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N. Chandrasekhar

ResearcherPublications, citations & collaboration network

N. Chandrasekhar is a researcher indexed in ScholarIQ from OpenAlex & ORCID. ScholarIQ records 106 works, 3,004 citations, an h-index of 32 and an i10-index of 62.

106
Works
3,004
Citations
32
h-index
62
i10-index

How has N. Chandrasekhar's publication output changed over time?

ScholarIQpublication output · 2001–2019

Output grew100% over the shown period — from 1 works in 2001 to 2 in 2019.

1
1
1
1
1
1
1
2
2
200120072011201220132014201520172019

What are the most-cited papers on N. Chandrasekhar?

ScholarIQmost cited works
Simulation of laser butt welding of AISI 316L stainless steel sheet using various heat sources and experimental validation
Jazeel Rahman Chukkan, M. Vasudevan, S. Muthukumaran, Rajiv Kumar, N. Chandrasekhar
S127530304. 2014198 Citations
Sc modified multiferroic BiFeO3 thin films prepared through a sol-gel process
Santiranjan Shannigrahi, Anqi Huang, N. Chandrasekhar, D. Tripathy, A. O. Adeyeye
S105243760. 2007120 Citations
Photonic Microrods Composed of Photoswitchable Molecules: Erasable Heterostructure Waveguides for Tunable Optical Modulation
Dasari Venkatakrishnarao, Md. Ahamad Mohiddon, N. Chandrasekhar, Rajadurai Chandrasekar
S108229257. 2015117 CitationsOPEN ACCESS
Intelligent modeling for estimating weld bead width and depth of penetration from infra-red thermal images of the weld pool
N. Chandrasekhar, M. Vasudevan, A.K. Bhaduri, T. Jayakumar
Journal of Intelligent Manufacturing. 2013107 Citations

Related on ScholarIQ

Homi Bhabha National Institute
Institution
Reversibly Shape‐Shifting Organic Optical Waveguides: Formation of Organic Nanorings, Nanotubes, and Nanosheets
Paper
Simulation of laser butt welding of AISI 316L stainless steel sheet using various heat sources and experimental validation
Paper
Sc modified multiferroic BiFeO3 thin films prepared through a sol-gel process
Paper
Photonic Microrods Composed of Photoswitchable Molecules: Erasable Heterostructure Waveguides for Tunable Optical Modulation
Paper
Intelligent modeling for estimating weld bead width and depth of penetration from infra-red thermal images of the weld pool
Paper
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