Flax nanofibrils production via supercritical carbon dioxide pre‐treatment and enzymatic hydrolysis
Bibliographic record
Abstract
Abstract Flax fibres are an agro‐industrial waste available in large quantities in several countries around the world. This resource can be properly used. The goal of this work was to extract lignocellulosic nanosized flax fibres using an environmentally friendly process based on a combination of supercritical carbon dioxide (SC‐CO2) pre‐treatment and enzymatic hydrolysis. Raw flax fibres (RFF) were submitted to a SC‐CO2 pre‐treatment at various temperatures (ie, 70°C and 80°C) and pressures (ie, 20 and 37.7 MPa) for 60 minutes. The enzymatic hydrolysis was performed at 40°C for 24 hours in a pH 4.0 buffer. Cellulase, xylanase, pectinase, and viscozyme were used as hydrolytic enzymes. The as‐received raw flax fibres, SC‐CO2 pretreated flax fibres, and extracted lignocellulosic nanofibrils (LCNF) were characterized by Fourier transformed infrared spectroscopy (FTIR), x‐ray diffraction (XRD), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). It was shown that the effect of the SC‐CO2 pre‐treatment of flax fibres was two‐fold. It helped to disorganize biomass without changing its chemical composition and it increased access to enzymes to extract LCNF. The FTIR analysis showed no changes in the functional groups after SC‐CO2 pre‐treatment. The XRD characterization revealed that the crystallinity increased with the SC‐CO2 pre‐treatment and LCNF extraction. SEM images showed holes, cracks, and erosion on the surface of the SC‐CO2 pretreated flax fibres (SC‐CO2‐PFF). TEM evidenced the production of nano/micro‐sized fibril and fibril aggregates.
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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) | 0.001 | 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".