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Record W2153017224 · doi:10.1139/p02-143

Nitrates in ice: uptake; dielectric response by the layered capacitor method

2003· article· en· W2153017224 on OpenAlexvenueno aff
Guenter W. Gross

Bibliographic record

VenueCanadian Journal of Physics · 2003
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Dynamics and Properties
Canadian institutionsnot available
FundersOffice of the Vice President for Research and Economic Development, University at Buffalo
KeywordsAnalytical Chemistry (journal)DielectricConductivitySodium nitrateRelaxation (psychology)Ammonium nitratePotassium nitrateDispersion (optics)PotassiumMaterials sciencePhysicsChemistryOpticsChromatography

Abstract

fetched live from OpenAlex

Ice columns of 0.20–0.25 m length and 0.038 m diameter were grown in the laboratory from dilute solutions (10 –5 to 10 –2 N) of potassium, sodium, or ammonium nitrate at a constant freezing rate of 0.002 m h –1 and stirring at 300 rpm. The distribution coefficient was computed at ~0.015 m intervals as the ratio of nitrate concentration in the melted ice and in the liquid phase as a function of interface position. The average distribution coefficients were (2.25 ± 0.55) × 10 –4 for the potassium and sodium nitrate, and (6.1 ± 1.4) × 10 –3 for the ammonium nitrate samples — about a 27-fold increase. These results are in line with other large anions such as sulfate and methanesulfonate that were previously investigated. The dielectric relaxation spectrum of ice slices (~0.012 m thick and sandwiched between thin fluoroplastic foils) was measured between –1 and –85°C at frequencies from 1 Hz to 100 kHz with a lock-in amplifier technique. First, the ice response was recovered from the (Maxwell–Wagner) layered capacitor. The dielectric relaxation ranges were then separated and their characteristic parameters computed. The complex conductivity (Grant plot) and the conductivity frequency-response plot have been the most useful tools for this purpose. Both (alkali-metal and ammonium nitrate) sample groups exhibit the Debye dispersion of polar molecules so characteristic for ice regardless of impurity content. There is also a dispersion range at lower frequencies, and a static or quasi-static conductivity. In the alkali-metal nitrates the low-frequency dispersion is a prominent space-charge dispersion, and the temperature-dependent interaction between orientational and ionic point defects in the ice lattice leads to the conductivity crossover phenomenon. Ammonium greatly reduces the ionic lattice defects responsible for space charge and static conductivity; there is no crossover. Both the Debye dispersion and the crossover support the concept of co-operative responses by the polar molecules making up the ice substance to physico-chemical stimuli. PACS Nos.: 61.72, 77.22G, 77.22J, 81.30F

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)0.001
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 score0.130
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.239
Teacher spread0.221 · 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

Citations5
Published2003
Admission routes1
Has abstractyes

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