A Uniform Electro-Thermal Film For Electronic Clothing
Bibliographic record
Abstract
Traditional printed elastic heating films are notable for providing active and controllable heating effects. But wires, cables, or large batteries lead to electron concentration at the power input and output ends, resulting in hot spots or poor signal quality. Using electronic clothing (E-textile) over extended periods can cause burns to the user's skin. In severe cases, excessive noise and impedance generated by the circuit may cause circuit backtracking, leading to an overload of the temperature sensing system in the controller. This can result in controller burnout and additional safety concerns. To address these issues, this study employs two types of elastic conductive slurries with various resistance characteristics (carbon slurry and silver slurry) in conjunction with a screen-printing process for precise alignment and presents a new printed electric heating module with uniform heating effects. Specifically, a new layer-by-layer stacking technology is utilized in this study to reduce the interface resistance between various materials, resulting in a more stable and efficient heating module. The temperature difference between the environment and the electrode decreased from 13.34°C to 3.12°C, the temperature difference in heating areas decreased from 24.36% to 19.26%, and the average temperature increased from 41.10°C to 46.20°C.
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".