Capacitance of graphene in aqueous electrolytes: The effects of dielectric saturation of water and finite size of ions
Bibliographic record
Abstract
We present a theoretical model for electrolytically top-gated graphene, in which we analyze the effects of dielectric saturation of water due to possibly strong electric fields near the surface of a highly charged graphene, as well as the steric effects due to the finite size of salt ions in an aqueous electrolyte. By combining two well-established analytical models for those two effects, we show that the total capacitance of the solution-gated graphene is dominated by its quantum capacitance for gating potentials $\ensuremath{\lesssim}1\phantom{\rule{0.28em}{0ex}}\mathrm{V}$, which is the range of primary interest for most sensor applications of graphene. On the other hand, at the potentials $\ensuremath{\gtrsim}1\phantom{\rule{0.28em}{0ex}}\mathrm{V}$ the total capacitance is dominated by a universal capacitance of the electric double layer in the electrolyte, which exhibits a dramatic decrease of capacitance with increasing gating potential due to the interplay of a fully saturated dielectric constant of water and ion crowding near graphene.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".