Synthesis of Boroxine and Dioxaborole Covalent Organic Frameworks via Transesterification and Metathesis of Pinacol Boronates
Bibliographic record
Abstract
Boroxine and dioxaborole are the first and some of the most studied synthons of covalent organic frameworks (COFs). Despite their wide application in the design of functional COFs over the last 15 years, their synthesis still relies on the original Yaghi’s condensation of boronic acids (with itself or with polyfunctional catechols), some of which are difficult to prepare, poorly soluble, or unstable in the presence of water. Here, we propose a new synthetic approach to boroxine COFs (on the basis of the transesterification of pinacol aryl boronates (aryl-Bpins) with methyl boronic acid (MBA) and dioxaborole COFs (through the metathesis of pinacol boronates with MBA-protected catechols). The aryl-Bpin and MBA-protected catechols are easy to purify, highly soluble, and bench-stable. Furthermore, the kinetic analysis of the two model reactions reveals high reversibility ( K eq ∼ 1) and facile control over the equilibrium. Unlike the conventional condensation, which forms water as a byproduct, the byproduct of the metathesis (MBA pinacolate) allows for easy kinetic measurements of the COF formation by conventional 1 H NMR. We show the generality of this approach by the synthesis of seven known boroxine/dioxaborole COFs whose crystallinity is better or equal to those reported by conventional condensation. We also apply metathesis polymerization to obtain two new COFs, Py4THB and B2HHTP, whose synthesis was previously precluded by the insolubility and hydrolytic instability, respectively, of the boronic acid precursors.
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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".