This ongoing project develops a computational framework to model the growth kinetics and optical/electronic evolution of biosynthesized silver nanoparticles (AgNPs) produced using Eichhornia crassipes (water hyacinth) as a green reducing and stabilizing system. The work links reaction kinetics → particle size distribution → UV-Vis plasmonic response → quantum confinement trends, enabling prediction and optimization of synthesis conditions for targeted nanoparticle properties.
Research Goal
To provide a reproducible, physics-grounded simulation workflow that predicts how time and temperature-dependent Ag⁺ reduction drives nanoparticle nucleation/growth, and how these changes control:
Surface Plasmon Resonance (SPR) peak position and intensity
Extinction/absorbance evolution during synthesis
Size dispersity and transport metrics (DLS-like outputs)
Bandgap modulation via quantum confinement
What the Model Includes
Pseudo-first-order (PFO) reduction kinetics to simulate Ag⁺ → Ag⁰ conversion over time, including temperature dependence (Arrhenius-style rate behavior).
Lognormal size distribution modeling to capture realistic nanoparticle population growth and dispersity trends.
DLS-inspired transport estimates using Stokes–Einstein diffusion to compute size-dependent diffusion coefficients and intensity-weighted Z-average behavior.
Optical response simulations using quasi-static Mie theory coupled with Drude dielectric modeling to predict size-dependent SPR shifts, linewidth changes, and extinction scaling.
Beer-Lambert coupling to connect Ag⁰ concentration and extinction behavior to time-resolved UV-Vis absorbance.
Quantum confinement interpretation using effective-mass concepts to examine size-dependent bandgap trends during growth.
Key Findings (Current Results)
Kinetics: Simulations show Ag⁰ formation rising under pseudo-first-order behavior.
Size Distributions: Predicted nanoparticle populations follow lognormal distributions supporting controlled growth in a cellulose-dominated matrix.
Plasmonics: The SPR peak redshifts with increasing size consistent with plasmonic theory and reported experimental trends.
Time-Resolved UV–Vis: Absorbance increases systematically during synthesis (growth and increasing metallic fraction), capturing nucleation → growth → saturation behavior.
Temperature Effects: Higher temperatures accelerate reduction and growth, producing faster nucleation and slightly broader distributions at the highest temperature regimes.
Quantum Confinement: Bandgap estimates decrease modestly with growth (reduced confinement), supporting the coupled kinetic-optical-electronic interpretation.
Why This Matters
This project provides a predictive design tool for green-synthesized AgNPs, allowing researchers to tune particle size, dispersity, SPR response, and electronic trends by adjusting synthesis conditions (time, temperature, and growth environment). Target applications include catalysis, sensing, photonics, and antimicrobial/biomedical materials.
Tools & Technologies
Python (NumPy, Matplotlib)
Kinetic modeling (PFO + temperature dependence)
Lognormal population modeling + dispersity metrics
Quasi-static Mie–Drude plasmonic simulations
UV-Vis modeling via Beer–Lambert coupling
Quantum confinement trend modeling (effective-mass concepts)
Status
Ongoing - Publication done in Applied Physics A
Planned next steps include:
model calibration against experimental UV-Vis/DLS/TEM datasets
uncertainty quantification and sensitivity analysis (rate constants, dielectric parameters, damping terms)
extension to aggregation/capping effects and medium refractive-index variability
packaging the workflow into a reproducible, shareable research codebase