INTERDISCIPLINARY EDUCATION THROUGH THE DEVELOPMENT OF A COST-EFFECTIVE PHOTOMETRIC pH METER SENSOR USING NATURAL PIGMENTS
Why this work is in the frame
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Bibliographic record
Abstract
There is a trend of development of analytical methodologies and technologies that allow in situ analysis, producing accurate information in real time, at low cost, using homemade experiments and devices to increase interest in scientific knowledge with a constructive approach in an interdisciplinary perspective in Chemistry Education. In this context, a photometric-chemometric method of analysis was developed to measure the pH of solutions using easily accessible and low-cost material based on the use of natural pigments found in red cabbage (Brassica oleracea L.). Calibrations and determinations were performed by RGB measurements of pigment coloration in solution at different pH values using the free app Photometrix, converted to HSV, YCbCr and YUV color spaces and processed by Partial Least Squares regression (PLS). The best PLS model found was HSV with mean central scale obtained pH measurements with RMSEP 0.98, R2 0.97 and bias close to zero. In addition, experimental data were statistically validated. Analyzes of predicted pH from three independents experiments revealed high recoveries (95-103%) and low relative standard deviations. Thus, the PhotoMetrix app was viable for colorimetric pH determinations using the low-cost photometric pH meter sensor and a smartphone, improving accessibility and applicability in Chemistry Education.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 it