Forces of Interactions between Bare and Polymer-Coated Iron and Silica: Effect of pH, Ionic Strength, and Humic Acids
Bibliographic record
Abstract
The interactions between a silica substrate and iron particles were investigated using atomic force microscopy-based force spectroscopy (AFM). The micrometer- and nanosized iron particles employed were either bare or coated with carboxymethyl cellulose (CMC), a polymer utilized to stabilize iron particle suspensions. The effect of water chemistry on the forces of interaction was probed by varying ionic strength (with 100 mM NaCl and 100 mM CaCl₂) or pH (4, 5.5, and 8) or by introducing 10 mg/L of humic acids (HA). When particles were uncoated, the forces upon approach between silica and iron were attractive at pH 4 and 5.5 and in 100 mM CaCl₂ at pH 8, but they were negligible in 100 mM NaCl buffered to pH 8 and repulsive in water buffered to pH 8 and in HA solutions. HA produced electrosteric repulsion between iron particles and silica, likely due to its sorption to iron particles. HA sorption to silica was excluded on the basis of experiments conducted with a quartz-crystal microbalance with dissipation monitoring. Repulsion with CMC-coated iron was attributed to electrosteric forces, which were damped at high ionic strength. An extended DLVO model and a modified version of Ohshima's theory were successfully utilized to model AFM data.
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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 | 0.001 |
| 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".