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VOLUMES OF WORTH—DELIMITING THE SAMPLE SIZE FOR RADIOCARBON DATING OF PARCHMENT

Published online by Cambridge University Press:  09 December 2020

Tuuli M Kasso*
Affiliation:
The Globe Institute, University of Copenhagen, Øster Farimagsgade 5, 1350 Copenhagen, Denmark
Markku J Oinonen
Affiliation:
Laboratory of Chronology, Finnish Museum of Natural History (LUOMUS), 00014 University of Helsinki, Finland
Kenichiro Mizohata
Affiliation:
Laboratory of Chronology, Finnish Museum of Natural History (LUOMUS), 00014 University of Helsinki, Finland Accelerator Laboratory, Faculty of Sciences, University of Helsinki, 00014 University of Helsinki, Finland
Jaakko K Tahkokallio
Affiliation:
National Library of Finland, 00014 University of Helsinki, Finland
Tuomas M Heikkilä
Affiliation:
Faculty of Theology, University of Helsinki, 00014 University of Helsinki, Finland
*
*Corresponding author. Email: tuuli@palaeome.org.
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Abstract

Medieval manuscripts are invaluable archives of the written history of our past. Manuscripts can be dated and localized paleographically, but this method has its limitations. The Fragmenta membranea manuscript collection at the National Library of Finland has proved difficult to date using paleographic methods. Radiocarbon dating has been applied to manuscripts of parchment before, but a systematic protocol for radiocarbon dating of parchment has not been established with a minimally destructive sampling strategy. In this work, we have established a radiocarbon dating procedure for parchments combining a clean-room based chemical pretreatment process, elemental analyzer combustion, automatic graphitization and accelerator mass spectrometry (AMS) measurements to reduce the AMS target size from a typical 1 mg of carbon. Prolonged acid treatment resulted in improved dating accuracy, since this is consistent with the manufacturing process of medieval parchment involving a lime bath. Two different combustion processes were compared. The traditional closed tube combustion (CTC) method provided a well-established though labor-intensive way to produce 1 mg AMS targets. The Elemental Analyzer-based process (EA-HASE, Elemental Analyzer Helsinki Adaptive Sample prEparation line), is designed for fast combustion and smaller sample sizes. The EA-HASE process was capable of reproducing the simulated radiocarbon ages of known-age samples with AMS graphite target sizes of 0.3 mg of carbon, corresponding to a 3 mm2 area of a typical medieval parchment. The full potential of the process to go down to as little as 50 μg will be further explored in the future in parallel to studies of sample-specific contamination issues.

Information

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
The Author(s), 2020. Published by Cambridge University Press on behalf of the University of Arizona
Figure 0

Figure 1 Example of a simulated 14C date of the calendar year AD 1484 by assuming ±30 14C year statistical uncertainty. Note that due to double-peak distribution, the mean value of the calibrated age is 1510 ± 60 calAD which is slightly later than the assumed age of AD 1484.

Figure 1

Table 1 Sample data for 14C-AMS measurements of parchments, msample = original mass of the parchment sample, mcomb = parchment mass for combustion process, method = combustion & graphitization method (CTC/EA-HASE), mC = estimated mass of graphite target for 14C-AMS measurements by assuming carbon content of 35% for collagen.

Figure 2

Table 2 Measurement data of the D and E parchment series. RA = radiocarbon age. σRA = uncertainty of the RA (1 σ). Difference = average of the mean differences of 20 simulated cpd and the measured one. σDiff = standard deviation of Difference.

Figure 3

Figure 2 Comparison of measured and simulated pMC values for D and E series. The z score analysis shows two statistically significant outliers (D5 and E4). Differences between the measured and simulated results are not statistically significant for most of the samples.

Figure 4

Figure 3 An example of a comparison of calendar year distributions of 10 simulated 14C ages of AD 1484 and the measurement of D6 sample.

Figure 5

Figure 4 Differences between 10 simulated calendar year probability distributions of AD 1484 and the measurement D6 in calendar years. Eventually, 20 simulations and corresponding differences were made for each comparison.

Figure 6

Figure 5 An example of a comparison of calendar year distributions of 10 simulated 14C ages of AD 1484 and the measurement of E6 sample.

Figure 7

Figure 6 Differences between simulated calendar year probability distributions of AD 1484 and the measurement E6 in calendar years. Eventually, 20 simulations and corresponding differences were made.

Figure 8

Figure 7 Differences between all the simulated and measured calendar year dates as a function of the AMS target masses.