Hydrolysis of semi‐industrial aqueous extracted xylan from birch (<i>Betula pendula</i>) employing commercial catalysts: kinetics and modelling
Bibliographic record
Abstract
BACKGROUND: Acidic hydrolysis of a birch (Betula pendula) xylan produced by a novel semi-industrial-scale aqueous-based and highly sustainable method was studied in a batch reactor.Five commercial acidic heterogeneous catalysts were screened and significant differences in their performance were observed.Dowex 50WX2-100 was selected for further studies and the influence of the reaction parameters, including stirring speed, pH (0.5-1.5), temperature (115-145 °C) and catalyst particle size (50-400 mesh) were studied.The goal was to maximize xylose yield by balancing between the kinetics of hydrolysis and the undesired degradation of monosaccharides. RESULTS:The results show that the maximum achieved yield of xylose was 76%, but higher yields were hindered by the consecutive dehydration of sugars.It was also observed that the hydrolysis and dehydration reactions do not follow the same dependence on the experimental parameters, which leaves room for optimization of the yield.A kinetic model was developed based on the data, which takes into account the consecutive reaction pathway and the influence of the experimental conditions, and a very good fit of the model to the experimental data was achieved.An activation energy of 119 and 88 kJ mol -1 was obtained for the hydrolysis and dehydration steps, respectively.CONCLUSION: Hydrolysis results of this novel, well-characterized hemicellulose extract have not been published previously, and they contribute significantly to the understanding of the hydrolysis and dehydration of real feedstock, instead of highly purified and typically very deacetylated model compounds with different characteristics and behaviour in hydrolysis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".