Natural clinoptilolite composite membranes on tubular stainless steel supports for water softening
Bibliographic record
Abstract
Disk membranes generated from high-purity natural clinoptilolite mineral rock have shown promising water desalination and de-oiling performance. In order to scale up production of these types of membranes for industrial wastewater treatment applications, a coating strategy was devised. A composite mixture of natural clinoptilolite from St. Cloud (Winston, NM, USA) and aluminum phosphate was deposited on the inner surface of porous stainless steel tubes by the slip casting technique. The commercial porous stainless steel tubes were pre-coated with a TiO2 layer of about 10 μm. Phase composition and morphology of the coating materials were investigated using X-ray diffraction and scanning electron microscopy. Water softening performance of the fabricated membranes was evaluated using Edmonton (Alberta, Canada) municipal tap water as feed source. Preliminary experimental results show a high water flux of 7.7 kg/(m(2) h) and 75% reduction of hardness and conductivity in a once-through membrane process at 95 °C and feed pressure of 780 kPa. These results show that natural zeolite coated, stainless steel tubular membranes have high potential for large-scale purification of oil sands steam-assisted gravity drainage water at high temperature and pressure requirements.
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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) | 0.000 | 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".