Streaming potential and electroviscous effects in soft nanochannels: towards designing more efficient nanofluidic electrochemomechanical energy converters
Bibliographic record
Abstract
In this paper we provide analytical solutions for the streaming potential and electroviscous effects in soft nanochannels. The analysis is based on the solution of the linearized Poisson-Boltzmann equation, valid for small electrostatic potentials. We identify the important dimensionless parameters that dictate these two effects. Results are provided for a large range of electric double layer (EDL) thickness values, spanning from the case of very thin to very large overlapped EDL thicknesses. We compare the results with those of a rigid nanochannel, having zeta potential equal to the electrostatic potential at the solid-polyelectrolyte interface of the soft nanochannels. For the soft nanochannel, the streaming potential varies very weakly with the EDL thickness and can be substantially larger than that corresponding to the rigid nanochannel. The electroviscous effects for the soft nanochannel, unlike the rigid nanochannel, virtually always exhibit a monotonic decrease with the EDL thickness, and for certain parameter ranges can be several times larger than that for a rigid nanochannel. Most importantly, for the soft nanochannels the electrochemomechanical energy conversion, associated with the generation of streaming potential, is found to be highly efficient, with the efficiency being several times higher than that of a rigid nanochannel.
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) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".