229 research outputs found

    A neural-network-based surrogate model for the properties of neutron stars in 4D Einstein-Gauss-Bonnet gravity

    Full text link
    Machine learning and artificial neural networks (ANNs) have increasingly become integral to data analysis research in astrophysics due to the growing demand for fast calculations resulting from the abundance of observational data. Simultaneously, neutron stars and black holes have been extensively examined within modified theories of gravity since they enable the exploration of the strong field regime of gravity. In this study, we employ ANNs to develop a surrogate model for a numerical iterative method to solve the structure equations of NSs within a specific 4D Einstein-Gauss-Bonnet gravity framework. We have trained highly accurate surrogate models, each corresponding to one of twenty realistic EoSs. The resulting ANN models predict the mass and radius of individual NS models between 10 and 100 times faster than the numerical solver. In the case of batch processing, we demonstrated that the speed up is several orders of magnitude higher. We have trained additional models where the radius is predicted for specific masses. Here, the speed up is considerably higher since the original numerical code that constructs the equilibrium models would have to do additional iterations to find a model with a specific mass. Our ANN models can be used to speed up Bayesian inference, where the mass and radius of equilibrium models in this theory of gravity are required.Comment: 12 pages, 8 figure

    Rapid and Efficient N-tert-butoxy carbonylation of Amines Catalyzed by Sulfated Tin Oxide Under Solvent-free Condition

    Get PDF
    A straightforward, rapid, and efficient protocol for the N-tert-butoxy carbonyl (N-Boc) protection of amines (aromatic, aliphatic) using sulfated tin oxide catalyst is illustrated. N-Boc protection of various amines was carried out with (Boc)2O using sulfated tin oxide as a catalyst at room temperature under solvent-free conditions. Rapid reaction times, ease of handling, cleaner reactions, easy work-up, reusable catalyst, and excellent isolated yields are the striking features of this methodology which can be considered to be one of the better methods for the protection of amines and alcohols. DOI: http://dx.doi.org/10.17807/orbital.v10i7.115

    An Overview of Recent Development in Composite Catalysts from Porous Materials for Various Reactions and Processes

    Get PDF
    Catalysts are important to the chemical industry and environmental remediation due to their effective conversion of one chemical into another. Among them, composite catalysts have attracted continuous attention during the past decades. Nowadays, composite catalysts are being used more and more to meet the practical catalytic performance requirements in the chemical industry of high activity, high selectivity and good stability. In this paper, we reviewed our recent work on development of composite catalysts, mainly focusing on the composite catalysts obtained from porous materials such as zeolites, mesoporous materials, carbon nanotubes (CNT), etc. Six types of porous composite catalysts are discussed, including amorphous oxide modified zeolite composite catalysts, zeolite composites prepared by co-crystallization or overgrowth, hierarchical porous catalysts, host-guest porous composites, inorganic and organic mesoporous composite catalysts, and polymer/CNT composite catalysts
    • …
    corecore