New insights into the removal of nitric oxide using UiO-66-NH2: Synergistic photooxidation and subsequent adsorption
Bibliographic record
Abstract
UiO-66 variants are widely used for photocatalysis and adsorption, but few studies reported their combined effects in one process. This paper reports that the mechanism behind NO removal using UiO-66-NH 2 includes photocatalytic oxidation and subsequent adsorption. First, the UiO-66-NH 2 was selected out of UiO-66-X variants (X = H, NH 2 , NO 2 , OH, Br, Br 2 ) because of its high NO removal efficiency ( i.e ., 80.54%), which is 8.55–64.95 times higher than those of other UiO-66 variants. Then, the NO reaction pathway and degradation mechanisms are proposed based on the experimental results and theoretical calculations. In an anhydrous environment, the NO removal efficiency increases from 80.54% at 40–60% of humidity to 96.23%. Moreover, NO 2 emissions and catalyst deactivate are not observed. These findings indicate that the photocatalytic NO degradation includes the photocatalytic oxidation of NO into NO 2 on the surface of UiO-66-NH 2 and subsequent NO 2 adsorption in micropores . The reason is that the formation of NO x - ions without H 2 O is theoretically impossible according to the principle of electroneutrality . In-situ DRIFTS also confirms the mechanism. Furthermore, density functional theory (DFT) and grand canonical Monte Carlo (GCMC) simulations were carried out to understand the improved NO removal caused by synergistic photocatalysis and adsorption. In summary, this work proposes a new mechanism for NO removal that combines the photocatalytic oxidation and adsorption capability of UiO-66-NH 2 , the new mechanism provides a new strategy to further improve the NO removal efficiency of UiO-66-NH 2 and a new way of inhibiting the deactivation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".