Comparative evaluation of the effect of microfluidisation on physicochemical properties and usability as food thickener and Pickering emulsifier of autoclaved and TEMPO‐oxidised nanofibrillated cellulose
Bibliographic record
Abstract
Summary Although chemical‐free production processes of nanofibrillated cellulose (NFC) have been investigated, comparative studies on the effect of chemical and chemical‐free processes to produce NFCs are limited. Combined effect of either of these production routes and defibrillation condition has also never been studied. Here, thermally treated NFCs were produced through microfluidisation for 6–12 passes (ANFC6‐ANFC12), while TEMPO‐oxidised NFC (TONFC) was prepared at six passes. Proportion of nanocellulose, viscosity, Gʹ and Gʺ values increased with increased microfluidisation pass in the cases of ANFCs. ANFC12 exhibited gel‐like behaviour with strongest three‐dimensional network structure and required lowest concentration to enhance thin liquid foods into honey‐like consistency. TONFC needed to be added by as much as 8.5, 1.3 and 2.3 times the amount of ANFC12 to achieve the same consistency in thickened water, milk and soup, respectively. ANFC12 can also be well used as Pickering emulsifier due to its higher viscosity and gel‐like property.
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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 | 0.001 |
| 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".