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Biopolymer Production Kinetics of Mixed Culture Using Wastewater Sludge as a Raw Material and the Effect of Different Cations on Biopolymer Applications in Water and Wastewater Treatment

2016· article· en· W2343329125 on OpenAlexafffund
Tanaji T. More, Shouke Yan, R. D. Tyagi, Rao Y. Surampalli

Bibliographic record

VenueWater Environment Research · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiopolymerWastewaterSewage treatmentKineticsWaste managementRaw materialPulp and paper industryChemistryWater treatmentActivated sludgeEnvironmental scienceEnvironmental engineeringEngineeringPolymer

Abstract

fetched live from OpenAlex

Thirteen extracellular polymeric substances (EPS) producing bacterial strains were cultivated (as mixed culture) in the sterilized sludge (suspended solids of 25 g/L) and the batch fermentation was carried out. Mixed culture revealed a high specific growth rate of 0.35/hr. The EPS production rate was higher up to 24 hours, which gradually decreased with further incubation. The kinetic estimates demonstrated growth-associated EPS production. Broth EPS revealed higher flocculation activity when combined with different cations (Ca(2+), Mg(2+), Fe(3+), and Al(3+)) in river water (≥90%), municipal wastewater (≥90%), and brewery wastewater (≥80%), respectively. A low dose (5 to 40 mg/L) of trivalent cations was required to achieve higher flocculation compared to the divalent cations (50 to 250 mg/L). Flocculation performance of EPS was comparable to Magnafloc-155 (chemical polymer) and, hence, it could be used as a flocculant.

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 score0.003
Threshold uncertainty score0.964

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 studies0.0000.001
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 head0.018
GPT teacher head0.270
Teacher spread0.252 · 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

Citations19
Published2016
Admission routes2
Has abstractyes

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