QCM Electrode Configurations for Enhanced Mass Distribution and Sensitivity
Bibliographic record
Abstract
This work presents the first fabrication and experimental validation of a novel electrode design approach for enhancing the mass sensitivity of quartz crystal microbalances (QCMs). The development of unique QCM electrode configurations includes a study of mass loading area distribution and its impact on resonant frequency shift, a key parameter that defines mass sensing performance. Finite element analysis (FEA) is conducted to identify areas of opportunity where localized energy trapping occurs and simulate the sensing performances of the configured electrode topologies compared to the conventional circular design. Theoretical models are experimentally validated through the fabrication of 5 MHz QCM sensors with nonconventional designs and the utilization of an automated controlled environment and sensor readout system. The unique QCMs presented herein exhibit noticeably higher resonant frequency shifts in response to variations in water vapor concentration, where the observed shift in frequency serves as an indicator for sensing performance. Experimental results reveal that unique topologies based on the novel distribution of area for improving mass sensitivity (DAIS) electrode design approach, featuring patterns of annularly distributed small electrodes, effectively utilize the energy trapping effect, and outperform the conventional QCM design.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".