Defect-Rich Metal–Organic Framework Nanocrystals for Removal of Micropollutants from Water
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide In this study, the effective removal of three major micropollutants (e.g., propranolol hydrochloride, 1-naphthylamine, and 2-naphthol) from water is investigated using defected UiO-66 and functionalized derivatives as adsorbents. The defects in UiO-66 are induced using two distinct strategies. The first one involves a process of selectively thermolyzing labile linkers, leading to the creation of a hierarchical mesoporous framework (HP-UiO-66). The second technique employs monocarboxylic acid modulator, such as acetic acid (AA) or trifluoroacetic acid (TFA), which is coordinated with the Zr-clusters during synthesis and removed upon activation, resulting in the creation of defective UiO-66 structures. The influence they have on structural features of metal–organic framework (MOF) nanocrystals is compared to the ideal nonmodulated UiO-66 MOF. The samples are fully characterized by powder X-ray diffraction (PXRD), scanning electron microscope (SEM), Brunauer–Emmett–Teller surface area analyzer (BET), and thermogravimetric analysis (TGA). Their effectiveness as adsorbents for the elimination of 1-naphthylamine, propranolol hydrochloride, and 2-naphthol from water is examined. The thermodynamic and kinetic parameters of the adsorption process are determined. Interestingly, among the UiO-66 samples examined, HP-UiO-66 exhibits remarkable adsorption capacities of approximately 743, 602, and 409 mg g –1 for 1-naphthylamine, propranolol hydrochloride, and 2-naphthol, respectively. These values surpass all of the reported adsorbents in the literature. The surface interactions between HP-UiO-66 and the three micropollutants were demonstrated by Fourier transform infrared (FT-IR) and X-ray photoelectron spectroscopy (XPS) analyses. This enhancement in adsorption performance is linked to the preferential adsorption mechanism, primarily by binding to coordinatively unsaturated zirconium atoms within the MOF structure. Through this study, an effective method for improving the adsorption capability of MOF-based adsorbents is presented through defects engineering in Zr-based MOFs. These findings open the path for the creation of effective MOF adsorbents for water treatment applications.
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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) | 0.001 | 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) | 0.015 | 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 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".