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Record W1582377853

Geochemical and mineralogical impacts of sulfuric acid on clays between pH 5.0 and -3.0

2008· article· en· W1582377853 on OpenAlexfundaboutno aff
Sean A. Shaw

Bibliographic record

VenueUniversity Library - University of Saskatchewan (University of Saskatchewan) · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaSyncrude
KeywordsSulfuric acidGeologyGeochemistryMineralogyClay mineralsChemistryEnvironmental chemistryInorganic chemistry
DOInot available

Abstract

fetched live from OpenAlex

Natural and constructed clay liners are routinely used to contain waste and wastewater. The impact of acidic solutions on the geochemistry and mineralogy of clays has been widely investigated in relation to acid mine drainage systems at pH > 1.0. The impact of sulfuric acid leachate characterized by pH < 1.0, including potentially negative pH values on the geochemistry and mineralogy of clays is, however, not clear. To address this deficiency a series of batch and diffusion cell studies, investigating the geochemical and mineralogical impacts of H2SO4 solutions (pH 5.0 to -3.0), were conducted on three mineralogically distinct clays (Kc, Km, and BK). Batch testing was conducted at seven pH treatments (5.0, 3.0, 1.0, 0.0, -1.0, -2.0 and -3.0) using standardized sulfuric acid solutions for four exposure periods (14, 90, 180, and 365 d). Aqueous geochemical, XRD, and Si and Al XANES analyses showed: increased dissolution of aluminosilicates with decreasing pH and increasing exposure period; preferential dissolution of aluminosilicate Al-octahedral layers relative to Si-tetrahedral layers; formation of an amorphous silica-like phase that was confined to the surface layer of the altered clay samples at pH ≤ 0.0 and t ≥ 90 d; and precipitation of anhydrite and a Al-SO4-rich phase (pH ≤ -1.0, t ≥ 90 d). The diffusive transport of H2SO4 (pH =1.0, -1.0, and -3.0) through the Kc and Km clays for 216 d was examined using single reservoir, constant concentration, diffusion cells. The diffusive transport of H+ within the cells was modeled using 1-D transport models that assumed no absorption, linear absorption, and non-linear absorption of H+. The absorption isotherms were calculated from the pH 5.0, 3.0, and 1.0 batch experiment results, which were assumed representative of H+ absorption at pH < 1.0. However, model results indicated that the batch test results can not account for the observed H+ consumption in all cells and greatly underestimate the amount of H+ consumption in the pH -1.0 and -3.0. In the Kc and Km diffusion cells, above-background Ca, Al, Fe, and Si aqueous concentrations were associated with depth intervals characterized by decreased pH values. Respective peak concentrations of 325, 403, 176, 11.7, and 1.38 x 10³ μmol g-1 (Kc) and 32.4, 426, 199, 7.2, and 1.22 x 10³ μmol g-1 (Km) were measured in the pH -3.0 cells. XRD results showed that the elevated concentrations corresponded to the loss of carbonates and montmorillonite peaks and decreased peak intensities for illite and kaolinite in depth intervals with pH ≤ 1.0, in the Kc and Km pH -1.0 and -3.0 cells. The combined results of these studies indicated that the long-term diffusion of H2SO4 through clays at pH < 1.0 will result in a large amount of primary phase dissolution; however, this will be accompanied by precipitation of soluble Ca and Al sulfate salts and amorphous silica, especially at pH ≤ 0.0. Additionally, the presence of even a small amount of carbonate will serve to greatly buffer the diffusive transport of H2SO4 through clays, even at a source pH of -3.0.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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 head0.008
GPT teacher head0.161
Teacher spread0.154 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2008
Admission routes2
Has abstractyes

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