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Record W4415957830 · doi:10.1021/acs.jchemed.5c00920

Qualitative and Quantitative Analysis of Spinach Extracts: A Modular, Inexpensive, and Bench-Friendly Protocol

2025· article· en· W4415957830 on OpenAlexaff
Melissa D’Ascenzio, A. L. Black, John Pokora

Bibliographic record

VenueJournal of Chemical Education · 2025
Typearticle
Languageen
FieldChemistry
TopicVarious Chemistry Research Topics
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsProtocol (science)Modular designSpinachQuantitative analysis (chemistry)Qualitative analysisThin-layer chromatographyBiological pigment

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.448
Teacher spread0.418 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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