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Record W2038602873 · doi:10.1002/ep.670200212

Comparison of biodegradation kinetic parameters for naphthalene in batch and sand column systems by <i>pseudomonas putida</i>

2001· article· en· W2038602873 on OpenAlexaboutno aff
Jeong‐Hun Park, Xianda Zhao, Thomas C. Voice

Bibliographic record

VenueEnvironmental Progress · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsnot available
Fundersnot available
KeywordsNaphthaleneBiodegradationPseudomonas putidaChemistryDegradation (telecommunications)Saturation (graph theory)Substrate (aquarium)KineticsChromatographyVolumetric flow rateLiquid flowReaction rate constantBiomass (ecology)Chemical engineeringEnvironmental chemistryEnvironmental engineeringThermodynamicsEnvironmental scienceEcologyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Kinetic parameters for the degradation of naphthalene by Pseudomonas putida ( ATCC 17484) were estimated in both batch and column assays, in order to evaluate the role of flow and cell attachment on biodegradation rates. Suspended cells and cells attached to Ottawa sand were used under a variety of biomass levels, column flow‐rates, and substrate concentrations. In batch systems, degradation followed zero order kinetics across the entire concentration range, while the columns exhibited decreased rates at concentrations less than 100 (μg/L), describable by Michaelis‐Menten kinetics. This is reflected in elevated values of the half‐saturation constant, K s , in columns. We offer the explanation that this may have resulted from reactive heterogeneity within the porous media, imposing a distribution of length‐scales for transfer of substrate to the cell surfaces. Well‐mixed batch systems are expected to have both shorter and more uniform transfer distances. When kinetic parameters obtained in batch system are used for prediction of degradation in columns, at least two factors—exposed reduction of exposed cell surface are a and heterogeneity of cell distribution—will likely reduce overall column degradation rates.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.276
Threshold uncertainty score0.562

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 head0.014
GPT teacher head0.253
Teacher spread0.239 · 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 designObservational
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

Citations11
Published2001
Admission routes1
Has abstractyes

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