Paper-Based Colorimetric pH Test Strip Using Bio-Derived Dyes
Bibliographic record
Abstract
Natural dyes have emerged as a promising alternative to synthetic dyes for industrial applications due to their advantages, namely, easy availability, low cost, and environmental friendliness. In this sense, natural dyes, due to their potential to react over the pH range, could offer an alternative to conventional pH measuring techniques for industrial products, such as potentiometers, sensors, or indicator drops. Therefore, this project aims to evaluate the potential of several natural organic dyes in response to changes in pH and develop an indicator for determining pH grades. We extracted and analyzed the pigments of forty natural vegetable species using two extraction methods with a mixture of solvents, specifically 70% MeOH/30% H2O. The results find that pigments of cabbage, hibiscus flower, radish, and turmeric in their dry state exhibit the best reaction over a broad pH range, and color can be easily distinguished according to its level. These findings demonstrate the potential of natural dyes as a novel approach for pH verification, providing a sustainable and cost-effective alternative to conventional techniques.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".