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Record W2922350846 · doi:10.2166/wqrj.2000.014

Determining Granular Activated Carbon Adsorption Isotherms of Benzene as a Volatile Organic Compound

2000· article· en· W2922350846 on OpenAlexaff
Jian Peng, Weithong Cui, Gordon Wilson, Wei Lin

Bibliographic record

VenueWater Quality Research Journal · 2000
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAdsorptionBenzeneActivated carbonChemistryVolatilisationFreundlich equationVolatile organic compoundChromatographyCarbon fibersGas chromatographyOrganic chemistryMaterials science

Abstract

fetched live from OpenAlex

Abstract The determination of the adsorption equilibrium isotherm is a fundamental requirement for the design of adsorption-based processes. A method using a headspace auto-sampler and gas chromatograph system was developed in this study to determine the adsorption isotherm of benzene as a volatile organic compound for granular activated carbon. Dynamic equilibria between chemical solution, headspace gas and activated carbon were established in the 22-mL headspace glass vials sealed with Teflon-faced silicone septa and aluminum cramp caps. Using the closed system of headspace glass vials minimized the volatilization loss of chemical during the experiment. The method was utilized to measure the adsorption of benzene on Filtrasorb 300 granular activated carbon under 25 ± 1°C. The Freundlich isotherm parameters were determined using the experimental result. A reproducibility study and comparison with other results found in the literature showed that the method developed in this research is a reliable technique for determination of adsorption isotherm of benzene as a volatile organic compound on activated carbon.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.054
GPT teacher head0.341
Teacher spread0.287 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2000
Admission routes1
Has abstractyes

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