Mobile App to Quantify pH Strips and Monitor Titrations: Smartphone-Aided Chemical Education and Classroom Demonstrations
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide pH determination and acid–base titrations are essential experiments performed by high school and university undergraduate students alike throughout their chemistry education. While these experiments often rely on conventional pH meters for quantification and pH test strips or indicators for qualitative assessments, we demonstrated herein that a smartphone-based pH determination technique, performing digital image analysis, particularly the determination of either the dominant wavelength or the RGB intensities, could readily replace all but one conventional pH meter in a classroom setting. Using an in-house developed smartphone-based pH reading application (app), students were able to determine the pH and perform titrations using pH strips and universal indicators, producing results matching those determined with a standard pH meter. The app and its “variants” are available for download ( https://tinyurl.com/2dashjyk and https://tinyurl.com/4d73wnxt ), and no prior knowledge of coding or programing was required from the students. All that was needed was an Android 11 phone or tablet with an Internet connection. Moreover, the students and instructors’ reactions to the mobile app alike were very positive and showcased the need and interest for such inexpensive technology, which allows for the running of an entire class for pH determination of multiple real-life samples or acid/base titration without using standard pH meters.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.128 | 0.048 |
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".