Deconstructing Electrostatics of Functionalized Metal Nanoparticles from Molecular Dynamics Simulations

Area of Science:

• Nanotechnology
• Computational Chemistry
• Biophysics

Background:

• Gold nanoparticles (NPs) offer versatile applications in biotechnology and biomedicine due to selective protein interactions.
• Surface functionalization of NPs is crucial for controlling aggregation and interactions with charged biomolecules, particularly for biosensing.
• Accurate computational modeling of NP behavior in solution, especially concerning electrostatic interactions and ionic strength, remains a challenge for low-resolution models.

Purpose of the Study:

• To systematically characterize the conformational dynamics and electrostatic properties of ligand-coated gold nanoparticles (NPs) of varying sizes, charges, and functionalizations.
• To elucidate the influence of NP characteristics and solvent ionic strength on interparticle interactions.
• To establish a foundation for developing generalizable coarse-grained models for NPs that account for ionic strength variations.

Main Methods:

• Atomistic molecular dynamics (MD) simulations were employed to investigate the dynamic behavior of ligand-coated gold NPs.
• Analysis of simulation trajectories focused on conformational dynamics, electrostatic properties, and interparticle interactions.
• The study systematically varied NP size, surface charge, ligand functionalization, and solvent ionic strength.

Main Results:

• Detailed characterization of the conformational dynamics of ligand-coated gold NPs was achieved.
• Electrostatic properties and long-range interactions were deconstructed, revealing the impact of NP features and ionic strength.
• A generalizable representation of average long-range interactions was proposed, applicable across different NP parameters and ionic strengths.

Conclusions:

• The study provides critical insights into the role of NP size, charge, structure, and ionic strength in governing interparticle interactions.
• Findings pave the way for more accurate computational modeling of nanoparticle behavior in complex biological environments.
• This work represents a significant step towards developing robust coarse-grained models for nanoparticles that accurately capture the effects of varying ionic strengths.