Experimental methods in chemical engineering: Reactive extrusion
Bibliographic record
Abstract
Abstract Reactive extrusion (REX) is a manufacturing technique that combines traditional melt extrusion with chemical reactions, including polymerization, polymer functionalization, and depolymerization (chemical recycling). Single screw, counter‐rotating, and co‐rotating twin‐screw extruders (TSE) are possible configurations, but the TSE is most effective for viscous media with better mixing capability, temperature and residence time distribution control, and self‐wiping (cleaning) performance. Compared to traditional polymer processing where monomers react in tanks, followed by compounding and pelletizing, REX can operate solvent‐free in a single step. This simplifies the downstream separation and saves equipment and operational costs. However, the short residence times (on the order of seconds to minutes) of extruders limit their applications to fast reactions. For longer reactions, a sequential design and string extruders with a batch reactor extend residence times. Furthermore, the surface‐to‐volume ratio decreases with increasing scale, which introduces design complexity to remove the heat of reaction. Here, we review REX working principles, apparatuses and their elements, applications and reactions, simulation/modelling and scale‐up considerations, as well as limitations and recommendations. Web of Science indexed 579 articles that mention REX between 2017 and 2021. A bibliometric analysis of these articles identified five research clusters: composite, nano‐composite, and thermal properties; degradation, crosslinking, rheology, and acid; crystallization, polypropylene, maleic anhydride, and rheological properties; morphology, compatibilization, and copolymer; and oxidative stress and mechanisms.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".