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1.
Order fluctuation induced tunable light emission from carbon nano system
Mohanachandran Nair Sindhu Swapna, Sankararaman S, 2019, original scientific article

Abstract: The paper reports the thermal-induced order fuctuations, in a carbon nanosystem with carbon nanotubes (CNTs) synthesized by the incomplete combustion of gingelly oil. The sample annealed at diferent temperatures (30–400 °C) is subjected to various morphological and spectroscopic characterizations. The ultraviolet–visible spectroscopic and thermogravimetric analyses reveal the CNTs in the sample. The high-resolution transmission electron microscopy (HR-TEM) also confrms the formation of CNTs in the sample. The Raman spectrum and X-ray difraction pattern show the signature of multi-walled to single-walled CNT transformation and thus an order fuctuation on annealing. The quantum yield of the sample, measured by integrating sphere method, yields 46.15% at an emission wavelength of 575 nm. When the excitation wavelength is varied from 350 to 510 nm, the CIE coordinate moves from the white region to the yellowish-green region. The varying amount of CNTs in the soot, upon annealing is found to vary the luminescence emission from the sample. The study reveals the thermal-induced oscillatory order in carbon nanosystem with carbon nanotubes (CNTs) leading to tunable excitation/ thermal-dependent luminescence emission and thereby suggesting the possibility of converting the futile soot for fruitful applications in photonics and nanoelectronics.
Keywords: Carbon nanosystem, Single-walled carbon nanotubes, Multi-walled carbon nanotubes, Raman spectroscopy, Thermogravimetric analysis, CIE plot, Quantum yield, gingelly oil
Published in RUNG: 05.07.2022; Views: 1281; Downloads: 0
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2.
Application of machine learning techniques for cosmic ray event classification and implementation of a real-time ultra-high energy photon search with the surface detector of the Pierre Auger Observatory : dissertation
Lukas Zehrer, 2021, doctoral dissertation

Abstract: Despite their discovery already more than a century ago, Cosmic Rays (CRs) still did not divulge all their properties yet. Theories about the origin of ultra-high energy (UHE, > 10^18 eV) CRs predict accompanying primary photons. The existence of UHE photons can be investigated with the world’s largest ground-based experiment for detection of CR-induced extensive air showers (EAS), the Pierre Auger Observatory, which offers an unprecedented exposure to rare UHE cosmic particles. The discovery of photons in the UHE regime would open a new observational window to the Universe, improve our understanding of the origin of CRs, and potentially uncloak new physics beyond the standard model. The novelty of the presented work is the development of a "real-time" photon candidate event stream to a global network of observatories, the Astrophysical Multimessenger Observatory Network (AMON). The stream classifies CR events observed by the Auger surface detector (SD) array as regards their probability to be photon nominees, by feeding to advanced machine learning (ML) methods observational air shower parameters of individual CR events combined in a multivariate analysis (MVA). The described straightforward classification procedure further increases the Pierre Auger Observatory’s endeavour to contribute to the global effort of multi-messenger (MM) studies of the highest energy astrophysical phenomena, by supplying AMON partner observatories the possibility to follow-up detected UHE events, live or in their archival data.
Keywords: astroparticle physics, ultra-high energy cosmic rays, ultra-high energy photons, extensive air showers, Pierre Auger Observatory, multi-messenger, AMON, machine learning, multivariate analysis, dissertations
Published in RUNG: 27.10.2021; Views: 2856; Downloads: 150
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