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Record W2044681584 · doi:10.1080/09593330.2014.952344

Extracellular polymeric substances production kinetics of 13 sludge isolates using wastewater sludge as raw material and its flocculation potential

2014· article· en· W2044681584 on OpenAlexafffund
Tanaji T. More, Amine Mahmoudi, R.D. Tyagi

Bibliographic record

VenueEnvironmental Technology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsInstitut National de la Recherche Scientifique
FundersCanada Research Chairs
KeywordsFlocculationExtracellular polymeric substanceSerratiaFood scienceSerratia marcescensChemistryFermentationMicrobiologyBacillus (shape)WastewaterBacteriaBiologyBiochemistryPseudomonasWaste managementEscherichia coliBiofilmOrganic chemistry

Abstract

fetched live from OpenAlex

The kinetics of batch fermentation of 13 extracellular polymeric substances (EPS) producing bacterial strains (9 Bacillus, 2 Serratia and 2 Yersinia) were carried out using sterilized sludge as a raw material. The most of Bacillus (µ(max): 0.11-0.27 h⁻¹), Serratia (µ(max): 0.23-0.27 h⁻¹) and Yersinia (µ(max): 0.18-0.19 h⁻¹) strains had capability to grow and produce EPS (1.36-2.12 g/L) in the sterilized sludge. In general, EPS production was mixed growth associated for all the bacterial strains cultivated independently. Bacillus sp. 7, Serratia sp. 2 and Yersinia sp. 2 produced higher concentration (1.95-2.12 g/L) of EPS than the other remaining bacterial strains. Protein and carbohydrate contents of EPS remained constant during fermentation. Broth EPS (B-EPS) exhibited high kaolin flocculation activity (≥ 75%) in most of the cases except Bacillus sp. 1, Bacillus sp. 5 and Bacillus sp. 9, respectively. In general, high flocculation activities (FAs) (≥ 75%), were attained using 1.31-1.70 mg B-EPS/g kaolin, 0.45-0.97 mg protein/g kaolin and 0.11-0.21 mg carbohydrates/g kaolin. The study suggests that further systematic exploration is required for optimizing the process of EPS production. EPS produced in the sludge can potentially be used for different water and wastewater treatments.

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 score0.006
Threshold uncertainty score0.895

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)0.0010.000

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 head0.006
GPT teacher head0.191
Teacher spread0.185 · 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

Citations6
Published2014
Admission routes2
Has abstractyes

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