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Garnet-biotite geothermometry revised; new Margules parameters and a natural specimen data set from Maine

The garnet-biotite thermometer has been recalibrated using recently obtained Margules parameters for iron-magnesium-calcium garnet, Mn interactions in garnet, and Al interactions in biotite, as well as the Fe oxidation state of both minerals. Fe-Mg and ΔWAl Margules parameters for biotite have been...

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Bibliographic Details
Published in:The American mineralogist 1997-06, Vol.82 (5-6), p.582-595
Main Authors: Holdaway, M. J, Mukhopadhyay, Biswajit, Dyar, M. D, Guidotti, C. V, Dutrow, B. L
Format: Article
Language:English
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Summary:The garnet-biotite thermometer has been recalibrated using recently obtained Margules parameters for iron-magnesium-calcium garnet, Mn interactions in garnet, and Al interactions in biotite, as well as the Fe oxidation state of both minerals. Fe-Mg and ΔWAl Margules parameters for biotite have been retrieved by combining experimental results on [6]Al-free and [6]Al-bearing biotite using statistical methods. Margules parameter, per mole of biotite are WBtMgFe = 40719-30T J/mole. ΔWBtAl = WBtFeAl-WBtMgAl = 210190-245.40T J/mole. ΔWBtTi = WBtFeTi-WBtMgTi = 310990-370.39T J/mole. Based on this model, the exchange reaction ΔH is 41952 J/mol and ΔS is 10.35 J/(K mol). Estimated uncertainty for this geothermometer is 25°C. This geothermometer was tested on two data sets. The first consisted of 98 specimens containing garnet and biotite from west-central Maine, which formed under reducing fO2 with graphite, a limited range of P (∼3 to 4.5 kbar), and a moderate range in T (∼550-650°C), and which were all analyzed on a single microprobe using the experimental data. The second set consisted of cordierite-garnet granulites without hypersthene from Ontario. Results here suggest an average T of 662°C, compared with significantly lower or higher TS calculated from other geothermometers. This model reproduces the PErchuk and Lavrent'eva (1983) experimental TS with a standard deviation of 12°C and discriminates the assemblages in the Maine data set better than other models.
ISSN:0003-004X
1945-3027
DOI:10.2138/am-1997-5-618