Design and Optimization of a Multichannel Quartz Crystal Microbalance Sensor Array for Multiple Target Gas Detection
Bibliographic record
Abstract
Quartz Crystal Microbalance is a candidate technology for high sensitivity gas detection that operates based on the thickness shear mode of piezoelectric crystal. These sensors can detect nanogram level of mass change on their electrode surface, which leads to a high accuracy of detection while benefiting from a simple geometry, low cost and ease of fabrication. Unlike conventional Quartz Crystal Microbalance sensors that are limited to a single-electrode structure hampering their detection capability, in this work a novel 5MHz multielectrode sensor is designed and optimized on a single substrate. This developed sensor can be used for detection of simultaneously present multiple gases in a complex environment. Therefore, this new design configuration eliminates the need for multiple sensors and reduces the complexity of fabrication and measurements. In order to enhance the proposed device's performance, analytical modeling and finite element analysis are further conducted to optimize the device structure for highsensitivity applications. A four-electrode 5MHz sensor configuration is developed on a single substrate with the optimized electrodes' thickness, radius, and center to center distance of 300nm, 500μm, and 6.5mm respectively. The sensor achieved a high sensitivity of 0.135 Hz.cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .ng <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> while eliminating the interference between the electrodes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".