Unlocking Water Adsorption Mechanisms in Y-BTC MOF: Insights from XAFS and SSNMR Spectroscopy
Bibliographic record
Abstract
Water plays a critical role in natural and technological processes, including atmospheric water harvesting, electrocatalysis, and biochemical reactions, all of which involve interactions between water and materials. Metal–organic frameworks (MOFs), with their tunable porosity and adsorption sites, offer significant potential in these fields. Understanding the interactions of water with MOFs is essential for optimizing their performance. This study investigates water adsorption behavior and dynamics in a yttrium-based MOF (Y-BTC) with open metal sites using X-ray absorption fine structure (XAFS) spectroscopy, solid-state nuclear magnetic resonance (SSNMR), and Monte Carlo (MC) simulations. XAFS reveals local structural changes upon coordinated water removal, producing open metal sites, while SSNMR provides insights into water mobility and adsorption site preferences under varying relative humidity (RH) conditions. MC simulations further validate these findings by mapping the water distribution within the framework. The results highlight that water adsorption in Y-BTC involves multiple adsorption sites and dynamic rearrangements, with the framework itself undergoing subtle structural evolution at lower temperatures. These findings enhance our understanding of water adsorption mechanisms in MOFs and offer valuable insights for the rational design of materials for water harvesting.
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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) | 0.000 | 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 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".