Preferential interaction of Na+ over K+ with carboxylate-functionalized silver nanoparticles
Bibliographic record
Abstract
Elucidating mechanistic interactions between monovalent cations (Na(+)/K(+)) and engineered nanoparticle surfaces to alter particle stability in polar media have received little attention. We investigated relative preferential interaction of Na(+) and K(+) with carboxylate-functionalized silver nanoparticles (carboxylate-AgNPs) to determine if interaction preference followed the Hofmeister series (Na(+)>K(+)). We hypothesized that Na(+) will show greater affinity than K(+) to pair with carboxylates on AgNP surfaces, thereby destabilizing the colloidal system. Destabilization upon Na(+) or K(+) interacting with carboxylate-AgNPs was evaluated probing changes in multiple physicochemical characteristics: surface plasmon resonance/optical absorbance, electrical conductivity, pH, hydrodynamic diameter, electrophoretic mobility, surface charge, amount of Na(+)/K(+) directly associated with AgNPs, and Ag(+) dissociation kinetics. We show that Na(+) and K(+) react differently, indicating local Na(+) pairing with carboxylates on AgNP surfaces is kinetically faster and remarkably favored over K(+), thus supporting Hofmeister ordering. Our results suggest that AgNPs may transform into micron-size aggregates upon release into aqueous environments and that the fate of such aggregates may need consideration when assessing environmental risk.
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 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 | 0.001 | 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".