The Ion Sensitivity of Surface Conductive Single Crystalline Diamond
Bibliographic record
Abstract
Charge build-up at the solid/aqueous interface is a ubiquitous phenomenon that determines the properties of interfacial electrical double layers. Due to its unique properties, the surface of diamond offers an attractive platform to investigate charging mechanisms in aqueous solutions. We investigate the surface charge by studying the ion sensitivity of H-terminated single crystalline diamond surface conductive layers. The effect of monovalent and divalent salts has been probed at different pH values. For a pH above 3.5, increasing the ionic strength results in a decrease of the surface conductivity, in contrast to the results obtained for pH below 3.5. Electrokinetic experiments are in good agreement with the surface conductivity measurements, showing an isoelectric point at pH 3.5 for the H-terminated diamond surface. We discuss the results in terms of the Coulombic screening by electrolyte ions of the surface potential, which is induced by a pH-dependent surface charge. The origin of this surface charge is discussed in terms of charge regulation by amphoteric hydroxyl surface groups and unsymmetrical adsorption of hydroxide and hydronium ions induced by the hydrophobic nature of the H-terminated diamond surface. This surface charge can have important consequences for processes governed by the diamond/aqueous interface, such as electron transfer to charged redox molecules, adsorption of charged molecules and proteins, and ion sensitivity.
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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 | 0.001 | 0.000 |
| 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".