All‐in‐One Hybrid Solar‐Driven Interfacial Evaporators for Cogeneration of Clean Water and Electricity
Bibliographic record
Abstract
Abstract Solar‐driven interfacial evaporators (SDIEs) have recently attracted considerable interest due to their ability to harvest abundant solar energy and treat seawater/wastewater for both freshwater production and electricity generation. However, during photothermal conversion in SDIEs, a portion of the incident sunlight is inevitably wasted, which presents an opportunity for potential alternative applications. There are also other types of harvestable energy like interactions between absorber materials’ surfaces and water/ions—called hydroelectricity (HE), as a form of renewable energy. This review paper provides an overview of studies focusing on utilizing SDIEs with a single structure capable of simultaneously producing freshwater and electricity, referred to as all‐in‐one hybrid SDIEs, with a particular emphasis on the HE power generation mechanism, which is the most commonly applied. An introduction to the photothermal conversion of sunlight into heat and fundamental aspects of the HE effect in hybrid SDIEs are discussed accordingly. The key results from studies on photothermal materials employed in all‐in‐one hybrid SDIEs are then explained and compared. This review will be concluded by spotlighting recent advancements, existing challenges, and promising opportunities that lie ahead for the materials used in these systems.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".