Nuclear Magnetic Resonance, a Powerful Tool in Cultural Heritage

Proietti, Noemi; Capitani, Donatella; Tullio, Valeria Di

doi:10.3390/magnetochemistry4010011

Cited by 11 publications

(5 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The reduction in the thickness represented by a weaker post-treatment hydrogen signal was used as an indicator of its removal and of the success of the cleaning treatment. [22] Understanding how varnishes and film layers affect the treatment of a painting is essential in establishing the best practices for the conservation of the painting. Fife et al [23] used portable NMR to examine the effect varnishing has on the stiffness of paintings and how it is influenced by the type of treatment solvents used.…”

Section: Cleaning Of Paintingsmentioning

confidence: 99%

How mobile NMR can help with the conservation of paintings

Awad

Baias

2020

Magnetic Reson in Chemistry

View full text Add to dashboard Cite

The conservation of paintings is fundamental to ensure that future generations will have access to the ideas of the grand masters who created these art pieces. Many factors, such as humidity, temperature, light, and pollutants, pose a risk to the conservation of paintings. To help with painting conservation, it is essential to be able to noninvasively study how these factors affect paintings and to develop methods to investigate their effects on painting degradation. Hence, the use of mobile nuclear magnetic resonance (NMR) as a method of investigation of paintings is gaining increased attention in the world of Heritage Science. In this mini‐review, we discuss how this method was used to better understand the stratigraphy of paintings and the effect different factors have on the painting integrity, to analyze the different cleaning techniques suitable for painting conservation, and to show how mobile NMR can be used to identify forgeries. It is also important to keep in mind its limitations and build upon this information to optimize it to extend its applicability to the study of paintings and other precious objects of cultural heritage.

show abstract

Section: Cleaning Of Paintingsmentioning

confidence: 99%

How mobile NMR can help with the conservation of paintings

Awad

Baias

2020

Magnetic Reson in Chemistry

View full text Add to dashboard Cite

show abstract

“…They are especially well-suited for the characterization and investigation of precious works of art from the cultural heritage, due to their mostly non-invasive nature, especially for mobile techniques [9]. A diverse array of experimental approaches has been used and developed to investigate artistic materials: Indeed, ultraviolet-visible (UV-Vis) [10], infrared (IR) [11] and Raman spectroscopies [12][13][14], as well as nuclear magnetic resonance (NMR) [15], with additional important contributions provided by other analytical tools like mass spectrometry (MS) [16][17][18], chromatography and X-Ray diffraction (XRD) [12], [13] have been reported. New spectroscopic techniques are developed and are becoming more and more sophisticated like hyperspectral spectroscopy [25].…”

Section: Introductionmentioning

confidence: 99%

Rapid and complete prediction of Madder natural dyes’ color and properties through computational method

TRAN,

BERRAUD-PACHE,

JABER

2024

Preprint

View full text Add to dashboard Cite

Experimental chemists traditionally hold experimentation in high regard, but computational chemistry has the potential to revolutionize this perspective. Our work exemplifies this shift, employing computational methods such as Density Functional Theory (DFT), Time-Dependent Density Functional Theory (TD-DFT), and Quantum Mechanic/Molecular Mechanic (QM/MM) to delve into the realm of photodegradation of color. Focused on Madder dye, a natural and historical pigment renowned for its diverse color properties, we tackle challenges that traditional experimentation struggled to address. The historical difficulty in deciphering the spectroscopic properties of Madder's colorants due to extraction challenges, impurities, and high costs is overcome through computational spectroscopy techniques. By marrying computational insights with experimental data, we predict UV-Vis, color, and NMR spectra, considering factors like pH, solvent effects, and conformers. The study underscores the impact of solute-solvent interactions on reproducing experimental measurements, laying the foundation for a comprehensive database to understand color properties in cultural heritage. These initial findings pave the way for future exploration of complex systems like Madder lake. This transformative shift empowers researchers and scientists to conquer crucial challenges in comprehending the properties of ancient colors for cultural heritage preservation, advancing the development of durable bio-sourced colors, and discovering new color applications.

show abstract

“…Molecules 2022, 27, 3653 2 of 16 metabolomics [7][8][9], drug discovery [10][11][12], food [13,14], natural products [15,16], flavors [17], environments [18], forensic [19], cultural heritage [20], etc. It is mainly used for three tasks: identification, verification and quantification [21].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Method for Compound Identification in NMR Spectra of Mixtures

et al. 2022

View full text Add to dashboard Cite

Nuclear magnetic resonance (NMR) spectroscopy is highly unbiased and reproducible, which provides us a powerful tool to analyze mixtures consisting of small molecules. However, the compound identification in NMR spectra of mixtures is highly challenging because of chemical shift variations of the same compound in different mixtures and peak overlapping among molecules. Here, we present a pseudo-Siamese convolutional neural network method (pSCNN) to identify compounds in mixtures for NMR spectroscopy. A data augmentation method was implemented for the superposition of several NMR spectra sampled from a spectral database with random noises. The augmented dataset was split and used to train, validate and test the pSCNN model. Two experimental NMR datasets (flavor mixtures and additional flavor mixture) were acquired to benchmark its performance in real applications. The results show that the proposed method can achieve good performances in the augmented test set (ACC = 99.80%, TPR = 99.70% and FPR = 0.10%), the flavor mixtures dataset (ACC = 97.62%, TPR = 96.44% and FPR = 2.29%) and the additional flavor mixture dataset (ACC = 91.67%, TPR = 100.00% and FPR = 10.53%). We have demonstrated that the translational invariance of convolutional neural networks can solve the chemical shift variation problem in NMR spectra. In summary, pSCNN is an off-the-shelf method to identify compounds in mixtures for NMR spectroscopy because of its accuracy in compound identification and robustness to chemical shift variation.

show abstract

Nuclear Magnetic Resonance, a Powerful Tool in Cultural Heritage

Cited by 11 publications

References 73 publications

How mobile NMR can help with the conservation of paintings

How mobile NMR can help with the conservation of paintings

Rapid and complete prediction of Madder natural dyes’ color and properties through computational method

Deep Learning-Based Method for Compound Identification in NMR Spectra of Mixtures

Contact Info

Product

Resources

About