Lightweight and flexible sensors based on environmental‐friendly poly(butylene adipate‐co‐terephthalate) composite foams with porous segregated conductive networks
Bibliographic record
Abstract
Abstract Constructing the microcellular structure in the conductive polymer composites (CPCs) is a promising approach to improve the sensitivity and durability of the piezoresistive sensor. Selectively placing the conductive fillers, to form the segregated network between the polymer region, can effectively improve the electric performance of CPCs. However, few researches have focused on the influence of the polymer bead size on the segregated network and the large‐scale production of spherical polymer beads with low cost is still difficult to realize. Herein, plenty of regular‐shaped poly(butylene adipate‐co‐terephthalate) (PBAT) beads were manufactured through underwater pelletizing process, which was further coated with carbon nanotube (CNT) particles with the assistance of ball milling technology, and eventually the sensor was successfully fabricated through supercritical carbon dioxide (scCO2) bead foaming technology. The lightweight and flexible sensor exhibited the uniform cell structure with the mean cell size of 51.0 μm and cell density of 3.9 × 108 cells/cm3 when the pelletizing cutter speed was 2500 rpm and the foaming temperature was 117.5°C. And the conductivity of the sensor reached 6.5 S/m incorporated with 3 wt% CNT, which possessed high sensitivity, good stability and long‐term durability, attributed to its excellent microcellular structure and segregated conductive network.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".