Impact of moisture content on instant catapult steam explosion pretreatment of sweet potato vine
Bibliographic record
Abstract
Lignocellulose originating from renewable and sustainable biomass is a promising alternative resource to produce biofuel. However, the complex component, especially high moisture content, leads to a higher cost of transportation and processing. The instant catapult steam explosion (ICSE) pretreatment can exploit the intracellular water of lignocellulosic materials and convert into vapors leading towards the breakdown of the feedstock during the explosion process. However, it is necessary to study the impact of moisture content on the pretreatment. The sugar yield of wet feedstock after ICSE pretreatment reached 88.05%, which was higher when compared to dried and untreated biomass. The utilization of wet feedstock decreased the production of inhibitor and improved the carbohydrate content in ICSE-treated biomass. There occurred a shrinkage of feedstock after drying process and the mechanical breakage upon ICSE pretreatment. Moreover, not all water was converted into vapor to cause breakage in the lignocellulose. ICSE has shown to be preferably suitable to pretreat wet sweet potato vine with high moisture content, either fresh or soaked biomass that has been dried before. By using these materials, it would have a higher sugar yield and lower inhibitor production after pretreatment. Based on these advantaged aspects of ICSE platform, two potential strategies are proposed to improve the economic and environmental impacts of pretreatment.
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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".