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Record W2146304013 · doi:10.1139/v01-104

Electrical conductivity, speeds of sound, and viscosity of aqueous ammonium nitrate solutions

2001· article· en· W2146304013 on OpenAlexvenueno aff
Abdul Wahab, Sekh Mahiuddin

Bibliographic record

VenueCanadian Journal of Chemistry · 2001
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsnot available
Fundersnot available
KeywordsChemistrySolvationAqueous solutionViscosityIonSolventConductivityIsentropic processAmmoniumElectrical resistivity and conductivityInorganic chemistryThermodynamicsPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Density, electrical conductivity, speeds of sound, and viscosity of aqueous ammonium nitrate solutions were measured as functions of concentration (m, mol kg –1 ) (0.1599 [Formula: see text] m [Formula: see text] 20.42) and temperature (T, K) (273.15 [Formula: see text] T [Formula: see text] 323.15). Experimental values are consistent with the reported data. Variation of isotherms of electrical conductivity, isentropic compressibility, and structural relaxation time with concentration evoke structural information on the ion solvation in aqueous ammonium nitrate solution at different concentration regions. The primary hydration numbers of ammonium nitrate were estimated at a particular concentration at which the isentropic compressibility isotherms converge. The existence of free hydrated ions, resulting from strong ion solvent interactions in dilute to 9.1 mol kg –1 , the solvent-separated ion-pairs resulting from the relative competition between the ion–solvent and the ion–ion interactions in 9.1 to 12.0 mol kg –1 , and the solvent-shared ion-pairs beyond 12.0 mol kg –1 resulting from a decrease in the number of solvent molecules, govern the transport process.Key words: electrical conductivity, speeds of sound, viscosity, ammonium nitrate, hydration number.

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 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 score0.174
Threshold uncertainty score0.374

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 science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.237
Teacher spread0.220 · 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.

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

Citations23
Published2001
Admission routes1
Has abstractyes

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