Root-based solar interfacial evaporation setup for efficient volumetric reduction of concentrated slurry waste
Bibliographic record
Abstract
Although solar interfacial evaporation stands as a promising application for seawater desalination, it has been scarcely studied for volumetric reduction of particle-containing wastewater. Taking inspiration from trees, this work presents a novel approach of root-based solar-interfacial evaporation for accelerated drying of slurries. By varying the total surface area of the roots, the water conduction rate was maximized under different intensities of solar radiation. The optimal setup displayed a high evaporation rate of 1.15 kg/(m2h1) under 1 sun irradiation. Additionally, the evaporator dried the slurry to 75 wt% solid concentration at this high evaporation rate. The setup could remove water further till solid concentration above 90 wt% in a total 40-h duration. The decrease in slurry evaporation rates observed at higher solid concentration was attributed to the break-up of continuous water capillary bridges present between the particles in the slurries. Long duration evaporation experiments for over 100 h of continuous operation displayed 75 % evaporation efficiency, underlining the feasibility of this setup for long-term usage. Large-scale outdoor experiments, scaling to 625 cm2, exhibited high evaporation rates comparable to the smaller setups, confirming the feasibility of this setup for large-scale volumetric reduction.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".