Spotting Local Environments in Self-Assembled Monolayer-Protected Gold Nanoparticles

Abstract

Organic−inorganic (O−I) nanomaterials are versatile platforms for an incredible high number of applications, ranging from heterogeneous catalysis to molecular sensing, cell targeting, imaging, and cancer diagnosis and therapy, just to name a few. Much of their potential stems from the unique control of organic environments around inorganic sites within a single O−I nanomaterial, which allows for new properties that were inaccessible using purely organic or inorganic materials. Structural and mechanistic characterization plays a key role in understanding and rationally designing such hybrid nanoconstructs. Here, we introduce a general methodology to identify and classify local (supra)molecular environments in an archetypal class of O−I nanomaterials, i.e., self-assembled monolayer-protected gold nanoparticles (SAM-AuNPs). By using an atomistic machine-learning guided workflow based on the Smooth Overlap of Atomic Positions (SOAP) descriptor, we analyze a collection of chemically different SAM-AuNPs and detect and compare local environments in a way that is agnostic and automated, i.e., with no need of a priori information and minimal user intervention. In addition, the computational results coupled with experimental electron spin resonance measurements prove that is possible to have more than one local environment inside SAMs, being the thickness of the organic shell and solvation primary factors in the determining number and nature of multiple coexisting environments. These indications are extended to complex mixed hydrophilic−hydrophobic SAMs. This work demonstrates that it is possible to spot and compare local molecular environments in SAM-AuNPs exploiting atomistic machine-learning approaches, establishes ground rules to control them, and holds the potential for the rational design of O−I nanomaterials instructed from data.

Pursuing the Complete OFF State in Photoswitchable Catalysis

“…To verify this hypothesis all PT ligands were replaced by aliphatic ligands (1-hexadecanethiol, HDT). From molecular calculations, we see that the lack of the terminal phenyl ring in HDT ligands enables stronger and persistent interactions between the alkyl portions of the chains, leading to clustering of ligands. The nonuniform distribution of the chains on the surface makes the monolayer locally more exposed to the external environment (Figures and S48).…”

Machine Learning Applications in Optical Fiber Sensing: A Research Agenda

“…The first used ML approaches to discover and categorize local chemical environments in self-assembled monolayer-protected gold nanoparticles [64]. These models were also used to predict the physicochemical properties and behavior of these nanoparticles in biological contexts [65]. On the other hand, several models using artificial neutral network (ANN), long short-term memory (LSTM), and pattern recognition algorithms have been developed to improve the response of fiber optic sensors when this technology is used to monitor the structural health of buildings [66,67].…”

Self-Assembled Monolayers for Uricase Enzyme Absorption Immobilization on Screen-Printed Gold Electrodes Modified

“…In Figure 2b, the view at ×10,000 shows the AuNP distribution on the gold surface, which presented an average diameter of 22.53 nm. The AuNPs increased the molecular level interactions on the working surface due to their nanometric size, facilitating the -SH group interaction with the Au ions for the SAM formation [15].…”