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

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.

Controlling the dopant dose in silicon by mixed-monolayer doping

Molecular monolayer doping (MLD) presents an alternative to achieve doping of silicon in a nondestructive way and holds potential for realizing ultrashallow junctions and doping of nonplanar surfaces. Here, we report the mixing of dopant-containing alkenes with alkenes that lack this functionality at various ratios to control the dopant concentration in the resulting monolayer and concomitantly the dopant dose in the silicon substrate. The mixed monolayers were grafted onto hydrogen-terminated silicon using well-established hydrosilylation chemistry. Contact angle measurements, X-ray photon spectroscopy (XPS) on the boron-containing monolayers, and Auger electron spectroscopy on the phosphorus-containing monolayers show clear trends as a function of the dopant-containing alkene concentration. Dynamic secondary-ion mass spectroscopy (D-SIMS) and Van der Pauw resistance measurements on the in-diffused samples show an effective tuning of the doping concentration in silicon.

Self-assembly of nanoparticle amphiphiles with adaptive surface chemistry

We investigate the self-assembly of amphiphilic nanoparticles (NPs) functionalized with mixed monolayers of hydrophobic and hydrophilic ligands in water. Unlike typical amphiphilic particles with "fixed" surface chemistries, the ligands used here are not bound irreversibly but can rearrange dynamically on the particles' surface during their assembly from solution. Depending on the assembly conditions, these adaptive amphiphiles form compact micellar clusters or extended chain-like assemblies in aqueous solution. By controlling the amount of hydrophobic ligands on the particles' surface, the average number of nearest neighbors--that is, the preferred coordination number--can be varied systematically from ∼ 1 (dimers) to ∼ 2 (linear chains) to ∼ 3 (extended clusters). To explain these experimental findings, we present an assembly mechanism in which hydrophobic ligands organize dynamically to form discrete patches between proximal NPs to minimize contact with their aqueous surroundings. Monte Carlo simulations incorporating these adaptive hydrophobic interactions reproduce the three-dimensional assemblies observed in experiment. These results suggest a general strategy based on reconfigurable "sticky" patches that may allow for tunable control over particle coordination number within self-assembled structures.

Controlling assembly of mixed thiol monolayers on silver nanoparticles to tune their surface properties

Modifying the surfaces of metal nanoparticles with self-assembled monolayers of functionalized thiols provides a simple and direct method to alter their surface properties. Mixed self-assembled monolayers can extend this approach since, in principle, the surfaces can be tuned by altering the proportion of each modifier that is adsorbed. However, this works best if the composition and microstructure of the monolayers can be controlled. Here, we have modified preprepared silver colloids with binary mixtures of thiols at varying concentrations and modifier ratios. Surface-enhanced Raman spectroscopy was then used to determine the effect of altering these parameters on the composition of the resulting mixed monolayers. The data could be explained using a new model based on a modified competitive Langmuir approach. It was found that the composition of the mixed monolayer only reflected the ratio of modifiers in the feedstock when the total amount of modifier was sufficient for approximately one monolayer coverage. At higher modifier concentrations the thermodynamically favored modifier dominated, but working at near monolayer concentrations allowed the surface composition to be controlled by changing the ratios of modifiers. Finally, a positively charged porphyrin probe molecule was used to investigate the microstructure of the mixed monolayers, i.e., homogeneous versus domains. In this case the modifier domains were found to be <2 nm.