In-situ probing of thermal desorption of vapor molecules on a nanowire via work function variance
Bibliographic record
Abstract
Nanowire sensors based on variations of their electrical properties show great potential for real-time, in situ monitoring of molecular adsorption and desorption. Although the molecular adsorption-induced change in the electronic work function is very sensitive, it does not have any specificity. However, the temperature dependency of the adsorption-induced work function variation can provide limited selectivity based on the desorption temperature. In this study, we report the in situ probing of molecular desorption by monitoring the work function variations of a single Pt nanowire as a function of temperature. The work function of a clean Pt nanowire shows a significant variation due to vapor adsorption at room temperature. Increasing the temperature of the nanowire results in a variation of the work function due to molecular desorption. Experimentally measured differential work function as a function of temperature shows desorption peaks at 36 and 44 °C for methanol and ethanol molecules respectively. Adsorption-induced variation of the Pt nanowire work function was further confirmed using ultraviolet photoelectron spectroscopy before and after exposure to methanol vapor. These results show that the molecular adsorption/desorption-induced variation of the work function and its temperature dependency can be used for developing nanoscale electro-calorimetric sensors.
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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) | 0.000 | 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) | 0.001 | 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".