Ammonia Fiber Expansion (AFEX) Treatment of Wheat Straw for Production of Bioethanol
Bibliographic record
Abstract
Ammonia Fiber Expansion (AFEX) treatment is a technique that is able to enhance the enzymatic hydrolysis yield of lignocellulosic materials. In this technique, lignocellulosic materials are treated by liquid ammonia under pressure followed by rapid release of pressure that expands the fiber structure and increases enzyme access to lignocellulose polysaccharides. However, the AFEX treatment variables such as the mass ratio of ammonia to lignocellulosic biomass, moisture of lignocellulose (moisture content of biomass), temperature, and residence time need to be evaluated to find the maximum efficiency of this treatment. The efficiency of the AFEX pretreatment was quantified by the yield of released sugars during enzymatic hydrolysis of the AFEX-treated wheat straw. The optimal treatment conditions for wheat straw were found to be: ammonia-to-wheat straw ratio, 1:1; temperature, 95°C; moisture content of wheat straw, 70% (dry weight basis); and residence time, 5 minutes. Under these conditions, almost 89% of the theoretical sugars were released by enzymatic hydrolysis of the AFEX-treated wheat straw. The enzymatic hydrolysis results showed the significance of AFEX pretreatment of wheat straw when compared to untreated wheat straw with released sugars yield of only 26 %.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".