Simple and Robust Microfabrication of Polymeric Piezoelectric Resonating MEMS Mass Sensors
Bibliographic record
Abstract
Resonating MEMS mass sensors are microdevices with broad applications in fields such as bioscience and biochemistry. Their advantageous surface-to-volume ratio makes their resonant frequency highly sensitive to variations in their mass induced by surface depositions. Recent global challenges, such as water quality monitoring or pandemic containment, have increased the need for low-cost (even disposable), rapidly fabricated microdevices as suitable detectors. Resonant MEMS mass sensors are among the best candidates. This paper introduces a simple and robust fabrication of polymeric piezoelectric resonating MEMS mass sensors. The microfabrication technology replaces the traditional layer-by-layer micromachining techniques with laser micromachining to gain extra simplicity. Membrane-based resonant sensors have been fabricated to test the technology. Their characterization results have proven that the technology is robust with good reproducibility (around 2% batch level variations in the resonant frequency). Initial tests for the MEMS mass sensors' sensitivity have indicated a sensitivity of 340 Hz/ng. The concept could be a starting point for developing low-cost MEMS sensing solutions for pandemic control, health examination, and pollution 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".