Critical Review of Transport Properties of HCl, KOH, and NaOH in High Temperature Water and Correlations for Transport Properties of H3O+ and OH−
Bibliographic record
Abstract
High-temperature tracer diffusion coefficients for H3O+ and OH− are important parameters in the modeling of diffusion-controlled reaction kinetics and mass transport processes under hydrothermal conditions, and these tracer diffusion coefficients are directly related to the ionic electrical conductivities in the limit of infinite dilution through the Nernst–Einstein relationship. The limiting conductivity of H3O+ and OH− in water is controlled by two separate mechanisms of ionic movement: (i) the bulk ionic diffusion and (ii) proton hopping, also known as “Grotthuss” mechanism and/or “prototropic transfer.” This work reports a critical assessment of the limiting electrical conductivity data (Λ°) for aqueous HCl, KOH, and NaOH measured above room temperature. The initial assessed dataset included temperatures from 273.15 K up to 873.15 K and water densities from 1000 kg m−3 down to 270 kg m−3 and was reduced down to a final critically evaluated dataset spanning temperatures between 273.15 and 678.15 K and densities between 346 and 1006 kg m−3. The results were used to derive values for the excess conductivity due to prototropic transfer, λE°, of H3O+ and OH− using correlations previously reported for aqueous KCl. Simple empirical correlations of water viscosity and density were derived for Λ°(HCl), Λ°(KOH), Λ°(NaOH), λE°(H3O+), and λE°(OH−). Tests using the λE°(OH−) correlation and a previously reported function for Λ°(NaCl) show that the NaOH data can be accurately reproduced to within the estimated uncertainties. The reported correlations provide a means to model more accurately the tracer diffusion coefficients for H3O+ and OH− to supercritical conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".