Energy from domestic refuse by enzymatic degradation of cellulosic fibre waste into sugars and ethanol: initial laboratory studies
Bibliographic record
Abstract
The search for commercially viable biogenic sources of transport fuel, such as ethanol, is nowa priority among developed countries. Sugar- and starch-containing crops currently supportmature industries producing ethanol by yeast fermentation. The potential of bulk plantmaterials (biomass crops, agricultural wastes and domestic refuse) is enormous, but suchligno-cellulosic compounds are difficult to degrade into simple sugar molecules. In the USAand Canada, commercial development programmes are under way to develop new enzymaticand fermentation technologies and to reduce process costs.We investigated the potential of processed waste material derived from domestic refuse as asource of simple sugars for conversion to ethanol. "Pure" cellulose was almost completelydegraded to reducing sugars by cellulase C0I3L, a mixed enzyme preparation, and byTrichoderma viride cellulase, whereas enzymes from other fungal species performed lesswell. T viride achieved less than I 0% (by weight) conversion of waste material to reducingsugars in 2 hour incubations, whereas C013L cellulase gave sugar yields of up to 35%.Extended incubation times gave little increase in yield. These results support the feedstockpotential of this material. Alternative techniques, such as pre-treatment with ferulic acidesterase to improve the effectiveness of degradation, are discussed.
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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 | 0.001 |
| 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".