Towards the scalable isolation of cellulose nanocrystals from tunicates
Bibliographic record
Abstract
In order for sustainable nanomaterials such as cellulose nanocrystals (CNCs) to be utilized in industrial applications, a large-scale production capacity for CNCs must exist. Currently the only CNCs available commercially in kilogram scale are obtained from wood pulp (W-CNCs). Scaling the production capacity of W-CNCs isolation has led to their use in broader applications and captured the interest of researchers, industries and governments alike. Another source of CNCs with potential for commercial scale production are tunicates, a species of marine animal. Tunicate derived CNCs (T-CNCs) are a high aspect ratio CNC, which can complement commercially available W-CNCs in the growing global CNC market. Herein we report the isolation and characterization of T-CNCs from the tunicate Styela clava, an invasive species currently causing significant harm to local aquaculture communities. The reported procedure utilizes scalable CNC processing techniques and is based on our experiences from laboratory scale T-CNC isolation and pilot scale W-CNC isolation. To our best knowledge, this study represents the largest scale where T-CNCs have been isolated from any tunicate species, under any reaction conditions. Demonstrating a significant step towards commercial scale isolation of T-CNCs, and offering a potential solution to the numerous challenges which invasive tunicates pose to global aquaculture communities.
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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) | 0.000 | 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 | 0.001 |
| 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".