Selective Water Transport in an Alanine-Functionalized Metal–Organic Framework: A Computational Study
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Applications of metal–organic frameworks (MOFs) functionalized with biomolecules have primarily focused on the use of these frameworks for bioimaging, catalysis, chiral separation, nanomotors, and drug delivery. However, their use in the design of artificial water channels (AWCs) has yet to be explored. In this work, we computationally explore the performance of a zwitterionic alanine-functionalized Ni-CPO-27 MOF as an AWC. Using density functional theory (DFT) calculations and equilibrium/nonequilibrium molecular dynamics (MD) simulations, the stability, water permeability, and ion selectivity of the proposed AWC are studied. The DFT calculations predict that zwitterionic alanine binds to the coordinatively unsaturated Ni sites almost twofold stronger compared to water. Using the quantum theory of atoms in molecules, it is also found that the zwitterionic alanine molecules are further stabilized through hydrogen bonding between their carboxylate (COO – ) and amino (NH 3 + ) groups. Nonequilibrium MD simulations show that the proposed AWC possesses a high osmotic water permeability of 2.2 ± 0.3 × 10 –15 cm 3 /s/channel, which lies between that observed in aquaporin-0 and aquaporin-1 proteins, while completely excluding Na + and Cl – ions from the channel. The free energies associated with the water and ion transport show that fast water transport may be attributed to the relatively low free energy barriers for water in the channel, whereas the ion exclusion is due to large free energy barriers that the ions cannot overcome even under 100 MPa of applied pressure. By using a crystalline material, the proposed design of an amino acid-functionalized MOF-based AWC represents a departure from previously developed AWCs, which rely on the self-assembly of curated molecules in lipid bilayers or polymer matrices and are susceptible to long-term stability issues.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".