Analysis of Malate and Other Di- and Tricarboxylic Acids Using Capillary Electrophoresis and Laser-Induced Photoluminescence Detection After Complexation with Europium Tetracycline
Bibliographic record
Abstract
Fumarate, succinate, maleate, dihydroxyfumarate, D–tartarate, L–tartarate, DL–tartarate, L-malate, D-malate, oxaloacetate, citrate, and DL-isocitrate in the 5–100 μM concentration range were incubated in 12.5 mM HEPES/25 mM TRIS base containing 200 μM Eu3+–tetracycline and 60% (v/v) formamide (pH unadjusted). After 30 min of incubation, they were separated at 4 °C by capillary electrophoresis utilizing laser-induced luminescence detection with 12.5 mM HEPES/25 mM TRIS base containing 60% formamide as the running buffer. All analytes yielded peaks, with the exception of fumarate, succinate, and maleate. L-Malate was detected down to 100 nM. The main component of this study was the analysis of malate. The objective was to develop a stereoselective methodology for the detection of L-malate. This was achieved by varying the formamide concentration and separation temperature. When the temperature was increased to 22 °C and the formamide concentration decreased to 40%, the sensitivity for L-malate was diminished about 10-fold, but that for D-malate was eliminated. This combination of conditions allowed for the stereospecific analysis of L-malate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".