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Spotting Local Environments in Self-Assembled Monolayer-Protected Gold Nanoparticles
Cristian Gabellini1, Maria Şologan2, Elena Pellizzoni2
1Department of Engineering and Architecture, University of Trieste, 34127 Trieste, Italy.
ACS Nano
|December 2, 2022
Summary
Researchers developed an automated machine learning method to identify and classify molecular environments in organic-inorganic nanomaterials like gold nanoparticles. This approach enables precise control and rational design of these versatile hybrid nanoconstructs.
Area of Science:
- Nanomaterials Science
- Computational Chemistry
- Machine Learning Applications
Background:
- Organic-inorganic (O-I) nanomaterials offer unique properties by combining organic and inorganic components.
- Understanding local molecular environments is crucial for designing O-I nanomaterials for applications like catalysis, sensing, and medicine.
- Characterization of these hybrid nanoconstructs is complex due to their intricate structures.
Purpose of the Study:
- To introduce a general, automated methodology for identifying and classifying local molecular environments in O-I nanomaterials.
- To analyze self-assembled monolayer-protected gold nanoparticles (SAM-AuNPs) using this novel approach.
- To establish ground rules for controlling and rationally designing O-I nanomaterials based on data-driven insights.
Main Methods:
- Utilized an atomistic machine learning workflow guided by the Smooth Overlap of Atomic Positions (SOAP) descriptor.
- Analyzed diverse chemically distinct SAM-AuNPs.
- Combined computational results with experimental electron spin resonance (ESR) measurements.
Main Results:
- Developed an agnostic and automated method to detect and compare local environments in SAM-AuNPs with minimal user intervention.
- Confirmed the existence of multiple local environments within SAMs, influenced by organic shell thickness and solvation.
- Extended findings to complex mixed hydrophilic-hydrophobic SAMs.
Conclusions:
- Atomistic machine learning approaches can effectively identify and compare local molecular environments in SAM-AuNPs.
- Organic shell thickness and solvation are key factors determining the nature and number of coexisting environments.
- This work provides a foundation for the data-instructed rational design of advanced O-I nanomaterials.
