2012
DOI: 10.1017/s0003598x00047967
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A new interpretative approach to the chemistry of copper-alloy objects: source, recycling and technology

Abstract: The metal composition of bronze alloys has been routinely examined as a means of inferring the source of the ore. But bronze is recycled, and the quantity of some components, such as arsenic, is depleted every time the alloy is melted down. Since the Early Bronze Age of the British Isles was largely supplied from a single mine on Ross Island, Co. Kerry, tracking arsenic content shows the number of re-melts and this gives the object a biography and a social context. Applying this ingenious new procedure to thei… Show more

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Cited by 118 publications
(89 citation statements)
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References 20 publications
(18 reference statements)
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“…impurities from the ore sources, which will affect the multi-element spatial correlations which can be inferred for metalworking in prehistory (based on impurity compositions in metals and/or slags, e.g. Bray and Pollard 2012).…”
Section: A C C E P T E D Accepted Manuscriptmentioning
confidence: 99%
“…impurities from the ore sources, which will affect the multi-element spatial correlations which can be inferred for metalworking in prehistory (based on impurity compositions in metals and/or slags, e.g. Bray and Pollard 2012).…”
Section: A C C E P T E D Accepted Manuscriptmentioning
confidence: 99%
“…For this reason, it is suggested that experimental work be conducted on representative alloys to supplement this research. The arsenical content in the alloys left in the archaeological record is the final product of a casting process that would have involved substantial arsenic loss, meaning that smiths had to start with even more arsenic than the artefacts contain due to the vaporization, sublimation or oxidation of arsenic gas, increasing the fuel requirements beyond what is documented here from the alloys (Lechtman and Klein ). Arsenical copper loses arsenic content the longer it is kept in its molten state due to oxidation effects (Bray and Pollard , 859). This is true during singular events such as the initial smelt or a recycling event, and continues over multiple events, as the decrease of arsenic content continues to occur each time the metal object is recast.…”
Section: Methodsmentioning
confidence: 99%
“…This means that the fuel levels required to melt arsenical coppers tend towards the amount needed to melt pure copper with each additional melt. In other words, the more a smith recycles an arsenical copper object, with each recycling episode arsenic sublimates and the fuel requirements approach the pure copper thermodynamic baseline (Bray and Pollard , 861; see also Table ). New alloying elements such as tin would then be necessary, or otherwise the addition of more arsenical copper ores in a co‐smelting or mixed smelting scenario to increase arsenic content.…”
Section: Methodsmentioning
confidence: 99%
“…However we might doubt the metallurgical groups which they identified. One of the elements included in the statistical analysis was Arsenic, which is potentially the result of alloying or, at the very least, the result of deliberate selection of ores for particular artefact classes (Pearce 2007, 84-86) and which can be lost through oxidation when an artefact is re-melted so that Arsenic content can be used as a proxy to detect recycling (Bray and Pollard 2012;Pollard, Bray and Gosden 2014). Another was Bismuth, which (like Lead) segregates during solidification and so will vary in different parts of an artefact (Slater and Charles 1970).…”
Section: Using Data From Past Analytical Programmesmentioning
confidence: 99%
“…Others, such as Liversage (2000) and de Marinis (1979;2006), have used Waterbolk and Butler's (1965) frequency distribution histograms. Bray and Pollard have recently suggested using the presence/absence (based generally on a cut-off at 0.1%, renormalizing to account for alloying) of Arsenic, Antimony, Silver and Nickel to define groups (Bray and Pollard 2012;Bray et al 2015). These approaches are of course limited to the lowest common denominator of the elements determined by the greatest number of analytical programmes, and it is by no means sure that these elements are those with the greatest heuristic value, but they have arguably yielded important results.…”
Section: Using Data From Past Analytical Programmesmentioning
confidence: 99%