Enhanced formaldehyde sensing with UV-LED activated Nafion-coated in₂O₃ gas sensor
Bibliographic record
Abstract
High-performance formaldehyde sensors are highly desirable in various industries and indoor gas monitoring systems. Recent advances in ultraviolet light-emitting diode (UV-LED) technology have provided the opportunity to develop UV-activated semiconductor sensors with many advantages. However, the typical UV-activated metal oxide semiconductor (MOS) sensing material is at fault of restructuring in the presence of ppm concentrations of formaldehyde, leading to inconsistent sensor measurements. In response, we developed a formaldehyde sensor featuring excellent response stability by exploiting Nafion-coated indium oxide (In 2 O 3 ) nanoparticles (NPs) as the sensing material, prepared via an indium nitrate precursor route along with Nafion solution, deposited using spin-coating on an electrode, under UV-LED irradiation. The results indicated that the presence of a thin layer of Nafion preserves the structure of the UV-activated sensing layer from agglomeration, resulting in stable and reproducible sensor responses. Sensor sensitivity (response) was found to be a function of two key parameters: Nafion concentration and UV irradiance. We studied the responses of the Nafion-coated In 2 O 3 NPs sensor with Nafion concentrations of 0.5 wt% and 2.5 wt% and under UV irradiances of 2.35, 2.49, and 2.56 mW/cm 2 . The characterization results revealed greater porosity and a reduction in particle size after reducing the Nafion concentration, leading to sensitivity improvement. Sensor sensitivity was further improved by increasing the UV irradiance due to the enhanced proton conductivity through the Nafion layer. The linear relationship between the transient response and formaldehyde concentration exhibited the feasibility of the developed sensor for fast formaldehyde measurement. The results showed a transient response of 0.15 for the 0.5 wt% Nafion-coated In 2 O 3 NPs sensor under 2.56 mW/cm 2 irradiance to 25 ppm of formaldehyde. This study presents a new approach in preventing sensing layer restructuring caused by formaldehyde (and likely other similar volatile organic compounds (VOCs) of semiconductor gas sensors), thereby providing a practical solution to formaldehyde and other VOCs detection. This approach enhanced sensor sensitivity to different concentrations of the target gas, with fast and consistent response and recovery.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".