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Effect of nanoparticle-enhanced biocementation in kaolinite clay by microbially induced calcium carbonate precipitation

2024· article· en· W4390848568 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueConstruction and Building Materials · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Applications in Construction Materials
Canadian institutionsPolytechnique Montréal
FundersPolytechnique Montréal
KeywordsKaoliniteCompressive strengthMaterials scienceCalcium carbonateCementation (geology)Scanning electron microscopeMicrostructureWater contentChemical engineeringMineralogyComposite materialChemistryCementMetallurgyGeotechnical engineeringGeology

Abstract

fetched live from OpenAlex

Microbially induced calcium carbonate precipitation (MICP) is a nature-based technique that has been developed over the past two decades for soil stabilization. However, the use of MICP for clay stabilization has received limited attention in the literature. On the other hand, the utilization of nanomaterials, such as nano-silica (nano-SiO2), for soil stabilization has been explored in numerous studies in the literature. This paper investigates the effect of nano-CaCO3 and nano-SiO2 on MICP stabilization in kaolinite clay. Nanoparticle-enhanced bio-cementation is introduced to effectively improve the strength of kaolinite clay at high water contents. Unconfined compressive strength (UCS) tests were conducted to evaluate the impact of the nano-bio-treatment (mixture of nano-additives and MICP) on soil strength. Additionally, the microstructure of the treated soils was examined using scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), Raman spectroscopy, and X-ray diffraction (XRD) analyses. The results indicate that the MICP stabilization method is effective in enhancing the strength of kaolinite soil through a mixing approach. The study of UCS in the treated samples revealed that the use of nano-CaCO3 and nano-SiO2 can have either detrimental or beneficial effects on the MICP method, depending on the degree of saturation of the treated samples. The highest UCS observed for the MICP method at a target water content of 0.25 was more than three times that of untreated soil at the same moisture content. Furthermore, the increase in UCS for the samples treated with MICP was 2.5 times that of the untreated soil at a 30% target water content. The most significant finding, however, is that, at a 30% target water content, the samples improved with MICP+ 1.5% nano − SiO2 exhibited UCS values that were six and fifteen times greater than those of the untreated and MIC-treated samples, respectively. SEM images illustrated that the addition of nano-SiO2 with the MICP method led to an agglomerated soil texture, and the formation of calcium carbonate attached to the clay minerals. This results in an increase in soil strength.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.018
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.274
Teacher spread0.266 · 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