Atmospheric Water Harvesting Using Thermoelectric Cooling Technology
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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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".