jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andriy E. Serebryannikov | Ekmel Özbay
Source: Scientific Reports (article, 2024-01-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Exploring localized ENZ resonances and their role in superscattering, wideband invisibility, and tunable scattering While the role and manifestations of the localized surface plasmon resonances (LSPRs) in anomalous scattering, like superscattering and invisibility, are quite well explored, the existence, appearance, and possible contribution of localized epsilon-near-zero (ENZ) resonances still invoke careful exploration. In this paper, that is done along with a comparison of the resonances of two types in the case of thin-wall cylinders made of lossy and loss-compensated dispersive materials. It is shown that the localized ENZ resonances exist and appear very close to the zero-permittivity regime, i.e., at near-zero but yet negative permittivity that is similar to the ENZ modes in thin planar films. Near- and far-field characteristics of the superscattering modes are investigated. The results indicate that the scattering regimes arising due to LSPRs and localized ENZ resonances are distinguishable in terms of the basic field features inside and around the scatterer and differ in their contribution to the resulting scattering mechanism, e.g., in terms of the occupied frequency and permittivity ranges as well as the sensitivity to the wall thickness variations. When the losses are either weak or tend to zero due to the doping with gain enabling impurities, the sharp peaks of the scattering cross-section that are yielded by the resonances can be said to be embedded into the otherwise wide invisibility range. In the case of lossy material, a wide and continuous invisibility range is shown to appear not only due to a small total volume of the scatterer in the nonresonant regime, but also because high-Q superscattering modes are suppressed by the losses. For numerical demonstration, indium antimonide, a natural lossy material, and a hypothetical, properly doped material with the same real part of the permittivity but lower or zero losses are considered. In the latter case, variations of permittivity with a control parameter can be adjusted in such a way that transitions from one superscattering mode to another can be achieved. In turn, transition from the strong-scattering to the invisibility regime is possible even for the original lossy material. The basic properties of the studied superscattering modes may be replicable in artificial structures comprising natural low-loss materials. Scattering ; Invisibility ; Condensed matter physics ; Permittivity ; Materials science ; Physics ; Planar ; Field (mathematics) ; Optics ; Computational physics ; Optoelectronics ; Dielectric Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Photonic Crystals and Applications
Authors: Pierre J. Walker | Andrés Riedemann | Zhen‐Gang Wang
Source: The Journal of Open Source Software (article, 2024-04-05)
Field: Chemistry — Spectroscopy

GCIdentifier.jl: A Julia package for identifyingmolecular fragments from SMILES Welcome to GCIdentifier! This module provides utilities needed to fragment a given molecular SMILES (or name) based on the groups provided in existing group-contribution methods (such as UNIFAC, Joback's method, etc.). Additional functionalities have been provided to automatically identify and propose new groups. R package ; Computer science ; Programming language Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Scientific Measurement and Uncertainty Evaluation
Authors: Jung‐Hyun Lee | Junhyuk Oh | Hyung‐gun Chi | Min-Seok Lee | Jehwan Hwang | Seung-Jin Jeong | Sang‐Woo Kang | Haeseong Jee | Hagyoul Bae | Jae‐Sang Hyun | Jun Oh Kim | Bongjoong Kim
Source: Advanced Materials Technologies (article, 2024-05-16)
Field: Materials Science — Surfaces, Coatings and Films

Deep Learning‐Assisted Design of Bilayer Nanowire Gratings for High‐Performance MWIR Polarizers Abstract Optical metamaterials have revolutionized imaging capabilities by manipulating light‐matter interactions at the nanoscale beyond the diffraction limit. Bilayer nanowire grating configurations exhibit significant potential as exceptional elements for high‐performance polarimetric imaging systems. However, conventional computational approaches for predicting electromagnetic responses are time‐consuming and labor‐intensive, and thereby, the practical implementation remains challenging through an iterative design, analysis, and fabrication process. Here, a deep learning‐based design process is presented utilizing an artificial neural network (ANN) trained on finite element method (FEM) simulations that enables the prediction of bilayer nanowire gratings‐based electromagnetic responses. The study validates predictions through nanoimprinted bilayer nanowire gratings, demonstrating the reliability of the ANN's predictions. Furthermore, the research identifies critical geometric parameters significantly influencing transverse magnetic (TM) and transverse electric (TE) transmission. The ANN model effectively tailors design for specific mid‐wavelength infrared (MWIR) wavelengths, which may provide a practical tool for rapidly designing and optimizing metamaterial for high‐performance polarizers. Polarizer ; Materials science ; Bilayer ; Nanowire ; Optoelectronics ; Optics ; Membrane ; Physics ; Chemistry Optical Coatings and Gratings ; Photonic and Optical Devices ; Plasmonic and Surface Plasmon Research
Authors: Pavel D. Terekhov | Shengyuan Chang | Md Tarek Rahman | Sadman Shafi | Hyun-Ju Ahn | Linghan Zhao | Xingjie Ni
Source: Nanophotonics (article, 2024-05-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Enhancing metasurface fabricability through minimum feature size enforcement The metasurfaces have shown great potential for miniaturizing conventional optics while offering extended flexibility. Recently, there has been considerable interest in using algorithms to generate meta-atom shapes for these metasurfaces, as they offer vast design freedom and not biased by the human intuition. However, these complex designs significantly increase the difficulty of fabrication. To address this, we introduce a design process that rigorously enforces the fabricability of both the material-filled (fill) and empty (void) regions in a metasurface design. This process takes into account specific constraints regarding the minimum feature size for each region. Additionally, it corrects any violations of these constraints across the entire device, ensuring only minimal impact on performance. Our method provides a practical way to create metasurface designs that are easy to fabricate, even with complex shapes, hence improving the overall production yield of these advanced meta-optical components. Computer science ; Intuition ; Metamaterial ; Engineering design process ; Feature (linguistics) ; Robustness (evolution) ; Flexibility (engineering) ; Computer engineering ; Fabrication ; Distributed computing ; Materials science ; Mechanical engineering ; Engineering ; Optoelectronics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Photonic Crystals and Applications
Authors: Seyedeh Akram Mousavi | Leila Nateghi | Elahesadat Hosseini
Source: Flavour and Fragrance Journal (article, 2024-10-28)
Field: Chemistry — Organic Chemistry

Exploring the Physicochemical, Antioxidant and Sensory Properties of the Flavoured Virgin Olive Oil With Chavir Extract During Storage ABSTRACT This study aimed to study the impact of Ferulago angulata ( Schlecht ) Boiss (known as an antioxidant known as Chavir) extract, obtained through ultrasound‐assisted extraction (UAE), on the physicochemical, antioxidant and sensory properties of virgin olive oil (VOO) during a 90‐day storage period at room temperature. For this purpose, different concentrations of Chavir extract (100, 200 and 300 ppm) were added to VOO. Key parameters, including total phenol content (TPC), extract composition by HPLC‐DAD and quality indicators like acidity value (AV), p‐anisidine value ( p ‐AV) and UV indices (parameters: K232, K270 and ΔK), were determined. AVs increased overall during the storage period, but adding Chavir extract at 300 ppm appeared to counteract the rise in free fatty acids, suggesting a potential role in reducing hydrolysis. p ‐AVs decreased in unflavoured and flavoured samples, indicating reduced oxidation in Chavir‐flavoured VOO. K232 values exhibited a decline in the presence of flavouring agents and their concentrations. In contrast, the values of K270 showed an increase over the storage period, which indicated the formation of aldehyde and ketones in the second oxidation step. All samples maintained ΔK values below permissible limits (≥ 0.01), signifying good stability. The sensory evaluation emphasised positive attributes such as bitterness, fruitiness and pungency, indicating the high quality of VOO. The addition of Chavir extract further enhanced all of these positive characteristics, particularly at the 300 ppm concentration. However, a gradual decline was observed in all these attributes over time. Notably, the Chavir extract played a crucial role in delaying the formation of secondary oxidation products, which contribute to negative attributes like rancidity in VOO. Moreover, adding Chavir extract increases oxidative stability, infuses the positive qualities in VOO and provides valuable insights into its potential applications for enhancing the overall quality of VOO during storage. This enhancement not only helps maintain high quality and nutritional value but also makes the product more appealing to consumers. Chemistry ; Food science ; Antioxidant ; Olive oil ; Sensory system ; Sensory analysis ; Organic chemistry Edible Oils Quality and Analysis ; Phytochemicals and Antioxidant Activities ; Essential Oils and Antimicrobial Activity
Authors: Florian Wätzold | Bartosz Popiela | Jonas Mayer
Source: Publications (article, 2024-12-14)
Field: Materials Science — Materials Chemistry

Methodology for AI-Based Search Strategy of Scientific Papers: Exemplary Search for Hybrid and Battery Electric Vehicles in the Semantic Scholar Database The rapid development of artificial intelligence (AI) has significantly enhanced productivity, particularly in repetitive tasks. In the scientific domain, literature review stands out as a key area where AI-based tools can be effectively applied. This study presents a methodology for developing a search strategy for systematic reviews using AI tools. The Semantic Scholar database served as the foundation for the search process. The methodology was tested by searching for scientific papers related to batteries and hydrogen vehicles with the aim of enabling an evaluation for their potential applications. An extensive list of vehicles and their operational environments based on international standards and literature reviews was defined and used as the main input for the exemplary search. The AI-supported search yielded approximately 60,000 results, which were subjected to an initial relevance assessment. For the relevant papers, a neighbourhood analysis based on citation and reference networks was conducted. The final selection of papers, covering the period from 2013 to 2023, included 713 papers assessed after the initial review. An extensive discussion of the results is provided, including their categorisation based on search terms, publication years, and cluster analysis of powertrains, as well as operational environments of the vehicles involved. This case study illustrates the effectiveness of the proposed methodology and serves as a starting point for future research. The results demonstrate the potential of AI-based tools to enhance productivity when searching for scientific papers. Computer science ; Scientific literature ; Relevance (law) ; Domain (mathematical analysis) ; Process (computing) ; Semantic search ; Data science ; Information retrieval ; Operations research ; Search engine ; Engineering Machine Learning in Materials Science ; Electric Vehicles and Infrastructure ; Fuel Cells and Related Materials
Authors: Éric Bertin | F. Ladieu
Source: Physical review. E (article, 2024-06-28)
Field: Materials Science — Materials Chemistry

Nonlinear dielectric response in glass formers: Restoring forces and avoided spin-glass criticality Experimental measurements of nonlinear dielectric response in glass formers like supercooled glycerol or propylene carbonate have been interpreted as providing evidence for a growing thermodynamic length scale when lowering temperature. A heuristic picture based on coherently flipping "superdipoles" with disordered internal structure has been argued to capture the essence of the experimentally reported behavior, pointing to the key role of effectively disordered interactions in structural glasses. We test these ideas by devising an explicit one-dimensional model of interacting spins incorporating both the spin-glass spirit of the superdipole argument and the necessary long-time decorrelation of structural disorder, encoded here in a slow dynamics of the coupling constants. The frequency-dependent third-order response of the model qualitatively reproduces the typical humped shape reported in experiments. The temperature dependence of the maximum value is also qualitatively reproduced. In contrast, the humped shape of the third-order response is not reproduced by a simple kinetically constrained spin model with noninteracting spins. To rationalize these results, we propose a two-length-scale scenario by distinguishing between the characteristic length of dynamical heterogeneities and a rigidity length that accounts for the local tendency of spins to flip coherently as a block, in the presence of interactions. We show that both length scales are identical in the kinetically constrained spin model, while they have significantly different dynamics in the model of interacting spins. Spins ; Frustration ; Condensed matter physics ; Spin glass ; Length scale ; Supercooling ; Nonlinear system ; Physics ; Spin ice ; Thermodynamics ; Quantum mechanics Material Dynamics and Properties ; Theoretical and Computational Physics ; Spectroscopy and Quantum Chemical Studies
Authors: Li Yang | Jiajun Hu | Haiyun Wu | Yongqi Wei | Huiyong Shan | Xin Song | Xiuping Hua | Wei Xu | Yongcheng Jiang
Source: Scientific Reports (article, 2024-07-05)
Field: Chemistry — Organic Chemistry

An appearance quality classification method for Auricularia auricula based on deep learning The intelligent appearance quality classification method for Auricularia auricula is of great significance to promote this industry. This paper proposes an appearance quality classification method for Auricularia auricula based on the improved Faster Region-based Convolutional Neural Networks (improved Faster RCNN) framework. The original Faster RCNN is improved by establishing a multiscale feature fusion detection model to improve the accuracy and real-time performance of the model. The multiscale feature fusion detection model makes full use of shallow feature information to complete target detection. It fuses shallow features with rich detailed information with deep features rich in strong semantic information. Since the fusion algorithm directly uses the existing information of the feature extraction network, there is no additional calculation. The fused features contain more original detailed feature information. Therefore, the improved Faster RCNN can improve the final detection rate without sacrificing speed. By comparing with the original Faster RCNN model, the mean average precision (mAP) of the improved Faster RCNN is increased by 2.13%. The average precision (AP) of the first-level Auricularia auricula is almost unchanged at a high level. The AP of the second-level Auricularia auricula is increased by nearly 5%. And the third-level Auricularia auricula AP is increased by 1%. The improved Faster RCNN improves the frames per second from 6.81 of the original Faster RCNN to 13.5. Meanwhile, the influence of complex environment and image resolution on the Auricularia auricula detection is explored. Auricularia ; Artificial intelligence ; Computer science ; Quality (philosophy) ; Traditional medicine ; Biology ; Food science ; Medicine Wood and Agarwood Research ; melanin and skin pigmentation ; Advanced Image Fusion Techniques
Authors: Philippe Lalanne | M. Chen | Carsten Rockstuhl | Alexander Sprafke | А. К. Дмитриев | K. Vynck
Source: arXiv (Cornell University) (preprint, 2024-08-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Disordered Optical Metasurfaces: Basics, Properties, and Applications Optical metasurfaces are conventionally viewed as organized flat arrays of photonic or plasmonic nanoresonators, also called metaatoms. These metasurfaces are typically highly ordered and fabricated with precision using expensive tools. However, the inherent imperfections in large-scale nanophotonic devices, along with recent advances in bottom-up nanofabrication techniques and design strategies, have highlighted the potential benefits of incorporating disorder to achieve specific optical functionalities. This review offers an overview of the key theoretical, numerical, and experimental aspects related to the exploration of disordered optical metasurfaces. It introduces fundamental concepts of light scattering by disordered metasurfaces and outlines theoretical and numerical methodologies for analyzing their optical behavior. Various fabrication techniques are discussed, highlighting the types of disorder they deliver and their achievable precision level. The review also explores critical applications of disordered optical metasurfaces, such as light manipulation in thin film materials and the design of structural colors and visual appearances. Finally, the article offers perspectives on the burgeoning future research in this field. Disordered optical metasurfaces offer a promising alternative to their ordered counterparts, often delivering unique functionalities or enhanced performance. They present a particularly exciting opportunity in applications demanding large-scale implementation, such as sustainable renewable energy systems, as well as aesthetically vibrant coatings for luxury goods and architectural designs. Computer science ; Optics ; Physics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Photonic Crystals and Applications
Authors: Mhammed El Bakkali | Redouane Messnaoui | Mustapha Elkhaoudi | Omar Cherkaοui | Aziz Soulhi
Source: Data & Metadata (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Predicting saturation for a new fabric using artificial intelligence (fuzzy logic): experimental part Weaving saturation can have harmful consequences, such as problems with loom performance, accelerated wear of mechanical parts and loss of raw materials. To avoid these problems, when designing and creating new fabrics, the densities and yarn qualities must be carefully matched with the weaves to ensure successful testing. To facilitate this task, this study focuses on the development of a practical fuzzy logic model for predicting the saturation of new fabrics. An experimental part was carried out to validate this fuzzy model. The fabric samples used in this study came from three different types of weaves, namely plain, twill and satin. These samples also included five weft counts (Nm) and eight different densities. The results obtained using the fuzzy logic model developed were compared with experimental values. The prediction results were satisfactory and precise, demonstrating the effectiveness of the fuzzy logic model developed. The mean absolute error of the calculated fuzzy model was 1,97 %. It was therefore confirmed that this fuzzy model was both fast and reliable for predicting the saturation of new fabric Fuzzy logic ; Artificial intelligence ; Saturation (graph theory) ; Computer science ; Mathematics Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Color Science and Applications
Authors: Aaron Scott | Lisa Mellhammar | Erik Malmström | Axel Goch Gustafsson | Anahita Bakochi | Marc Isaksson | Tirthankar Mohanty | Louise Thelaus | Fredrik Kahn | Lars Malmström | Johan Malmström | Adam Linder
Source: medRxiv (preprint, 2024-03-22)
Field: Chemistry — Spectroscopy

Population scale proteomics enables adaptive digital twin modelling in sepsis Abstract Sepsis is one of the leading causes of mortality in the world. Currently, the heterogeneity of sepsis makes it challenging to determine the molecular mechanisms that define the syndrome. Here, we leverage population scale proteomics to analyze a well-defined cohort of 1364 blood samples taken at time-of-admission to the emergency department from patients suspected of sepsis. We identified panels of proteins using explainable artificial intelligence that predict clinical outcomes and applied these panels to reduce high-dimensional proteomics data to a low-dimensional interpretable latent space (ILS). Using the ILS, we constructed an adaptive digital twin model that accurately predicted organ dysfunction, mortality, and early-mortality-risk patients using only data available at time-of-admission. In addition to being highly effective for investigating sepsis, this approach supports the flexible incorporation of new data and can generalize to other diseases to aid in translational research and the development of precision medicine. Scale (ratio) ; Computer science ; Population ; Proteomics ; Data science ; Biology ; Geography ; Medicine ; Cartography ; Genetics Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; Cell Image Analysis Techniques
Authors: Zahra Izadi | Sajad Kiani
Source: Journal of Food Science (article, 2024-05-25)
Field: Chemistry — Analytical Chemistry

Pomegranate molasses authentication using hyperspectral imaging system coupled with automatic clustering algorithm Pomegranate molasses is made from concentrated pomegranate juice with nothing added. Due to its nutritional value, limitation in production, and high production cost, this product may be adulterated by date syrup. This study was done to differentiate various types of pomegranate molasses and investigate the possibility of nonauthenticity detection in pomegranate molasses samples using the hyperspectral imaging (HSI) technique compared with physicochemical measurement analysis. The physicochemical properties (brix index, sucrose, acidity, ash content, pH, and formalin index) of 24 samples were measured as the reference analysis method, and it was found that the formalin index was a good factor for pomegranate molasses authenticity evaluation. Additionally, an HSI system (400-1000 nm) was used as a nondestructive and rapid screening method to capture spectral data of the samples. The evolutionary wavelength selection algorithm was applied to select effective wavelengths in sample clustering based on the obtained Davies-Bouldin index. Next, principal component analysis was used to visually interpret the spectral data of the sample when using the selected wavelengths and the whole spectra of the samples. Finally, an automatic clustering algorithm by the artificial bee colony as an unsupervised method was developed for the clustering of the authentic and nonauthentic samples. The method did not need descriptively labeled samples and obtained agreed satisfactorily with the degree of nonauthenticity in the samples. This study showed that the developed HSI technique coupled with an automatic clustering algorithm could detect date syrup nonauthenticity in pomegranate samples from the level of 5% adulteration. Hyperspectral imaging ; Cluster analysis ; Computer science ; Authentication (law) ; Algorithm ; Artificial intelligence ; Pattern recognition (psychology) ; Computer security Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Smart Agriculture and AI