Understanding the Surfaces of Nanodiamonds
Bibliographic record
Abstract
Functional groups and their associated charges are responsible for the binding and release of molecules from the surfaces of particles in nanodiamond colloids. In this work, we describe a combined set of experimental and computational techniques that are used to characterize these functional groups quantitatively. The surfaces of the particles examined during this study are amphoteric, as one would expect for surfaces made of carbon, with high concentrations of phenols, pyrones, and sulfonic acid groups; the average 50-nm-diameter nanodiamond aggregate has approximately 22000 phenols, 7000 pyrones, and 9000 sulfonic acids. The aggregates also have at least 2000 fixed positive charges, stabilized within pyrones and/or chromenes. No evidence for a significant concentration of carboxylic acid groups was found, although some are probably present. Hydroxyl and epoxide groups are present on some areas of the surfaces. The surfaces are graphitized, so the presence of phenols and pyrones is not surprising because such groups are common on graphitic surfaces. The sulfonic acid is due to the sulfuric acid treatment used to remove amorphous carbon and graphite during particle cleaning. The fixed charges are also due to the cleaning procedure that includes the use of KMnO 4 with the sulfuric acid. Based on titration and zeta potential experiments, elemental and particle size analyses, and modeling using semiempirical quantum mechanics, a model is proposed for the types and concentrations of surface groups. The modeling shows how functional groups form during the bead milling and cleaning used in the preparation of the colloid. It also shows that the p K a associated with the phenols and pyrones that are formed (p K a = 7.6–10.0) is consistent with that predicted using titration experiments (p K a ≥ 7.3). The positive surface potential means that the latter p K a value is significantly larger than a Henderson–Hasselbalch-based estimate. The model is shown to be useful in explaining a number of recent experiments in which nanodiamonds were used to bind and release therapeutic drug and polymer molecules.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".