Coextrusion of Multifunctional Smart Sensors
Bibliographic record
Abstract
Three‐dimensional (3D) printing of a piezoelectric sensor conventionally involves a minimum of three steps: fabrication of the sensor structure, electrode deposition to collect the generated charges, and electrical poling. Here, the authors report a novel approach to fabricate a working piezoelectric sensor with its electrodes in a single step. The authors optimize the rheological characteristics of a piezoelectric nanocomposite ink, formulated and processed to work without the need for poling, and a metallic conductive paint for coextrusion. The authors then employ solvent evaporation‐assisted 3D printing to coextrude ready‐to‐use sensors. The process fabricates conformal sensors, 3D self‐supported cat whiskers with aspect ratios over 15, and filaments spanning over 2 cm. The authors present potential applications in the form of aero‐elastic sensors and smart active thread for wearable electronics. The authors print sensors directly on fused deposition modeling printed miniature wings to monitor aero‐elastic stability. In another application, the authors use the coextruded filament in the form of a piezoelectric thread for wearable sensors for knee‐joint and respiration monitoring. The self‐powered piezoelectric sensing elements are an attractive alternative for customized, multi‐material applications where each watt and each gram counts such as wearables, and micro‐drones. The process is adaptable to other multi‐material and multi‐component printing needs beyond piezoelectric materials.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".