Quantification of Full Range Ethanol Concentrations by using pH Sensor
Bibliographic record
Abstract
The aim of this study is to develop a method for determining full range of ethanol concentrations with high accuracy and low cost. Since applying other methods (e.g. non-enzymatic, optical...etc) suffer from difficulties, it was intended to develop method which overcomes them. A differential pH measurement device was used to achieve operation conditions of alcohol dehydrogenase reaction. Optimum operating conditions were temperature of 30°C, 10 µl of alcohol dehydrogenase enzyme volume (with a final activity of 563.75 units ml-1) per 50 µl of sample, NAD+ concentration of 0.05 mM and 20 mM glycine-pyrophosphate buffer solution of pH 9.1. In this method a range of ethanol concentrations from 0 - 99,985 %, which means 0.000001714 - 17.14 M, were used. The maximum obtained change in pH, delta pH, was (-33) mpH.End point of equilibrium concentrations of reactants and products of ethanol oxidation reaction was measured within spectrophotometer. The results indicated 100 seconds to reach the end point for all ethanol standard samples. This required time was satisfied with results of measuring change in pH within differential pH analyzer system. It seems that there is no method of determining a wide range of ethanol concentrations available yet. In contrary, this work was effective in quantifying full range of ethanol concentrations. Applying this method will reduce cost of assaying of samples as small amount of one enzyme, alcohol dehydrogenase, and low NAD+ concentration are used.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".