Closed-loop air recirculation architecture for alkaline urine dehydration and water recovery using regenerable superabsorbent polymers
Bibliographic record
Abstract
We evaluated a novel closed-loop evaporative system designed to concentrate alkalised human urine while simultaneously recovering water using regenerable superabsorbent polymers (SAPs). This architecture recirculates air and physically isolates urine from atmospheric CO₂, thereby maintaining high alkalinity and preventing enzymatic urea hydrolysis. The system was operated at ∼30 °C using sodium polyacrylate, potassium polyacrylate, or a 1:1 ( w /w) blend of both SAPs across eight absorption–desorption cycles. All treatments exhibited high initial water uptake (>1.4 kg m −2 day −1 ) and gravimetric absorption (>0.8 g g −1 ), with performance declining due to polymer fatigue after repeated use. FT-IR spectra revealed the depolymerisation of the acrylate backbone leading to the formation of acrylic acid residues, confirming chemical deterioration during the thermal regeneration of the SAPs. Notably, the closed-loop design eliminated the need for supersaturating urine with Ca(OH)₂, which is required in open evaporative systems to buffer against CO₂-induced acidification. Colorimetric and targeted metabolomic analyses confirmed complete nitrogen retention and > 99 % recovery of the 30 most abundant endogenous organic solutes in urine, including urea, creatinine, and hippuric acid. These results demonstrate that low-temperature evaporation can preserve the full biochemical complexity of urine, producing a dry, sanitised fertiliser as well as water with extremely low organic content.
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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".