Sodium adsorption by reusable zeolite adsorbents: Integrated adsorption cycles for salinized groundwater treatment
Bibliographic record
Abstract
Using Canadian (CMZ), Bear River (BRZ), and St. Cloud (SCZ) zeolites, this study investigates the application of natural and pre-treated zeolites for Na+ removal from salinized groundwater. Natural BRZ achieved better Na+ removal for initial concentrations of 250–10 000 mg Na+/L and had the highest maximum adsorption capacity (14.3 ± 0.4 mg/g) compared to natural CMZ (5.8 ± 0.5 mg/g) and SCZ (5.6 ± 0.7 mg/g). Natural BRZ exhibited a higher cation exchange capacity (CEC), mineralogical purity, and natural abundance of exchangeable calcium. The natural abundance of Na+ on CMZ and SCZ may have reduced Na+ adsorption. H-form BRZ and H-form CMZ were also prepared through conventional acidic pre-treatment. Acid treatment improved zeolite properties for adsorption (surface area and CEC). Synchrotron-based X-ray scanning transmission microscopy (STXM) indicated that Na+ adsorption sites in the H-form zeolites were associated with the mineral framework. However, sorption effluents were highly acidic (pH ∼2) and Al3+ leached significantly due to the dealumination induced by acid treatment. Alternatively, hard water softening was cyclically integrated with sodium adsorption as a zeolite treatment to generate Ca/Mg-form CMZ. This integration suggested the feasibility of combining CMZ cycles for water softening and sodium reduction for an extended CMZ lifecycle. Natural CMZ was first used to treat hard water, which enriched the CMZ with Ca2+ and Mg2+ and increased its subsequent Na+ removal rate by over 77%, without producing acidic effluents. The Canadian zeolite adsorbed more sodium when water softening was integrated with sodium removal, which is a repeatable dual-treatment.
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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".