Hierarchically MOF‐Based Porous Monolith Composites for Atmospheric Water Harvesting
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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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".