Reactive Multidentate Block Copolymer Stabilization to Carbon Nanotubes for Thermoreversible Cross-Linked Network Gels
Bibliographic record
Abstract
Control over surface chemistry is essential for various applications of colloidal carbon-based and inorganic nanomaterials. Here, a reactive multidentate block copolymer (rMDBC) strategy is demonstrated with the synthesis of rMDBC composed of a furfuryl block designed to react with maleimides through a Diels–Alder (DA) reaction and a pyrene block designed to bind to carbon materials through a π–π interaction. The synthesized rMDBC enables the stabilization of carbon nanotube (CNT) surfaces to form colloidally stable rMDBC/CNT colloids, and it has a greater binding affinity to CNTs compared with those of its counterparts, such as a homopolymer bearing pendant pyrene groups and a monodentate homopolymer bearing a pyrene terminal group. Furthermore, the resultant colloids bearing multiple furfuryl groups are highly applicable as reactive cross-linkers for the fabrication of thermally induced cross-linked networks exhibiting thermoreversibility with a dimaleimide. These results suggest that the rMDBC strategy is an effective platform for the stabilization of nanomaterial surfaces in single layers and, thus, the development of high performance, dynamic, cross-linked self-healable materials.
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".