Density Functional Theory (DFT) as a Nondestructive Probe in the Field of Art Conservation: Small-Molecule Adsorption on Aragonite Surfaces.
ACS Appl Mater Interfaces
; 14(11): 13858-13871, 2022 Mar 23.
Article
em En
| MEDLINE
| ID: mdl-35258292
Humans have incorporated minerals in objects of cultural heritage importance for millennia. The surfaces of these objects, which often long outlast the humans that create them, are undeniably exposed to a diverse mixture of chemicals throughout their lifetimes. As of yet, the art conservation community lacks a nondestructive, accurate, and inexpensive flexible computational screening method to evaluate the potential impact of chemicals with art, as a complement to experimental studies. In this work, we propose periodic density functional theory (DFT) studies as a way to address this challenge, specifically for the aragonite phase of calcium carbonate, a mineral that has been used in pigments, marble statues, and limestone architecture since ancient times. Computational models allow art conservation scientists to better understand the atomistic impact of small-molecule adsorbates on common mineral surfaces across a wide variety of environmental conditions. To gain insight into the surface adsorption reactivity of aragonite, we use DFT to investigate the atomistic interactions present in small-molecule-surface interfaces. Our adsorbate set includes common solvents, atmospheric pollutants, and emerging contaminants. Chemicals that significantly disrupt the surface structure such as carboxylic acids and sulfur-containing molecules are highlighted. We also focus on comparing adsorption energies and changes in surface bonds, which allows for the identification of key features in the electronic structure presented in a projected-density-of-state analysis. The trends outlined here will guide future experiments and allow art conservators to gain a better understanding of how a wide range of molecules interact with an aragonite surface under variable conditions and in different environments.
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01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
ACS Appl Mater Interfaces
Ano de publicação:
2022
Tipo de documento:
Article