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Enregistrement W6921549094 · doi:10.7939/81922

Environmental and Crystal Chemical Controls on the Products and Efficiency of Carbon Mineralization Reactions

2025· dissertation· en· W6921549094 sur OpenAlexaboutno aff

Notice bibliographique

RevueUniversity of Alberta Library · 2025
Typedissertation
Langueen
DomaineEnvironmental Science
ThématiqueCO2 Sequestration and Geologic Interactions
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCarbonateCarbonate mineralsWeatheringMineralization (soil science)Carbon cycleCarbon dioxideBiogeochemical cycleGlobal warmingSustainable energy

Résumé

récupéré en direct d'OpenAlex

Negative global consequences associated with the climate crisis are expected to become more extreme and continually worsen as Earth’s mean global temperature approaches, or exceeds, a 2 °C rise from pre-industrial revolution temperatures. To combat this, countries have pledged to transition to sustainable energy systems to limit emissions, primarily as CO2; however, this is a slow process. In order to minimize environmental and societal damage related to anthropogenic climate change, rapid implementation of large-scale carbon dioxide removal (CDR) technologies is necessary to mediate historic and current emissions. While many strategies for CDR are being explored, CO2 storage in geologic environments or as benign carbonate minerals presents a safe, long-term repository for anthropogenic emission. In my thesis, I examine how environmental conditions and biogeochemical processes influence CO2 storage in carbonate minerals to help advance carbon mineralization and enhanced rock weathering (ERW) technologies. Furthermore, this research examines how cation substitution in the Mg–Ca–Fe(II) –H2O–CO2 system affects the long-term stability of secondary carbonate minerals targeted for CDR. This was achieved by integrating data from four research projects (Chapters 2–5): Chapter 2 describes detailed temporal mineral transformation and recrystallization pathways for Ca, Mg, and Ca Mg-carbonates in simulated saline/diagenetic conditions, between 40 and 80 °C, while concomitantly tracking metal partitioning (Sr and Li) and stable oxygen isotope fractionation. Multi-phase assemblages of carbonate minerals formed in a laboratory experiment following the transformation of amorphous Ca-Mg-carbonate. Mineralogical and chemical compositions varied depending on reaction temperature. These results have implications for predicting the evolution of carbonate precipitation reactions and their long-term stability, as well as associated metal release, during CO2 sequestration in saline environments. Chapter 3 examines how Fe(II)-substitution in brucite [Mg(OH)2] and prevailing environmental conditions — reduction-oxidation (redox) state and background anions — affects the long-term stability of secondary Mg–Fe carbonates and the overall carbonation efficiency. These results highlight the importance of considering metal substitution when estimating the CO2 sequestration potential of both mine wastes in surficial environments and serpentinite deposits in subsurface conditions. Long-term carbon sequestration potential in Fe bearing phases is typically limited to siderite (FeCO3) and pyroaurite [Mg6FeIII2CO3(OH)16∙4H2O]. But, as a consequence of the redox sensitive tendencies of Fe bearing phases, captured CO2 can be released from Fe bearing phases during redox fluctuations and environmental changes. Chapter 4 elucidates the influence of dissolved silica and Fe(II) on Mg-carbonate precipitation and transformation pathways, along with co-evolving (low-temperature) silicate formation. This research shows that dissolved silica can accelerate Mg-carbonate nucleation; however, the amount of Mg-carbonate formed depended on the initial silica concentration as amorphous silicates scavenge Mg cations. In Mg-only experiments, the rate of dypingite formation increased as the initial silica concentration increased and, interestingly, nesquehonite did not form as an intermediary phase in experiments containing 100 mM Si but rather dypingite was the sole phase. Carbonate (re)crystallization in solutions containing a mixture of Mg and Fe(II) is controlled by redox conditions and, to a lesser extent, Si concentration. This work demonstrates the importance of considering relationships between carbonate and silicate precipitation reactions and, therefore, these results support our current understanding of silicate–carbonate cycling (i.e., enhanced rock weathering) in alkaline systems. Chapter 5 evaluates whether burial of sulfide minerals, derived from chemoheterotrophic microorganisms (i.e., sulfate reducing microorganisms), and organic carbon facilitates long-term storage of carbonate minerals in Mg- and Fe-rich saline environments. To achieve this, I studied a unique saline playa lake, Basque Lake #2 (near Ashcroft, British Columbia, Canada), where formation of low-temperature magnesite (MgCO3) is associated with sulfidic sediments. Field observations were integrated with laboratory microbial experiments, to determine the role of chemoheterotrophic microorganisms during carbon mineralization. Based on analysis of core samples, it was estimated that only ~1.0% of the total magnesite was generated by alkalinity derived from sulfate reduction: this finding is also supported by microcosm experiments. Additionally, results suggest that previous studies that examined the capability of sulfate reducing microorganisms to induce carbonate precipitation may have overestimated their contribution. The implications from this work extend to developing biogeochemical CDR methods in alkaline mining environments and subsurface geologic systems. This research improves the current understanding of mineralogical, chemical, and biological controls on secondary carbonate precipitation in geologic systems. Overall, my thesis will support future development of CDR methods in geologic environments by integrating these novel findings into future practices.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,700
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,004
Tête enseignante GPT0,167
Écart entre enseignants0,163 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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