Evaluating polythiophenes as temperature sensing materials using combinatorial inkjet printing
Bibliographic record
Abstract
Abstract Polythiophenes comprise a class of emerging materials with potential applications in the field of temperature sensing. In this article, we validate and apply an integrated blending and printing methodology to combinatorially study libraries of pristine and compositionally graded blends of polythiophenes PEDOT:PSS and P(S-EDOT) (a PEDOT-like self-doped conjugated polymer) to understand their intrinsic electrical conductivity behaviour and along with its temperature dependence on blend composition and ambient temperature. Hypothesis testing is conducted to identify optima in electrical conductivity from combinations of input material proportions intended to meet multiple requirements otherwise difficult to achieve in any single-component solution-processable material. We chose PEDOT:PSS as a commercial developed intrinsically conductive polythiophene and with it, compared a novel self-doped polythiophene P(S-EDOT) as its potential replacement or complement as a sensor material. The electrical and morphological characteristics for both polymers and their blends were investigated for use as different components of temperature sensing applications. Different error sources within the process flow were considered for statistically significant conclusions regarding the utility of different compositions for different aspects of temperature sensing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".