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Enregistrement W4401006392 · doi:10.1093/mam/ozae044.062

Challenges in Silver Conservation: Characterizing the Composition and Sources of Unusual Tarnish on Seleucid Silver Coins Using SEM-EDS

2024· article· en· W4401006392 sur OpenAlexaff
Maria Stanko, Dian Yu, Laura Lipcsei, Jane Y. Howe, Doug D. Perovic

Notice bibliographique

RevueMicroscopy and Microanalysis · 2024
Typearticle
Langueen
DomaineArts and Humanities
ThématiqueCultural Heritage Materials Analysis
Établissements canadiensRoyal Ontario MuseumUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésTarnishComposition (language)Materials scienceMetallurgyMineralogyNanotechnologyChemistryArtCopper

Résumé

récupéré en direct d'OpenAlex

Due to its status as a noble metal, one might expect that silver, a material abundant in antiquity and cultural heritage, would require little or no conservation, particularly in controlled museum environments. While silver demonstrates greater resistance to oxidation compared with other ancient metals like copper, iron, lead, and tin [1], it possesses a vulnerability that complicates its long-term preservation. Silver exhibits an electrochemical affinity with sulfur, a contaminant that is naturally found in unregulated indoor environments, primarily in the form of hydrogen sulfide (H2S) and carbonyl sulfide (COS) [2]. This process, commonly known as ‘silver tarnishing’, is well-documented and characterized by the growth of silver sulfide (acanthite, Ag2S) on the metal’s surface [3]. Remarkably, airborne sulfur concentrations as minute as 0.2 ppb have been reported as sufficient to trigger silver sulfidation, with increased levels of moisture and sulfur accelerating the tarnishing rate [4]. Prolonged exposure to ambient indoor conditions can result in tarnish layers beyond 100 nm thick [5], giving the originally sleek and shiny metal a black/grey discoloration and dull appearance [6,7,8,9] which, by many curators and patrons, may be considered aesthetically displeasing. Consequently, conservators may be tasked with removing the tarnish chemically or mechanically using particles to abrade the surface, as necessary to reduce the crystalline silver sulfide [10]. At the same time, unless the underlying environmental factors contributing to tarnishing are addressed, the silver artifacts will continue to react with the sulfur, requiring continuous treatment to maintain their appearance. This tarnishing/treatment cycle will cause the progressive removal of surface atoms, resulting in the loss of surface details that characterize the cultural heritage of the object. As such, the long-term preservation of silver artifacts necessitates the maximally achievable elimination of tarnish-inducing agents from their storage and/or exhibition environments. The exact sources responsible for the accelerated development of silver tarnish, however, are not always evident. Featured in this work, is a case study of two extensively tarnished silver coins originating from the Seleucid dynasty of the Hellenistic period (∼117-118 BCE). Having been on display for nearly four decades, these coins had developed significant surface tarnish, as inferred from visual inspection. Contrary to the typical description of advanced silver tarnish in literature, their physical appearance, as shown in Figure 1, is neither entirely ‘black/grey’ nor ‘dull’. Notably, localized areas exhibiting vivid and distinct colouration alongside a ‘glossy’ almost ‘oily’ surface quality prompted an inquiry into the contaminants responsible for the clearly non-silver appearance of the coins. To identify the causes behind the coins’ pronounced tarnishing and ‘glossiness’, as well as to formulate targeted remediations for eliminating the tarnish-inducing contaminants in and around the display case, elemental characterization of the coin surfaces was conducted using non-destructive analytical techniques. Micrographs and elemental spectra were captured using a Hitachi SU-7000 Schottky Field Emission Scanning Electron Microscope (SEM) combined with an Oxford Ultim Max SDD for Energy Dispersive X-ray Spectroscopy (EDS), operating with an acceleration voltage of 20kV. Prior to SEM imaging, the coins were gently swabbed with ethanol to eliminate loose surface particulates and excess carbon contamination. As illustrated in Figures 2 and 3, EDS analysis of both tarnished coins revealed the presence of sulfur, chlorine, and carbon within the uniformly distributed surface tarnish. While the detection of sulfur was anticipated, the significant presence of chlorine was surprising. Although conservation literature acknowledges silver as prone to corrosion attacks by chlorine [3] and identifies chlorine as a common constituent of silver tarnish [7], its study in the context of silver tarnish is limited, with experimental simulations predominantly focused on mechanisms of the sulfidation process. The accumulation of sulfur on the coins was mainly attributed to airborne emissions from gastrointestinal and metabolic processes (humans) within the gallery, whereas chlorine was linked to potential off-gassing from polymer-based materials, like polyvinyl chloride (PVC) components within the display case and/or the microclimate unit feeding its air. Given the observed ‘glossy’ surface texture of the coins, it was hypothesized that an organic film composed of hydrocarbons might be overlaying the silver tarnish. However, solely relying on EDS spectra, which reported significant carbon signals, made it challenging to definitively classify it as an organic coating rather than copious amounts of carbon contamination, as expected due to limitations with sample cleaning. To provide supplementary evidence for the presence of a hydrocarbon film, an SEM technique for visualizing the response of organics to a focused electron beam was employed, with results depicted in Figure 4. This methodology, utilizing a low-energy beam at an accelerating voltage of 3kV and a magnification of up to 45,000x, enabled the observation of the surface actively excited into motion by the beam. This surface activity is attributed to radiolysis, which involves the breaking of weak covalent bonds [11]. Given that silver sulphide is an inorganic crystalline corrosion product, the observation of radiolysis on the surface of the coins, combined with the significant amounts of carbon detected by EDS, suggests the presence of an organic coating. This ‘glossy’ film is attributed to hydrocarbons (oil and grease) sourced from a kitchen environment [3] which likely shares ventilation and piping systems with the Greek gallery that supplies the air into the display case. X-ray Photoelectron Spectroscopy (XPS) analysis is underway to isolate the nature of the organic top-layer as possible triglycerides (fatty acids composing culinary oils [12]), and to generate a depth profile that can confirm the suspected multi-layer contamination on the coins’ surfaces. This profile is anticipated to consist of the outermost organic film, followed by a silver-sulfide/chloride tarnish layer, a possible copper oxide/sulfide layer (as observed in experimentally simulated silver tarnish [6]), and finally the silver-copper alloy of the original coin. Data points will be collected at various locations on the coin surfaces to assess the accuracy of the thin film interference phenomenon as an explanation for the observed differences in colouration resulting from varying tarnish layer thicknesses [5,6,7]. Additionally, this analysis may elucidate whether variations in silver tarnish chemistry and corrosion products play a role in to the observed colouration [6]. This study, implementing a non-destructive SEM-EDS characterization approach, facilitated the correlation of visual and elemental characteristics of silver tarnish to environmental contaminants in the context of a museum exhibit. It explores constituents seldom encountered in literature studies of both real and archaeological silver tarnish and experimentally simulated silver sulfidation. Notably, this work discusses the likely sources of heavy chlorine content in silver tarnish and introduces a procedure for the analytical identification of hydrocarbon accumulation on artifacts along with its attribution to unanticipated environmental contaminants [13]. Digital photographs of the two tarnished coins 5.5 (a) and 6.3 (b), with the areas of analysis boxed out in white (Optical Microscopy) and yellow (SEM-EDS). Indexing of the data is done using the provided descriptive labels referencing the features of the coin on or near the areas of analysis. On the bottom, digital photographs of tarnished coin 5.5 (c), 6.3 (d) and a reference corroded coin (e) under a raking light, highlighting the relative reflectivity or ‘glossiness’ of the surfaces. The visibly lower light diffusion indicates a possible organic film or coating on the two tarnished coins. Typical EDS elemental maps for areas imaged on the tarnished coins. These elemental maps correspond with the Back Scattered Electron (BSE) SEM micrograph, captured at 20kv, of the halo on the obverse side of coin 6.3. Ag and Cu are characterized as ‘bulk’ elements, while C, S and Cl are characterized as ‘tarnish’ elements, and O, Si and Al are characterized as ‘grime’ elements. (a) Plot of the elemental distributions on both tarnished coins, indicating relative amounts detected for each element (in Wt%) based on the location of the SEM-EDS area. Plots (b) and (c) provide elemental distributions for coins 5.5 and 6.3 respectively, with the darkest colored bars corresponding with the SEM-EDS area reporting the highest amounts of sulfur. Screenshots of recordings captured during SEM imaging showing the motion of the contaminants on the coins’ surfaces; Middle Detector (MD) Secondary Electron (SE) images captured at high magnification and low voltage. As highlighted by the blue boxes, the tarnished coins 5.5 (a) and 6.3 (b) display motion on the surface over imaging time, suggesting the presence of an organic film. The reference silver corroded coin (c), which visually exhibited no signs of tarnish, nor an organic coating, shows no such effect under the same imaging parameters. Images captured using a 3kV accelerating voltage, 7.2-9.9 mm working distances, and 40-45k magnifications.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,004

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0000,000
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,055
Tête enseignante GPT0,270
Écart entre enseignants0,215 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2024
Routes d'admission1
Résumé présentoui

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