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Record W3006023896 · doi:10.1180/emu-notes.20.6

The struggle between thermodynamics and kinetics: Phase evolution of ancient and historical ceramics

2019· book-chapter· en· W3006023896 on OpenAlexaboutno aff
Robert B. Heimann, Marino Maggetti

Bibliographic record

VenueMineralogical Society of Great Britain and Ireland eBooks · 2019
Typebook-chapter
Languageen
FieldArts and Humanities
TopicCultural Heritage Materials Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsDiagenesisWeatheringCeramicMineralogyGeologyClay mineralsMetamorphic rockThermodynamic equilibriumMaterials scienceGeochemistryThermodynamicsMetallurgyPhysics

Abstract

fetched live from OpenAlex

This contribution is dedicated to the memory of Professor Ursula Martius Franklin, a true pioneer of archaeometric research, who passed away at her home in Toronto on July 22, 2016, at the age of 94. Making ceramics by firing of clay is essentially a reversal of the natural weathering process of rocks. Millennia ago, potters invented simple pyrotechnologies to recombine the chemical compounds once separated by weathering in order to obtain what is more or less a rock-like product shaped and decorated according to need and preference. Whereas Nature reconsolidates clays by long-term diagenetic or metamorphic transformation processes, potters exploit a ‘short-cut’ of these processes that affects the state of equilibrium of the system being transformed thermally. This ‘short-cut’ is thought to be akin to the development of mineral-reaction textures resulting from disequilibria established during rapidly heated pyrometamorphic events (Grapes, 2006) involving contact aureoles or reactions with xenoliths. In contrast to most naturally consolidated clays, the solidified rock-like ceramic material inherits non-equilibrium and statistical states best described as ‘frozen-in’. The more or less high temperatures applied to clays during ceramic firing result in a distinct state of sintering that is dependent on the firing temperature, the duration of firing, the firing atmosphere, and the composition and grain-size distribution of the clay. Hence, the salient properties of the ceramics have to be assessed in a temperature-time-composition space. Owing to the variability of clay composition, the mineralogical processes during thermal transformation of clay minerals can be very complex, not least because most reactions occur far removed from thermodynamic equilibrium and hence are kinetically controlled; that is, they are time- and temperature-dependent. Indeed, kinetics imposes constraints on thermodynamics by retarding reaction rates because of low temperatures, large temperature gradients present in primitive pottery kilns, short reaction times, inhomogeneously distributed reaction partners, and varying redox conditions triggered, for example, by ingress of air during reducing firing cycles. In the context of ceramic technological development over time, the role and development of pottery technology within complex societies is discussed. The close relationship between pottery development and changes in life/societal organization appears to be a major driver in this endeavour. In this chapter, the phase evolution of some typical ancient and historical ceramics will be traced using ceramic phase diagrams, i.e. chemographical expressions of Goldschmidt’s mineralogical phase rule. In particular, the systems CaO–Al2O3–SiO2 (in which most ancient low- to medium-fired ceramics can be accommodated), K2O–Al2O3–SiO2 (applicable to high-fired Chinese stoneware and European hard-paste porcelain) and Na2O–CaO–(Al2O3)–SiO2 (typical of some ancient Egyptian and Mesopotamian alkaline glazes and French soft-paste porcelain) are discussed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.007
Scholarly communication0.0030.005
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.212
Teacher spread0.177 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations41
Published2019
Admission routes1
Has abstractyes

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