110 research outputs found
Entropy data for the 3D core-collapse supernova simulation of a 40 solar mass progenitor done by Fornax.
No full textEntropy data (unit: kb per baryon) for the 3D core-collapse supernova simulation of a 40 solar mass progenitor done by Fornax. The entropies are saved as Numpy arrays with shape (1024, 128, 256) corresponding to the (r, theta, phi) directions.
The grid information can be read from grid.h5. The file read.py shows a demo on how to read the grid.h5 file. The demo prints R, theta, and phi of the grid as 1D arrays. Use numpy.meshgrid(R, theta, phi, indexing="ij") to create the 3D meshgrid.
The "*.xmf" files provided make it simple to be read by the open-source visualization engine Paraview (https://www.paraview.org/). Open the ".xmf" files from Paraview, and the variables will be automatically imported.
In real simulations, they are written every millisecond. However, the data in this repository are saved every 10 milliseconds because otherwise it takes too long to upload. Contact the corresponding author to acquire other data
Individual Differences and Category Learning Performance and Strategy - A scoping review protocol
No full textThis registration is the protocol of a scoping review under the working titled "Individual Differences and Category Learning Performance and Strategy - A scoping review". This report is not an update of any previous review. Author Contacts: Tianshu Zhu (corresponding author): [email protected] John Paul Minda, PhD: [email protected] Western Interdisciplinary Research Building, 1151 Richmond Street, London, ON N6A 3K7, Room 5158. Both authors contributed to the search strategy and analytic plan of this review. Any deviations from the protocol will be documented and discussed in the supplementary material of the review. Important deviations will be discussed in the review itself. This review is supported by the University of Western Ontario, which provides subscriptions to academic database and to Covidence. This institution has played no role in developing this protocol. This registration was dveloped using the PRISMA-ScR checklist
Antifungal heteroresistance causes prophylaxis failure and facilitates breakthrough Candida parapsilosis infections
No full textTowards the Controlled Synthesis and Industrial Applications of Two-Dimensional (2D) Materials Guided by Machine Learning
No full textSince the first exfoliation of graphene from graphite in 2004, atomically thin two-dimensional (2D) materials have gained significant attention due to their unique properties that emerge during the transition from bulk to monolayer form. These characteristics have enabled a broad range of applications spanning nanoelectronics, optoelectronics, and energy systems. While substantial progress has been made in the discovery and synthesis of 2D materials, challenges remain in achieving controlled growth and scalable, cost-effective production.
Recent advances in integrating machine learning (ML) into practical engineering processes have accelerated its adoption in 2D materials research by reducing the labor required for large-scale data analysis. This thesis addresses the pressing challenges in 2D material development by establishing a data-driven framework to investigate both the top-down exfoliation and bottom-up synthesis mechanisms, while also exploring their potential for industrial applications.
A customized miniature CVD platform was developed to facilitate real-time optical monitoring of MoS₂ monolayer growth. Through image processing techniques, real-time growth footage was digitized, enabling the extraction of key morphological parameters such as nucleation density, crystal coverage, and growth rate. Machine learning algorithms were employed to correlate these parameters with process conditions, enabling predictive modeling and CVD optimization. This closed-loop system lays the foundation for future autonomous material synthesis platforms.
In parallel, a polymer-assisted dry ball-milling method was introduced for scalable exfoliation of hexagonal boron nitride (hBN). Using ML-based feature selection, critical polymer properties responsible for high exfoliation efficiency were identified. The method was successfully extended to exfoliate various layered materials, supporting its potential for large-scale manufacturing. Additionally, atomic-layer structured photovoltaics (ALSPs) incorporating 2D heterostructures were fabricated, demonstrating strong performance, stability, and flexibility, with promising applications in self-powered electronics.
In general, these studies establish a robust, data-driven strategy for controlled synthesis and industrial implementation of 2D materials
Towards the Controlled Synthesis and Industrial Applications of Two-Dimensional (2D) Materials Guided by Machine Learning
No full textSince the first exfoliation of graphene from graphite in 2004, atomically thin two-dimensional (2D) materials have gained significant attention due to their unique properties that emerge during the transition from bulk to monolayer form. These characteristics have enabled a broad range of applications spanning nanoelectronics, optoelectronics, and energy systems. While substantial progress has been made in the discovery and synthesis of 2D materials, challenges remain in achieving controlled growth and scalable, cost-effective production.
Recent advances in integrating machine learning (ML) into practical engineering processes have accelerated its adoption in 2D materials research by reducing the labor required for large-scale data analysis. This thesis addresses the pressing challenges in 2D material development by establishing a data-driven framework to investigate both the top-down exfoliation and bottom-up synthesis mechanisms, while also exploring their potential for industrial applications.
A customized miniature CVD platform was developed to facilitate real-time optical monitoring of MoS₂ monolayer growth. Through image processing techniques, real-time growth footage was digitized, enabling the extraction of key morphological parameters such as nucleation density, crystal coverage, and growth rate. Machine learning algorithms were employed to correlate these parameters with process conditions, enabling predictive modeling and CVD optimization. This closed-loop system lays the foundation for future autonomous material synthesis platforms.
In parallel, a polymer-assisted dry ball-milling method was introduced for scalable exfoliation of hexagonal boron nitride (hBN). Using ML-based feature selection, critical polymer properties responsible for high exfoliation efficiency were identified. The method was successfully extended to exfoliate various layered materials, supporting its potential for large-scale manufacturing. Additionally, atomic-layer structured photovoltaics (ALSPs) incorporating 2D heterostructures were fabricated, demonstrating strong performance, stability, and flexibility, with promising applications in self-powered electronics.
In general, these studies establish a robust, data-driven strategy for controlled synthesis and industrial implementation of 2D materials
Global Production Networks and Industrial Upgrading in China: The Case in Electronics Contract Manufacturing.
No full textThe paper analyzes the networks of U.S. and Taiwan based electronics contract manufacturers in South China, today the world´s most important location for low-cost mass production in the electronics industry. Based on extensive empirical research, the paper traces the production sites, the organization of manufacturing, and the workforce policies of contract manufacturers in the region, and discusses perspectives and limits of industrial upgrading, especially with regard to the role of labor. In theoretical terms, the author attempts to integrate an analysis of "global flagship networks" with concepts of industrial sociology.
Probing Cation Displacements in Antiferroelectrics: A Joint NMR and TEM Approach
No full textHigh-resolution scanning transmission electron microscopy (STEM) enjoys great advantages for atomic-resolution visualization of the atomic structure, while failing to disclose structural information along the atomic columns. On the other hand, solid-state nuclear magnetic resonance (NMR) spectroscopy is highly sensitive to the three-dimensional, local structure around atoms in the bulk sample but typically cannot provide an intuitive visualization of the structure. Thus, the combination of atomic-resolution (S)TEM and solid-state NMR spectroscopy has the potential to establish an in-depth, multidimensional structural understanding. Here, we explore this novel strategy to probe the structure of antiferroelectric perovskite oxides PbZrO3 and (Pb,La)(Zr,Sn,Ti)O3. We combine complementary information regarding the in-plane displacement vector mapping from STEM with the analysis of local PbO12 environments from 207Pb NMR spectroscopy to provide unprecedented insight into Pb displacements. For PbZrO3, an ordered 4-fold in-plane displacement modulation is clearly revealed via STEM imaging; meanwhile, the out-of-plane information is provided by two discrete 207Pb NMR signals attributed to two crystallographic Pb sites in the 2D-PASS NMR spectrum. In the chemically modified (Pb,La)(Zr,Sn,Ti)O3 system, disorder of the structure manifests in not only an inhomogeneous displacement modulation but also a broad distribution of 207Pb chemical shifts, related to significant disorder of displacement magnitudes and a favoring of larger displacements. We show that the displacement distribution depends on whether both in-plane and out-of-plane displacements or only out-of-plane displacements are considered. Our findings demonstrate the advantages in the structural analysis using combined TEM and NMR approaches, hence laying the foundation work for controlling and optimizing functional properties.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.RST/Storage of Electrochemical Energ
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