Smartphone-Based Microfluidic Colorimetric Sensor for Gaseous Formaldehyde Determination with High Sensitivity and Selectivity
Bibliographic record
Abstract
Formaldehyde is one of the most dangerous air pollutants, which can cause sick building syndrome. Thus, it is very crucial to precisely determine formaldehyde with a low cost and simple operation. In this paper, a smartphone-based microfluidic colorimetric sensor is devised for gaseous formaldehyde determination with high sensitivity and selectivity. Specifically, a novel microfluidic chip is proposed based on the 4-aminohydrazine-5-mercapto-1,2,4-triazole (AHMT) method to determine formaldehyde; the chip consists of two reagent reservoirs, one reaction reservoir and a mixing column. In this design to prevent the fluid from flowing out while letting the gas molecule in, a hydrophobic porous poly tetra fluoroethylene (PTFE) membrane is put on the top of the reaction reservoir. Using the microfluidic chip sensor, a smartphone-based formaldehyde determination system is developed, which makes the measuring process automated and simple. As per the experiment results, the limit-of-detection (LOD) of the system is as low as 0.01 ppm, which is much lower than the maximum exposure concentration (0.08 ppm) recommended by the World Health Organization (WHO). Moreover, the sensor is hardly affected by acetaldehyde, volatile organic compounds (VOCs) or acidic-alkaline, which shows great selectivity. Finally, the performance of the proposed sensor is verified by using it for the determination of formaldehyde in a newly decorated house.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".