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Enregistrement W4387706398 · doi:10.3897/aca.6.e108666

Using a novel approach to characterize the surface reactivities of silica-rich ferrihydrite and biogenic cyanobacteria-ferrihydrite aggregates and the implications for Archean ocean geochemistry

2023· article· en· W4387706398 sur OpenAlexaff
Yuhao Li, Lingyi Tang, Daniel S. Alessi, Janice Kenney, Murray K. Gingras, Kurt O. Konhauser

Notice bibliographique

RevueARPHA Conference Abstracts · 2023
Typearticle
Langueen
DomaineEarth and Planetary Sciences
ThématiqueGeochemistry and Elemental Analysis
Établissements canadiensMacEwan UniversityUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésFerrihydriteGeologyBanded iron formationSeawaterGeochemistryNontroniteEnvironmental chemistryBirnessiteChemistryInorganic chemistryAdsorptionArcheanClay mineralsManganeseOceanography

Résumé

récupéré en direct d'OpenAlex

Precambrian banded iron formations (BIF) are iron- and silica-rich chemical sedimentary rocks that are commonly used as paleo-redox proxies for Archean and Paleoproterozoic seawater geochemistry. At the onset of the Great Oxidation Event (herein GOE) around 2.4 Ga, cyanobacteria flourished with increasing nutrient fluxes due to oxidative weathering on land. In turn, this led to increased primary productivity that facilitated the permanent shift from a reducing Earth atmosphere to an oxidizing one. Interestingly, the duration of GOE also overlapped with one of the most prolific periods of BIF deposition.It is widely accepted that cyanobacteria were likely responsible for BIF formation during the GOE. Oxidation of dissolved Fe(II) by oxygen produced from cyanobacteria forms a metastable and amorphous mineral phase ferrihydrite, Fe(OH) 3 . As an essential component in both ancient BIF deposits and various modern ecosystems, the surface reactivity of ferrihydrite has been extensively studied under different conditions (i.e., pH and ionic strengths). Not only are the highly reactive surfaces of ferrihydrite particles important shuttles for trace element transport from the water column to the sediment pile, but previous studies have also demonstrated that cyanobacterial cells and ferrihydrite tend to aggregate at seawater pH. This means that ferrihydrite was also a vector for the transport of organic carbon to the seafloor. However, a complicating issue is how co-ions affect the surface reactivity of ferrihydrite, specifically dissolved silica which was abundant in ancient seawater. Although previous studies have demonstrated that silica can passivate the surface reactivity of ferrihydrite, what remains unclear is how silica impacts ferrihydrite-biomass aggregation. To fill this knowledge gap, we formed both silica-spiked ferrihydrite and cyanobacteria-ferrihydrite aggregates in situ and subsequently conducted empirical potentiometric acid-base titrations and Cd adsorption experiments on the fresh aggregate samples at three different ionic strengths (0.56 M, 0.1 M and 0.01 M). We minimized sample processing (i.e., drying and powdering) to a simple washing step, in which the aggregate pellets remained hydrated to avoid any mineral transformation thus altering their true surface reactivity in seawater. Experimental results were then fitted with non-electrostatic model to predict both surface charges and metal-adsorption behavior of ferrihydrite aggregates. Different from previous surface-complexation modelling studies, here we used a novel and more powerful modelling program called Phreefit. It utilizes the global optimization algorithms instead of more commonly used Newton-Raphson method in FITEQL program, which is often too limited for precisely modelling complex systems such as the two samples in this study. Furthermore, we also measured the surface charges of both samples over the pH range from 3 to 9 on a Malvern Zetasizer and characterized the surface functional groups through Fourier-Transform Infrared Spectroscopy to help with our interpretation of the experimental data.Preliminary results show that cyanobacteria-ferrihydrite aggregates formed primarily due to ionic bridging. Cyanobacterial cells likely facilitated the precipitation of dissolved silica. Findings from titration and Cd adsorption experiments indicate that the surface reactivity and capacity of both silica-rich ferrihydrite cyanobacteria-ferrihydrite aggregates to adsorb trace elements differ from their individual components, likely due to site blockage. This distinction is particularly prominent when considering the expected Archean seawater pH from 6 to 8. This disparity implies that the biogenic ferrihydrite aggregates do not exhibit an additive surface reactivity, which is in agreement with similar previous studies. Our combined results are crucial to accurately predict the adsorption of trace elements onto the aggregate surface and, ultimately, comprehend the archive of trace elements in sedimentary rocks used to reconstruct Precambrian ocean chemistry.

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)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,854
Score d'incertitude au seuil0,516

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
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)0,0000,000

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 Opus0,070
Tête enseignante GPT0,256
Écart entre enseignants0,185 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
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
Publié2023
Routes d'admission1
Résumé présentoui

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