3D Printing‐Induced Hierarchically Aligned Nanocomposites With Exceptional Multidirectional Strain Sensing Performance
Bibliographic record
Abstract
High-performance sensors capable of detecting multidirectional strains are indispensable to understand the complex motions involved in flexible electronics. Conventional isotropic strain sensors can only measure uniaxial deformations or single stimuli, hindering their practical application fields. The answer to such challenge resides in the construction of engineered anisotropic sensing structures. Herein, a hierarchically aligned carbon nanofiber (CNF)/polydimethylsiloxane nanocomposite strain sensor is developed by one-step 3D printing. The precisely controlled printing path and shear flow bring about highly aligned nanocomposite filaments at macroscale and orientated CNF network within each filament at microscale. The periodically orientated nanocomposite filaments along with the inner aligned CNF network successfully control the strain distribution and the appearance of microcracks, giving rise to anisotropic structural response to external deformations. The synergetic effect of the multiscale structural design leads to distinguishable gauge factors of 164 and 0.5 for applied loadings along and transverse to the alignment direction, leading to an exceptional selectivity of 3.77. The real-world applications of the hierarchically aligned sensors in multiaxial movement detector and posture-correction device are further demonstrated. The above findings propose new ideas for manufacturing nanocomposites with engineered anisotropic structure and properties, verifying promising applications in emerging wearable electronics and soft robotics.
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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".