Application of Conductive Polymer-Based Hydrogel in Multi-robot Balance Control
Bibliographic record
Abstract
At present, the flexible electronic materials with both good flexibility and conductivity have become a major development trend of electronic materials in the future. Due to their flexibility in construction, controllable mechanical properties and electrical conductivity, the conductive polymer-based hydrogels (CPHs) are expected to become one of the candidate materials in flexible electronics and other fields, and have received extensive attention from researchers. However, the existing CPH systems generally have shortcomings such as low mechanical strength and single function. To this end, the authors attempt to construct a hydrogel using polyaniline (PANI) as the conductive component and polyacrylic acid (PAA) as the flexible component through in-situ polymerization and physical blending. The designed PAA/PANI conductive polymer-based hydrogel enjoys ultra-long stretchability and high strength. It can be used in the flexible strain sensors and pressure sensors to detect the step cycle of the multi-legged robot in real time, and adjust their rhythm during the walking gait, thereby achieving the physical balance. This paper provides a new idea for the application of the CPHs, and especially offers a wealth of theoretical foundation and practical experience for the research on its application to the flexible strain 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 | 0.000 |
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".