Faculty Publications

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Experimental and Computational Studies of N-(4,6-Dimethylpyrimidin-2-yl)-4-[(quinazoline-4-yl)amino]-benzene-1-sulfonamide as an Effectual Corrosion Inhibitor for Mild Steel in Hydrochloric Acid
(Pleiades journals, 2021) D’Souza, P.R.; Kudva, J.; Kumar, A.S.; Nityananda Shetty, A.N.
Abstract: The inhibition efficacy of N-(4,6-dimethylpyrimidin-2-yl)-4-[(quinazoline-4-yl)amino]benzene-1-sulfonamide (DMQS) as a new corrosion inhibitor on mild steel (MS) in 0.1 M HCl medium at varied temperature has been analysed and it has ensued 97.6% high efficiency at 4 × 10–5 M of DMQS. The electrochemical techniques employed were impedance spectroscopy and potentiodynamic polarization. Surface analysis of MS was carried out through scanning electron microscopy (SEM) and energy dispersion spectroscopy (EDS). Inhibitory effect of DMQS escalated with elevated concentration and declined with elevation in solution temperature. The adsorption of DMQS on MS has consent with the Langmuir adsorption isotherm. The computational study using density functional theory (DFT) with the B3LYP/3-21G basis set level for DMQS was investigated through the association between their molecular and electronic structure. The parameters involved in quantum chemical calculations were energy gap, EHOMO, ELUMO, absolute electronegativity, electrophilicity, softness, global hardness, energy gap, ionisation energy, electron affinity and dipole moment. DMQS shows EHOMO value of –6.256 eV, fortifying MS from corrosive attack and implicating it to be an effectual inhibitor. The results of the implemented studies show the corrosion inhibition efficiency of the DMQS is in good accord. © 2021, Pleiades Publishing, Ltd.
Colorimetric sensors for discriminatory detection of arsenite ions: Application in milk, honey and water samples and molecular logic gates
(Elsevier Inc., 2024) K, K.; Nityananda Shetty, A.N.; Trivedi, D.R.
Millions of people are exposed to dangerous arsenic levels in drinking water, highlighting the urgent need for affordable, continuous on-site arsenic monitoring methods. It is crucial to specifically detect arsenite among inorganic arsenic anions because it is more poisonous than arsenate. Addressing these concerns, the present study developed 5-(4-nitrophenyl)-2-furaldehyde based two colorimetric chemosensors, N5R3 and N5R4, with different signaling groups for the selective detection of arsenite anions over arsenate in DMSO/H2O (6:4, v/v). The red-shift in the UV–Vis absorption spectra supported the distinct color changes of sensors N5R3 and N5R4 displayed upon binding with arsenite. Sensors demonstrated stability over a pH range of 6 to 12 and exhibited stability over a considerable time period. Among the chemosensors, N5R3 exhibited the lowest detection limit of 7.41 ppb with a high binding constant of 2.9976 × 106 M?1 for arsenite. The 1:1 binding interactions between the chemosensors and arsenite were confirmed using B-H plot and Job's plot analysis. The intramolecular charge transfer (ICT) mechanism for detecting arsenite was proposed through UV and 1H NMR titrations, electrochemical studies, mass spectral analysis and DFT calculations. The interactions between the sensor and arsenite anions were further analyzed using global reactivity parameters (GRPs). Practical applications were demonstrated through the utilization of test strips and molecular logic gates. Both chemosensors efficiently recognized arsenite in real water, honey, and milk samples. © 2024 Elsevier B.V.
Performance of anionic dimeric surfactant on AZ31 Magnesium alloy in neutral medium unveiled through experimental and theoretical investigation
(Springer, 2024) Acharya, M.G.; Nityananda Shetty, A.N.
The synthesis of a novel eco-friendly anionic Gemini surfactant, pursuing three parameters of green chemistry: economic, environmental factor, and mass reaction efficiency is developed as a corrosion inhibitor for AZ31 Mg alloy. Herein, the corrosion inhibition ability of novel EDTA-based dimeric surfactant on AZ31 Mg alloy in corrosive media with varying concentrations of NaCl and Na2SO4 at temperatures between 30–50 °C were studied. The surfactant’s molecular structure is affirmed by FT-IR, NMR, and LC–MS techniques. Electrochemical techniques and surface morphology were employed to evaluate the corrosion inhibition efficiency. The inhibitor studied exhibited appreciable corrosion inhibition at 30 °C. The surfactant shows physical adsorption as per the data obtained in the Gibbs free energy and enthalpy of adsorption studies. The adsorption of the inhibitor was found to be a film-like layer on the surface of AZ31 Mg alloy and, is confirmed by SEM–EDX and XPS techniques. In addition, theoretical simulations were performed to compare with experimental results. Conclusively, the work provides a deeper understanding of the intricacies involved in the development of a new anionic dimeric surfactant as an effective corrosion inhibitor. © The Author(s) 2024.
Selective chromogenic nanomolar level sensing of arsenite anions in food samples using dual binding site probes
(Elsevier Ltd, 2025) K, K.; Nityananda Shetty, A.N.; Trivedi, D.R.
In the present study, two chromogenic probes, N7R2 and N7R3, each containing two binding sites, were designed and synthesized for the selective detection of arsenite in DMSO/H2O (1:1, v/v). The probes exhibited stability across a pH range spanning from 5 to 12. The lower detection limits of 2.01 ppb (18.86 nM) for N7R2 and 1.79 ppb (16.75 nM) for N7R3, which are much lower than the WHO recommended permissible limit of arsenite, confirmed the superior efficiency of the probes in detecting arsenite. The detection mechanism for arsenite was proposed through UV and 1H NMR titrations, electrochemical studies, and DFT calculations. Practical applications were demonstrated through the fabrication of test strips and molecular logic gates. The probes efficiently recognized arsenite in real water, honey, milk samples, and fruit/vegetable juices. Both N7R2 and N7R3 exhibited excellent recovery rates in the analysis of food samples, demonstrating the probes' usefulness in real sample analysis. © 2024

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