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Record W2064918951 · doi:10.1080/09593330802422803

Electrochemical regeneration of granular activated carbons loaded with phenol and natural organic matter

2008· article· en· W2064918951 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEnvironmental Technology · 2008
Typearticle
Languageen
FieldMaterials Science
TopicCovalent Organic Framework Applications
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhenolAdsorptionActivated carbonElectrochemistryRegeneration (biology)ChemistryChemical engineeringMaterials scienceOrganic chemistryElectrode

Abstract

fetched live from OpenAlex

The main objective of this study was to assess the performance of electrochemical regeneration of granular activated carbon via a set of bench-scale experiments using different operating conditions in the regeneration of several different activated carbons loaded with phenol or natural organic matter. The regeneration efficiency can be increased by increasing the charge applied, whether this was achieved by an increase in current or regeneration time. The degree of phenol-adsorption saturation did not significantly affect the regeneration efficiencies. The regeneration efficiencies of the various types of phenol-loaded activated carbon were quite similar despite the differences in their conductivity. The activated carbon exhibiting fully reversible adsorption of phenol had slightly higher regeneration efficiencies than those involving partially irreversible adsorption. Electrochemical regeneration of activated carbon is feasible at a laboratory scale as regeneration efficiencies up to 80% were achieved during electrochemical regeneration of phenol-loaded or natural organic matter-loaded activated carbons.

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.

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 score0.002
Threshold uncertainty score0.569

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 head0.003
GPT teacher head0.175
Teacher spread0.172 · 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