Microwave-Assisted Alkali Pre-Treatment, Densification and Enzymatic Saccharification of Canola Straw and Oat Hull
Bibliographic record
Abstract
The effects of microwave-assisted alkali pre-treatment on pellets' characteristics and enzymatic saccharification for bioethanol production using lignocellulosic biomass of canola straw and oat hull were investigated. The ground canola straw and oat hull were immersed in distilled water, sodium hydroxide and potassium hydroxide solutions at two concentrations (0.75% and 1.5% w/v) and exposed to microwave radiation at power level 713 W and three residence times (6, 12 and 18 min). Bulk and particle densities of ground biomass samples were determined. Alkaline-microwave pre-treated and untreated samples were subjected to single pelleting test in an Instron universal machine, pre-set to a load of 4000 N. The measured parameters, pellet density, tensile strength and dimensional stability were evaluated and the results showed that the microwave-assisted alkali pre-treated pellets had a significantly higher density and tensile strength compared to samples that were untreated or pre-treated by microwave alone. The chemical composition analysis showed that microwave-assisted alkali pre-treatment was able to disrupt and break down the lignocellulosic structure of the samples, creating an area of cellulose accessible to cellulase reactivity. The best enzymatic saccharification results gave a high glucose yield of 110.05 mg/g dry sample for canola straw ground in a 1.6 mm screen hammer mill and pre-treated with 1.5% NaOH for 18 min, and a 99.10 mg/g dry sample for oat hull ground in a 1.6 mm screen hammer mill and pre-treated with 0.75% NaOH for 18 min microwave-assisted alkali pre-treatments. The effects of pre-treatment results were supported by SEM analysis. Overall, it was found that microwave-assisted alkali pre-treatment of canola straw and oat hull at a short residence time enhanced glucose yield.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".