MétaCan
Menu
Back to cohort
Record W2006638306 · doi:10.1081/ese-200060651

Removal of Phenolic Compounds in Water by Ultrafiltration Membrane Treatments

2005· article· en· W2006638306 on OpenAlexfundno aff
Juan L. Acero, F. Javier Benítez, I. Leal, Francisco J. Real

Bibliographic record

VenueJournal of Environmental Science and Health Part A · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsnot available
FundersUniversity of Guelph
KeywordsUltrafiltration (renal)MembraneChemistryChromatographyBiochemistry

Abstract

fetched live from OpenAlex

The ultrafiltration (UF) of aqueous solutions containing mixtures of three phenolic compounds (gallic acid, acetovanillone, and esculetin) was studied in a tangential UF laboratory system. These substances were selected as model pollutants present in the tannic fraction of the cork processing wastewaters. The two membranes used were a polyethersulfone membrane (Biomax5K) and a regenerated cellulose membrane (Ultracel5K), both with a molecular weight cut-off (MWCO) of 5000 Da. Previous experiments for the characterization of the membranes led to values for the water hydraulic permeability of 70.3 and 18.1 L/h x m2 x bar for the Biomax5K and Ultracel5K membranes, respectively. During the UF experiments, the permeate flow rate remained almost constant with processing time and the evolution of the pollutants concentrations varied depending on the nature of the membranes and the substances. The influence of the main operating variables (tansmembrane pressure and feed flow rate) on the permeate flux was established, and values for the apparent and intrinsic rejection coefficients were evaluated. Cork processing wastewater UF experiments were also conducted under similar operating conditions to those applied to the ultrapure water solutions. Removals of chemical oxygen demand, aromatic and tannic contents, and color were determined in these experiments, and the elimination of the three model compounds in the wastewater was also followed, with the evaluation of their apparent rejection coefficients.

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)0.001
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 score0.201
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.021
GPT teacher head0.283
Teacher spread0.262 · 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

Citations30
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Environmental Science and Health Part ASame topicMembrane Separation TechnologiesFrench-language works237,207