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Record W4403645975 · doi:10.1128/spectrum.01176-24

Dynamics of microbial-induced oil degradation at the microscale

2024· article· en· W4403645975 on OpenAlexaff
Hong Zhang, Wenchao Zhang, Yiwu Zong, Dongyang Kong, Z. Luyan, Xiao‐Lei Wu, Kun Zhao

Bibliographic record

VenueMicrobiology Spectrum · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsPetro-Canada
FundersNational Key Research and Development Program of ChinaPetroChina Company LimitedChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsMicroscale chemistryDegradation (telecommunications)BiofilmHexadecaneBioremediationBiodegradationChemistryEnvironmental chemistryBioreactorChemical engineeringEnvironmental scienceEnvironmental engineeringContaminationBacteriaEcologyBiologyOrganic chemistryComputer science

Abstract

fetched live from OpenAlex

ABSTRACT Microbial-induced oil degradation (MIOD) has a wide range of applications, such as microbial enhanced oil recovery and bioremediation of oil pollution. However, our understanding of MIOD is still far from complete. Particularly, how is the dynamics of degradation process at the microscale level with a single-cell resolution remains to be disclosed. In this work, using hexadecane droplets in water as a model system, we have studied the dynamics of hexadecane degradation by different strains, including Pseudomonas aeruginosa PAO1, IMP68, O-2-2, and Dietzia sp. DQ12-45-1b, at the microscale. Based on visualization of MIOD, the dynamics of MIOD can be characterized by a three-stage process, including adhesion, adaptation, and degradation stages. Although different strains showed similar three-stage dynamics of MIOD, the effective degradation rate varied and followed an order of PAO1 > O-2-2 > IMP68 > DQ12-45-1b under aerobic conditions. Different oxygen conditions were also tested, and the dynamics of MIOD was slowed down under anaerobic conditions in comparison to under aerobic conditions. Further investigations at the degradation stage revealed that biofilms formed at the oil-water interface enhanced oil degradation, but a prerequisite for such enhanced degradation is proper stimulation of biofilm cells in the course of biofilm formation. The findings in this work provided a detailed picture on the dynamics of MIOD at the microscale and would be beneficial for better applications of MIOD. IMPORTANCE Microbial-induced oil degradation is environmental friendly and economic and has become a promising technique in the fields of enhanced oil recovery and remediation of crude oil-polluted environments. For better applications of microbial-induced oil degradation, understanding the degradation dynamics particularly at the microscale is crucial. In this study, we investigated the degradation dynamics of hexadecane oil droplets incubated with different strains, including Pseudomonas aeruginosa PAO1, O-2-2, IMP68, and Dietzia sp. DQ12-45-1b at the microscale by employing microdroplet-based methods and bacterial tracking techniques. The findings in this study provided a detailed picture on the dynamics of microbial-induced oil degradation at the microscale, which will deepen our understandings on the biodegradation mechanisms of alkanes and shed insights for developing more effective biodegradation techniques.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.028
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0070.003

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 head0.210
Teacher spread0.203 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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