Crystalline Cyclophane–Protein Cage Frameworks
Bibliographic record
Abstract
Cyclophanes are macrocyclic supramolecular hosts famous for their ability to bind atomic or molecular guests via noncovalent interactions within their well-defined cavities. In a similar way, porous crystalline networks, such as metal-organic frameworks, can create microenvironments that enable controlled guest binding in the solid state. Both types of materials often consist of synthetic components, and they have been developed within separate research fields. Moreover, the use of biomolecules as their structural units has remained elusive. Here, we have synthesized a library of organic cyclophanes and studied their electrostatic self-assembly with biological metal-binding protein cages (ferritins) into ordered structures. We show that cationic pillar[5]arenes and ferritin cages form biohybrid cocrystals with an open protein network structure. Our cyclophane-protein cage frameworks bridge the gap between molecular frameworks and colloidal nanoparticle crystals and combine the versatility of synthetic supramolecular hosts with the highly selective recognition properties of biomolecules. Such host-guest materials are interesting for porous material applications, including water remediation and heterogeneous catalysis.
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) | 0.002 | 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".