Water Adsorption Capacity of UiO-66 Metal Organic Framework (MOF) Nanoparticles for Applications in Water Harvesting
Bibliographic record
Abstract
Metal organic framework (MOF) nanoparticles, characterized by their substantial surface area, tunable porosity, and exceptional adsorption capacity, emerge as promising candidates for water harvesting via efficient water vapor capture in arid regions.This investigation presents an experimental evaluation of zirconium-based UiO-66 MOF nanoparticles exposed to both liquid water and controlled humidity conditions.FTIR spectroscopic analysis revealed distinct spectral signatures associated with O-H bonds for dry and wetted UiO-66 particles.Dry UiO-66 particles kept at five different relative humidity conditions demonstrated different performances with increased water adsorption capacity at elevated humidity values, consistent with the porous structure of MOF particles and previous literature.Comparative analysis of UiO-66 adsorption behaviour in both liquid and vapor phases provides valuable insights into the feasibility of utilizing these materials for water harvesting applications.
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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) | 0.002 | 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".