ZnO Nanorod-Immobilized Pt Single-Atoms as an Ultrasensitive Sensor for Triethylamine Detection
Bibliographic record
Abstract
Triethylamine (TEA) is a flammable and highly toxic gas, and the fast, accurate, and sensitive detection of gas TEA remains greatly challenging. Herein, we report a ZnO nanorod anchored with a single-atom Pt catalyst (Pt 1 /ZnO) as a gas sensor for TEA detection. The sensor shows high selectivity and high response to gas TEA with a response value of 4170 at 200 °C, which is 92 times higher than that of pure ZnO. Moreover, the Pt 1 /ZnO sensor has very short response and recovery times of only 34 and 76 s, respectively, and also has a high response to ppb-level TEA gas (100 ppb-21.6). The gas-sensing enhancement mechanism of the Pt 1 /ZnO sensor to gas TEA was systematically investigated using band structure analysis, in situ diffuse reflectance infrared Fourier transformation spectroscopy, and density functional theory calculations. The results show that the oxygen vacancies on Pt 1 /ZnO can effectively activate the adsorbed oxygen. Moreover, chemical bonds can be formed between Pt single atoms and N atoms in TEA to achieve effective adsorption and activation of TEA molecules, facilitating the reaction between TEA and the adsorbed oxygen on Pt 1 /ZnO, and thereby obtaining high gas-sensing performance. This work highlights the crucial role of Pt single-atom in improving the sensing performance for gas TEA detection, paving the way for developing more advanced gas 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".