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26,793 results on '"Manoharan, A"'

59. Exploring Structural and Electronic Properties of Topological Insulator/Graphene Nano-heterostructures

Author

Gallardo, Valentina, Arce, Barbara, Muñoz, Francisco, Martín, Rodolfo San, Zubritskaya, Irina, Giraldo-Gallo, Paula, Manoharan, Hari, and Parra, Carolina

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

There is great interest in the study of topological insulator-based heterostructures due to expected emerging phenomena. However, a challenge of topological insulator (TI) research is the contribution of the bulk conduction to the TI surface states. Both strain engineering and thickness control routes, which have been proposed to compensate for bulk doping, can be accessed through the use of nano-heterostructures consisting of topological insulator nanostructures grown on 2D materials. In this work, we report the synthesis of TI/graphene nano-heterostructures based on Bi2Te3 and Sb2Te3 nanoplatelets (NPs) grown on single-layer graphene. Various techniques were used to characterize this system in terms of morphology, thickness, composition, and crystal quality. We found that most of the obtained NPs are mainly < 20 [nm] thick with thickness-dependent crystal quality, observed by Raman measurements. Thinner NPs (1 or 2 QL) tend to replicate the topography of the underlying SLG, according to roughness analysis, and observed buckling features. Finally, we show preliminary studies of their band structure obtained by LT-STM (STS) and by DFT. We observe a highly negative doping which can be attributed to the presence of defects., Comment: 10 pages, 6 figures

Published
2023

71. Factor XII signaling via uPAR-integrin β1 axis promotes tubular senescence in diabetic kidney disease

Author

Elwakiel, Ahmed, Gupta, Dheerendra, Rana, Rajiv, Manoharan, Jayakumar, Al-Dabet, Moh’d Mohanad, Ambreen, Saira, Fatima, Sameen, Zimmermann, Silke, Mathew, Akash, Li, Zhiyang, Singh, Kunal, Gupta, Anubhuti, Pal, Surinder, Sulaj, Alba, Kopf, Stefan, Schwab, Constantin, Baber, Ronny, Geffers, Robert, Götze, Tom, Alo, Bekas, Lamers, Christina, Kluge, Paul, Kuenze, Georg, Kohli, Shrey, Renné, Thomas, Shahzad, Khurrum, and Isermann, Berend

Published
2024
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83. Efficacy and tolerability of a 4-month ofloxacin-containing regimen compared to a 6-month regimen in the treatment of patients with superficial lymph node tuberculosis: a randomized trial

Author

Hissar, Syed, Velayutham, Banurekha, Tamizhselvan, Manoharan, Rathinam, Sridhar, Arunbabu, Chinnadurai, Vidhya, Jayanthi Bharathi, Vargunapandian, Gurusamy, Sundararajaperumal, Anandakrishnan, Sivaramakrishnan, Gomathi Narayan, Chelvi, Silambu, Ramesh, Paranchi Murugesan, Arun, Damodharan, Reddy, Sirasanambati Devarajulu, Kumaran, Paramasivam Paul, Kumar, Marimuthu Makesh, Kalaiselvi, Dharuman, Hanna, Luke Elizabeth, Kumar, Hemanth, Gowrisankar, Alagarsamy, Rajavelu, Ramasamy, Jayabal, Lavanya, Ponnuraja, Chinnayan, and Baskaran, Dhanaraj

Published
2024
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100. Efficient Net-Based Deep Learning for Visual Fault Detection in Solar Photovoltaic Modules

Author

R. Priyadarshini, P. S. Manoharan, and S. Mohamed Mansoor Roomi

Subjects
deep neural network, RGB images, solar photovoltaic modules, visual anomalies, Engineering (General). Civil engineering (General), TA1-2040
Abstract

Detecting faults in solar photovoltaic modules (PVM) is crucial for enhancing their longevity, power output, and overall reliability. Visual anomalies such as soiling, partial shading, cell damage, and glass breakage pose significant challenges for fault identification, particularly in harsh environmental conditions. Therefore, it is essential to maintain healthy PV systems with extended lifecycles and optimal performance through the quick and efficient detection of faults. This work introduces a comprehensive approach that encompasses dataset creation, preprocessing, and PV fault classification utilizing the EfficientNet B0 model. Processed RGB images serve as input for the model, enabling the classification of visual faults in PVM. The performance evaluation of the proposed deep neural network model includes metrics such as classification accuracy, F1 score, specificity, and recall. The results highlight the exceptional performance of the proposed model, achieving a classification accuracy of 97.24% for visual fault identification in PV modules. Moreover, the study underscores the model's robustness and efficacy through a comparative analysis with other classification techniques reported in the literature.

Published
2025
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