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Record W2577612781 · doi:10.1021/acs.est.6b03781

Importance of Interlayer Equivalent Pores for Anion Diffusion in Clay-Rich Sedimentary Rocks

2017· article· en· W2577612781 on OpenAlexfundno aff
Cornelia Wigger, Luc R. Van Loon

Bibliographic record

VenueEnvironmental Science & Technology · 2017
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
FundersPaul Scherrer InstitutNuclear Waste Management Organization
KeywordsPorosityMarlIonic strengthDiffusionIonic bondingIonMineralogyClay mineralsChemistrySedimentary rockGeologyChemical engineeringGeochemistryPhysical chemistryOrganic chemistryThermodynamicsGeomorphology

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide The anion exclusion behavior in two different clay stones, Opalinus Clay (OPA) and Helvetic Marl (HM), was studied using a well-established experimental through-diffusion technique. The ionic strength of the pore water was varied between 0.01 and 5 M to evaluate its effect on the diffusion of HTO and 36 Cl – . The total porosity determined by HTO-diffusion was independent of the ionic strength, while the anion accessible porosity varies with the ionic strength of the pore water. In the case of Opalinus Clay, the anion accessible porosity increases from 3% at low ionic strength (0.01 M) up to 8.4% at high ionic strength (5 M), whereas the anion accessible porosity of Helvetic Marl increases from 0.6% up to only 1.1%. The anion exclusion effect in HM is thus more pronounced than that in OPA, even at high ionic strength. This observation can be correlated to differences in mineralogy and to the fact that HM has a larger fraction of interlayer equivalent pores. Interlayer equivalent pores are small pores in compressed clay stones that are small enough to have, because of overlapping electric double layers, properties similar to those of interlayers and are therefore rather inaccessible for anions.

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 score0.021
Threshold uncertainty score0.425

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.008
GPT teacher head0.226
Teacher spread0.218 · 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

Citations57
Published2017
Admission routes1
Has abstractyes

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