At-Home Real-Life Sample Preparation and Colorimetric-Based Analysis: A Practical Experience outside the Laboratory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As teaching laboratories stand empty in light of COVID-19, we extended the practical experience from the laboratory to the safety of the students’ homes. We developed a simple, robust, and versatile at-home experiment that teaches solution preparation, calibration curves, real-life sample preparation, and data analysis to second-year analytical chemistry students. Solutions were prepared using common kitchen tools and readily available corn starch, syringes, and trophic iodine for a low cost below $20. A calibration curve for the brightness of corn starch–iodine solutions as a function of starch concentration was prepared. Solutions were imaged using a smartphone camera, and the brightness of each solution was quantified using ImageJ. Starch was extracted from a ripe banana and quantified using the calibration curve. Extending the practical experience to students’ homes in the age of COVID-19 not only provides them with a better sense of the real chemistry laboratory they will one day return to but also helps solidify and expand on key concepts learned in the virtual classroom.
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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 | 0.001 |
| 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