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Dielectric Relaxation-Based Capacitive Heating of Oil Sands

2016· article· en· W2284982941 on OpenAlexafffund
Tinu Abraham, C. W. Van Neste, Artin Afacan, Thomas Thundat

Bibliographic record

VenueEnergy & Fuels · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsUniversity of Alberta
FundersInstitute for Oil Sands Innovation, University of AlbertaCanada Excellence Research Chairs, Government of CanadaCanada Research Chairs
KeywordsCapacitive sensingDielectric spectroscopyMaterials scienceRelaxation (psychology)Electrical impedanceDielectricSilicateCapacitanceAnalytical Chemistry (journal)Electric heatingComposite materialChemistryOptoelectronicsElectrical engineeringElectrodeElectrochemistryChromatography

Abstract

fetched live from OpenAlex

An electrothermal method of capacitive heating of oil sands was investigated. Temperature-based impedance spectroscopy experiments for a given rich grade of oil sands were conducted to identify a suitable dielectric relaxation frequency for carrying out capacitive heating, which was observed to be around 65 kHz having a full width at half-maximum of two decades. This was attributed to interfacial polarizations at water, bitumen, and silicate mineral interfaces. The relaxation frequency changed with temperature rise, indicating that frequency tuning could be suitable to optimize capacitive heating. Hence, capacitive heating of oil sands was demonstrated by exposing it to high alternating electric fields (10 4 V/m) at a frequency in the dispersion regime of its relaxation frequency. As temperature increased, the overall impedance of oil sands decreased as determined from impedance spectroscopy. Frequency was retuned to match the changed impedance of the system, which ensured that temperature further increased.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.981
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 science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.215
Teacher spread0.203 · 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 designOther design
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
Published2016
Admission routes2
Has abstractyes

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