Nucleobase-Templated Polymerization: Copying the Chain Length and Polydispersity of Living Polymers into Conjugated Polymers
Bibliographic record
Abstract
Conjugated polymers synthesized by step polymerization mechanisms typically suffer from poor molecular weight control and broad molecular weight distributions. We report a new method which uses nucleobase recognition to read out and efficiently copy the controlled chain length and narrow molecular weight distribution of a polymer template generated by living polymerization, into a daughter conjugated polymer. Aligning nucleobase-containing monomers on their complementary parent template using hydrogen-bonding interactions, and subsequently carrying out a Sonogashira polymerization, leads to the templated synthesis of a conjugated polymer. Remarkably, this daughter strand is found to possess a narrow molecular weight distribution and a chain length nearly equivalent to that of the parent template. On the other hand, nontemplated polymerization or polymerization with the incorrect template generates a short conjugated oligomer with a significantly broader molecular weight distribution. Hence, nucleobase-templated polymerization is a useful tool in polymer synthesis, in this case allowing the use of a large number of polymers generated by living methods, such as anionic polymerization, controlled radical polymerizations (NMP, ATRP, and RAFT) and other mechanisms to program the structure, length, and molecular weight distribution of polymers normally generated by step polymerization methods and significantly enhance their properties.
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 | 0.001 |
| 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 | 0.001 |
| 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".