Mn Bharath
Mn Bharath has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Alankar Alankar
Indian Institute of Technology Bombay
3 shared publications - Amal Sibi2 shared publications
- Dandapani Vijayshankar
Indian Institute of Technology, Bombay
2 shared publications - Jafar Albinmousa
Mechanical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
2 shared publications - M. J. N. V. Prasad
Department of Metallurgical Engineering and Materials Science, Indian Institute of Technology Bombay, Mumbai, India
2 shared publications R.K. Singh Raman
2 shared publications
Latest Publications
- Open Access
- 17 October 2026
- Published by Elsevier in Electrochimica Acta
- Vol. 581
Bayesian statistical analysis for assessing variable effect of glucose content of simulated body fluid on the corrosion of ZK60 Mg alloy
- 17 October 2026
- Published by Elsevier in Electrochimica Acta
- Vol. 581
Open AccessBayesian statistical analysis for assessing variable effect of glucose content of simulated body fluid on the corrosion of ZK60 Mg alloy
- Open Access
- 5 September 2026
- Published by Elsevier in SSRN Electronic Journal
Bayesian Statistical Analysis for Assessing Variable Effect of Glucose Content of Simulated Body Fluid in Corrosion of a Biodegradable Magnesium Alloy
Glucose contents of simulated body fluid may both inhibit and accelerate corrosion of magnesium alloys. The nature of the influence may depend on the type and content of glucose and the characteristics of simulated body fluid. Corrosion of a Mg-Zn-Zr alloy (i.e., ZK60) in modified-simulated body fluid (m-SBF) was studied with and without added glucose, employing potentiodynamic polarisation (PDP) and electrochemical impedance spectroscopy (EIS). The morphology and chemical characteristics of the corrosion product layer were investigated by scanning electron microscopy (SEM), grazing-incidence X-ray diffraction (GI-XRD), and X-ray photoelectron spectroscopy (XPS), which enabled a mechanistic understanding of the role of glucose addition in corrosion of the alloy. Principal component analysis (PCA) through a Bayesian inference pipeline was applied to the full experimental dataset to provide a statistically rigorous and quantitative assessment of the electrochemical data beyond conventional mean-comparison. This was meant not to replace the mechanistic interpretation derived from PDP, EIS, and surface characterization, but to quantify pattern structure in the raw electrochemical data and to express uncertainty in the condition-level electrochemical summaries in probabilistic terms.
- 5 September 2026
- Published by Elsevier in SSRN Electronic Journal
Open AccessBayesian Statistical Analysis for Assessing Variable Effect of Glucose Content of Simulated Body Fluid in Corrosion of a Biodegradable Magnesium Alloy
Glucose contents of simulated body fluid may both inhibit and accelerate corrosion of magnesium alloys. The nature of the influence may depend on the type and content of glucose and the characteristics of simulated body fluid. Corrosion of a Mg-Zn-Zr alloy (i.e., ZK60) in modified-simulated body fluid (m-SBF) was studied with and without added glucose, employing potentiodynamic polarisation (PDP) and electrochemical impedance spectroscopy (EIS). The morphology and chemical characteristics of the corrosion product layer were investigated by scanning electron microscopy (SEM), grazing-incidence X-ray diffraction (GI-XRD), and X-ray photoelectron spectroscopy (XPS), which enabled a mechanistic understanding of the role of glucose addition in corrosion of the alloy. Principal component analysis (PCA) through a Bayesian inference pipeline was applied to the full experimental dataset to provide a statistically rigorous and quantitative assessment of the electrochemical data beyond conventional mean-comparison. This was meant not to replace the mechanistic interpretation derived from PDP, EIS, and surface characterization, but to quantify pattern structure in the raw electrochemical data and to express uncertainty in the condition-level electrochemical summaries in probabilistic terms.
- 11 December 2025
- Published by Elsevier in Progress in Materials Science
- Vol. 158
The role of protein content in body fluids in magnesium alloy bioimplant degradation: A machine learning approach
- 11 December 2025
- Published by Elsevier in Progress in Materials Science
- Vol. 158
The role of protein content in body fluids in magnesium alloy bioimplant degradation: A machine learning approach