Aqueous Sol–Gel Synthesis and Shaping of Covalent Organic Frameworks
Bibliographic record
Abstract
The intrinsic fragility and insoluble nature of covalent organic frameworks (COFs) have strongly impeded their processability for practical applications. Herein, an aqueous-based sol–gel synthetic strategy is reported for the synthesis and shaping of COFs with task-specific applications that satisfy the principles of green chemistry for gram-scale production of crystalline materials. Our successful approach involves three pivotal aspects: the “prodrug mimic” design of water-soluble monomers, the utilization of hydrolyzable bonds, and the manipulation of reaction kinetics. The generality of the method is demonstrated by the successful preparation of representative high-surface area two-dimensional (2D) COFs with several commonly used amines. By virtue of this strategy, a COF colloidal dispersion is achieved and can be formulated into processable fluids, structured films, and COF monoliths. Remarkably, the obtained lightweight (∼0.020 g cm –3 ) and robust aerogels displayed outstanding adsorption capacity (exceeding 57 times its own weight) toward a variety of organic solvents and exhibited superior thermal insulating properties compared to the widely used sponge and cotton. This work demonstrates a versatile strategy for the synthesis and shaping of processable COF materials in water that will contribute to the development of COF monoliths for advanced applications.
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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 | 0.000 |
| 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".