This data article presents a simulation model based on quantum mechanics and energy potentials for obtaining simulation data that allows, from the perspective of materials informatics, the prediction of the electrodeposition mechanism for forming nanostructured metallic coatings. The development of the research is divided into two parts ) the formulation (Quantum mechanical model and Corrected model for electron prediction; using a modified Schrödinger equation) and ) the implementation of the theoretical prediction model (Discretization of the model). For the simulation process, the finite element method (FEM) was used considering the equation of electric potential and electroneutrality with and without the inclusion of quantum leap. We also provide the code to perform QM simulations in CUDA®, and COMSOL® software, the simulation parameters, and data for two metallic arrangements of chromium nanoparticles (CrNPs) electrodeposited on commercial steel substrate. (CrNPs-AISI 1020 steel and CrNPs-A618 steel). Data collection shows the direct relationship between applied potential (), current (), concentration (), and time () for the homogeneous formation of the coating during the electrodeposition process, as estimated by the theoretical model developed. Their potential reuse data is done to establish the precision of the theoretical model in predicting the formation and growth of nanostructured surface coatings with metallic nanoparticles to give surface-mechanical properties.
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http://dx.doi.org/10.1016/j.dib.2023.109269 | DOI Listing |
Acta Crystallogr A Found Adv
March 2025
Pennsylvania State University, University Park, PA 16802, USA.
X-ray diffraction is ideal for probing the sub-surface state during complex or rapid thermomechanical loading of crystalline materials. However, challenges arise as the size of diffraction volumes increases due to spatial broadening and because of the inability to deconvolute the effects of different lattice deformation mechanisms. Here, we present a novel approach that uses combinations of physics-based modeling and machine learning to deconvolve thermal and mechanical elastic strains for diffraction data analysis.
View Article and Find Full Text PDFJACS Au
January 2025
Department of Chemistry, University of Warwick, Coventry CV4 7AL, U.K.
Polyketide synthases (PKSs) are multidomain enzymatic assembly lines that biosynthesize a wide selection of bioactive natural products from simple building blocks. In contrast to their -acyltransferase (AT) counterparts, -AT PKSs rely on stand-alone ATs to load extender units onto acyl carrier protein (ACP) domains embedded in the core PKS machinery. -AT PKS gene clusters also encode stand-alone acyl hydrolases (AHs), which are predicted to share the overall fold of ATs but function like type II thioesterases (TEs), hydrolyzing aberrant acyl chains from ACP domains to promote biosynthetic efficiency.
View Article and Find Full Text PDFOphthalmol Sci
November 2024
Department of Ophthalmology, National Center for Global Health and Medicine, Tokyo, Japan.
Objective: To evaluate the effect of disease stage, frequency and clustering of visual field (VF) tests, inclusion of 1 or both eyes, and 1 (1 arm; before and after a treatment) or 2 groups (2 arms; treatment and control arm) on sample size calculation in clinical trials.
Design: Clinical cohort study.
Participants: A series of VFs were simulated based on test-retest VF data in the early, moderate, and advanced stages of glaucoma with 231, 204, and 226 eyes, respectively.
Water used in post-harvest handling and processing operations is an important risk factor for microbiological cross-contamination of fruits, vegetables and herbs (FVH). Industrial data indicated that the frozen FVH sector is characterised by operational cycles between 8 and 120 h, variable product volumes and no control of the temperature of process water. Intervention strategies were limited to the use of water disinfection treatments such as peroxyacetic acid and hydrogen peroxide.
View Article and Find Full Text PDFA dynamic mass balance model was developed to simulate contamination dynamics in the process water of fresh and frozen fruits, vegetables and herbs (ffFVH) during processing and handling operations. The mass balance relates to the flux of water and product in a wash tank and the number of microbial cells released in the water, inactivated by the water disinfectant or transferred from the water back to the product. Critical variables describing microbial dynamics in water are: (i) the chemical oxygen demand (COD), as an indicator of the concentration of organic matter; (ii) free chlorine (FC) and particularly its antimicrobial fraction, hypochlorous acid (HOCl); and (iii) the microbial population levels.
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