jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Debdas Dhabal | Valeria Molinero
Source: ChemRxiv (preprint, 2023-01-18)
Field: Materials Science — Materials Chemistry

Kinetics and Mechanisms of Pressure-induced Ice Amorphization and Polyamorphic Transitions in a Machine-learned Coarse-Grained Water Model Water glasses have attracted considerable attention due to their potential connection to a liquid-liquid transition in supercooled water. Here we use molecular simulations to investigate the formation and phase behavior of water glasses using the ML-BOP water model. We produce glasses through hyperquenching of water, pressure-induced amorphization (PIA) of ice, and pressure-induced polyamorphic transformations. Our simulations show that PIA of polycrystalline ice occurs at a lower pressure than for mono-crystalline ice, and through a different mechanism. The temperature dependence of the amorphization pressure of polycrystalline ice for ML-BOP agrees with experiments. We also find that ML-BOP accurately reproduces the density, coordination number, and struc-tural features of LDA, HDA and VHDA water glasses. We examine the kinetics and mechanism of the transformation between low-density and high-density glasses, and find that the sharp nature of these transitions in ML-BOP is similar to that in experiments and all-atom water models with a liquid-liquid transition. Transitions between ML-BOP glasses occur through a spinodal-like mechanism, similar to ice crystallization from LDA. Both glass-to-glass and glass-to-ice transformations have Avrami-Kolmogorov kinetics with exponent n=1.5±0.2 in experiments and simulations. Importantly, ML-BOP reproduces the competition between crystallization and HDA→LDA transition above the glass transition temperature Tg, and separation of their time scales below Tg, observed also in experi-ments. These findings demonstrate the ability of ML-BOP to accurately reproduce water properties across various regimes, making it a promising model for addressing the competition between polyamorphic transitions and crystallization in water and solutions. Supercooling ; Crystallite ; Glass transition ; Materials science ; Thermodynamics ; Crystallization ; Clear ice ; Kinetics ; Chemical physics ; Mineralogy ; Chemistry ; Sea ice ; Physics ; Arctic ice pack ; Meteorology ; Metallurgy ; Polymer ; Composite material Material Dynamics and Properties ; Glass properties and applications ; Theoretical and Computational Physics
Authors: Meera Singh | Shweta Takawale | Rajesh B. Patil | Somdatta Chaudhari | Aarifkhan Pathan | Jaiprakash N. Sangshetti | Abu Tayab Moin | Talha Zubair | Mohammad Helal Uddin
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-09-12)
Field: Materials Science — Materials Chemistry

Development and Characterization of Co-crystals Assisted with <i>In-silico</i> Screening for Solubility and Permeability Enhancement of Curcumin Abstract Despite being a promising phytochemical, Curcumin’s potential applications are limited due to its classification in BCS class IV, which is associated with low water solubility and permeability. Enhancing the bioavailability of BCS class IV drugs presents a significant challenge, but crystal chemistry provides a hopeful avenue for overcoming this hurdle. In this research, co-crystals of Curcumin were developed to improve both solubility and permeability. Unlike traditional methods that require extensive trial-based lab work and time-consuming screening of co-formers, the use of molecular docking in In-silico co-former screening offers a scientific and rational approach to selecting suitable partners. In this study, two distinct co-crystals were synthesized using a solvent evaporation technique with methanol as the solvent, employing a 1:1 molar ratio. L-proline and piperine were chosen as co-formers to enhance solubility and permeability, respectively. The co-crystals underwent optimization and characterization through Design of Experiments (DOE). Comparing the dissolution study results for the same curcumin concentration, the cumulative drug release (CDR) after 8 hours was 20% for pure curcumin and an impressive 71% for curcumin-L-proline co-crystals. The permeability study, conducted over four hours using the everted gut sac method in phosphate buffer pH 6.8, revealed curcumin’s permeability to be less than 0.05 mg/mL, while curcumin-piperine co-crystals exhibited a five-fold increase (0.2545 mg/mL) in permeability. The co-crystals formed through a molecular ratio of 1:1 for curcumin-L-proline to enhance solubility and 1:1 for curcumin-piperine to enhance permeability, both demonstrated positive outcomes with support from optimization analysis, FTIR, DSC, SEM, PXRD analysis, and dissolution studies. Curcumin ; Solubility ; Piperine ; Bioavailability ; Dissolution ; Chemistry ; Solvent ; Permeability (electromagnetism) ; Nuclear chemistry ; Chromatography ; Chemical engineering ; Organic chemistry ; Pharmacology ; Medicine ; Biochemistry ; Membrane Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography ; Crystallography and molecular interactions
Authors: Yuelong Wang | Weiqing Wang
Source: Processes (article, 2023-05-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Research on the Control Strategy of Urban Integrated Energy Systems Containing the Fuel Cell As a new type of energy with the advantages of high efficiency, clean and pollution-free, fuel cells have attracted the attention of many experts and scholars. The efficient utilization of fuel cells will certainly become the mainstay of energy transformation and environmental protection. However, fuel cells have low power density, soft electrical output characteristics, and significantly delayed response to sudden load changes. When fuel cells are used as power supply energy alone, the output voltage fluctuates greatly, and the power supply reliability could be higher. To increase the fuel cell’s service life in real world applications, a DC converter and an appropriate auxiliary energy storage power supply are combined to form a fuel cell hybrid power supply system that makes efficient use of the auxiliary energy storage system’s availability, enhances the power supply system’s adaptability through dynamic reconfiguration, and provides better flexibility overall. This work proposes a method for managing the energy produced by an urban integrated power supply system that includes fuel cells, supercapacitors, and solar cells. Applying the IF-THEN rule of load power and the state of charge of the supercapacitors, the power balance is adjusted between the su-percapacitors, photovoltaic cells, and fuel cells according to the defined fuzzy logic control. The intermittent nature of solar power production and the erratic nature of fuel cell output may both be mitigated using this technique, allowing the load power to operate more reliably. The simulation results show that the control strategy adopted in this paper is able to not only meet the load requirements but also reasonably allocate the functional requirements and improve the working efficiency of the system, resulting in a clear optimization effect on the system’s control. In this paper, we focus on the fuel cell hybrid power supply system design, and then we use the idea of fuzzy logic control energy management to build the structure of the fuzzy logic control system, design the fuzzy controller, determine the functions, and verify the solutions through simulation and experimentation. Automotive engineering ; Energy storage ; Load following power plant ; Power (physics) ; Supercapacitor ; Electricity generation ; Photovoltaic system ; Flexibility (engineering) ; Distributed generation ; Energy supply ; Computer science ; Electrical engineering ; Engineering ; Base load power plant ; Renewable energy ; Energy (signal processing) Supercapacitor Materials and Fabrication ; Fuel Cells and Related Materials ; Electric and Hybrid Vehicle Technologies
Authors: A. Mark Payne | Haoyang Wu | Hao‐Wei Pang | Colin A. Grambow | Duminda S. Ranasinghe | Xiaorui Dong | Alon Grinberg Dana | William H. Green
Source: ChemRxiv (preprint, 2023-11-29)
Field: Chemistry — Organic Chemistry

Towards Accurate Quantum Mechanical Thermochemistry: (1) Extensible Implementation and Comparison of Bond Additivity Corrections and Isodesmic Reactions To obtain accurate enthalpies of formation of a chemical species, Hf, one needs a procedure to quantitatively connect the results from quantum mechanical (QM) calculations with the experimental enthalpies of elements in their standard state, often along with additional empirical corrections. Although QM methods continue to improve, accuracy gains from new methods are usually realized if the calculations for Hf are properly corrected. One such approach is to use atomization energy corrections (AECs) followed by bond additivity corrections (BACs), such as those defined by Petersson et al. or Anantharaman and Melius. Another approach is to utilize isodesmic reactions (IDRs) which are error-canceling reactions that can be constructed systematically as shown by Buerger et al. In this work, we implement all of these approaches in Arkane, an open-source software that can calculate species thermochemistry from computations done by a wide variety of QM software packages. As part of this work, we curate a diverse database of over 400 reference species from the available literature for use in fitting BACs or to participate in IDRs, and also provide AECs and BACs for 15 commonly used levels of theory using the workflow implemented. When comparing the approaches, we find that both BAC types yield results of similar accuracy, though the Anantharaman and Melius-type BACs seem to generalize better. Further, although the IDRs approach is often as accurate as the BAC approaches and sometimes even more advantageous, we find that this method appears to be less robust than the BAC methods. In particular, the accuracy of the IDRs approach is sensitive to the chosen reference species and reactions. Therefore, in addition to providing the automated AECs and BACs fitting procedure, we also propose improved methods to select IDRs. Isodesmic reaction ; Thermochemistry ; Workflow ; Computer science ; Additive function ; Quantum ; Software ; Quantum chemical ; Bond energy ; Computational science ; Computational chemistry ; Algorithm ; Chemistry ; Mathematics ; Density functional theory ; Physics ; Physical chemistry ; Molecule ; Quantum mechanics ; Programming language Chemical Thermodynamics and Molecular Structure ; Machine Learning in Materials Science ; Thermal and Kinetic Analysis
Authors: Rafic Ayass | Samir Mustapha | Darine A. Salam
Source: Proceedings of the World Congress on Civil, Structural, and Environmental Engineering (article, 2023-03-01)
Field: Chemistry — Analytical Chemistry

Quantification of Hydrocarbon Contamination in Soil Using Hyperspectral Data and Deep Learning Petroleum and its products undergo large scale production, transportation, and storage which make them prone to spills and leakages into the environment.Petroleum contamination in terrestrial environments, particularly soil bodies, is common and holds major consequences on food and crops, microbial communities, the atmosphere, the water sphere, public health and safety, and the soil itself, and therefore, requires immediate detection and assessment in case contamination is present.In this paper, advanced machine learning methods are used to predict petroleum hydrocarbon contamination in soil using hyperspectral image data.Hyperspectral imaging combines imaging and spectroscopy and can detect petroleum hydrocarbons using the characteristic absorption features in hydrocarbon reflectance spectra.Laboratory prepared soil samples are contaminated with crude oil and scanned with a hyperspectral camera in a laboratory setup.The data collected is used to train deep learning models to predict, quantitatively, the amount of petroleum present in soil samples.To make predictions, a first model is built to use spectral data from a single pixel while a second model is built to use spectral and spatial data by using two adjacent pixel spectra as input.The results show good performance for both models, with the twopixel model achieving a better mean square error of 0.48 on a testing dataset compared to the mean square error of 0.628 for the single pixel model on the same testing data.Therefore, hyperspectral imaging contains valid spectral and spatial information that are beneficial for assessing petroleum contamination in soil with good accuracy. Hyperspectral imaging ; Contamination ; Environmental science ; Remote sensing ; Hydrocarbon ; Geology ; Chemistry Spectroscopy and Chemometric Analyses
Authors: Tobias Knoke | Sebastian Kinnewig | Sven Beuchler | Ayhan Demircan | Uwe Morgner | Thomas Wick
Source: Selecciones Matemáticas (article, 2023-05-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Domain Decomposition with Neural Network Interface Approximations for time-harmonic Maxwell’s equations with different wave numbers In this work, we consider the time-harmonic Maxwell's equations and their numerical solution with a domain decomposition method. As an innovative feature, we propose a feedforward neural network-enhanced approximation of the interface conditions between the subdomains. The advantage is that the interface condition can be updated without recomputing the Maxwell system at each step. The main part consists of a detailed description of the construction of the neural network for domain decomposition and the training process. To substantiate this proof of concept, we investigate a few subdomains in some numerical experiments with low frequencies. Therein the new approach is compared to a classical domain decomposition method. Moreover, we highlight current challenges of training and testing with different wave numbers and we provide information on the behaviour of the neural-network, such as convergence of the loss function, and different activation functions. Artificial neural network ; Domain decomposition methods ; Domain (mathematical analysis) ; Interface (matter) ; Time domain ; Maxwell's equations ; Feedforward neural network ; Convergence (economics) ; Computer science ; Frequency domain ; Harmonic ; Wave equation ; Decomposition method (queueing theory) ; Algorithm ; Applied mathematics ; Mathematics ; Mathematical analysis ; Physics ; Artificial intelligence ; Acoustics ; Finite element method ; Discrete mathematics Magnetic Properties and Applications ; Electromagnetic Simulation and Numerical Methods ; Model Reduction and Neural Networks
Authors: Qiquan Wei | Yurui Zheng | Zhaoqing Chen | Yun Huang | Chang Chen | Zhenbo Wei | Shuiqin Zhou | Hongwei Sun | Fengnong Chen
Source: International journal of agricultural and biological engineering (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Nondestructive perception of potato quality in actual online production based on cross-modal technology Nowadays, China stands as the global leader in terms of potato planting area and total potato production. The rapid and nondestructive detection of the potato quality before processing is of great significance in promoting rural revitalization and augmenting farmers’ income. However, existing potato quality sorting methods are primarily confined to theoretical research, and the market lacks an integrated intelligent detection system. Therefore, there is an urgent need for a post-harvest potato detection method adapted to the actual production needs. The study proposes a potato quality sorting method based on cross-modal technology. First, an industrial camera obtains image information for external quality detection. A model using the YOLOv5s algorithm to detect external green-skinned, germinated, rot and mechanical damage defects. VIS/NIR spectroscopy is used to obtain spectral information for internal quality detection. A convolutional neural network (CNN) algorithm is used to detect internal blackheart disease defects. The mean average precision (mAP) of the external detection model is 0.892 when intersection of union (IoU) = 0.5. The accuracy of the internal detection model is 98.2%. The real-time dynamic defect detection rate for the final online detection system is 91.3%, and the average detection time is 350 ms per potato. In contrast to samples collected in an ideal laboratory setting for analysis, the dynamic detection results of this study are more applicable based on a real-time online working environment. It also provides a valuable reference for the subsequent online quality testing of similar agricultural products. Keywords: cross-modal technology, potato quality, YOLOv5s, VIS/NIR spectroscopy, online nondestructive detection DOI: 10.25165/j.ijabe.20231606.8076 Citation: Wei Q Q, Zheng Y R, Chen Z Q, Huang Y, Chen C Q, Wei Z B, et al. Nondestructive perception of potato quality in actual online production based on cross-modal technology. Int J Agric & Biol Eng, 2023; 16(6): 280-290. Production (economics) ; Modal ; Perception ; Quality (philosophy) ; Nondestructive testing ; Engineering ; Agricultural engineering ; Acoustics ; Computer science ; Psychology ; Materials science ; Economics ; Physics ; Composite material ; Microeconomics Spectroscopy and Chemometric Analyses
Authors: Jie Hou | Runar Strand-Amundsen | Ørjan G. Martinsen
Source: Journal of Electrical Bioimpedance (article, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Detection of physiological concentrations of GABA using dielectric spectroscopy - A pilot study Gamma-aminobutyric acid (GABA) is a major inhibitory neurotransmitter that is present at a relatively low level throughout the normal adult human brain. Abnormal GABA levels are found in people with neurodegenerative disorders such as Parkinson's disease, epilepsy, schizophrenia, depression, and others. Being able to measure the GABA concentration would be beneficial for patient groups with fluctuating GABA levels for better diagnosis and treatment. In this study, we explore the feasibility of using dielectric relaxation spectroscopy for the detection of GABA concentrations within a physiological range, with the perspective of miniaturization and use during implantation. Utilizing machine learning techniques, we were able to differentiate GABA concentrations down to 5 μm. This work investigates a novel use of dielectric relaxation spectroscopy, to assess if physiological GABA concentrations can be detected through permittivity measurements. Neurotransmitter ; Dielectric spectroscopy ; Glutamate receptor ; Neuroscience ; Epilepsy ; Dielectric ; Relaxation (psychology) ; Chemistry ; Spectroscopy ; Medicine ; Psychology ; Materials science ; Biochemistry ; Receptor ; Physics ; Central nervous system ; Optoelectronics Conducting polymers and applications ; Neuroscience and Neural Engineering ; Advanced Chemical Sensor Technologies
Authors: Efthymia Ramou | Susana Palma | Ana Cecília A. Roque
Source: Nano Select (article, 2023-02-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A room temperature 9CB‐based chemical sensor Abstract The gas sensing potential of the 9CB (4‐cyano‐4′‐nonylbiphenyl) liquid crystal compound at ambient conditions is investigated. The solid at room temperature 9CB is incorporated in a hybrid gel formulation of ionic liquid/liquid crystal droplets immobilized in a gelatin matrix. Several ionic liquids are tested in order to tackle the crystallization of 9CB and also a 9CB‐based mixture with a different smectogen is considered. The resulting formulation demonstrates great analyte responding capabilities and can be considered an alternative format for artificial olfaction purposes. Ionic liquid ; Analyte ; Crystallization ; Materials science ; Gelatin ; Liquid crystal ; Matrix (chemical analysis) ; Crystal (programming language) ; Chemical engineering ; Analytical Chemistry (journal) ; Chromatography ; Chemistry ; Organic chemistry ; Computer science ; Optoelectronics ; Composite material Liquid Crystal Research Advancements ; Polydiacetylene-based materials and applications ; Mesoporous Materials and Catalysis
Authors: Ugochukwu Elele | Azam Nekahi | Arshad Ali | Kate McAulay | I. Fofana
Source: Preprints.org (preprint, 2023-10-03)
Field: Chemistry — Analytical Chemistry

Sensitivity Analysis of Intensity-Modulated Plastic Optical Fiber Sensors for Effective Ageing Detection in Rapeseed Transformer Oil In the realm of power delivery to end-users, transformers are indispensable, with their malfunctions leading to substantial economic, safety, and environmental repercussions. The need for persistent surveillance is accentuated for in-situ oil-filled transformers, given the potential degradation of oil and the emergence of related ageing by-products. As the focus tilts towards online detection methodologies for transformer oil ageing, bypassing challenges associated with traditional offline methods such as sample contamination and misinterpretation, fiber optic sensors are gaining trac-tion due to their compact nature, cost-effectiveness, and resilience to electromagnetic disturbances typical in high-voltage environments. This work delves into the sensitivity analysis of intensi-ty-modulated plastic optical fiber sensors. The investigation encompasses key determinants such as the influence of optical source wavelengths, noise response dynamics, ramifications of varying sensing lengths, and repeatability assessments. Findings underscore that elongating the sensing length detrimentally affects both the linearity response and repeatability, largely attributed to the diminished resistance to external noise. Additionally, the choice of the optical source wavelength proved to be a critical variable in assessing sensor sensitivity. Optical fiber ; Transformer ; Materials science ; Repeatability ; Computer science ; Electronic engineering ; Environmental science ; Voltage ; Electrical engineering ; Telecommunications ; Engineering Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Sensors ; Power Transformer Diagnostics and Insulation
Authors: Matevž Zupančič | Donato Fontanarosa | Matic Može | Mattia Bucci | Matevž Vodopivec | Balasubramanian Nagarajan | Maria Rosaria Vetrano | Sylvie Castagne | Iztok Golobič
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Enhanced Nucleate Boiling of Novec 649 on Thin Metal Foils Via Laser-Induced Periodic Surface Structures Boiling ; Materials science ; Nucleation ; Surface (topology) ; Laser ; Metal ; Composite material ; Metallurgy ; Optics ; Geometry ; Thermodynamics ; Physics ; Mathematics Nuclear Materials and Properties ; nanoparticles nucleation surface interactions