Unifying Efficiency Metrics for Solar Evaporation and Thermal Desalination
Bibliographic record
Abstract
Worsening water crises and climate change drive the need for solar evaporation and thermal desalination. Yet, diverse performance metrics, siloed communities, and a research shift away from high-efficiency technologies pose challenges to their advancement. We present a thermodynamic framework for unifying performance measurements across technologies, categorizing 17 leading performance metrics by their local- or system-level application and by thermodynamics laws. These are then organized into four categories of conceptually equivalent “sister” metrics. We clarify their best applications and measurement methods, detailing old and new conversion techniques using the temperature, recovery ratio, and salinity. Additionally, we compare six leading solar evaporation and thermal desalination technologies, identifying their second law efficiency and the specific exergy consumption. Furthermore, we reveal the unifying role of least work for solar desalination and steam generators and identify that many first law metrics become identical in these processes. Additionally, we create contour plots that link the energy efficiency metrics, recovery ratio, salinity, and temperature across a wider range than previously modeled, providing intuitive and easy comparisons and efficiency calculations. These findings contribute to enhancing comparisons and expediting optimal technology development.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".