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Record W4323354558 · doi:10.1016/j.envpol.2023.121343

Graphene oxide addition to anaerobic digestion of waste activated sludge: Impact on methane production and removal of emerging contaminants

2023· article· en· W4323354558 on OpenAlexfundno aff
Oriol Casabella-Font, S. Zahedi, Meritxell Gros, José Luís Balcázar, Jelena Radjenović, Maite Pijuan

Bibliographic record

VenueEnvironmental Pollution · 2023
Typearticle
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsnot available
FundersEuropean Social FundAgencia Estatal de InvestigaciónGeneralitat de CatalunyaCanadian Institute for Advanced Research
KeywordsGrapheneOxideAnaerobic digestionMethaneChemistryMaterials scienceEnvironmental chemistryOrganic chemistryNanotechnology

Abstract

fetched live from OpenAlex

The effect of graphene oxide on the anaerobic digestion of waste activated sludge was investigated at two graphene oxide concentrations (0.025 and 0.075 g graphene oxide per g volatile solids) using biochemical methane potential tests. The occurrence of 36 pharmaceuticals was monitored in the solid and liquid phases before and after the anaerobic treatment. The addition of graphene oxide improved the removal of most pharmaceuticals detected, even those that are considered persistent to biological degradation, such as azithromycin, carbamazepine, and diclofenac. No significant differences were observed in the final specific methane production without graphene oxide and with the lowest graphene oxide concentration, yet the highest graphene oxide concentration partially inhibited methane production. The relative abundance of antibiotic resistance genes was not affected by the graphene oxide addition. Finally, significant changes in the microbial community including bacteria and archaea were detected with graphene oxide addition.

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 score0.252
Threshold uncertainty score0.493

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 head0.008
GPT teacher head0.219
Teacher spread0.212 · 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
Published2023
Admission routes1
Has abstractyes

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