Hairy cellulose nanocrystalloids: a novel class of nanocellulose
Bibliographic record
Abstract
Nanomaterials have secured such a promising role in today's life that imagining the modern world without them is almost impossible. A large fraction of nanomaterials is synthesized from environmentally-dangerous elements such as heavy metals, which have posed serious side-effects to ecosystems. Despite numerous advantages of synthetic nanomaterials, issues such as renewability, sustainability, biocompatibility, and cost efficiency have drawn significant attention towards natural products such as cellulose-based nanomaterials. Within the past decade, nanocelluloses, most remarkably nanocrystalline cellulose (NCC) and nanofibrillated cellulose (NFC), have successfully been used for a wide spectrum of applications spanning from nanocomposites, packaging, and mechanical and rheological property modifications, to chemical catalysis and organic templating. Yet, there has been little effort to introduce fundamentally new polysaccharide-based nanomaterials. We have been able to develop the first kind of cellulose-based nanoparticles bearing both crystalline and amorphous regions. These nanoparticles comprise a crystalline body, similar to conventional NCC, but with polymer chains protruding from both ends; therefore, these particles are called hairy cellulose nanocrystalloids (HCNC). In this article, we touch on the philosophy of HCNC synthesis, the striking superiority over existing nanocelluloses, and applications of this novel class of nanocelluloses. We hope that the emergence of hairy cellulose nanocrystalloids extends the frontiers of sustainable, green nanotechnology.
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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 | 0.000 |
| 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".