Renewable Power Generation by Reverse Electrodialysis Using an Ion Exchange Membrane
Bibliographic record
Abstract
Reverse electrodialysis (RED) is a promising technology to extract sustainable salinity gradient energy. However, the RED technology has not reached its full potential due to membrane efficiency and fouling and the complex interplay between ionic flows and fluidic configurations. We investigate renewable power generation by harnessing salinity gradient energy during reverse electrodialysis using a lab-scaled fluidic cell, consisting of two reservoirs separated by a nanoporous ion exchange membrane, under various flow rates (qf) and salt-concentration difference (Δc). The current-voltage (I-V) characteristics of the single RED unit reveals a linear dependence, similar to an electrochemical cell. The experimental results show that the change of inflow velocity has an insignificant impact on the I-V data for a wide range of flow rates explored (0.01–1 mL/min), corresponding to a low-Peclet number regime. Both the maximum RED power density (Pc,m) and open-circuit voltage (ϕ0) increase with increasing Δc. On the one hand, the RED cell’s internal resistance (Rc) empirically reveals a power-law dependence of Rc∝Δc−α. On the other hand, the open-circuit voltage shows a logarithmic relationship of ϕ0=BlnΔc+β. These experimental results are consistent with those by a nonlinear numerical simulation considering a single charged nanochannel, suggesting that parallelization of charged nano-capillaries might be a good upscaling model for a nanoporous membrane for RED applications.
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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".