Development of a mayenite composite pellet towards salinity remediation: Experimental demonstration of passive chloride removal from saline oilfield groundwater
Bibliographic record
Abstract
This study investigates newly developed sand/gravel-sized mayenite composite pellets with cellulose-based binders as a chloride (Cl − ) remover that is compatible with saline groundwater also impacted by hydrocarbons. The mayenite pellets remove Cl − from impacted oilfield groundwater with 60–90 % efficiency in the range of salinity values found at field sites (< 20,000 mg/L total dissolved solids, TDS) in the presence of hydrocarbons, without agitation. The pellet reactions are driven by hydration, anion exchange, adsorption, and mineral phase reconstruction with increasing intraparticle diffusion and pellet heterogeneity during Cl − removal. Cl − removal is significantly correlated with changes in the quantities of pellet components. Mayenite is a key mineral for Cl − removal, during which hydrocalumite is formed. However, high mayenite purity is not required due to the contributions of non-mayenite pellet components (CaO and Al 2 O 3 ) to Cl − removal. Drastic morphological transformations of pellet minerals occurred from honeycomb-like to cubic or hexagonal structures. Sand/gravel-sized pellets had a comparable Langmuir q max of 74.5 mg/g. Mayenite pellets are not affected by hydrocarbons in both aqueous and nonaqueous phases. The removal of hydrocarbons observed was likely associated with porous pellet structures. TDS and competitive anions in impacted groundwater regulate the mayenite pellet reactions for Cl − removal. • Porous mayenite pellets were developed to remove Cl - from oilfield groundwater. • Mayenite pellets remove Cl - at various field salinities up to 20,000 mg/L TDS. • Rapid Cl - removal occurs via mineral reconstruction of multiple pellet components. • Impure mayenite pellets remove Cl - without mixing in the presence of hydrocarbons. • Mayenite pellets remove both Cl - and hydrocarbons from oilfield groundwater.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".