Now showing 1 - 3 of 3
  • Publication
    Site‐Selective MoS2‐Based Sensor for Detection and Discrimination of Triethylamine from Volatile Amines Using Kinetic Analysis and Machine Learning
    (2024)
    Snehraj Gaur
    ;
    Sukhwinder Singh
    ;
    Jyotirmoy Deb
    ;
    Vansh Bhutani
    ;
    Rajkumar Mondal
    ;
    Vishakha Pareek
    ;
    Detection and discrimination of volatile organic compounds (VOCs) is important to provide a more realistic assessment of their potential implication in complex environments and medical diagnostics based on volatile biomarkers. Herein, chemiresistive sensors are fabricated using stacked MoS2 nanoflakes with defects and exposed-edge sites. The sensor is found to be extremely selective to triethylamine (TEA) over polar, non-polar VOCs and atmospheric gases. The sensor exhibits a sensitivity of 1.72% ppm−1, fast response/recovery (19 s/39 s) to 100 ppm TEA at room temperature, low limit of detection (64 ppb), device reproducibility, humidity tolerance (RH 90%) and stability tested up to 60 days. The kinetic analysis of sensing curves reveals two discrete adsorption sites corresponding to edge and basal sites of interaction, with a higher rate constant of association and dissociation for TEA. The Density Functional Theory (DFT) studies support higher adsorption energy of TEA on MoS2 surface with respect to other volatile amines. The sensor demonstrates TEA recognition and composition estimation capability in a binary mixture of a similar class of VOCs using Machine Learning driven analysis with 95% accuracy. The ability to discriminate amines in binary mixture of other volatile amines paves the way for the advancement of next-generation devices in the field of disease diagnosis.
  • Publication
    Facile synthesis of nanostructured Ni/NiO/N-doped graphene electrocatalysts for enhanced oxygen evolution reaction
    (2024)
    Roshni Madampadi
    ;
    Avit Bhogilal Patel
    ;
    C. P. Vinod
    ;
    ;
    Dinesh Jagadeesan
    Electrocatalysts containing a Ni/NiO/N-doped graphene interface have been synthesised using the ligand-assisted chemical vapor deposition technique. NiO nanoparticles were used as the substrate to grow N-doped graphene by decomposing vapours of benzene and N-containing ligands. The method was demonstrated with two nitrogen-containing ligands, namely dipyrazino[2,3-f:2′,3′-h]quinoxaline-2,3,6,7,10,11-hexacarbonitrile (L) and melamine (M). The structure and composition of the as-synthesized composites were characterized by XRD, Raman spectroscopy, SEM, TEM and XPS. The composite prepared using the ligand L had NiO sandwiched between Ni and N-doped graphene and showed an overpotential of 292 mV at 10 mA cm−2 and a Tafel slope of 45.41 mV dec−1 for the OER, which is comparable to the existing noble metal catalysts. The composite prepared using the ligand M had Ni encapsulated by N-doped graphene without NiO. It showed an overpotential of 390 mV at 10 mA cm−2 and a Tafel slope of 78.9 mV dec−1. The ligand-assisted CVD route demonstrates a facile route to control the microstructure of the electrocatalysts.
  • Publication
    SnO2–MWCNT and SnO2–rGO Nanocomposites for Selective Electrochemical Detection in a Mixture of Heavy Metal Ions
    (2024)
    Mohit Verma
    ;
    Ankita Kumari
    ;
    Gaurav Bahuguna
    ;
    Vikas Singh
    ;
    Vishakha Pareek
    ;
    Anandita Dhamija
    ;
    Shubhendra Shukla
    ;
    Dibyajyoti Ghosh
    ;
    Metal oxide-carbon nanocomposites offer an interesting platform for electrochemical sensing due to the synergistic effect of a highly active semiconducting surface and conducting carbon as the supporting backbone. In this work, the in situ synthesis of SnO2 with reduced graphene oxide (rGO) led to the formation of small, uniform SnO2 nanoparticles, measuring 10-20 nm in size, whereas the inclusion of multiwalled carbon nanotubes (MWCNT) resulted in the formation of (200) oriented SnO2 nanoplatelets of ∼200 nm. X-ray photoelectron spectroscopy (XPS) demonstrates a chemical interaction between Sn and C rather than physical adherence. The cyclic voltammograms (CVs) of SnO2-rGO and SnO2-MWCNT display high peak current density and small ΔE in comparison to SnO2, signifying fast electron transfer, reversibility, and enhanced electrochemically active sites. Under optimized experimental conditions of square wave anodic stripping voltammetry (SWASV), the nanocomposites demonstrate high sensitivity (3.9, 9.9, 45.5, and 25.4 mA cm-1 ppb-1) and a low detection limit (in ppb) toward Cd2+, Pb2+, Cu2+, and Hg2+, respectively. The high selectivity of SnO2-rGO for Cd2+ and Pb2+ ions and SnO2-MWCNT for Hg2+ and Cu2+ in a complex metal ion environment is encouraging and is probed by using density functional theory (DFT). Additionally, an artificial neural network (ANN)-based model justifies the sensor’s accuracy and precision for real-time, on-site detection of heavy metal ions directly in tap water.