Hydrogel Microelectromechanical System (MEMS) Resonators: Beyond Cost‐Effective Sensing Platform
Bibliographic record
Abstract
Abstract Mechanical resonators have been used for various applications including timing references, filters, accelerometers, and inertial sensors. Mostly, silicon‐based materials have been thought to be ideal considering robustness and stability and thus used to fabricate micro and nanoscale mechanical resonators. When enhanced sensitivity becomes more important than long‐term stability, materials repertoires other than silicon might be better suited. Herein, a novel manufacturing approach is proposed, which rapidly fabricates microelectromechanical system resonators with hydrogel by single UV exposure via dynamic mask and dry‐state “plugging out” sacrificial process where hydrogel structures are defined by spatially modulated UV light. For practical demonstrations, rectangular cantilevers and closed circular membranes are employed for humidity and pressure sensing applications, respectively. The cost‐effective fabrication route suggested herein not only enables rapid prototyping of suspended hydrogel structures outside a cleanroom, but also offers spatially tunable elastic modulus. Most remarkably, sensitivity enhancement resulting from high swelling rate and/or low elastic modulus exceeds stability deterioration, one of the major concerns for polymeric materials. Such exclusive beneficial features, yet demonstrated with any microfabrication materials and methods or their combinations, open a new avenue for photocurable polymeric materials to be used for specific applications as well as fundamental investigations.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".