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Record W1994985755 · doi:10.1002/bit.20295

Stimulating in‐soil rhamnolipid production in a bioslurry reactor by limiting nitrogen

2004· article· en· W1994985755 on OpenAlexaff
Andrew J. Hudak, Daniel P. Cassidy

Bibliographic record

VenueBiotechnology and Bioengineering · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsRhamnolipidChemistryBiodegradationEnvironmental chemistryNitrogenSoil contaminationHydrocarbonMicroorganismContaminationNutrientCritical micelle concentrationBioremediationPetroleumSoil waterMicellePseudomonas aeruginosaBacteriaEnvironmental scienceAqueous solutionBiologyOrganic chemistryEcologySoil science

Abstract

fetched live from OpenAlex

A soil with aged contamination from lubricating oil (LO) and polychlorinated biphenyls (PCBs) was treated in a bioslurry reactor to investigate in-soil biosurfactant production by Pseudomonas aeruginosa, the most abundant indigenous, culturable, hydrocarbon-degrading microorganism. After 2 days of growth on LO, a depletion of nitrogen stimulated the production and accumulation of rhamnolipids to levels roughly 20 times the critical micelle concentration. Surface tensions and concentrations of monorhamnolipid and dirhamnolipid, PCBs, and total petroleum hydrocarbons (TPH) were measured in a slurry filtrate. Soil-bound PCBs and TPH were also quantified. Rhamnolipid production was observed within 1 to 2 days after nitrogen depletion in each of the 10 batches tested. By day 6, total rhamnolipid concentrations increased from below detection to average values over 1,000 mg/L, which caused over 98% of soil-bound PCBs and over 99% of TPH to be emulsified and recovered in the filtrate. After 70 days, rhamnolipid concentrations were only reduced by 15%, because of nitrogen-limited rates of rhamnolipid biodegradation. The results show that in-soil biosurfactant production can be stimulated in a controlled way with nutrient limitation and can be used to achieve soil washing.

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 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 score0.035
Threshold uncertainty score0.459

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)0.0000.000

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 head0.006
GPT teacher head0.188
Teacher spread0.182 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations24
Published2004
Admission routes1
Has abstractyes

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