Tunable 2D Conjugated Porous Organic Polymer Films for Precise Molecular Nanofiltration and Optoelectronics
Bibliographic record
Abstract
Structural design of 2D conjugated porous organic polymer films (2D CPOPs), by tuning linkage chemistries and pore sizes, provides great adaptability for various applications, including membrane separation. Here, four free-standing 2D CPOP films of imine- or hydrazone-linked polymers (ILP/HLP) in combination with benzene (B-ILP/HLP) and triphenylbenzene (TPB-ILP/HLP) aromatic cores are synthesized. The anisotropic disordered films, composed of polymeric layered structures, can be exfoliated into ultrathin 2D-nanosheets with layer-dependent electrical properties. The bulk CPOP films exhibit structure-dependent optical properties, triboelectric nanogenerator output, and robust mechanical properties, rivaling previously reported 2D polymers and porous materials. The exfoliation energies of the 2D CPOPs and their mechanical behavior at the molecular level are investigated using density function theory (DFT) and molecular dynamics (MD) simulations, respectively. Exploiting the structural tunability, the comparative organic solvent nanofiltration (OSN) performance of six membranes having different pore sizes and linkages to yield valuable trends in molecular weight selectivity is investigated. Interestingly, the OSN performances follow the predicted transport modeling values based on theoretical pore size calculations, signifying the existence of permanent porosity in these materials. The membranes exhibit excellent stability in organic solvents at high pressures devoid of any structural deformations, revealing their potential in practical OSN applications.
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 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) | 0.001 | 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".