Carbohydrate-Containing Conjugated Polymers: Solvent-Resistant Materials for Greener Organic Electronics
Bibliographic record
Abstract
Organic semiconducting polymers are exciting materials for electronic applications because of their good mechanical and optoelectronic properties. A major advantage of organic semiconductors is their solution processability. This allows access to a variety of simple and cost-effective device fabrication methods compared to the expensive, high-temperature processing methods required for silicon-based electronics. However, these materials often have low solubility, which limits their processing to toxic halogenated solvents. Also, their limited solubility often leads to interfacial mixing during device fabrication. This work explores the incorporation of environmentally friendly carbohydrate side chains in conjugated polymers to enhance processability in eco-friendly solvents. Moreover, a mild postprocessing treatment was designed to enable solvent resistance. Isoindigo-based polymers with varied ratios of acetyl-protected galactose side chains were synthesized to improve solubility in o -anisole in the protected state, while inducing solvent resistance through intramolecular hydrogen bonding in the deprotected state. Solvent resistance was confirmed both visually upon submersion in various solvents and using UV–visible spectroscopy. Importantly, the mild basic treatment to achieve solvent resistance has no negative impact on the electronic performance of these materials in organic field-effect transistors, even after subsequent submersion in various solvents, making them a valuable platform for the production of green processable multilayer electronics.
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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".