Modulation of Surface Charge by Mediating Surface Chemical Structures in Nonpolar Solvents with Nonionic Surfactant Used as Charge Additives
Bibliographic record
Abstract
One of the major challenges in controlling the colloidal stability in nonpolar solvents concerns their surface charging strength. By tuning surface chemical structures in nonpolar solvents, the surface charge can be modulated with nonionic surfactants used as charge additives. In this study, various self-assembled monolayers (SAMs) were coated onto specific surfaces and nanoparticles to obtain different chemical structures. X-ray photoelectron spectroscopy (XPS) and atomic force microscope (AFM) measurements were used to quantify surface chemical structures, whereas dynamic adsorption experiments and molecular dynamics (MD) simulation were utilized to study the influence of these structures on surfactant adsorption behavior. Surface charging optimization was achieved by mediating the concentration of electron acceptors and donors, which is reflected as matching the alkyl length of SAMs with an appropriate concentration of hydroxyl group on the surfaces. The charging strength of particles decreased above a certain surfactant concentration, which is attributed to the competition between surfactant adsorption stability and charge neutralization effect generated via the disproportionation process.
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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".