Advancing pressure sensors performance through a flexible MXene embedded interlocking structure in a microlens array
Bibliographic record
Abstract
Piezoresistive composite elastomers have shown great potentials for wearable and flexible electronic applications due to their high sensitivity, excellent frequency response, and easy signal detection. A composition membrane sensor with an interlocked structure has been developed and demonstrated outstanding pressure sensitivity, fast response time, and low temperature drift features. Compared with a flexible MXene-based flat sensor (Ti3C2), the interlocked sensor exhibits a significantly improved pressure sensitivity of two magnitudes higher (21.04 kPa−1), a fast reaction speed of 31 ms, and an excellent cycle life of 5000 test runs. The viability of sensor in responding to various external stimuli with high deformation capacity has been confirmed by calculating the force distribution of a polydimethylsiloxane (PDMS) film model with a microlens structure using the solid mechanics module in COMSOL. Unlike conventional process, we utilized three-dimensional (3D) laser-direct writing lithography equipment to directly transform high-precision 3D data into a micro-nano structure morphology through variable exposure doses, which reduces the hot melting step. Moreover, the flexible pressure device is capable of detecting and distinguishing signals ranging from finger movements to human pulses, even for speech recognition. This simple, convenient, and large-format lithographic method offers new opportunities for developing novel human–computer interaction devices.
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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".