jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yu Liu | Jingqiu Liang | Shuping Xu | Yijia Geng
Source: ACS Omega (article, 2023-10-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Surface Plasmon Field-Enhanced Raman Scattering Co-Excited by P-Polarized and S-Polarized Light Based on Waveguide-Coupled Surface Plasmon Resonance Configuration We constructed a waveguide-coupled surface plasmon resonance (WCSPR) structure to enhance Raman scattering. In this structure, P-polarized and S-polarized incident lasers can simultaneously coexcite the evanescent field, thereby further enhancing Raman scattering. This configuration is a five-phase Kretschmann resonance setup that consists of a SF10 prism/inner Ag film/SiO<sub>2</sub> film/outer Ag film/water structure. The WCSPR configuration effectively concentrates and confines the evanescent field excited by the incident light. Ag nanoparticles assembled on the outer Ag film surface enhance the evanescent field further by means of surface plasmon resonance. By finely tuning the thickness of the Ag and SiO<sub>2</sub> films, it is possible to achieve a coincidence between the SPR angle of P-polarized light and that of S-polarized light. At this angle, both P- and S-polarized light can jointly elevate the evanescent field intensity, leading to the simultaneous enhancement of the electric fields at the upper, lower, left, and right parts of the silver nanoparticles and generating maximum evanescent field enhancement. We achieved an electric field enhancement of up to 10<sup>3</sup> around the nanoparticles, leading to more SERS hotspots and comparable SERS enhancement capability to gap-type hotspots. Our WCSPR structure combined with the nanoparticles offers a feasible strategy for the SERS detection of large molecules that cannot be placed in traditional gap-type hotspots. It is highly convenient for SERS detection of large molecules. Surface plasmon resonance ; Raman scattering ; Materials science ; Surface plasmon ; Electric field ; Optics ; Plasmon ; Prism ; Optoelectronics ; Localized surface plasmon ; Scattering ; Raman spectroscopy ; Resonance (particle physics) ; Excited state ; Nanoparticle ; Total internal reflection ; Ray ; Light scattering ; Nanotechnology ; Physics ; Atomic physics Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Advanced biosensing and bioanalysis techniques
Authors: Jessica Arcudia | Filiberto Ortíz‐Chi | O. A. Sánchez-Valenzuela | Alán Aspuru‐Guzik | Gabriel Merino
Source: Matter (article, 2023-03-21)
Field: Materials Science — Materials Chemistry

Joining and arrangement of multilayers: A string representation for honeycomb layered materials Stacking ; Character (mathematics) ; String (physics) ; Representation (politics) ; Lattice (music) ; Computer science ; Honeycomb ; Set (abstract data type) ; Exploit ; Notation ; Symmetry (geometry) ; Topology (electrical circuits) ; Theoretical computer science ; Combinatorics ; Theoretical physics ; Geometry ; Mathematics ; Physics ; Arithmetic ; Programming language 2D Materials and Applications ; Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Hira Zulfiqar | Iqbal Ahmed | Muhammad Furqan Farooq | Iqra Rani | Ijaz Hussain | Hunain Zulfiqar | Abid Jan
Source: Journal of Chemistry and Environment (review, 2023-05-21)
Field: Chemistry — Organic Chemistry

Unraveling the Challenges of Oxidative Stability and Methods for Assessing Oxidation of Commercial Oils: A Review Edible oils are among the most crucial nutritional constituents as these are utilized for frying or cuisine. Though, these oils are prone towards oxidative deterioration from manufacturing to transportation due to environmental exposure as well as storage conditions which result in generation of different types of oxidation products. The generation of primary and secondary products result in harmful impacts on human health and may also result in cardiovascular diseases in consumers. The mechanism of oxidative deterioration cannot be assessed easily due to complexity of the reaction under which it may take place. Different types of approaches have been proposed to determine the products generated as a result of oxidation so that safety for their user can be assured. Different types of analytical techniques can be employed in addition to chemical methods so that analysis of oxidative deterioration can be done completely. The proposed study is aimed to assess the oxidative stability of oils in addition to the factors which can influence oxidation of these oils under different conditions. This review also summarize the classical and advanced state of the art analytical techniques which has recently been developed in different possible ways and effectively can be employed for analysis of oxidative deterioration of edible oils. This article possibly will oblige as a reference in the assortment, establishing, and enhancement of approaches for investigating the oxidative stability of edible oil. Oxidative phosphorylation ; Biochemical engineering ; Human health ; Mechanism (biology) ; Risk analysis (engineering) ; Chemistry ; Environmental science ; Computer science ; Business ; Engineering ; Medicine ; Biochemistry ; Environmental health Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity
Authors: Alexander Buccheri | Fabian Peschel | Benedikt Maurer | Mara Voiculescu | Daniel T. Speckhard | Hannah Kleine | Elisa Stephan | Martin Kubáň | Claudia Draxl
Source: The Journal of Open Source Software (article, 2023-05-03)
Field: Materials Science — Materials Chemistry

excitingtools: An exciting Workflow Tool Workflow ; Computer science ; Database Machine Learning in Materials Science ; Scientific Computing and Data Management ; Catalysis and Oxidation Reactions
Authors: Braian Saimon Frota da Silva | Nelson Rosa Ferreira | Priscila Domingues Alamar | Thiago de Melo e Silva | Wandson Braamcamp de Souza Pinheiro | Lucely Nogueira dos Santos | Cláudio Nahum Alves
Source: Molecules (article, 2023-09-29)
Field: Chemistry — Analytical Chemistry

FT-MIR-ATR Associated with Chemometrics Methods: A Preliminary Analysis of Deterioration State of Brazil Nut Oil Brazil nut oil is highly valued in the food, cosmetic, chemical, and pharmaceutical industries, as well as other sectors of the economy. This work aims to use the Fourier transform infrared (FTIR) technique associated with partial least squares regression (PLSR) and principal component analysis (PCA) to demonstrate that these methods can be used in a prior and rapid analysis in quality control. Natural oils were extracted and stored for chemical analysis. PCA presented two groups regarding the state of degradation, subdivided into super-degraded and partially degraded groups in 99.88% of the explained variance. The applied PLS reported an acidity index (AI) prediction model with root mean square error of calibration (RMSEC) = 1.8564, root mean square error of cross-validation (REMSECV) = 4.2641, root mean square error of prediction (RMSEP) = 2.1491, R<sup>2</sup><sub>cal</sub> (calibration correlation coefficient) equal to 0.9679, R<sup>2</sup><sub>val</sub> (validation correlation coefficient) equal to 0.8474, and R<sup>2</sup><sub>pred</sub> (prediction correlation coefficient) equal to 0, 8468. The peroxide index (PI) prediction model showed RMSEC = 0.0005, REMSECV = 0.0016, RMSEP = 0.00079, calibration R<sup>2</sup> equal to 0.9670, cross-validation R<sup>2</sup> equal to 0.7149, and R<sup>2</sup> of prediction equal to 0.9099. The physical-chemical analyses identified that five samples fit in the food sector and the others fit in other sectors of the economy. In this way, the preliminary monitoring of the state of degradation was reported, and the prediction models of the peroxide and acidity indexes in Brazil nut oil for quality control were determined. Partial least squares regression ; Chemometrics ; Correlation coefficient ; Mean squared error ; Calibration ; Nut ; Principal component analysis ; Coefficient of determination ; Statistics ; Mathematics ; Cross-validation ; Chemistry ; Chromatography ; Engineering Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Meat and Animal Product Quality
Authors: Tao Lü | Fanqianhui Yu | Baokun Han | Jingying Guo | Kunhua Liu | Shuai He
Source: Foods (article, 2023-09-28)
Field: Chemistry — Analytical Chemistry

An Intelligent Method for Predicting Pacific Oyster (Crassostrea gigas) Freshness Using Deep Learning Fused with Malondialdehyde and Total Sulfhydryl Groups Information To achieve a non-destructive and rapid detection of oyster freshness, an intelligent method using deep learning fused with malondialdehyde (MDA) and total sulfhydryl groups (SH) information was proposed. In this study, an "MDA-SH-storage days" polynomial fitting model and oyster meat image dataset were first built. AleNet-MDA and AlxNet-SH classification models were then constructed to automatically identify and classify four levels of oyster meat images with overall accuracies of 92.72% and 94.06%, respectively. Next, the outputs of the two models were used as the inputs to "MDA-SH-storage days" model, which ultimately succeeded in predicting the corresponding MDA content, SH content and storage day for an oyster image within 0.03 ms. Furthermore, the interpretability of the two models for oyster meat image were also investigated by feature visualization and strongest activations techniques. Thus, this study brings new thoughts on oyster freshness prediction from the perspective of computer vision and artificial intelligence. Oyster ; Interpretability ; Malondialdehyde ; Artificial intelligence ; Crassostrea ; Pacific oyster ; Pattern recognition (psychology) ; Computer science ; Machine learning ; Food science ; Chemistry ; Biology ; Fishery ; Biochemistry Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Identification and Quantification in Food
Authors: Seunghwan Moon | Jihun Kang | Jong‐Souk Yeo
Source: Applied Science and Convergence Technology (article, 2023-11-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Review of Generative Models for the Inverse Design of Nanophotonic Metasurfaces Evolving nanotechnologies and further understanding of nanophotonics have recently enabled the control of electromagnetic waves using metasurfaces.Since metasurfaces can provide diverse optical characteristics depending on their geometries, the forward design of metasurfaces conventionally has been employed through an understanding of the physical effects of each geometrical parameter.In contrast, the inverse design approach optimizes the metasurface geometry using computational algorithms.This review discusses recent studies on constructing generative models for the inverse design of nanophotonic metasurfaces.The generative model for inverse design is constructed mainly with three components: an evaluator, a generator, and a criterion.The evaluator, which can be implemented by physical simulators or deep neural networks, determines whether the input metasurface geometry satisfies the target optical characteristics.The generator suggests new possible design candidates that may have optical properties close to the target.The criterion, which includes algorithms based on mathematical optimization and artificial intelligence, manages the operation of a generative model while satisfying the convergence of optimal solutions.Inverse design takes advantage of larger design space for customized applications along with the possibility of investigating new physics, and hence it is expected to improve metasurfaces further with the emerging computational algorithms. Nanophotonics ; Inverse ; Generative grammar ; Computer science ; Generative Design ; Nanotechnology ; Artificial intelligence ; Mathematics ; Materials science ; Geometry Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Photonic Crystals and Applications
Authors: Maomao Li | Jing Zuo | Kailin Yang | Ping Wang | Shengtao Zhou
Source: Mass Spectrometry Reviews (review, 2023-04-12)
Field: Chemistry — Spectroscopy

Proteomics mining of cancer hallmarks on a single‐cell resolution Dysregulated proteome is an essential contributor in carcinogenesis. Protein fluctuations fuel the progression of malignant transformation, such as uncontrolled proliferation, metastasis, and chemo/radiotherapy resistance, which severely impair therapeutic effectiveness and cause disease recurrence and eventually mortality among cancer patients. Cellular heterogeneity is widely observed in cancer and numerous cell subtypes have been characterized that greatly influence cancer progression. Population-averaged research may not fully reveal the heterogeneity, leading to inaccurate conclusions. Thus, deep mining of the multiplex proteome at the single-cell resolution will provide new insights into cancer biology, to develop prognostic biomarkers and treatments. Considering the recent advances in single-cell proteomics, herein we review several novel technologies with particular focus on single-cell mass spectrometry analysis, and summarize their advantages and practical applications in the diagnosis and treatment for cancer. Technological development in single-cell proteomics will bring a paradigm shift in cancer detection, intervention, and therapy. Chemistry ; Proteomics ; Computational biology ; Cancer ; Resolution (logic) ; Nanotechnology ; Biochemistry ; Artificial intelligence ; Internal medicine ; Computer science Advanced Proteomics Techniques and Applications ; Cell Image Analysis Techniques ; Advanced Biosensing Techniques and Applications
Authors: T. Yokoyama | Yusei Kobayashi | Noriyoshi Arai | Arash Nikoubashman
Source: Soft Matter (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Aggregation of amphiphilic nanocubes in equilibrium and under shear We investigate the self-assembly of amphiphilic nanocubes into finite-sized aggregates in equilibrium and under shear, using molecular dynamics (MD) simulations and kinetic Monte Carlo (KMC) calculations. These patchy nanoparticles combine both interaction and shape anisotropy, making them valuable models for studying folded proteins and DNA-functionalized nanoparticles. The nanocubes can self-assemble into various finite-sized aggregates ranging from rods to self-avoiding random walks, depending on the number and placement of the hydrophobic faces. Our study focuses on suspensions containing multi- and one-patch cubes, with their ratio systematically varied. When the binding energy is comparable to the thermal energy, the aggregates consist of only few cubes that spontaneously associate/dissociate. However, highly stable aggregates emerge when the binding energy exceeds the thermal energy. Generally, the mean aggregation number of the self-assembled clusters increases with the number of hydrophobic faces and decreases with increasing fraction of one-patch cubes. In sheared suspensions, the more frequent collisions between nanocube clusters lead to faster aggregation dynamics but also to smaller terminal steady-state mean cluster sizes. The results from the MD and KMC simulations are in excellent agreement for all investigated two-patch cases, whereas the three-patch cubes form systematically smaller clusters in the MD simulations compared to the KMC calculations due to finite-size effects and slow aggregation kinetics. By analyzing the rate kernels, we are able to identify the primary mechanisms responsible for (shear-induced) cluster growth and breakup. This understanding allows us to tune nanoparticle and process parameters to achieve desired cluster sizes and shapes. Amphiphile ; Monte Carlo method ; Kinetic Monte Carlo ; Molecular dynamics ; Shear (geology) ; Statistical physics ; Amphiphilic molecule ; Kinetic energy ; Chemical physics ; Materials science ; Chemistry ; Computational chemistry ; Physics ; Copolymer ; Classical mechanics ; Polymer ; Mathematics ; Composite material Pickering emulsions and particle stabilization ; Surfactants and Colloidal Systems
Authors: Fengfeng Gao | Fukun Gui | Dejun Feng | Xiaoyu Qu | Fuxiang Hu | Xu Yang
Source: Journal of Marine Science and Engineering (article, 2023-05-04)
Field: Materials Science — Materials Chemistry

Study on the Corrosion Behavior of D36 Steel Plate and H62 Copper Alloy Net for Marine Aquaculture Facilities in Simulated Seawater Marine aquaculture facilities have been working in a high salt and humidity marine environment for a long time, which makes them be inevitably affected by seawater corrosion, especially the main structures such as metal mesh and frame. Therefore, studying the corrosion behavior of net and frame steel is of great significance for the selection of materials and corrosion protection of marine aquaculture facilities. The influence of NaCl concentrations and immersion state on self-corrosion behavior and the influence of layer thickness and overlapping area on galvanic corrosion behavior of H62 copper alloy mesh/D36 steel plate was discussed in this study using weight loss and electrochemical measurements. The tensile tests were conducted to observe the influence of different corrosion conditions on maximum force and tensile strength of the net. The corrosion rate of the two materials increased rapidly with the increase of NaCl concentrations; the corrosion rate of both materials showed the decreasing trend with the extension of time, and the corrosion rate of H62 was always lower than D36 steel. When two materials were coupled, the galvanic corrosion rate would decrease with the increase of the layer thickness and overlapping area. The tensile results were consistent with the corrosion results. When these two materials work together, adding layer thickness or increasing the overlapping area is one of the ways to protect the frame steel to a certain extent. Corrosion ; Materials science ; Metallurgy ; Galvanic cell ; Alloy ; Seawater ; Galvanic corrosion ; Ultimate tensile strength ; Galvanization ; Cathodic protection ; Copper ; Layer (electronics) ; Electrochemistry ; Composite material ; Geology ; Chemistry ; Oceanography ; Electrode Corrosion Behavior and Inhibition ; Calcium Carbonate Crystallization and Inhibition
Authors: Habib Amiri Savadroodbari | Milad Razbin | Mohsen Reza Hasani | Majid Safar Johari
Source: Research Square (preprint, 2023-10-04)
Field: Materials Science — Polymers and Plastics

Tuning Drafting Zone Parameters for Polyester Yarn within a Ring Spinning System: Modeling and Optimization Abstract Most textile products incorporate yarn as a fundamental element in the production process. Among various yarn manufacturing methods, the ring spinning system stands out as a crucially employed technology due to its advantages including yarn quality, evenness, low hairiness, and ease of handling. The parameters of drafting zone in this technology have a great impact on the quality of yarns. Typically, tuning this drafting zone parameters is time-consuming and costly through trial and error method. This study introduces an algorithmic procedure based on response surface methodology (RSM), experimental modeling, and multi-objective optimization to reduce unevenness percentage (U%) and imperfection index (IPI). Input parameters, including cots hardness of front and back top rollers, spacer size, and break draft, are optimized. Results indicate superior prediction performance of the artificial neural network (ANN) (average vaule of TGF = 1.9996) compared to RSM (average value of TGF = 1.8668). Consequently, ANN is selected for optimization. Furthermore, coupling the genetic algorithm with two ANN-based models reduced IPI from 39 to 33.67 and a reduction from 9.73–9.67% occurred in terms of U%. The final setting of Input parameters were cots hardness of front roller of 70 shore and cots hardness of back roller of 76 shore, spacer size 2.8 mm, and break draft of 1.26. This method efficiently optimizes the drafting zone parameter, enhancing yarn quality. Yarn ; Spinning ; Shore durometer ; Response surface methodology ; Artificial neural network ; Process (computing) ; Mechanical engineering ; Process variable ; Spiral (railway) ; Engineering ; Structural engineering ; Computer science ; Materials science ; Composite material ; Artificial intelligence Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Industrial Vision Systems and Defect Detection
Authors: Wenqing Yan | Jingwei Tang | Sandro Stucki
Source: IEEE Access (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

Design and Implementation of a Lightweight Deep CNN-Based Plant Biometric Authentication System The wide application of personal biometric information such as face, fingerprint, iris, and voiceprint has simultaneously created many new ethical and legal issues, including the fraudulent use of biometrics. A non-human biometric system is demanded as an alternative, which features no human private information and can be replaced or renewed from time to time. The main objective of this study is to identify wood or leaf biometric patterns and verify their identities by building their respective datasets. On this basis, a plant biometric feature-based recognition system and authentication application were developed and implemented by employing a deep convolutional neural network (CNN) architecture to learn the embedding model using a distance-based triplet-loss similarity metric. We used two kinds of small datasets based on wood and leaves, which are Spruce Cross-Section (SCS) dataset and Collinsonia Canadensis Leaf Abaxial Surface (CCLAS) dataset. A series of artificial augmentations have been integrated into training to mimic the changes in the images during the usage of keys in real-world scenarios. The final results achieve accuracy values of 97.56% (validation set) and 96.06% (test set) on the Spruce Cross-Section (SCS) dataset and 99.11% (validation set) and 98.61% (test set) on the Collinsonia Canadensis Leaf Abaxial Surface (CCLAS) dataset, indicating the high reliability of this non-human biometric authentication system. Biometrics ; Computer science ; Convolutional neural network ; Artificial intelligence ; Authentication (law) ; Palm print ; Set (abstract data type) ; Pattern recognition (psychology) ; Iris recognition ; Test set ; Metric (unit) ; Fingerprint (computing) ; Feature extraction ; Deep learning ; Feature (linguistics) ; Face (sociological concept) ; Data mining ; Computer security Wood and Agarwood Research ; Smart Agriculture and AI ; Biometric Identification and Security