Effect of Water Chemistry and Aging on Iron—Mica Interaction Forces: Implications for Iron Particle Transport
Bibliographic record
Abstract
The transport of particles through groundwater systems is governed by a complex interplay of mechanical and chemical forces that are ultimately responsible for binding to geological substrates. To understand these forces in the context of zero valent iron particles used in the remediation of groundwater, atomic force microscopy (AFM)-based force spectroscopy was employed to characterize the interactions between AFM tips modified with either carbonyl iron particles (CIP) or electrodeposited Fe as a function of counterion valency, temperature, particle morphology, and age. The measured interaction forces were always attractive for both fresh and aged CIP and electrodeposited iron, except in 100 mM NaCl, as a consequence of electrostatic attraction between the negatively charged mica and positively charged iron. In 100 mM NaCl, repulsive hydration forces appeared to dominate. Good agreement was found between the experimental data and predictions based on the extended DLVO (XDLVO) theory. The effect of aging on iron particle composition and morphology was assessed by X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS) revealing that the aged particles comprising a zero valent iron core passivated by a mixture of iron oxides and hydroxides. Force spectroscopy showed that aging caused variations in the adhesive force due to the changes in particle morphology and contact area.
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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 | 0.001 |
| 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".