Printed Acoustic Sensor for Low Concentration Volatile Organic Compound Monitoring
Bibliographic record
Abstract
Printed electronics employing flexible substrate offers prospective features for various applications such as, tactile sensing, energy harvesting, wearable electronics and acoustic wave sensors. In this work, an acoustic FPW (flexural plate wave) sensor is printed on thin and flexible PZT-PDMS (lead zirconate titanate-poly dimethyl siloxane) composite film with silver ink. The prototype FPW resonator has a resonant frequency of 22.65 MHz with an attenuation of -1.552 dBm. Gravimetric mass sensitivity of the sensor was measured by applying PDMS layers in between the input and output interdigital transducers (IDTs). The mass sensitivity was measured to be -7.8 cm2/g. The sensor is highly responsive to VOCs (volatile organic compounds) with PDMS as a sensing layer. Gas sensitivities with acetic acid and toluene concentrations were measured to be 0.66 and 160.63 kHz/ppm, respectively. The limit of detection for acetic acid and toluene were 10.9 and 0.03 ppm, respectively. Further, the sensor shows good repeatability and response time with both the VOCs. The thermal stability and high piezoelectric charge coefficient of PZT-PDMS composite compared to other piezoelectric composite and polymer substrates are advantageous for the flexible printed sensor. The reported FPW gas sensor shows potential for low concentration measurement of VOCs.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".