At-Home Real-Life Sample Preparation and Colorimetric-Based Analysis: A Practical Experience outside the Laboratory
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".