Remarkably Selective Binding, Behavior Modification, and Switchable Release of (Bipyridine)<sub>3</sub>Ru(II) vis-à-vis (Phenanthroline)<sub>3</sub>Ru(II) by Trimeric Cyclophanes in Water
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide A recurring dream of molecular recognition is to create receptors that distinguish between closely related targets with sufficient accuracy, especially in water. The more useful the targets, the more valuable the dream becomes. We now present multianionic trimeric cyclophane receptors with a remarkable ability to bind the iconic (bipyridine) 3 Ru(II) (with its huge range of applications) while rejecting the nearly equally iconic (phenanthroline) 3 Ru(II). These receptors not only selectively capture (bipyridine) 3 Ru(II) but also can be redox-switched to release the guest. 1D- and 2D(ROESY)-NMR spectroscopy, luminescence spectroscopy, and molecular modeling enabled this discovery. This outcome allows the control of these applications, e.g., as a photocatalyst or as a luminescent sensor, by selectively hiding or exposing (bipyridine) 3 Ru(II). Overall, a 3D nanometric object is selected, picked-up, and dropped-off by a discrete molecular host. The multianionic receptors protect excited states of these metal complexes from phenolate quenchers so that the initial step in photocatalytic phenolate oxidation is retarded by nearly 2 orders of magnitude. This work opens the way for (bipyridine) 3 Ru(II) to be manipulated in the presence of other functional nano-objects so that many of its applications can be commanded and controlled. We have a cyclophane-based toolkit that can emulate some aspects of proteins that selectively participate in cell signaling and metabolic pathways by changing shape upon environmental commands being received at a location remote from the active site.
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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) | 0.002 | 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".