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Enregistrement W2800182278 · doi:10.31274/td-20240329-111

Biocementation of soils through calcium carbonate precipitation using microbial catalysis

2021· dissertation· en· W2800182278 sur OpenAlexaboutno aff
Rayla Pinto Vilar

Notice bibliographique

Revuenon disponible
Typedissertation
Langueen
DomaineEnvironmental Science
ThématiqueMicrobial Applications in Construction Materials
Établissements canadiensnon disponible
Organismes subventionnairesIowa State University
Mots-clésAdsorptionCementation (geology)UreaseChemistryPrecipitationReagentSoil waterSiltChemical engineeringUreaEnvironmental chemistryOrganic chemistryEnvironmental scienceSoil scienceMaterials scienceGeologyMetallurgyEngineering

Résumé

récupéré en direct d'OpenAlex

The need for sustainable alternative methods for soil stabilization has received significant attention in the past decades, due to the highly detrimental effects the currently used methods pose to the environment. Biocementation techniques based on urea hydrolysis have been widely studied as an environmentally-friendly soil stabilization method. However, most biocementation studies performed thus far have only considered injection methods, reagent concentrations, and enzyme kinetics as operational constraints instead of thoroughly assessing reaction bottlenecks in a stepwise, systematic way. To our knowledge, no studies have conducted a thoroughly analysis of the mechanisms encompassed within biocementation and their impact in the degree of cementation achieved. The overall goal of this work was to study the mechanisms and effectiveness of biocementation through the Bacterial Induced Calcite Precipitation (BEICP) technique. We hypothesize that the spatial distribution of catalytic active adsorbed urease dictates the locations where CaCO3 precipitation and subsequent cementation will occur. Therefore, we first investigated the adsorption behavior and retained enzymatic activity of the biocatalyst urease when present in a complex protein mixture through batch adsorption experiments on sand and silt soil mixtures Our results from Chapters 2 showed the presence of other proteins in the BEICP crude protein extract did not hinder urease adsorption in the soil mixtures tested. In chapter 2, ours results also suggested larger molecular weight proteins from the BEICP crude extract, were preferentially adsorbed in the soil, whereas smaller size proteins tended to stay in the supernatants. Thus, suggesting that size exclusion could be used to encourage adsorption of targeted proteins within a complex protein mixture. This is a relevant finding for the field of protein adsorption as a whole, since the adsorption behavior of targeted proteins within a multi-mixture of protein onto solid surfaces is largely unknown. In chapter 2, more proteins adsorbed onto sand-silt soil mixtures and retained urease activity increased with higher silt contents for samples containing 10% and 20% silt contents compared to sand. However, above the 20% silt threshold no changes in the amount of protein adsorbed/ urease activity were observed. An overall loss of urease activity was found in all sand and silt soil mixtures and it increased for samples with higher silt contents. The results from chapter 2 suggested that the soil surface chemistry was an important factor in the level of activity lost after adsorption. For this reason, in chapter 3 we investigated the relationship between the amount of urease and total proteins adsorbed, retained enzymatic activity of adsorbed urease, and the overall loss of activity upon adsorption, and how this relationship is influenced by changes in soil surface chemistry. Our results showed that in soils with hydrophobic contents higher than 20% (w/w) ratio, urease was preferentially adsorbed compared to the total amount of proteins present in the crude BEICP protein extract. Conversely, adsorption of urease onto Ottawa silica sand and soils mixtures of Ottawa sand and iron coated sand was much lower compared to the total proteins. The highest overall loss of urease activity upon adsorption was observed in 10% and 20% iron containing soil mixtures (up to 58%), whereas the lowest loss of activity was found in 100% hydrophobic coated soils (less than 25%). In chapter 4, we conducted a comprehensive analysis of each rate limiting step within the biocementation process and correlated the outcomes of each individual step to the degree of cementation achieved. Soil specimens were treated with the BEICP technique in flow through columns with repetitive treatment cycles. Our results showed that higher levels of protein adsorption and urease activity were found in columns containing 10% hydrophobic sand, but that did not translate to higher amounts of calcium precipitates produced. In addition, approximately 23% more proteins adsorbed onto the 10% IRON columns compared to 100% SAND, but the urease retained activity was similar among these columns. Moreover, the strength gain was 100% higher in the 10% IRON columns when compared to 100% SAND. Thus, suggesting that CaCO3 bridging was highly effective in the 10% IRON columns. Overall, the results from this study highlights the importance of obtaining an in-depth understanding of the mechanisms behind each rate-limiting steps within the biocementation process. In particular, this study provides valuable information regarding the adsorption behavior and retained enzymatic activity of the biocatalyst urease onto soils. This information is extremely relevant because these two processes have been largely underestimated by researchers in the biocementation field. In addition, due to the importance of urease to agricultural and medical applications, the results of this study constitutes a significant contribution to understanding the adsorption of protein mixtures onto solid surfaces and its effects in enzymatic activity of targeted proteins. Finally, this study proposes a new framework to study and optimize biocementation techniques. One that considers each step individually, but also how they correlate to each other and to the overall degree of cementation achieved.

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 candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,096
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
Bibliométrie0,0000,001
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)0,0100,000

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 Opus0,024
Tête enseignante GPT0,301
Écart entre enseignants0,277 · 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
Publié2021
Routes d'admission1
Résumé présentoui

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