Optimizing Mixtures of Metal–Organic Frameworks for Robust and Bespoke Passive Atmospheric Water Harvesting
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide 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 (i.e., relying on ambient cooling and, for heating, natural sunlight─as opposed to an external power source), 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 natural 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 (temperature, humidity, and solar flux) and (2) the structure(s) of the MOF(s) comprising the bed, which dictate MOF-water interactions. Notably, the capture and release conditions vary from region-to-region and season-to-season and fluctuate from day-to-day, while different MOFs offer different water adsorption isotherms. Consequently, we propose (1) comprising the adsorbent bed for passive AWH with a mixture of MOFs and (2) tailoring this MOF mixture to particular geographic regions and time frames. We hypothesize each MOF in the mixture can specialize in delivering water under different capture and release conditions, ensuring the adsorbent bed delivers adequate water on every day─despite fluctuations in temperature, humidity, and solar flux. Herein, we develop an optimization framework to determine the total mass and composition of a MOF mixture for comprising a bespoke (i.e., tailored to a declared geographic region and time frame) adsorbent bed for robust (i.e., delivering adequate water every day) passive AWH. We combine weather data in the declared region, equilibrium water adsorption data in the candidate MOFs, and thermodynamic water adsorption models (as a simplifying assumption, we neglect heat and water transfer limitations) to frame a linear program expressing our optimal design principle: adjust the mass of each candidate MOF comprising the adsorbent bed to minimize mass (important for portability and a proxy for cost) while satisfying daily water delivery constraints. Based on case studies in the Chihuahuan and Sonoran Deserts, we find (1) a mixed-MOF adsorbent bed can be, but is not always, lighter (e.g., ≈40% lighter) than the optimized single-MOF counterpart; and (2) the optimal composition and mass of the adsorbent bed differ 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 an AWH field study. Our work is a starting point for optimizing the composition of bespoke adsorbent beds for robust, passive AWH.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".