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Record W1965477322 · doi:10.1021/je600556d

Measurement of Low Air−Water Partition Coefficients of Organic Acids by Evaporation from a Water Surface

2007· article· en· W1965477322 on OpenAlexaff
Hongxia Li, David Ellis, Don Mackay

Bibliographic record

VenueJournal of Chemical & Engineering Data · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Chemistry and Analysis
Canadian institutionsTrent University
Fundersnot available
KeywordsPartition coefficientIsothermal processChemistryEvaporationActivity coefficientFormic acidAnalytical Chemistry (journal)ThermodynamicsChromatographyAqueous solutionPhysical chemistry

Abstract

fetched live from OpenAlex

A novel system is described for the determination of the air−water partition coefficient ( K AW ) for substances that have low air−water partition coefficients, i.e., K AW < 10 -3, and may aggregate in solution, ionize, and display surface activity. The compound is evaporated isothermally from solution through an undisturbed air−water interface at a known gas flow rate, and its concentrations in the water and gas phases are measured. Although equilibrium is not achieved, the extent of departure from equilibrium can be determined using estimated mass transfer coefficients. K AW was determined for formic, acetic, benzoic, and perfluorooctanoic acids (PFOA), and assuming an approximately 50 % approach to equilibrium, which is in accord with theoretical prediction. Agreement with available literature data was satisfactory. The experimentally determined K AW of PFOA was 1.02·10 -3 with a standard deviation of 9.1 % ( n = 9). The method is suitable for fluorinated surfactants, aggregating and ionizing substances for which K AW may not be readily measured with existing techniques.

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)0.000
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.009
GPT teacher head0.196
Teacher spread0.188 · 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

Citations54
Published2007
Admission routes1
Has abstractyes

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