Polydiacetylene‐Crosslinked Oligosiloxanes for Dual‐Mode Temperature Sensing
Bibliographic record
Abstract
Abstract Polydiacetylenes (PDAs) are versatile smart materials due to their unique optoelectronic properties and sensitivity to environmental changes such as temperature, pH, and pressure, leading to distinct color transitions. Despite advantageous features, the limited solubility and challenging processing of PDAs often restrict their application in sensor manufacturing. Addressing the limitations of PDAs, this work combines PDAs with oligosiloxanes to create a material exhibiting good solubility in common organic solvents, facilitating the formation of thin films through solution deposition. A meticulous characterization strategy is used, including Raman spectroscopy, optical spectroscopy, and differential scanning calorimetry, to explore the thermochromic and electronic properties of the new crosslinked materials for the fabrication of optical‐electronic temperature sensors. The synthesized material displayed reversible thermochromism from 25 to 47 °C and a nonreversible transition beyond this temperature range. Dual‐mode capacitive temperature sensors fabricated from the new materials exhibited sensitivity (0.1 pF/°C) in the 25–80 °C range. The hybrid sensing mechanism developed enhances accuracy and reliability by monitoring temperature changes through both colorimetric shifts and capacitance variation. The development of new PDA‐crosslinked oligosiloxane not only marks an advancement in smart material technology but also opens new possibilities for diverse sensor applications.
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".