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Record W2060302835 · doi:10.1002/aic.10039

On the activity of ions and the junction potential: Revised values for all data

2004· article· en· W2060302835 on OpenAlexafffund
Grażyna Wilczek-Vera, Eva Rodil, Juan H. Vera

Bibliographic record

VenueAIChE Journal · 2004
Typearticle
Languageen
FieldChemical Engineering
TopicChemical and Physical Properties in Aqueous Solutions
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaXunta de Galicia
KeywordsIonElectrolyteWork (physics)CalibrationSign (mathematics)Interpretation (philosophy)ThermodynamicsChemistryActivity coefficientElectrodeStatistical physicsMathematicsPhysicsMathematical analysisStatisticsPhysical chemistryComputer science

Abstract

fetched live from OpenAlex

Abstract This work presents an in‐depth study of the interrelation between the junction potential EJ and the activity of individual ions. The need to correct for the sign of the junction potential employed in previous publications is used to confirm that the calibration of the ion‐selective electrodes largely cancels errors in the estimation of EJ. Revised values of the activity coefficients of individual ions are presented for all systems previously measured. A rederivation of Henderson equation is used to clarify the interpretation of its terms for asymmetric electrolytes. A new analytical equation to calculate EJ is derived and tested. This equation corrects for the nonideality of solutions and for the concentration dependence of the conductivity. The correction for nonideality is only necessary for the ions present in both the sample and the reference electrode solutions. The new equation represents a major improvement over the Henderson equation for EJ calculations. © 2004 American Institute of Chemical Engineers AIChE J, 50: 445–462, 2004

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)0.004
metaresearch head score (Gemma)0.031
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.031
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.004
Open science0.0030.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.043
GPT teacher head0.274
Teacher spread0.230 · 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

Citations92
Published2004
Admission routes2
Has abstractyes

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