jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Micheal Arockiaraj | J. Celin Fiona | S. Kavitha | Arul Jeya Shalini | K. Balasubramanian
Source: Molecules (article, 2022-12-24)
Field: Materials Science — Materials Chemistry

Topological and Spectral Properties of Wavy Zigzag Nanoribbons Low-dimensional graphene-based nanomaterials are interesting due to their cutting-edge electronic and magnetic properties. Their large surface area, strong mechanical resistance, and electronic properties have enabled potential pharmaceutical and opto-electronic applications. Graphene nanoribbons (GNRs) are graphene strips of nanometer size possessing zigzag and armchair edge geometries with tunable widths. Despite the recent developments in the characterization, design and synthesis of GNRs, the study of electronic, magnetic and topological properties, GNRs continue to pose a challenge owing to their multidimensionality. In this study, we obtain the topological and electronic properties of a series of wave-like nanoribbons comprising nanographene units with zigzag-shaped edges. The edge partition techniques based on the convex components are employed to compute the mathematical formulae of molecular descriptors for the wave-like zigzag GNRs. We have also obtained the spectral and energetic properties including HOMO-LUMO gaps, bond delocalization energies, resonance energies, 13C NMR and ESR patterns for the GNRs. All of these computations reveal zero to very low HOMO-LUMO gaps that make these nanoribbons potential candidates for topological spintronics. Zigzag ; Delocalized electron ; Spintronics ; Graphene nanoribbons ; Graphene ; Materials science ; Topology (electrical circuits) ; Condensed matter physics ; Electronic structure ; Nanotechnology ; Physics ; Geometry ; Quantum mechanics ; Ferromagnetism ; Mathematics Graphene research and applications ; Graph theory and applications ; Synthesis and Properties of Aromatic Compounds
Authors: Mayan Si | Ankit Izardar | Claude Ederer
Source: Physical Review Research (article, 2022-08-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Effect of chemical disorder on the magnetic anisotropy in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>L</mml:mi><mml:msub><mml:mn>1</mml:mn><mml:mn>0</mml:mn></mml:msub></mml:mrow></mml:math> FeNi from first-principles calculations We use first-principles calculations to investigate how deviations from perfect chemical order affect the magnetocrystalline anisotropy energy (MAE) in $L{1}_{0}$ FeNi. We first analyze the local chemical environment of the Fe atoms in various partially ordered configurations, using the orbital magnetic moment anisotropy (OMA) as proxy for a local contribution to the MAE. We are able to identify a specific nearest neighbor configuration and use this ``favorable environment'' to successfully design various structures with MAE higher than the perfectly ordered system. However, a systematic analysis of the correlation between local environment and OMA using smooth overlap of atomic positions indicates only a partial correlation, which exists only if the deviation from full chemical order is not too large, whereas in general no such correlation can be identified even using up to third nearest neighbors. Guided by the observation that the identified favorable environment implies an Fe-rich composition, we investigate the effect of randomly inserting additional Fe into the nominal Ni planes of the perfectly ordered structure. We find that the MAE increases with Fe content, at least up to 62.5% Fe. Thus, our paper shows that the perfectly ordered case is not the one with highest MAE and that an increased MAE can be obtained for slightly Fe-rich compositions. Anisotropy ; Magnetocrystalline anisotropy ; Order (exchange) ; k-nearest neighbors algorithm ; Anisotropy energy ; Energy (signal processing) ; Magnetic moment ; Magnetic anisotropy ; Algorithm ; Condensed matter physics ; Physics ; Chemistry ; Mathematics ; Computer science ; Artificial intelligence ; Magnetization ; Quantum mechanics ; Magnetic field Magnetic and transport properties of perovskites and related materials ; Magnetic properties of thin films ; Magnetic Properties of Alloys
Authors: Taghrid S. Alomar | Najla AlMasoud | Yun Xu | Cássio Lima | Barış Akbalı | Simon Maher | Royston Goodacre
Source: Sensors (article, 2022-10-15)
Field: Chemistry — Analytical Chemistry

Simultaneous Multiplexed Quantification of Banned Sudan Dyes Using Surface Enhanced Raman Scattering and Chemometrics Azo compounds such as the Sudan dyes I-IV are frequently used illegally as colorants and added to a wide range of foods. These compounds have been linked to a number of food safety hazards. Several methods have been proposed to detect food contamination by azo compounds and most of these are laboratory based; however, the development of reliable and portable methods for the detection and quantification of food contaminated by these chemicals in low concentration is still needed due to their potentially carcinogenic properties. In this study, we investigated the ability of surface enhanced Raman scattering (SERS) combined with chemometrics to quantify Sudan I-IV dyes. SERS spectra were acquired using a portable Raman device and gold nanoparticles were employed as the SERS substrate. As these dyes are hydrophobic, they were first dissolved in water: acetonitrile (1:10, <i>v/v</i>) as single Sudan dyes (I-IV) at varying concentrations. SERS was performed at 785 nm and the spectra were analyzed by using partial least squares regression (PLS-R) with double cross-validations. The coefficient of determination (<i>Q</i><sup>2</sup>) were 0.9286, 0.9206, 0.8676 and 0.9705 for Sudan I to IV, respectively; the corresponding limits of detection (LOD) for these dyes were estimated to be 6.27 × 10<sup>-6</sup>, 5.35 × 10<sup>-5</sup>, 9.40 × 10<sup>-6</sup> and 1.84 × 10<sup>-6</sup> M. Next, quadruplex mixtures were made containing all four Sudan dyes. As the number of possible combinations needed to cover the full concentration range at 5% intervals would have meant collecting SERS spectra from 194,481 samples (21<sup>4</sup> combinations) we used a sustainable solution based on Latin hypercubic sampling and reduced the number of mixtures to be analyzed to just 90. After collecting SERS spectra from these mixture PLS-R models with bootstrapping validations were employed. The results were slightly worse in which the <i>Q</i><sup>2</sup> for Sudan I to IV were 0.8593, 0.7255, 0.5207 and 0.5940 when PLS1 models (i.e., one model for one dye) was employed and they changed to 0.8329, 0.7288, 0.5032 and 0.5459 when PLS2 models were employed (i.e., four dyes were modelled simultaneously). These results showed the potential of SERS to be used as a high-throughput, low-cost, and reliable methods for detecting and quantifying multiple Sudan dyes in low concentration from illegally adulterated samples. Chemometrics ; Detection limit ; Raman scattering ; Chemistry ; Raman spectroscopy ; Surface-enhanced Raman spectroscopy ; Analytical Chemistry (journal) ; Chromatography ; Optics Dye analysis and toxicity ; Identification and Quantification in Food ; Spectroscopy and Chemometric Analyses
Authors: Jinghua Wang | Lei Yan | Fan Wang | Shanshan Qi
Source: Journal of Sensors (article, 2022-04-22)
Field: Chemistry — Analytical Chemistry

SVM Classification Method of Waxy Corn Seeds with Different Vitality Levels Based on Hyperspectral Imaging The vitality of corn seeds is a significant indicator for assessing the quality and yield of crops. In recent years, numerous information technologies have been adopted to analyze the seed vitality and provide support for efficient equipment. However, there are still some shortcomings in these technologies, which decrease the accuracy of identifying the seed vitality for various practical applications. In this paper, a synthesized classification method for seed vitality was proposed based on multisensor hyperspectral imaging. Firstly, hyperspectral images in the range of 370-1042 nm were collected for waxy corn seeds, which were subjected to aging processing with four periods of time (0, 3, 6, and 9 d). Besides, some preprocessing techniques including standard normal variate, multiplicative scatter correction, Savitzky-Golay smoothing, and first-order and second-order derivatives were employed to suppress noise interference in raw spectra. In addition, principal component analysis (PCA), 2nd derivatization, and successive projection algorithm (SPA) were adopted to select feature wavelengths. Moreover, SVM classification models based on full spectra and feature wavelengths were established. The results showed that, based on feature wavelengths selected by SPA, the SVM model preprocessed by multiplicative scatter correction (MSC) had the optimal performance. The training accuracy and testing accuracy of this model were 100% and 97.9167%, respectively. RMSE was 0.018 and <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:msup> <a:mrow> <a:mi>R</a:mi> </a:mrow> <a:mrow> <a:mn>2</a:mn> </a:mrow> </a:msup> </a:math> was 0.875. Therefore, it can be demonstrated that the pattern recognition algorithm could achieve a high accuracy in classifying accelerated aging seeds. This algorithm provides a new method for machine learning (ML) in nondestructive detection of crops. Vitality ; Hyperspectral imaging ; Support vector machine ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Juha Tiihonen | Paul R. C. Kent | Jaron T. Krogel
Source: The Journal of Chemical Physics (article, 2022-01-07)
Field: Materials Science — Materials Chemistry

Surrogate Hessian accelerated structural optimization for stochastic electronic structure theories We present an efficient energy-based method for structural optimization with stochastic electronic structure theories, such as diffusion quantum Monte Carlo (DMC). This method is based on robust line-search energy minimization in reduced parameter space, exploiting approximate but accurate Hessian information from a surrogate theory, such as density functional theory. The surrogate theory is also used to characterize the potential energy surface, allowing for simple but reliable ways to maximize statistical efficiency while retaining controllable accuracy. We demonstrate the method by finding the minimum DMC energy structures of the selected flake-like aromatic molecules, such as benzene, coronene, and ovalene, represented by 2, 6, and 19 structural parameters, respectively. In each case, the energy minimum is found within two parallel line-search iterations. The method is near-optimal for a line-search technique and suitable for a broad range of applications. It is easily generalized to any electronic structure method where forces and stresses are still under active development and implementation, such as diffusion Monte Carlo, auxiliary-field Monte Carlo, and stochastic configuration interaction, as well as deterministic approaches such as the random-phase approximation. Accurate and efficient means of geometry optimization could shed light on a broad class of materials and molecules, showing high sensitivity of induced properties to structural variables. Hessian matrix ; Monte Carlo method ; Diffusion Monte Carlo ; Quantum Monte Carlo ; Energy minimization ; Statistical physics ; Mathematical optimization ; Stochastic optimization ; Density functional theory ; Computer science ; Mathematics ; Applied mathematics ; Physics ; Monte Carlo molecular modeling ; Markov chain Monte Carlo ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalytic Processes in Materials Science
Authors: Nikolina Novosel | David Rivas Góngora | Zvonko Jagličić | Emil Tafra | Mario Basletić | A. Hamzić | Teodoro Klaser | Željko Skoko | Krešimir Salamon | Ivna Kavre Piltaver | M. Petravić | Bojana Korin-Hamzić | S. Tomić | B. P. Gorshunov | Tao Zhang | Tomislav Ivek | Matija Čulo
Source: Crystals (article, 2022-05-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Grain-Size-Induced Collapse of Variable Range Hopping and Promotion of Ferromagnetism in Manganite La0.5Ca0.5MnO3 Among transition metal oxides, manganites have attracted significant attention because of colossal magnetoresistance (CMR)—a magnetic field-induced metal–insulator transition close to the Curie temperature. CMR is closely related to the ferromagnetic (FM) metallic phase which strongly competes with the antiferromagnetic (AFM) charge ordered (CO) phase, where conducting electrons localize and create a long range order giving rise to insulator-like behavior. One of the major open questions in manganites is the exact origin of this insulating behavior. Here we report a dc resistivity and magnetization study on manganite La1−xCaxMnO3 ceramic samples with different grain size, at the very boundary between CO/AFM insulating and FM metallic phases x=0.5. Clear signatures of variable range hopping (VRH) are discerned in resistivity, implying the disorder-induced (Anderson) localization of conducting electrons. A significant increase of disorder associated with the reduction in grain size, however, pushes the system in the opposite direction from the Anderson localization scenario, resulting in a drastic decrease of resistivity, collapse of the VRH, suppression of the CO/AFM phase and growth of an FM contribution. These contradictory results are interpreted within the standard core-shell model and recent theories of Anderson localization of interacting particles. Manganite ; Condensed matter physics ; Colossal magnetoresistance ; Ferromagnetism ; Variable-range hopping ; Antiferromagnetism ; Magnetoresistance ; Charge ordering ; Electrical resistivity and conductivity ; Materials science ; Metal–insulator transition ; Magnetization ; Grain boundary ; Curie temperature ; Magnetic field ; Metal ; Physics ; Charge (physics) ; Microstructure Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics ; Rare-earth and actinide compounds
Authors: Dilanga Abeyrathna | Md Ashaduzzaman | Milind Malshe | Kalimuthu Jawaharraj | Venkataramana Gadhamshetty | Parvathi Chundi | Mahadevan Subramaniam
Source: Frontiers in Microbiology (article, 2022-12-01)
Field: Materials Science — Materials Chemistry

An AI-based approach for detecting cells and microbial byproducts in low volume scanning electron microscope images of biofilms Microbially induced corrosion (MIC) of metal surfaces caused by biofilms has wide-ranging consequences. Analysis of biofilm images to understand the distribution of morphological components in images such as microbial cells, MIC byproducts, and metal surfaces non-occluded by cells can provide insights into assessing the performance of coatings and developing new strategies for corrosion prevention. We present an automated approach based on self-supervised deep learning methods to analyze Scanning Electron Microscope (SEM) images and detect cells and MIC byproducts. The proposed approach develops models that can successfully detect cells, MIC byproducts, and non-occluded surface areas in SEM images with a high degree of accuracy using a low volume of data while requiring minimal expert manual effort for annotating images. We develop deep learning network pipelines involving both contrastive (Barlow Twins) and non-contrastive (MoCoV2) self-learning methods and generate models to classify image patches containing three labels-cells, MIC byproducts, and non-occluded surface areas. Our experimental results based on a dataset containing seven grayscale SEM images show that both Barlow Twin and MoCoV2 models outperform the state-of-the-art supervised learning models achieving prediction accuracy increases of approximately 8 and 6%, respectively. The self-supervised pipelines achieved this superior performance by requiring experts to annotate only ~10% of the input data. We also conducted a qualitative assessment of the proposed approach using experts and validated the classification outputs generated by the self-supervised models. This is perhaps the first attempt toward the application of self-supervised learning to classify biofilm image components and our results show that self-supervised learning methods are highly effective for this task while minimizing the expert annotation effort. Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Volume (thermodynamics) ; Scanning electron microscope ; Deep learning ; Biofilm ; Supervised learning ; Computer vision ; Grayscale ; Visualization ; Biological system ; Image (mathematics) ; Machine learning ; Materials science ; Artificial neural network ; Geology ; Biology ; Bacteria ; Physics Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques ; Machine Learning in Materials Science
Authors: Devashish Gokhale | Ian Chen | Patrick S. Doyle
Source: Soft Matter (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

Coarse-grained molecular dynamics simulations of immobilized micelle systems and their interactions with hydrophobic molecules Micelles immobilized in polymer materials are of emerging interest in drug delivery, water treatment and other applications. Immobilization removes the need for membrane-based separation to eliminate micelles from the medium, enabling facile extraction and delivery in diverse industries. This work lays out a coarse-grained molecular dynamics simulations framework for the rapid identification of surfactants for use in immobilized micelle systems. Micelles are immobilized by constraining one end of the constituent surfactants in space, mimicking what would occur in a copolymer system. We demonstrate that constraints affect how the micelles interact with small hydrophobic molecules, making it important to account for their effects in various drug-micelle and pollutant-micelle simulations. Our results show that in several systems there is stronger interaction between hydrophobic small molecules and micelles in immobilized systems compared to unconstrained systems. These strengthened interactions can have important implications for the design of new micelle-based extraction and delivery processes. Micelle ; Molecular dynamics ; Drug delivery ; Molecule ; Chemistry ; Polymer ; Critical micelle concentration ; Copolymer ; Chemical engineering ; Chemical physics ; Materials science ; Organic chemistry ; Aqueous solution ; Computational chemistry Surfactants and Colloidal Systems ; Electrostatics and Colloid Interactions ; Spectroscopy and Quantum Chemical Studies
Authors: Wanhua Shen | Narayan Koirala | Debjani Mukherjee | Kenneth Lee | Min Zhao | Jianbing Li
Source: Frontiers in Environmental Science (article, 2022-04-29)
Field: Materials Science — Materials Chemistry

Tween 20 Stabilized Conventional Heavy Crude Oil-In-Water Emulsions Formed by Mechanical Homogenization This study investigated the preparation of stable conventional heavy crude oil-in-water (O/W) emulsions by mechanical homogenization with the addition of a non-ionic surfactant, Tween-20. A four-factor, five-level central composite design was carried out to investigate the effects of four independent variables, including mixing intensity (4,000–10,000 rpm), mixing duration (5–45 min), water salinity (0–40 g/L), and the concentration of emulsifier (0.1–2.1 wt%) on the emulsion stability. Emulsion stability was determined by quantification of creaming index, turbidity change rate, and average oil droplet size. The results demonstrated that the salinity of 30 g/L, mixing intensity of 8,500 rpm, mixing duration of 35 min, and emulsifier concentration of 1.6 wt% led to the formation of the most stable emulsion. Emulsion ; Creaming ; Homogenization (climate) ; Turbidity ; Pulmonary surfactant ; Salinity ; Chromatography ; Chemistry ; Mixing (physics) ; Crude oil ; Materials science ; Chemical engineering ; Organic chemistry Pickering emulsions and particle stabilization ; Enhanced Oil Recovery Techniques ; Surfactants and Colloidal Systems
Authors: Adam M. Gilmore | Qiang Sui | Bryant Blair | Bruce S. Pan
Source: OENO One (article, 2022-11-09)
Field: Chemistry — Analytical Chemistry

Accurate varietal classification and quantification of key quality compounds of grape extracts using the absorbance-transmittance fluorescence excitation emission matrix (A-TEEM) method and machine learning Rapid and accurate quantification of grape berry phenolics, anthocyanins and tannins and identification of grape varieties are both important for effective quality control of harvesting and initial processing for winemaking. Current reference technologies, including High-Performance Liquid Chromatography (HPLC), can be rate-limiting and too complex and expensive for effective field operations. In this paper, we analyse robotically prepared grape extracts from several key varieties (n = Calibration/n = Prediction samples), including Cabernet-Sauvignon (64/10), Grenache (16/4), Malbec (14/4), Merlot (56/10), Petite Sirah (52/10), Pinot noir (54/8), Syrah (20/2), Teroldego (14/2) and Zinfandel (62/12). Key phenolic and anthocyanin parameters measured by HPLC included Catechin, Epicatechin, Quercetin Glycosides, Malvidin 3-glucoside, Total Anthocyanins and Polymeric Tannins. Split samples diluted 50-fold in 50 % EtOH pH 2 were analysed in parallel using the A-TEEM method following Multi-block Data Fusion of the absorbance and unfolded EEM data. A-TEEM chemical data were calibrated (n = 390) using Extreme Gradient Boosting (XGB) Regression and evaluated based on the Root Mean Square Error of the Prediction (RMSEP), the Relative Error of Prediction (REP) and Coefficient of Variation (R2P) of the Prediction data (n = 62). The regression results yielded an average Relative Error of Prediction (REP) of 5.89 ± 2.47 % and an R2P of 0.941 ± 0.025. While we consider the REP values to be in the acceptable range at significantly &lt; 10 %, we acknowledge that both the grape extraction method repeatability and HPLC reference method sample repeatability (5-8 % RSD) likely constituted the major sources of variation compared to the A-TEEM instrumental sample repeatability (&lt; 2 % RSD). The varietal classification was analysed using Agglomerative Hierarchical Cluster Analysis (HCA) and XGB discrimination analysis of the multi-block data. The classification results yielded 100 % True Positive and True Negative responses for the Calibration and Prediction Data for all tested varieties. We conclude that the A-TEEM method requires a minimum of sample preparation and rapid acquisition times (&lt; 1 min) and can serve as an accurate secondary method for both grape varietal identification and phenolic quantification. Importantly, the software application of the regression and classification models can be effectively automated for operators. Repeatability ; Chemistry ; Chromatography ; Absorbance ; Anthocyanin ; Coefficient of determination ; Flavonols ; Mathematics ; Grape wine ; Partial least squares regression ; Wine ; Quercetin ; Analytical Chemistry (journal) ; Food science ; Statistics Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Horticultural and Viticultural Research
Authors: Tiankai Yao | Xiang Liu | Yachun Wang | Fei Teng | Daniel J. Murray | M. K. Meyer | Michael Benson | Luca Capriotti
Source: Journal of Nuclear Materials (article, 2022-06-08)
Field: Materials Science — Materials Chemistry

Transmission Electron Microscopy based Characterization of a U-20Pu-10Zr Fuel Irradiated in Experimental Breeder Reactor-II Nuclear engineering ; Burnup ; Irradiation ; Transmission electron microscopy ; Materials science ; Breeder (animal) ; Breeder reactor ; Cladding (metalworking) ; Hfr cell ; Analytical Chemistry (journal) ; Chemistry ; Nuclear physics ; Composite material ; Nanotechnology ; Physics ; Engineering Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Fusion materials and technologies