Hierarchically MOF‐Based Porous Monolith Composites for Atmospheric Water Harvesting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Water scarcity, a critical global challenge, has intensified due to the adverse effects of climate change on ecosystems and its detrimental impact on human activities. Addressing this issue requires solutions capable of providing clean water in regions facing hydroclimatic challenges and limited infrastructure. Atmospheric water harvesting (AWH) offers a promising solution, particularly in arid regions, by extracting moisture from the air. This review explores AWH technologies that leverage material porosity and hygroscopicity, focusing on highly porous materials such as Metal-Organic Frameworks (MOFs) and monolithic scaffolds. While MOFs exhibit exceptional water uptake due to their tunable chemistry and nanoscale porosity, their powdery nature poses stability and processability challenges. To overcome these limitations, integrating MOFs into multiscale porous monoliths-such as foams, aerogels, cryogels, and xerogels-enhances structural integrity and performance. The role of hierarchical porosity, engineered across nano-scale in MOF (<2 nm) and micro-scales (>2 nm) is emphasized in porous monoliths, in optimizing water capture efficiency. This review also highlights recent advancements in MOF-based composite monoliths, their working mechanisms, and the potential for large-scale implementation. By integrating nanotechnology with material chemistry, this work outlines strategies to enhance sorption capacity, desorption kinetics, and scalability, ultimately providing a roadmap for developing efficient, sustainable, and scalable AWH systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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