Alkaline Earth Element Adsorption onto PAA-Coated Magnetic Nanoparticles
Bibliographic record
Abstract
In this paper, we present a study on the adsorption of calcium (Ca2+) onto polyacrylic acid-functionalized iron-oxide magnetic nanoparticles (PAA-MNPs) to gain an insight into the adsorption behavior of alkaline earth elements at conditions typical of produced water from hydraulic fracturing. An aqueous co-precipitation method was employed to fabricate iron oxide magnetic nanoparticles, whose surface was first coated with amine and then by PAA. To evaluate the Ca2+ adsorption capacity by PAA-MNPs, the Ca2+ adsorption isotherm was measured in batch as a function of pH and sodium chlorite (electrolyte) concentration. A surface complexation model accounting for the coulombic forces in the diffuse double layer was developed to describe the competitive adsorption of protons (H+) and Ca2+ onto the anionic carboxyl ligands of the PAA-MNPs. Measurements show that Ca2+ adsorption is significant above pH 5 and decreases with the electrolyte concentration. Upon adsorption, the nanoparticle suspension destabilizes and creates large clusters, which favor an efficient magnetic separation of the PAA-MNPs, therefore, helping their recovery and recycle. The model agrees well with the experiments and predicts that the maximum adsorption capacity can be achieved within the pH range of the produced water, although that maximum declines with the electrolyte concentration.
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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".