Organic-inorganic (O-I) nanomaterials are versatile platforms for an incredible high number of applications, ranging from heterogeneous catalysis, molecular sensing, cell targeting, imaging, 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 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 thickness of the organic shell and solvation primary factors in determining number and nature of multiple co-existing environments. These indications are extended to complex mixed hydrophilic-hydrophobic SAMs. This work demonstrates that it is possible to spot out and compare local molecular environments in SAM-AuNPs exploiting atomistic machine-learning approaches, establishes ground rules to control them, and holds the potential for rational design of O-I nanomaterials instructed from data.

Spotting local environments in self-assembled monolayer-protected gold nanoparticles

Cristian Gabellini;Maria Sologan;Elena Pellizzoni;Domenico Marson;Mario Daka;Luca Bignardi;Alessandro Baraldi;Paolo Pengo;Lucia Pasquato;Paola Posocco
2022-01-01

Abstract

Organic-inorganic (O-I) nanomaterials are versatile platforms for an incredible high number of applications, ranging from heterogeneous catalysis, molecular sensing, cell targeting, imaging, 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 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 thickness of the organic shell and solvation primary factors in determining number and nature of multiple co-existing environments. These indications are extended to complex mixed hydrophilic-hydrophobic SAMs. This work demonstrates that it is possible to spot out and compare local molecular environments in SAM-AuNPs exploiting atomistic machine-learning approaches, establishes ground rules to control them, and holds the potential for rational design of O-I nanomaterials instructed from data.
https://pubs.acs.org/doi/full/10.1021/acsnano.2c08467
File in questo prodotto:
File Dimensione Formato  
acsnano.2c08467 ONLINE_compressed.pdf

accesso aperto

Tipologia: Documento in Versione Editoriale
Licenza: Creative commons
Dimensione 1.56 MB
Formato Adobe PDF
1.56 MB Adobe PDF Visualizza/Apri
nn2c08467_si_001.pdf

accesso aperto

Descrizione: supporting material
Tipologia: Altro materiale allegato
Licenza: Creative commons
Dimensione 3.06 MB
Formato Adobe PDF
3.06 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/3035458
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact