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Record W4220836992 · doi:10.5194/egusphere-egu22-11602

Thermal degradation of biological carbonates

2022· preprint· en· W4220836992 on OpenAlexaboutno aff
Pedro Álvarez‐Lloret, Adriana Torres-Mansilla, Luis Monasterio-Guillot

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMaterials Science
TopicCalcium Carbonate Crystallization and Inhibition
Canadian institutionsnot available
Fundersnot available
KeywordsAragoniteCalciteMineralBiomineralizationCalcium carbonateDifferential scanning calorimetryMineralogyVateriteChemistryCrystallinityCalciumChemical engineeringCrystallographyOrganic chemistry

Abstract

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<p>The study of mineral decarbonation mechanisms is of great interest for its application to various geological and industrial processes. Biomineralization is a phenomenon by which living organisms are able to produce mineral phases, the most abundant of which are calcium carbonates and phosphates. Among the more abundant polymorphs of calcium carbonate are calcite and aragonite, being the most thermodynamically stable structures under biological environments (Weiner & Addadi, 1997). In general, mineral phases formed by biologically controlled mineralisation processes have lower crystallinity characteristics than their geological analogues. In the current communication, the thermal degradation of biogenic calcium carbonates is comparatively studied with their respective ones of geological origin. During the transformation, chemical and microstructural alterations occur from the original polymorphs of biogenic calcite (eggshell; <em>Gallus gallus</em>) and aragonite (mollusc shell; <em>Ruditapes philippinarum</em>) to the final calcium oxide mineral phase. The samples were gradually heated from room temperature to 1100°C in order to remove the water content and organic matter components contained in the biogenic phases and to induce progressive mineral decarbonation. The kinetics of these compositional transformations were analysed by means of differential scanning calorimetry (DSC). Different analytical techniques, such as attenuated total reflectance infrared spectroscopy (ATR-FTIR), X-ray diffraction (XRD) and scanning electron microscopy (SEM), were used for the chemical and structural characterisation of the mineral transformation of these phases. During thermal degradation, changes are observed in the molecular composition of these biogenic phases related to the distortion of the carbonate group and its association with the organic components. Furthermore, the mineral decarbonation process of calcium carbonates involves different structural transformation mechanisms that depend on the modification of unit cell parameters, thermal expansion coefficients and microstructural reorganisation (Rodriguez-Navarro et al., 2009). Accordingly, CaCO3 crystalline structures are transformed from the original calcite and aragonite phases to the cubic structure of lime, with a calcite-aragonite transformation prior to decarbonation explained by the reorientation of the CO3 group towards Ca and by changes in the packing of the Ca atoms, followed by the increase of the unit cell volume (Antao & Hassan, 2010). The results show some significant differences during mineral transformation depending on their geological or biological origin. The characterization of this mineral decarbonation process has important implications in natural and industrial processes (i.e., cement production, CO2 capture).</p><ul><li>Weiner, S.; Addadi, L. (1997) Design strategies in mineralized biological materials. Journal of Materials Chemistry, 7(5), 689-702.</li> <li>Rodriguez-Navarro, C.; Ruiz-Agudo, E.; Luque, A.; Rodriguez-Navarro, A.B.; Ortega-Huertas, M. (2009) Thermal decomposition of calcite: mechanisms of formation and textural evolution of CaO nanocrystals. American Mineralogist 94, 578–593.</li> <li>Antao, S. M., Hassan, I. (2010) Temperature Dependence of the Structural Parameters in the Transformation of Aragonite to Calcite, as Determined from in Situ Synchrotron Powder X-Ray-Diffraction Data. Canadian Mineralogist 48, 1225– 1236.</li> </ul>

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0220.000

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 head0.046
GPT teacher head0.276
Teacher spread0.230 · 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 teacher head, not a consensus.

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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