Novel Lab‐Scale System for Lyocell Fiber Manufacturing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Lyocell man‐made cellulosic fibers (L‐MMCFs) are typically produced from wood sources, but sustainability efforts are driving interest in agricultural biomass feedstocks. This study presents a lab‐scale system for evaluating feedstock viability and processing conditions using a rotary evaporator, syringe pump, and microreactor vessel. Results show that lower processing temperatures (≤ 90°C for dissolution, 65°C for spinning) lead to better fiber formation, while higher spinning temperatures (95°C) result in irregular morphology. Lower processing temperatures also reduce the risk of exothermic reactions and n ‐methylmorpholine‐ n ‐oxide (NMMO) decomposition, improving process safety. This is further confirmed by scanning electron microscopy (SEM). Viscosity measurements, thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and X‐ray diffraction (XRD) validate optimized processing conditions. Fourier transform infrared spectroscopy (FTIR) analysis verifies NMMO recovery efficiency, showing post‐evaporation concentrations reaching 51.4 wt.%, similar to commercial stock solutions. Even if the final proposed lab‐scale method did not apply fiber drawing to the fiber, this approach provides a repeatable, cost‐effective framework for evaluating alternative cellulose sources and refining processing conditions for sustainable L‐MMCF manufacturing. While mechanical testing is not relevant to this method due to the absence of fiber drawing, this limitation is acknowledged and highlights an area for future development in lab‐scale L‐MMCF evaluation.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it