Harvesting Blue Energy Based on Salinity and Temperature Gradient: Challenges, Solutions, and Opportunities
Bibliographic record
Abstract
Greenhouse gas emissions associated with power generation from fossil fuel combustion account for 25% of global emissions and, thus, contribute greatly to climate change. Renewable energy sources, like wind and solar, have reached a mature stage, with costs aligning with those of fossil fuel-derived power but suffer from the challenge of intermittency due to the variability of wind and sunlight. This study aims to explore the viability of salinity gradient power, or "blue energy", as a clean, renewable source of uninterrupted, base-load power generation. Harnessing the salinity gradient energy from river estuaries worldwide could meet a substantial portion of the global electricity demand (approximately 7%). Pressure retarded osmosis (PRO) and reverse electrodialysis (RED) are more prominent technologies for blue energy harvesting, whereas thermo-osmotic energy conversion (TOEC) is emerging with new promise. This review scrutinizes the obstacles encountered in developing osmotic power generation using membrane-based methods and presents potential solutions to overcome challenges in practical applications. While certain strategies have shown promise in addressing some of these obstacles, further research is still required to enhance the energy efficiency and feasibility of membrane-based processes, enabling their large-scale implementation in osmotic energy harvesting.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".