Dual-Modal Assay Kit for the Qualitative and Quantitative Determination of the Total Water Hardness Using a Permanent Marker Fabricated Microfluidic Paper-Based Analytical Device
Bibliographic record
Abstract
A dip-and-read microfluidic paper-based analytical device (µPAD) was developed for the qualitative and quantitative detection of the total hardness of water. To create well-defined hydrophobic barriers on filter paper, a regular office printer and a commercially available permanent marker pen were utilized as a quick and simple technique with easily accessible equipment/materials to fabricate µPAD in new or resource-limited laboratories without sophisticated equipment. After a wettability and barrier efficiency analysis on the permanent marker colors, the blue and green ink markers exhibited favorable hydrophobic properties and were utilized in the fabrication of the developed test devices. The device had five reaction and detection zones modeled after the classification given by the World Health Organization (WHO), so qualitatively it determined whether the water was ‘soft’, ‘moderately hard’, ‘hard’, or ‘very hard’ by changing color from blue to pink in about 3 min. The device was also used to introduce an alternative colorimetric reaction for quantitative analysis of the water hardness without the need for ethylenediaminetetraacetic acid (EDTA) and without compromising the simplicity and low cost of the device. The developed µPAD showed a calculated limit of detection (LOD) of 0.02 mM, which is at least 80% less than those of commercially available test strips and other reported µPADs, and the results of the real-world samples were consistent with those of the standard titration (with EDTA). In addition, the device exhibited stability for 2 months at room and frigid condition (4 °C) and at varying harsh temperatures from 25 to 100 °C. The results demonstrate that the developed paper-based device can be used for rapid, on-site analysis of water with no interferences and no need for a pipette for sample introduction during testing.
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 teacher head, 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".