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pH-Metric Determination of the Equilibrium Constants in Aqueous Heterogeneous Systems

2022· book-chapter· en· W4225282818 on OpenAlexaff
Igor Povar, Oxana Spînu, Alfonso Mucci

Bibliographic record

VenueAdvances in environmental engineering and green technologies book series · 2022
Typebook-chapter
Languageen
FieldChemical Engineering
TopicChemical and Physical Properties in Aqueous Solutions
Canadian institutionsMcGill University
Fundersnot available
KeywordsSolubility equilibriumSolubilityChemistryAqueous solutionEquilibrium constantChemical equilibriumInorganic chemistryThermodynamicsPhysical chemistry

Abstract

fetched live from OpenAlex

In this chapter, original methods for determining thermodynamic properties such as the equilibrium activity product or solubility product of sparingly soluble compounds and the stability constants of complexes are described. Methods for determining concentration product and solubility are based solely on pH measurements of the saturated aqueous solutions for a known initial composition of the heterogeneous mixture and the equilibrium constants of an arbitrary set of possible side reactions in the solution. In the case of sparingly soluble acids or hydroxides, the determination of the solubility (Sol) and solubility product (KS) is also possible in the presence of other acids and bases of known concentrations. Sol and KS were calculated from known experimental pH data for a series of hydroxides, acids, neutral and acidic salts MmHxAy(S), as well as basic/mixed salts Mm(OH)xAy(S) of arbitrary composition.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.909
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.005
GPT teacher head0.178
Teacher spread0.172 · 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 designNot applicable
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

Citations0
Published2022
Admission routes1
Has abstractyes

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