Impact\nof Sodium Humate Coating on Collector Surfaces on Deposition of Polymer-Coated\nNanoiron Particles
Bibliographic record
Abstract
The\naffinity between nanoscale zerovalent iron (nano-ZVI) and mineral\nsurfaces hinders its mobility, and hence its delivery into contaminated\naquifers. We have tested the hypothesis that the attachment of poly(acrylic\nacid)-coated nano-ZVI (PAA-nano-ZVI) to mineral surfaces could be\nlimited by coating such surfaces with sodium (Na) humate prior to\nPAA-nano-ZVI injection. Na humate was expected to form a coating over\nfavorable sites for PAA-nano-ZVI attachment and hence reduce the affinity\nof PAA-nano-ZVI for the collector surfaces through electrosteric repulsion\nbetween the two interpenetrating charged polymers. Column experiments\ndemonstrated that a low concentration (10 mg/L) Na humate solution\nin synthetic water significantly improved the mobility of PAA-nano-ZVI\nwithin a standard sand medium. This effect was, however, reduced in\nmore heterogeneous natural collector media from contaminated sites,\nas not an adequate amount of the collector sites favorable for PAA-nano-ZVI\nattachment within these media appear to have been screened by the\nNa humate. Na humate did not interact with the surfaces of acid-washed\nglass beads or standard Ottawa sand, which presented less surface\nheterogeneity. Important factors influencing the effectiveness of\nNa humate application in improving PAA-nano-ZVI mobility include the\nsolution chemistry, the Na humate concentration, and the collector\nproperties.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.012 | 0.001 |
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 teacher head, 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".