Qualitative and Quantitative Analysis of Spinach Extracts: A Modular, Inexpensive, and Bench-Friendly Protocol
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The chromatographic separation of plant pigments is one of the most popular and visually appealing experiments encountered by young scientists when they are introduced to the principles of analytical chemistry. As such, it has found widespread application in schools, public engagement activities, and undergraduate laboratories. However, most of the methods used in these contexts suffer from a range of limitations, including the difficulty of separating and isolating individual pigments when using paper-based or thin layer chromatography techniques or the need for specialized spaces in order to contain volatile, harmful solvents and dry silica powder. This new modular laboratory protocol has been developed following green chemistry principles and employs a series of inexpensive, bench-friendly methods. The protocol allows for the separation and quantitation of chlorophyll pigments in plant extracts through the use of prepacked, inexpensive reversed-phase chromatography columns and UV-vis spectrophotometry. A synthetic component is added to the more traditional separation experiments through the development of the bench-friendly synthesis of coloring agent E141, a chlorophyll-derived food dye. The protocol embeds analytical and mathematical components that encourage students to engage in problem solving activities and mathematical modeling early on in their studies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.018 |
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".