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Impact of graphene oxide addition on pharmaceuticals removal in anaerobic membrane bioreactor

2023· article· en· W4379058274 on OpenAlexfundno aff
Oriol Casabella-Font, Michele Ponzelli, Melina Papapanou, José Luís Balcázar, Maite Pijuan, Jelena Radjenović

Bibliographic record

VenueBioresource Technology · 2023
Typearticle
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsnot available
FundersAgencia Estatal de InvestigaciónGeneralitat de CatalunyaEuropean CommissionEuropean Social FundCentres de Recerca de CatalunyaCanadian Institute for Advanced Research
KeywordsBioreactorMembrane bioreactorGrapheneWastewaterOxideChemistryAnaerobic digestionAnaerobic exerciseMicrobial population biologyMembraneMicrobiologyPulp and paper industryBacteriaMaterials scienceEnvironmental engineeringBiologyMethaneBiochemistryNanotechnologyEnvironmental scienceOrganic chemistry

Abstract

fetched live from OpenAlex

The addition of conductive materials to the anaerobic digestion bioreactor was suggested to enhance microbial activity. In the present work, an anaerobic membrane bioreactor treating municipal wastewater was operated for 385 days. The impact of different graphene oxide concentrations on the removal target pharmaceuticals and microbial community dynamics was investigated. The addition of graphene oxide did not impact the reactor stability, whereas the removals of antibiotics (e.g., trimethoprim and metronidazole) were enhanced. A shift in the microbial community was detected after the addition of 50–900 mg L−1 of graphene oxide, with the proliferation hydrogenotrophic methanogens. The proliferation of syntrophic microorganisms may indicate interactions via direct interspecific electron transfer. The obtained results suggest that the addition of graphene oxide at low mg L−1 concentrations to an anaerobic membrane bioreactor may be considered to improve the removal of antibiotics from municipal wastewater.

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 score0.105
Threshold uncertainty score0.606

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.017
GPT teacher head0.277
Teacher spread0.260 · 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

Citations16
Published2023
Admission routes1
Has abstractyes

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