Skip to Main Content (Press Enter)

Logo CNR
  • ×
  • Home
  • Persone
  • Pubblicazioni
  • Strutture
  • Competenze

UNI-FIND
Logo CNR

|

UNI-FIND

cnr.it
  • ×
  • Home
  • Persone
  • Pubblicazioni
  • Strutture
  • Competenze
  1. Pubblicazioni

Photoacoustic signal attenuation analysis for the assessment of thin layers thickness in paintings

Articolo
Data di Pubblicazione:
2018
Abstract:
This study introduces a novel method for the thickness estimation of thin paint layers in works of art, based on photoacoustic signal attenuation analysis (PAcSAA). Ad hoc designed samples with acrylic paint layers (Primary Red Magenta, Cadmium Yellow, Ultramarine Blue) of various thicknesses on glass substrates were realized for the specific application. After characterization by Optical Coherence Tomography imaging, samples were irradiated at the back side using low energy nanosecond laser pulses of 532 nm wavelength. Photoacoustic waves undergo a frequencydependent exponential attenuation through the paint layer, before being detected by a broadband ultrasonic transducer. Frequency analysis of the recorded time-domain signals allows for the estimation of the average transmitted frequency function, which shows an exponential decay with the layer thickness. Ultrasonic attenuation models were obtained for each pigment and used to fit the data acquired on an inhomogeneous painted mock-up simulating a real canvas painting. Thickness evaluation through PAcSAA resulted in excellent agreement with cross-section analysis with a conventional brightfield microscope. The results of the current study demonstrate the potential of the proposed PAcSAA method for the non-destructive stratigraphic analysis of painted artworks.
Tipologia CRIS:
01.01 Articolo in rivista
Keywords:
photoacoustic analysis; paint layer thickness
Elenco autori:
DAL FOVO, Alice; Fontana, Raffaella
Autori di Ateneo:
FONTANA RAFFAELLA
Link alla scheda completa:
https://iris.cnr.it/handle/20.500.14243/421073
Pubblicato in:
MICROCHEMICAL JOURNAL
Journal
  • Dati Generali

Dati Generali

URL

https://doi.org/10.1063/1.5022749
  • Utilizzo dei cookie

Realizzato con VIVO | Designed by Cineca | 26.5.0.0 | Sorgente dati: PREPROD (Ribaltamento disabilitato)