperature will help to determine the shelf life at various storage temperatures. Ascorbic acid ; Chemistry ; Shelf life ; Food science ; Orange juice ; Kinetics ; Pulp and paper industry Coconut Research and Applications ; Advanced Chemical Sensor Technologies ; Hydrogen's biological and therapeutic effects
Authors: О. Е. Narivskyi | С. Б. Беликов | С. А. Субботин | Tеtiana Pulina
Source: Materials Science (article, 2021-09-01)
Field: Materials Science — Materials Chemistry

Influence of Chloride-Containing Media on the Pitting Resistance of AISI 321 Steel Materials science ; Austenite ; Metallurgy ; Manganese ; Chloride ; Chromium ; Structural material ; Microstructure Material Properties and Failure Mechanisms ; Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels
Authors: Eric Ashalley | Cuiping Ma | Yisong Zhu | Hongxing Xu | Peng Yu | Zhiming Wang
Source: Journal of Electronic Science and Technology (article, 2021-04-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent progress in chiral absorptive metamaterials Chiral metamaterial absorbers (CMMAs), a particular class of chiral metamaterials that refuse the transmission of incident radiation and exhibit different optical responses upon interactions with left and right circularly polarized (LCP, RCP) light, have gained research traction in recent years. CMMAs demonstrate numerous exotic and specialized applications owing to their achievable compatibility with various physical, chemical, and biomolecular systems. Aside from their well-evolved fabrication modalities for a broad range of frequencies, CMMAs exhibit strong chiroptical effects, making them central to various detection, imaging, and energy harvesting applications. Consequently, within the past decade, studies encompassing the design, optimization, and fabrication, as well as demonstrating the diverse applications of CMMAs have emerged. In this review, the theory, design, and fabrication of CMMAs are discussed, highlighting their top-down fabrication techniques as well as recent algorithmic and machine-learning (ML)-based approaches to their design and optimization. Some of their broad-spectrum applications are also discussed, spanning their roles in enantioselective photodetection, chiral imaging, generation of hot electrons, selective temperature sensing, and active chiral plasmonics. Metamaterial ; Fabrication ; Photodetection ; Nanotechnology ; Design elements and principles ; Photonics ; Materials science ; Computer science ; Optoelectronics ; Photodetector Metamaterials and Metasurfaces Applications ; Electromagnetic Scattering and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: Huazhou Chen | Hanli Qiao | Quanxi Feng | Lili Xu | Qinyong Lin | Ken Cai
Source: Frontiers in Bioengineering and Biotechnology (article, 2021-01-12)
Field: Chemistry — Analytical Chemistry

Rapid Detection of Pomelo Fruit Quality Using Near-Infrared Hyperspectral Imaging Combined With Chemometric Methods Pomelo is an important agricultural product in southern China. Near-infrared hyperspectral imaging (NIRHI) technology is applied to the rapid detection of pomelo fruit quality. Advanced chemometric methods have been investigated for the optimization of the NIRHI spectral calibration model. The partial least squares (PLS) method is improved for non-linear regression by combining it with the kernel Gaussian radial basis function (RBF). In this study, the core parameters of the PLS latent variables and the RBF kernel width were designed for grid search selection to observe the minimum prediction error and a relatively high correlation coefficient. A deep learning architecture was proposed for the parametric scaling optimization of the RBF-PLS modeling process for NIRHI data in the spectral dimension. The RBF-PLS models were established for the quantitative prediction of the sugar (SU), vitamin C (VC), and organic acid (OA) contents in pomelo samples. Experimental results showed that the proposed RBF-PLS method performed well in the parameter deep search progress for the prediction of the target contents. The predictive errors for model training were 1.076% for SU, 41.381 mg/kg for VC, and 1.136 g/kg for OA, which were under 15% of their reference chemical measurements. The corresponding model testing results were acceptably good. Therefore, the NIRHI technology combined with the study of chemometric methods is applicable for the rapid quantitative detection of pomelo fruit quality, and the proposed algorithmic framework may be promoted for the detection of other agricultural products. Partial least squares regression ; Hyperspectral imaging ; Artificial intelligence ; Correlation coefficient ; Radial basis function ; Mathematics ; Kernel (algebra) ; Pattern recognition (psychology) ; Gaussian function ; Computer science ; Gaussian ; Artificial neural network ; Statistics ; Chemistry Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Liangrui Pan | Pronthep Pipitsunthonsan | Chalongrat Daengngam | Sittiporn Channumsin | Suwat Sreesawet | Mitchai Chongcheawchamnan
Source: IEEE Sensors Journal (article, 2021-02-18)
Field: Chemistry — Analytical Chemistry

Identification of Complex Mixtures for Raman Spectroscopy Using a Novel Scheme Based on a New Multi-Label Deep Neural Network With a noisy environment caused by fluorescence and additive white noise as well as complicated spectra fingerprints, the identification of complex mixture materials remains a significant challenge in Raman spectroscopy application. This paper proposes a new scheme based on a constant wavelet transform (CWT) and a deep network for classifying complex mixture. The scheme first transforms the noisy Raman spectra to a two-dimensional scale map using CWT. Then, the multi-label deep neural network model (MLDNN) is simulated for material classification. The proposed model accelerates the feature extraction and expands the feature graph using the global averaging pooling layer. The Sigmoid function is implemented in the last layer of the model. The MLDNN model was trained, validated, and tested with data collected from the samples prepared from palm oil substances. During the training and validating process, data augmentation is applied to overcome the imbalance of data and enrich Raman spectra's diversity. From the test results, MLDNN successfully surpassed VGG16, VGG19, ResNet50, DenseNet121, InceptionResNetV2, MobileNet50 in average precision, F1 macro averaging and F1 micro averaging, which were 0.9900, 0.9874 and 0.9874, respectively. In Hamming loss, one error, coverage, ranking loss, the MLDNN model reached 0.014, 0.020, 1.738 and 0.000, respectively. The values of other models in Hamming loss, one error, coverage, ranking loss are higher than those of MLDNN. In the comparison of AUC value, MLDNN outperforms other models on each label. The average detection time of the MLDNN is 5.3123 s, which is much faster than those of VGG16, VGG19, ResNet50, DenseNet121, InceptionResNetV2 and MobileNetV2, which are 7.1245, 7.5046, 8.6300, 12.3294, 16.1131 and 6.6451, respectively. Artificial intelligence ; Pattern recognition (psychology) ; Artificial neural network ; Computer science ; Hamming code ; Raman spectroscopy ; Mathematics ; Algorithm ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Żaneta Światkowska-Warkocka
Source: Applied Sciences (article, 2021-02-24)
Field: Chemistry — Organic Chemistry

Bimetal CuFe Nanoparticles—Synthesis, Properties, and Applications Bimetal CuFe (copper-iron) nanoparticles, which are based on the earth-abundant and inexpensive metals, have generated a great deal of interest in recent years. The possible modification of the chemical and physical properties of these nanoparticles by changing their size, structure, and composition has contributed to the development of material science. At the same time, the strong tendency of these elements to oxidize under atmospheric conditions makes the synthesis of pure bimetallic CuFe nanoparticles still a great challenge. This review reports on different synthetic approaches to bimetallic CuFe nanoparticles and bimetallic CuFe nanoparticles supported on various materials (active carbide, carbide nanotubes, silica, graphite, cellulose, mesoporous carbide), their structure, physical, and chemical properties, as well as their utility as catalysts, including electrocatalysis and photocatalysis. Bimetal ; Bimetallic strip ; Materials science ; Carbide ; Nanoparticle ; Nanotechnology ; Chemical engineering ; Metallurgy ; Metal Nanomaterials for catalytic reactions ; Catalytic Processes in Materials Science ; Copper-based nanomaterials and applications
Authors: Arsalan Hashemi | Christopher Linderälv | Arkady V. Krasheninnikov | Tapio Ala-Nissilä | Paul Erhart | Hannu‐Pekka Komsa
Source: Physical review. B./Physical review. B (article, 2021-03-29)
Field: Materials Science — Materials Chemistry

Photoluminescence line shapes for color centers in silicon carbide from density functional theory calculations Silicon carbide with optically and magnetically active point defects offers unique opportunities for quantum technology applications. Since interaction with these defects commonly happens through optical excitation and deexcitation, a complete understanding of their light-matter interaction in general and optical signatures in particular is crucial. Here, we employ quantum mechanical density functional theory calculations to investigate the photoluminescence line shapes of selected, experimentally observed color centers (including single vacancies, double vacancies, and vacancy-impurity pairs) in 4H-SiC. The analysis of zero-phonon lines as well as Huang-Rhys and Debye-Waller factors is accompanied by a detailed study of the underlying lattice vibrations. We show that the defect line shapes are governed by strong coupling to bulk phonons at lower energies and localized vibrational modes at higher energies. Generally, good agreement with the available experimental data is obtained, and thus we expect our theoretical work to be beneficial for the identification of defect signatures in the photoluminescence spectra and thereby advance the research in quantum photonics and quantum information processing. Photoluminescence ; Silicon carbide ; Density functional theory ; Materials science ; Line (geometry) ; Time-dependent density functional theory ; Silicon ; Optoelectronics ; Computational chemistry ; Chemistry ; Geometry ; Composite material ; Mathematics Silicon Nanostructures and Photoluminescence ; Thin-Film Transistor Technologies ; ZnO doping and properties
Authors: Z. Karimi | Reza Khalili | Mohammad Ali Zazouli
Source: Water Science & Technology (article, 2021-06-10)
Field: Materials Science — Polymers and Plastics

Surface modified polythiophene/Al2O3 and polyaniline/Al2O3 nanocomposites using poly(vinyl alcohol) for the removal of heavy metal ions from water: kinetics, thermodynamic and isotherm studies In this study, polythiophene/Al<sub>2</sub>O<sub>3</sub> (PTh/Al<sub>2</sub>O<sub>3</sub>) and polyaniline/Al<sub>2</sub>O<sub>3</sub> (PAn/Al<sub>2</sub>O<sub>3</sub>) nanocomposites in the presence of poly(vinyl alcohol) (PVA) as the surfactant were synthesized via in situ chemical oxidative polymerization method in aqueous medium. The synthesized nanocomposites were characterized by scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy and X-ray diffraction (XRD). Results indicated that the Al<sub>2</sub>O<sub>3</sub> and poly(vinyl alcohol) influenced the properties of synthesized nanocomposites. The aim of this research was to investigate the sorption characteristics of polythiophene and polyaniline nanocomposites for the removal of heavy metal cations including Pb(II), Zn(II) and Cd(II) from aqueous solution. The factors that affected the adsorption equilibrium as well as the removal efficiency of the nanoadsorbents, i.e., contact time, metal ion concentration, pH and adsorption conditions were investigated in detail. From the kinetic results, it was concluded that the pseudo-second-order kinetic model was found to be the best at describing the adsorption process for Pb(II), Zn(II) and Cd(II) on PTh-PVA/Al<sub>2</sub>O<sub>3</sub> and PAn-PVA/Al<sub>2</sub>O<sub>3</sub>. In addition, thermodynamic analysis suggests the endothermic and spontaneous nature of the present adsorption process with increased entropy on PTh-PVA/Al<sub>2</sub>O<sub>3</sub> and PAn-PVA/Al<sub>2</sub>O<sub>3</sub>. The results suggest polythiophene, polyaniline and their nanocomposites have great potential to be used as efficient absorbent for the removal of heavy metal ions from water. Polyaniline ; Vinyl alcohol ; Polythiophene ; Nanocomposite ; Aqueous solution ; Fourier transform infrared spectroscopy ; Adsorption ; Materials science ; Metal ions in aqueous solution ; Chemical engineering ; Polymerization ; Nuclear chemistry ; Inorganic chemistry ; Polymer chemistry ; Metal ; Chemistry ; Conductive polymer ; Organic chemistry ; Polymer ; Nanotechnology ; Composite material ; Metallurgy Conducting polymers and applications ; Adsorption and biosorption for pollutant removal ; Extraction and Separation Processes
Authors: Aleksandar Bojkovic | Florence H. Vermeire | Maja Kuzmanović | Hang Dao Thi | Kevin M. Van Geem
Source: Energy & Fuels (article, 2021-12-21)
Field: Chemistry — Analytical Chemistry

Analytics Driving Kinetics: Advanced Mass Spectrometric Characterization of Petroleum Products The current state-of-the-art analysis techniques for petroleum fractions has progressed substantially during the past decade. This has helped to further improve the lumping procedures and modeling approaches of these complex systems. Recent advances in gas chromatography (GC), GC-field ionization mass spectrometry (GC-FIMS), and comprehensive gas chromatography (GC×GC) have made it possible to determine the compositions of fractions with up to 45 carbon atoms and in some cases up to C80. The combination of MS techniques with other selective detectors and reversed-phase column combinations has made it possible to quantify even traces of heteroatomic compounds in these complex hydrocarbon matrices. Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS), in some cases combined with GC×GC for the lighter part, has pushed the characterization of larger macromolecules in particular asphaltenes. Matrix-assisted laser desorption/ionization (MALDI) is also used widely for this purpose but has the disadvantage that quantification is not obvious. The development of more detailed characterization techniques has not remained unnoticed in the petrochemical society and more recently in the petrochemical kinetic modeling society. More detailed characterization of petrochemical fractions has made the implementation of detailed kinetic models for simulation and optimization possible including more and more molecular detail. Additionally, advances in photoionization mass spectrometry (PI-MS) have allowed the detection of reactive intermediates and direct kinetic measurements in time-resolved experiments. It can only be expected that this trend will continue and that the application field will move from now primarily petrochemistry (from catalytic cracking, over hydrotreating, and hydrocracking to pyrolysis, combustion, and steam cracking) to larger-scale chemical recycling and biomass conversion processes. Mass spectrometry ; Chemistry ; Petrochemical ; Fourier transform ion cyclotron resonance ; Asphaltene ; Characterization (materials science) ; Field desorption ; Gas chromatography ; Photoionization ; Analytical technique ; Analytical Chemistry (journal) ; Ionization ; Chromatography ; Ion ; Organic chemistry ; Nanotechnology ; Materials science Petroleum Processing and Analysis ; Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography