An Ultra‐Shapeable, Smart Sensing Platform Based on a Multimodal Ferrofluid‐Infused Surface
Bibliographic record
Abstract
The development of wearable, all-in-one sensors that can simultaneously monitor several hazard conditions in a real-time fashion imposes the emergent requirement for a smart and stretchable hazard avoidance sensing platform that is stretchable and skin-like. Multifunctional sensors with these features are problematic and challenging to accomplish. In this context, a multimodal ferrofluid-based triboelectric nanogenerator (FO-TENG), featuring sensing capabilities to a variety of hazard stimulus such as a strong magnetic field, noise level, and falling or drowning is reported. The FO-TENG consists of a deformable elastomer tube filled with a ferrofluid, as a triboelectric layer, surrounded by a patterned copper wire, as an electrode, endowing the FO-TENG with excellent waterproof ability, conformability, and stretchability (up to 300%). In addition, The FO-TENG is highly flexible and sustains structural integrity and detection capability under repetitive deformations, including bending and twisting. This FO-TENG represents a smart multifaceted sensing platform that has a unique capacity in diverse applications including hazard preventive wearables, and remote healthcare monitoring.
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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".