Development of an Efficient System for Blue Energy Production Based on Reverse Electrodialysis (RED) by Optimizing Electrolyte Composition: Experimental and Theoretical Simulations
Bibliographic record
Abstract
Herein, the effect of electrolyte composition (single vs salt mixture) on the performance of reverse electrodialysis (RED) has been investigated using lab-made sulfonated poly(ether ether ketone) (sPEEK) cation exchange membrane (CE) membrane and Neosepta, a commercially available anion exchange (AE) membrane. The efficiency of the RED cell was monitored by measuring open-circuit voltage (OCV), power density (PD), and gross power density (PD gross ). The effect of feed solution flow and concentration was analyzed by using several electrolytes (LiCl, NaCl, KCl, and NH 4 Cl) and mixed composition (NaKCl and NaNH 4 Cl). NaCl solution among single electrolytes exhibited the highest performance with a PD of 1.77 Wm –2, which was improved further by intermixing with KCl and NH 4 Cl. For the case of binary mixtures, NaNH 4 Cl showed a PD of 2.51 Wm –2, which is 42% higher compared to that of NaCl possibly due to the inferior stack resistance. A molecular dynamics (MD) simulation was performed to further investigate the adsorption-diffusion properties of CEM and AEM at the molecular scale. A positive correlation was observed between MD simulation and experimental measurements regarding the competitive adsorption of cations into the sPEEK membrane with the following trend NH 4 + > K + > Na + > Li +, which is associated with the ionic radius and hydration energies of respective cations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".