Detection of Volatile Alcohol Vapors Using PMMA-Coated Micromechanical Sensors: Experimental and Quantum Chemical DFT Analysis
Bibliographic record
Abstract
Micromechanical sensors, in which the sensor response is created as a result of molecular interactions on the sensors’ surfaces, have been employed as a powerful technique for rapid and sensitive detection of low concentrations of chemical and biological materials. In the study reported herein, poly(methyl methacrylate) (PMMA)-coated microcantilever (MCL) sensors were used to detect the vapors of volatile alcohols (methanol, ethanol, and isopropanol) at three different concentrations. A vapor generator was used to generate and flow the alcohol vapor onto the PMMA coated MCL surface in a closed system chamber. The vapor adsorption onto the MCL surface results in a rapid and measurable deflection of the MCL. No significant deflections of the uncoated MCL occurred when the different vapors were passed through into the microcantilever chamber. Linear concentration–deflection responses were observed, with the highest sensitivity shown with methanol, followed by ethanol and then isopropanol. Density functional theory (DFT) quantum chemical calculations were conducted to estimate the electronic interaction energies (ΔIE) between the alcohol molecules and MMA and two different model tetrameric segments of PMMA. The computed ΔIEs were in the same order as the experimentally observed order: methanol > ethanol > isopropanol.
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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 | 0.001 |
| 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".