Atmospheric Water Harvesting Using Thermoelectric Cooling Technology
Why this work is in the frame
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Bibliographic record
Abstract
Given Indonesia's average atmospheric humidity of 75% to 85%, this study explores the potential of atmospheric air as an alternative clean water source to mitigate water shortage.The research employs a thermoelectric cooler (TEC 1-12706), supplemented with a heatsink and fan on its hot side to enhance heat dissipation.A copper-made cooling coil serves as both a heat absorber and a condenser for atmospheric air passing through it.The cooling source for the coil (diameter=7.9mm;length=1000mm) is derived from a waterblock attached to the cooler's cold side.Experiments were conducted across three environmental conditions: laboratory, residential area, and coastal area, with the air flow rate of the heatsink cooling fan varied.Data collection spanned a humidity range of 72.27%-83.01%.Findings revealed a direct correlation between the air mass flow rate of the heatsink cooling fan and the amount of water extractable from the air.In initial testing at the Laboratory, at a mass flow rate of 0.046 kg/s it produced 4.25 ml/hour and at 0.069 kg/s it produced 4.625 ml/hour and at 0.092 kg/s it produced 5.5 ml/hour.Furthermore, from the three environmental conditions tested, more water can be extracted on the coast than in laboratories and residential areas.In coastal areas, the air mass flow rate is 0.092 kg/s, water that could be extracted is 7.75 ml/hour, while in the laboratory environment it is 5.5 ml/hour and in residential areas it is 4.75 ml/hour.These promising results encourage further research to augment water extraction by maximizing the contact surface between the air cooler and the coil surface, potentially offering a viable solution for clean water shortage.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it