Optimizing mixtures of metal–organic frameworks for robust and bespoke passive atmospheric water harvesting
Bibliographic record
Abstract
Atmospheric water harvesting (AWH) is a method to obtain clean water in remote or underdeveloped regions including, but not limited to, those with an arid or desert climate. For passive, adsorbent-based AWH, an adsorbent bed is employed to capture water from cold, humid air at nighttime, while during the daytime the bed is then exposed to ambient sunlight to heat it and desorb the water for collection. Metal–organic frameworks (MOFs) are tunable, nanoporous materials with suitable water adsorption properties for comprising this adsorbent bed. The water delivery by the MOF adsorbent bed in a passive AWH device depends on (1) the nighttime, capture conditions (temperature and humidity) and daytime, release conditions and (2) the structure(s) of the MOF(s) comprising the bed, which dictate MOF-water interactions. We hypothesize that (1) an adsorbent bed comprised of a mixture or portfolio of MOFs, each specialized for different capture and release conditions, may deliver water with robustness to day-to-day weather fluctuations, and (2) the optimal composition of the adsorbent bed depends on the geographic region and time frame. Herein, we develop a method to optimally design the total mass and composition of a MOF mixture comprising an adsorbent bed for bespoke and robust passive AWH---i.e., tailored to a declared geographic region and time frame and satisfying minimum daily water delivery requirements. We combine historical weather data in the declared region, water adsorption data in the candidate MOFs, and thermodynamic adsorption models to frame a linear program expressing our optimal design principle: adjust the mass of each candidate MOF comprising the mixture to yield the minimal-mass adsorbent bed that satisfies the daily water delivery constraints. Based on our three case studies in the Chihuahuan Desert in June and the Sonoran Desert in June and in August, we find: (1) a mixed-MOF adsorbent bed can be, but is not always, lighter than its optimized single-MOF counterpart; and (2) the optimal composition and mass of the adsorbent bed differs by both geographic region and time frame. Finally, we visualize the linear program for a reduced problem with a two-dimensional design space to gain intuition, conduct a sensitivity analysis, and compare to a AWH field study. Our work is a starting point for optimizing the composition of adsorbent beds for robust, passive AWH and tailoring them for specific AWH missions.
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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".