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Characterization of the Pore Structure and Surface Properties of Shale Using the Zeta Adsorption Isotherm Approach

2015· article· en· W2328074046 on OpenAlexafffund
Seyed Hadi Zandavi, C. A. Ward

Bibliographic record

VenueEnergy & Fuels · 2015
Typearticle
Languageen
FieldChemistry
TopicAnalytical Chemistry and Chromatography
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOil shaleAdsorptionZeta potentialSpecific surface areaMesoporous materialMaterials scienceThermodynamicsMineralogyChemical engineeringChemistryPhysical chemistryGeologyOrganic chemistryNanotechnologyNanoparticle

Abstract

fetched live from OpenAlex

The determination of the specific surface area and pore structure parameters of natural materials have been a long-standing issue. We propose a method for determining the specific surface area and pore size distribution that is based on the zeta adsorption isotherm and apply that method for each of two hydrocarbon vapors, octane and heptane, adsorbing on two types of shale, as-received and milled shale. We determined the specific surface areas of both materials, approximate the pores as cylindrical, and determine the average pore mouth radius. The standard deviation in the mean values of the specific surface areas and average pore radius determined with each of the two vapors is less than 3%. The experimental isotherms on the as-received shale indicate the existence of a pronounced adsorption–desorption hysteresis loop that results from the mesoporous structure of the material. We previously showed that liquid forms in the pores because of coalescence of molecular clusters inside the pores. In this study, we show that the pore emptying in the as-received shale is delayed because of the pore blocking effect. The zeta isotherm theory and the necessary conditions for thermodynamic equilibrium along with the measured amount adsorbed are used to determine the distribution in the pore mouth radii.

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 score0.002
Threshold uncertainty score0.207

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 head0.023
GPT teacher head0.203
Teacher spread0.179 · 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

Citations27
Published2015
Admission routes2
Has abstractyes

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