A High Frequency Dual Inverted Mesa QCM Sensor Array with Concentric Electrodes
Bibliographic record
Abstract
Quartz Crystal Microbalance (QCM) is a candidate technology for high sensitivity application. This mass sensor's property depends on the piezoelectric property of the AT-cut quartz crystal. QCM has been widely employed for gas detection due to its comparative advantages including high mass sensitivity, potential for array configuration, low cost, ease of fabrication as well as wide range of available sensitive material compatibility. However, its application has been limited due to two main disadvantages of the non-uniform mass sensitivity across the electrodes and lower frequency of operation. In this work, in order to overcome these disadvantages, a novel concentric electrode structure combined with the dual inverted mesa structure has been proposed and implemented. It is demonstrated that the developed and optimized concentric electrode has provided a uniform displacement across the QCM's sensing electrode. In addition, the dual inverted mesa design has been implemented in the QCM array in which a high fundamental resonant frequency of 33 MHz has been achieved without interference between the adjacent channels in the sensor array. The interference between the adjacent high frequency QCM channels has been eliminated and therefore, each QCM can function as an individual gas sensor when coated with the designed sensing layers. For 33 MHz, the interference has been eliminated at the optimal center to center between electrode distance (c2c). In the case of 10 MHz array, c2c value of 6 mm is achieved which is lower than the value of c2c achieved on a traditionally un-etched QCM array which was found equal to 6.5 mm. Therefore, the proposed QCM array design is shown to reduce the overall size while enhancing the device sensitivity, uniformity and performance.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".