Water/Alcohol‐Processable Low‐Cost Dihydropyrazine‐Based Polymers for Highly Sensitive, Stable and Flexible Temperature Sensors
Bibliographic record
Abstract
Abstract Flexible temperature sensors based on π–conjugated polymers are well‐suited for diverse applications, including food packaging and human health monitoring. Herein, novel dihydropyrazine (DHP)‐based polymers designed for the development of flexible temperature sensors are introduced. The DHP‐based polymers are synthesized via direct arylation polymerization, eliminating toxic byproducts. Polymers with carboxylate potassium salt side chains, which exhibit high solubility in green solvents like water and alcohol are obtained via post‐polymerization hydrolysis of carboxylate ester chains. Furthermore, a post‐deposition treatment converts the carboxylate potassium salt side chains into carboxylic acid side chains, resulting in highly solvent‐resistant polymers. Notably, these DHP‐based polymers exhibit moderate electrical conductivity in the range of ≈10−4 to 10−1 S cm−1 without the need for additional dopants. Resistor‐type temperature sensors based on the self‐doped DHP‐based polymers, processed with ethylene glycol (EG) on flexible polyethylene terephthalate (PET) substrates via blade coating, demonstrate an impressive temperature coefficient of resistance (TCR) of up to −1.5% °C−1 (20–60 °C) and outstanding long‐term stability under ambient conditions. This work presents a well‐founded design of π–conjugated polymers that simultaneously fulfill performance, stability, processability, and cost criteria, paving the way for practical applications of flexible and printable temperature sensors.
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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 | 0.001 |
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".