Biocementation of soils through calcium carbonate precipitation using microbial catalysis
Bibliographic record
Abstract
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".