Transparent TiO<sub>2</sub>/MoO<sub>3</sub> Heterojunction-Based Photovoltaic Self-Powered Triethylamine Gas Sensor with IoT-Enabled Smartphone Interface
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Bibliographic record
Abstract
Conventional gas sensors encounter a significant obstacle in terms of power consumption, making them unsuitable for integration with the next generation of smartphones, wireless platforms, and the Internet of Things (IoT). Energy-efficient gas sensors, particularly self-powered gas sensors, can effectively tackle this problem. The researchers are making significant strides in advancing photovoltaic self-powered gas sensors by employing diverse materials and their compositions. Unfortunately, several of these sensors seem complex in fabrication and mainly target oxidizing species detection. To address these issues, we have successfully employed a transparent, cost-efficient solution processed bilayer TiO 2 /MoO 3 heterojunction-based photovoltaic self-powered gas sensor with superior VOC sensing capabilities, marking a significant milestone in this field. The scanning Kelvin probe (SKP) measurement reveals the remarkable change in contact potential difference (−23 mV/kPa) of the TiO 2 /MoO 3 bilayered film after UV light exposure in a triethylamine (TEA) atmosphere, indicating the highest reactivity between TEA molecules and TiO 2 /MoO 3 . Under photovoltaic mode, the sensor further demonstrates exceptional sensitivity (∼2.35 × 10 –3 ppm –1 ) to TEA compared to other studied VOCs, with an admirable limit of detection (22 ppm) and signal-to-noise ratio (1540). Additionally, the sensor shows the ability to recognize TEA and estimate its composition in a binary mixture of VOCs from a similar class. The strongest affinity of TiO 2 /MoO 3 toward the TEA molecule, the lowest covalent bond energy, and the highest electron-donating nature of TEA may be mainly attributed to the highest adsorption between TiO 2 /MoO 3 and TEA. We further demonstrate the practical applicability of the TEA sensor with a prototype device connected to a smartphone via the IoT, enabling continuous surveillance of TEA.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it