Determination of the Acetyl Group in Biomass and Its Products by Headspace Gas Chromatography
Bibliographic record
Abstract
This paper reports on a robust method to quantify the acetyl group in biomass and its derived liquid or solid samples using headspace gas chromatography (HS–GC). The method was based on the sample pretreatment in an oxalic acid medium (0.6 mol/L) at 150 °C and 10 min for the liquid samples or 70 min for the solid samples to convert the acetyl group to acetic acid, which can be quantified by a full evaporation HS–GC technique. It was found that ∼100 μm was the suitable solid particle size to be used in the sample analysis at the suggested pretreatment condition. The results showed that the present method has good measurement precision (relative standard deviation was less than 2.09% for the liquid sample and 2.83% for the solid sample) and accuracy (with the recoveries of 96.2–104% in the acetyl group quantification). The limit of quantification of the method was 163 mg/L for the biomass liquid samples and 0.11% for the biomass solid samples. The present method can be a valuable tool to provide the high-throughput testing for the acetyl group in biomass and its derived samples in biorefinery-related development.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".