jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dongsun Lee
Source: AIP Advances (article, 2023-08-01)
Field: Materials Science — Materials Chemistry

Gradient-descent-like scheme for the Allen–Cahn equation The phase-field equations have many attractive characteristics. First, phase separation can be induced by the phase-field equations. It transforms from a single homogeneous mixture to two distinct phases in a nascent state. Second, the solution of the phase-field equations is bounded by a finite value. It is beneficial to ensure numerical stability. Third, the motion of the interface can be described by geometric features. It is helpful for expressing natural phenomena in mathematical terms. Fourth, the phase-field equations possess the energy dissipation law. This law is about degeneration and decay. It tells us in thermodynamics that all occurrences are irreversible processes. In this paper, we would like to investigate the numerical implementation of the Allen–Cahn (AC) equation, which is the classical one of the phase-field equations. In phase field modeling, the binary phase system is described using a continuous variable called the order parameter. The order parameter can be categorized into two forms: conserved, which represents the physical property such as concentration or mass, and non-conserved, which does not have the conserved physical property. We consider both the non-conservative and conservative AC equations. Our interest is more precisely to scrutinize the utilization of the discrete Laplacian operator in the AC equation by considering the conservative and non-conservative order parameter ϕ. Constructing linearly implicit methods for solving the AC equation, we formulate a gradient-descent-like scheme. Therefore, reinterpreting the implicit scheme for the AC equation, we propose a novel numerical scheme in which solutions are bounded by 1 for all t &amp;gt; 0. Together with the conservative Allen–Cahn equation, our proposed scheme is consistent when mass is conserved as well. From a numerical point of view, a linear, unconditionally energy stable splitting scheme is transformed into a gradient-descent-like scheme. Various numerical simulations are illustrated to demonstrate the validity of the proposed scheme. We also make distinctions between the proposed one and existing numerical schemes. Cahn–Hilliard equation ; Mathematics ; Bounded function ; Conservation law ; Field (mathematics) ; Gradient descent ; Conservation of mass ; Mathematical analysis ; Relaxation (psychology) ; Applied mathematics ; Physics ; Differential equation ; Computer science ; Thermodynamics Solidification and crystal growth phenomena ; Advanced Mathematical Modeling in Engineering ; nanoparticles nucleation surface interactions
Authors: Jack W. Hart | Helena S. Knowles
Source: Frontiers in Quantum Science and Technology (article, 2023-08-01)
Field: Materials Science — Materials Chemistry

Multimodal quantum metrology in living systems using nitrogen-vacancy centres in diamond nanocrystals Nitrogen-vacancy centres in diamond nanocrystals are among the leading candidates for realising nanoscale quantum sensing under ambient conditions. Due to their exceptional electronic spin coherence at room temperature and optical addressability, these solid state spin-based sensors can achieve a wide selection of sensing modalities, including probing temperature, external magnetic and electric field, as well as the detection of nearby electronic and nuclear spins. In this article, we discuss recent progress made utilizing nanodiamond quantum sensors in living systems and explore both opportunities for future advances and challenges that lie ahead. Nanodiamond ; Diamond ; Quantum sensor ; Nitrogen-vacancy center ; Vacancy defect ; Spins ; Quantum metrology ; Nanocrystal ; Metrology ; Coherence (philosophical gambling strategy) ; Nanotechnology ; Materials science ; Quantum ; Quantum technology ; Spin (aerodynamics) ; Quantum dot ; Optoelectronics ; Physics ; Condensed matter physics ; Open quantum system ; Optics ; Quantum mechanics Diamond and Carbon-based Materials Research ; Advanced Fiber Laser Technologies ; Force Microscopy Techniques and Applications
Authors: Satoru Iwata | Taihei Oki | Shinsaku Sakaue
Source: arXiv (Cornell University) (preprint, 2023-12-09)
Field: Materials Science — Materials Chemistry

Rate Constant Matrix Contraction Method for Stiff Master Equations with Detailed Balance This paper considers master equations for Markovian kinetic schemes that possess the detailed balance property. Chemical kinetics, as a prime example, often yields large-scale, highly stiff equations. Based on chemical intuitions, Sumiya et al. (2015) presented the rate constant matrix contraction (RCMC) method that computes approximate solutions to such intractable systems. This paper aims to establish a mathematical foundation for the RCMC method. We present a reformulated RCMC method in terms of matrix computation, deriving the method from several natural requirements. We then perform a theoretical error analysis based on eigendecomposition and discuss implementation details caring about computational efficiency and numerical stability. Through numerical experiments on synthetic and real kinetic models, we validate the efficiency, numerical stability, and accuracy of the presented method. Computation ; Applied mathematics ; Constant (computer programming) ; Contraction (grammar) ; Computer science ; Stability (learning theory) ; Matrix (chemical analysis) ; Property (philosophy) ; Detailed balance ; Mathematics ; Algorithm ; Statistical physics ; Physics Machine Learning in Materials Science ; Advanced Thermodynamics and Statistical Mechanics
Authors: Wenjing Wei | Peter Samuelsson | Pär G. Jönsson
Source: steel research international (article, 2023-12-16)
Field: Materials Science — Materials Chemistry

Alloy Design Optimization of Stainless Steel's Performance and Environmental Impact Through Taguchi‐Based Grey Relational Analysis The current work presents an optimization approach for improving stainless steel's performance and environmental impact. A Taguchi‐based grey relational analysis is used to determine the optimal alloy design of six stainless steels (304, 316L, 904L, 2304, 2205, 2507). The alloy design of a multivariable system (C, Mn, Cr, Ni, N, Mo, and Cu) is initially formulated based on the Taguchi orthogonal array. It combines the multiperformances (pitting corrosion resistance, proof strength, tensile strength, and greenhouse gas emissions) to determine a numerical score and suggests the optimal combination of alloy contents. The results are further analyzed using a range analysis to detect the significance of each alloy parameter and its level. The results show that nitrogen plays the most important role in determining the stainless steel's combined performance. Besides, the optimum alloy design consists of a high content of nitrogen, chromium, molybdenum, and copper combined with a low content of nickel. The study has a focus on proposing the systematic approach, Taguchi‐based grey relational analysis, to optimize the stainless steel's different mechanical properties with respect to environmental impact. Taguchi methods ; Grey relational analysis ; Materials science ; Orthogonal array ; Alloy ; Ultimate tensile strength ; Metallurgy ; Molybdenum ; Corrosion ; Composite material ; Mathematics Corrosion Behavior and Inhibition ; Welding Techniques and Residual Stresses ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Marek Florkowski
Source: IEEE Transactions on Power Electronics (article, 2023-11-17)
Field: Materials Science — Materials Chemistry

Magnetic Field Effects on Partial Discharges in Electrical Insulation Subjected to PWM Excitation This article depicts an original measurement methodology and detection approach for determining the influence of a magnetic field on partial discharge (PD) dynamics in electrical insulation while subjected to pulse-width modulated (PWM) excitation. Unlike conventional PD measurements that are only carried out in an electric field, it was demonstrated that the interplay of electric and magnetic fields enhances a PD's intensity. The quantitative effect of a magnetic field was captured by phaseresolved acquisition and visualization on time-sequence intensity diagrams. The increase in the PWM carrier frequency resulted in enhanced intensity and was also elevated by the effect of the magnetic field. The influence of the magnetic field on the PDs was associated with the elongation of the charged particle trajectory and effects that were caused by the Lorentz force. The presented study may contribute to PD measurement methodology in the power electronic environment in both electric and magnetic fields as well as a better understanding of the underlying physical mechanisms. Since the endurance and reliability of electrical insulation that is subjected to fast switching, PWM-modulated power electronic-based excitation is an actual topic in many segments, such as power grids, industry, and transportation; the awareness of PD-intensity modulation that originates from the presence of a magnetic field should be raised and investigated Excitation ; Magnetic field ; Electric field ; Lorentz force ; Pulse-width modulation ; Intensity (physics) ; Partial discharge ; Materials science ; Electromagnetic field ; Physics ; Nuclear magnetic resonance ; Electrical engineering ; Voltage ; Computational physics ; Optics ; Engineering High voltage insulation and dielectric phenomena ; Electrostatic Discharge in Electronics ; Lightning and Electromagnetic Phenomena
Authors: Giulia Bernagozzi | Rossella Arrigo | Alberto Frache
Source: Polymers (article, 2023-12-25)
Field: Materials Science — Polymers and Plastics

Evolution of the Microstructure of PP-LDHs Nanocomposites during Melt Compounding: A Simulation Approach In the context of polymer-based nanocomposites containing layered nanofillers, the achievement of good extents of dispersion and distribution of the embedded nanoparticles and, even more, the obtainment of intercalated and/or exfoliated structures through melt compounding still represents a persistent challenge, especially in the case of anionic layered double hydroxides (LDHs)-containing systems and non-polar polymeric matrices. In this work, a simulation approach is proposed to evaluate the influence of the processing conditions on the morphology of polypropylene (PP)-based nanocomposites containing organomodified LDHs. In particular, the effect of the screw rotation speed and the feed rate on the final microstructure of the materials formulated through melt compounding in a twin-screw extruder was assessed. The rheological and morphological characterizations demonstrated that a more homogeneous morphology was achieved when high levels of both exploited processing parameters are selected. The results coming from the simulation of the processing were used to establish some relationships between the flow parameters and the microstructure of the nanocomposites, demonstrating that low residence times coupled with high local shear rates are required to ensure the achievement of homogenous morphologies, likely involving the occurrence of intercalation phenomena. Compounding ; Materials science ; Nanocomposite ; Microstructure ; Layered double hydroxides ; Rheology ; Polypropylene ; Composite material ; Context (archaeology) ; Plastics extrusion ; Dispersion (optics) ; Shear rate ; Polymer nanocomposite ; Chemical engineering ; Hydroxide Polymer Nanocomposites and Properties ; biodegradable polymer synthesis and properties ; Dielectric materials and actuators
Authors: Akio Tomiya | Yuki Nagai
Source:  (article, 2023-12-27)
Field: Materials Science — Materials Chemistry

Equivariant transformer is all you need Machine learning, deep learning, has been accelerating computational physics, which has been used to simulate systems on a lattice. Equivariance is essential to simulate a physical system because it imposes a strong induction bias for the probability distribution described by a machine learning model. This reduces the risk of erroneous extrapolation that deviates from data symmetries and physical laws. However, imposing symmetry on the model sometimes occur a poor acceptance rate in self-learning Monte-Carlo (SLMC). On the other hand, Attention used in Transformers like GPT realizes a large model capacity. We introduce symmetry equivariant attention to SLMC. To evaluate our architecture, we apply it to our proposed new architecture on a spin-fermion model on a two-dimensional lattice. We find that it overcomes poor acceptance rates for linear models and observe the scaling law of the acceptance rate as in the large language models with Transformers. Extrapolation ; Equivariant map ; Computer science ; Transformer ; Monte Carlo method ; Lattice (music) ; Homogeneous space ; Architecture ; Artificial intelligence ; Statistical physics ; Mathematics ; Physics ; Pure mathematics ; Mathematical analysis ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Topic Modeling ; Quantum many-body systems
Authors: Alirio Benavides | Carlos Mario Zapata Jaramillo | Pedro Benjumea | Camilo A. Franco | Farid B. Cortés | Marco A. Ruíz
Source: Processes (article, 2023-05-09)
Field: Chemistry — Analytical Chemistry

Predicting Octane Number of Petroleum-Derived Gasoline Fuels from MIR Spectra, GC-MS, and Routine Test Data Petroleum-derived gasoline is still the most widely used liquid automotive fuel for ground vehicles equipped with spark-ignition engines. One of the most important properties of gasoline fuels is their antiknock performance, which is experimentally evaluated via the octane number (ON). It is widely accepted that the standard methods for ON measuring (RON: research octane number and MON: motor octane number) are very expensive due to the costs of the experimental facilities and are generally not suitable for field monitoring or online analysis. To overcome these intrinsic problems, it is convenient that the ON of gasoline fuels is estimated via faster methods than the experimental tests and allows for acceptable results with acceptable reproducibility. Various ON prediction methods have been proposed in the literature. These methods differ in the type of fuels for which they are developed, the input features, and the analytical method used to underlie the link between input features and ON. The aim of this work is to develop and evaluate three empirical methods for predicting the ON of petroleum-derived gasoline fuels using MIR spectra, GC-MS, and routine test data as input features. In all cases, the chosen analytical method was partial least squares regression (PLSR). The best performance for both MON and RON prediction corresponded with the composition-based model, since it presented lesser evaluation indices (RMSE, MAE, and R2) and more than 80% of residuals were within the established criteria (sum of the reproducibility and the uncertainty of the standard method). Although the routine-test-data-based method performed poorly according to the established criterion, its use could be recommended in cases of scarce data since it showed an acceptable value of R2 and physical consistency. Despite their empirical nature, the proposed prediction models based on MIR (mid-infrared) spectra, GC-MS, and routine test data had the potential to predict the RON and MON of real gasoline fuels commercialized in Colombia. Gasoline ; Octane rating ; Octane ; Petroleum product ; Reproducibility ; Petroleum ; Partial least squares regression ; Computer science ; Process engineering ; Statistics ; Environmental science ; Automotive engineering ; Mathematics ; Chemistry ; Engineering ; Waste management ; Organic chemistry Spectroscopy and Chemometric Analyses ; Pesticide Residue Analysis and Safety ; Analytical Chemistry and Chromatography
Authors: Nemanja Koljančić | Olga Vyviurska | Ivan Špánik
Source: Plants (article, 2023-06-18)
Field: Chemistry — Spectroscopy

Aroma Compounds in Essential Oils: Analyzing Chemical Composition Using Two-Dimensional Gas Chromatography–High Resolution Time-of-Flight Mass Spectrometry Combined with Chemometrics Analyzing essential oils is a challenging task for chemists because their composition can vary depending on various factors. The separation potential of volatile compounds using enantioselective two-dimensional gas chromatography coupled with high-resolution time-of-flight mass spectrometry (GC×GC-HRTOF-MS) with three different stationary phases in the first dimension was evaluated to classify different types of rose essential oils. The results showed that selecting only ten specific compounds was enough for efficient sample classification instead of the initial 100 compounds. The study also investigated the separation efficiencies of three stationary phases in the first dimension: Chirasil-Dex, MEGA-DEX DET-β, and Rt-βDEXsp. Chirasil-Dex had the largest separation factor and separation space, ranging from 47.35% to 56.38%, while Rt-βDEXsp had the smallest, ranging from 23.36% to 26.21%. MEGA-DEX DET-β and Chirasil-Dex allowed group-type separation based on factors such as polarity, H-bonding ability, and polarizability, whereas group-type separation with Rt-βDEXsp was almost imperceptible. The modulation period was 6 s with Chirasil-Dex and 8 s with the other two set-ups. Overall, the study showed that analyzing essential oils using GC×GC-HRTOF-MS with a specific selection of compounds and stationary phase can be effective in classifying different oil types. Chemometrics ; Chemistry ; Chromatography ; Mass spectrometry ; Gas chromatography ; Two-dimensional chromatography ; Resolution (logic) ; Two-dimensional gas ; Essential oil ; Time-of-flight mass spectrometry ; Gas chromatography–mass spectrometry ; Analytical Chemistry (journal) ; Organic chemistry ; Ion Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Mass Spectrometry Techniques and Applications
Authors: Liangjun Li | Chen Li | Fang Jing | Xiaolong Chen | Wen Qin | Hanhan Zhang | Jing Xu | Bing Jia | Wei Heng | Xiu Jin | Li Liu
Source: Horticulturae (article, 2023-07-06)
Field: Chemistry — Analytical Chemistry

Quantitatively Determine the Iron Content in the Fruit of ‘Huangguan’ Pear Using Near-Infrared Spectroscopy ‘Huangguan’ pear has excellent quality, strong adaptability, and good socioeconomic value. Iron is one of the important trace elements in plants, and iron imbalance seriously affects the growth and development of pear trees and reduces their economic benefits. If the iron content in pear fruit can be easily and non-destructively detected using modern technology during the critical period of fruit development, it will undoubtedly help guide actual production. In this study, ‘Huangguan’ pear fruit was used as the research object, and the possibility of using the more convenient near-infrared spectroscopy (900~1700 nm) technology for nondestructive detection of the iron content in the peel and pulp of ‘Huangguan’ pear was explored. First, 12 algorithms were used to preprocess the original spectral data, and based on the original and the preprocessed spectral data, partial least squares regression and gradient boosting regression tree algorithms were used. A full-band prediction model of the iron content in the peel and pulp of ‘Huangguan’ pear was established, and the genetic algorithm was used to extract characteristic wavelengths, establish a characteristic wavelength prediction model, and evaluate the prediction effect of each model according to the coefficient of determination R² and the relative analysis error RPD. After comparison, we found that the prediction model with the best prediction of the iron content in the peel and pulp of ‘Huangguan’ pear reaches class A, and the prediction effect is good and meets expectations. This experiment shows that the use of near-infrared spectroscopy can achieve better prediction of the iron content in the peel and pulp of ‘Huangguan’ pear. PEAR ; Near-infrared spectroscopy ; Computer science ; Optics Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Water Quality Monitoring and Analysis
Authors: Alyona Grishina | Oksana Sherstneva | Anna Zhavoronkova | Maria Ageyeva | Tatiana A. Zdobnova | Maxim Lysov | Anna A. Brilkina | Vladimir Vodeneev
Source: Plants (article, 2023-11-12)
Field: Chemistry — Analytical Chemistry

Comparison of the Efficiency of Hyperspectral and Pulse Amplitude Modulation Imaging Methods in Pre-Symptomatic Virus Detection in Tobacco Plants Early detection of pathogens can significantly reduce yield losses and improve the quality of agricultural products. This study compares the efficiency of hyperspectral (HS) imaging and pulse amplitude modulation (PAM) fluorometry to detect pathogens in plants. Reflectance spectra, normalized indices, and fluorescence parameters were studied in healthy and infected areas of leaves. Potato virus X with GFP fluorescent protein was used to assess the spread of infection throughout the plant. The study found that infection increased the reflectance of leaves in certain wavelength ranges. Analysis of the normalized reflectance indices (NRIs) revealed indices that were sensitive and insensitive to infection. NRI<sub>700/850</sub> was optimal for virus detection; significant differences were detected on the 4th day after the virus arrived in the leaf. Maximum (F<sub>v</sub>/F<sub>m</sub>) and effective quantum yields of photosystem II (<i>Φ</i><sub>PSII</sub>) and non-photochemical fluorescence quenching (NPQ) were almost unchanged at the early stage of infection. <i>Φ</i><sub>PSII</sub> and NPQ in the transition state (a short time after actinic light was switched on) showed high sensitivity to infection. The higher sensitivity of PAM compared to HS imaging may be due to the possibility of assessing the physiological changes earlier than changes in leaf structure. Photosystem II ; Fluorescence ; Hyperspectral imaging ; Virus ; Quenching (fluorescence) ; Pulse (music) ; Materials science ; Horticulture ; Biology ; Virology ; Photosynthesis ; Optics ; Botany ; Remote sensing Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Remote Sensing in Agriculture