Determination of Absolute Injection Volume in Capillary Electrophoresis by the “Capillary–Volume–Plateau Calibration (CVPC)” Method
Bibliographic record
Abstract
Accurate knowledge of injection volumes is essential for quantitative capillary electrophoresis (CE), yet volumes predicted by the Hagen–Poiseuille equation become unreliable at low pressures or with short injection times. We present a self-referenced approach, termed the Capillary–Volume–Plateau Calibration (CVPC) method, that uses capillary volume as a reference and requires only a one-point calibration to obtain absolute injection volumes ( V inj ) without measuring pressure, time, or viscosity. The working relation, V inj = VA /( kCt m ), depends only on five readily measured quantities: ( i ) the capillary volume, V = π r 2 l ( r is the inner radius and l is the injection-to-detector length), ( ii ) the detector constant k (signal per concentration unit), ( iii ) the analyte concentration C, ( iv ) the peak area A, and ( v ) the peak migration time t m . If the detector responds linearly with concentration, the constant k is obtained from a single pressure-driven run: a reference solution of known concentration C ref is introduced just long enough to generate a flat-top plateau, and the average plateau signal ⟨ S ⟩ gives k = ⟨ S ⟩/ C ref . Error analysis shows that the uncertainly in peak area is the dominant contributor to the overall uncertainly in CVPC-determined V inj, which remains below 4% under typical CE conditions. A test on a commercial instrument with a built-in Hagen–Poiseuille-based predictor revealed overestimates in V inj of up to approximately 120%, a bias eliminated by the CVPC method. Because the CVPC procedure relies solely on a detector calibration and fixed geometric dimensions, it is based on first principles and remains valid unless detector settings or buffer composition change, offering a practical and broadly applicable route to accurate volume determination in CE.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".