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Record W4311914333 · doi:10.21203/rs.3.rs-2383297/v1

Incorporating Ion Pairing in an Extended Specific Ion Theory: 2-2 Electrolytes

2022· preprint· en· W4311914333 on OpenAlexaff
Alex De Visscher, Nazli Chavoshpoor

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldChemical Engineering
TopicChemical and Physical Properties in Aqueous Solutions
Canadian institutionsConcordia University
Fundersnot available
KeywordsMolalityElectrolyteActivity coefficientPairingChemistryIonWork (physics)Ionic bondingThermodynamicsIonic strengthExtension (predicate logic)Computational chemistryPhysicsPhysical chemistryCondensed matter physicsAqueous solutionOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The specific ion theory (SIT) is a relatively simple model for the description of activity coefficients of ions in electrolyte solutions. However, the SIT model does not account for ion pairing, a phenomenon that is pronounced in 2–2 electrolytes. In this work, a recent extension of the SIT model is further extended to account for ion pairing. The model extension is tested with reported data for BeSO 2 , MgSO 4 , MnSO 4 , NiSO 4 , CuSO 4 , ZnSO 4 , and CdSO 4 . The standard error of the fit is typically around 1%. To get this accuracy in model fits of the activity coefficients that are consistent with literature data of the MgSO 4 0 ion pair concentration, it was necessary to work in a modified molality scale and to reduce the SIT parameter B from 1.5 to 1.2. The drawback of this choice is that a value of B equal to 1.2 leads to worse predictions of the activity coefficients of 1–1 electrolytes. It is concluded that not accounting for ion pairing in 2–2 electrolytes leads to overestimations of the ionic strength of up to 30% and leads to overestimations of the Debye-Huckel term for the activity coefficient of 2–2 electrolytes by up to 20%.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0000.005
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 head0.070
GPT teacher head0.353
Teacher spread0.283 · 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.

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

Citations1
Published2022
Admission routes1
Has abstractyes

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