A hybrid theoretical method for predicting electrokinetic energy conversion in nanochannels
Bibliographic record
Abstract
The traditional methods to predict electrokinetic energy conversion (EKEC) in nanochannels are mostly based on the Navier-Stokes (NS) equation for ionic flow and the Poisson-Boltzmann (PB) equation for charge distributions, which is questionable for ion transport through highly charged nanochannels. In this work, the classical density functional theory (cDFT) is used together with molecular dynamics (MD) simulation and the Navier-Stokes (NS) equation to predict the electrical current and the thermodynamic efficiency of electrokinetic energy conversion in nanochannels. By introducing numerical results for the slip length calculated from MD simulation, a significant increase of the electrokinetic current is predicted in comparison to that obtained from the traditional electrokinetic equations with the non-slip boundary condition, leading to the theoretical predictions of the thermodynamic efficiency for electrokinetic energy conversion in nanochannels in good agreement with recent experiments. The hybrid method predicts that maximum electrokinetic efficiency can be achieved by tuning the channel height and solution conditions including electrolyte concentrations, ion valences, and surface energies. The theoretical results provide new insights into pressure-driven electrical energy generation processes and helpful guidelines for engineering design and optimization of electrokinetic energy conversion.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".