Process Intensification of Thermoplastic Lignocellulose Production through High-Solids Reactive Extrusion Enabled by a Novel Recycle Loop
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide To reduce the reactant concentration necessary for producing a newly synthesized thermoplastic lignocellulose from forestry waste, this study explores the concept of a recycle stream as a chemical unit operation in a reactive extrusion process. Modified lignocellulose from the first pass was returned to the start of the extrusion process to act as a lubricant for the lignocellulose feedstock. By this action, a high lignocellulose content could be extruded without requiring costly lubrication alternatives such as plasticizing additives, solvents, or excessive quantities of liquid reactants. With 25% recycled material, a significantly improved processing state was found, allowing for a reduction in total reactant usage by 50% without change to the degree of modification and ultimately leaving less unreacted species in the final product. The thermoplastic nature of the modified lignocellulose was characterized by thermal and rheological analysis and was found to demonstrate greater flowability with any recycle stream fraction.
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".