Functionalization of Metal–Organic Framework Nanochannels for Water Transport and Purification
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Artificial water nanochannels (AWCs) have drawn great attention due to their potential use in water purification. Herein, we propose an AWC design, which is based on coordinatively functionalizing unsaturated metal sites found in metal–organic frameworks (MOFs) with one-dimensional nanochannels. As a computational demonstration, we consider two MOFs, namely, Ni-CPO-27 and Ni-CPO-54, and graft proline, imidazolecarboxylic acid, imidazolecarboxaldehyde, pyrazolecarboxylic acid, and pyrazole carbaldehyde molecules into the MOF nanochannels. To assess the strength of the molecule-metal binding, binding energies were calculated using density functional theory. The results indicate that the MOFs containing either proline or 2-imidazolecarboxylic acid form water-stable AWCs with binding energies twice that of the binding energy of water. To shed light on the water diffusion mechanism in the proline-Ni-CPO-27/54 and 2-imidazolecarboxylic-Ni-CPO-27/54 AWCs, molecular dynamics simulations were performed to calculate the mean-squared displacement of water molecules and nonbonded interaction energies between select pairs of atoms in water and coordinated molecules were analyzed. It was found that the fastest water diffusion occurs in proline-Ni-CPO-54 with a self-diffusion coefficient of 7.2 ± 0.5 × 10 –8 cm 2 /s. In comparison, the fastest water self-diffusion coefficient reported in a carbon nanotube-based AWC is 9 × 10 –6 cm 2 /s. Nonbonded interactions between specific atom pairs regulate water diffusion in the functionalized MOF nanochannels. In particular, the change in water mean-squared displacement with changing water loading correlates well with the nonbonded energies between the partially positively charged hydrogen atoms in water and the partially negatively charged oxygen and nitrogen atoms in the proline and 2-imidazolecarboxylic acid molecules. The results presented herein indicate that water-stable MOFs could perform well as AWCs, thereby lending support to the further design and synthesis of MOF-based AWCs for water purification.
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".