Bibliographic record
Abstract
Nanoscience, the study of materials so small that not even light can capture them, seeks to unravel and understand the building blocks of our planet. Nature, perhaps the most talented nanoscientist, has created very remarkable biological nanomaterials including proteins, lipids and polysaccharides. This article will explore a very unique nanomaterial, derived from cellulose, that has received great academic and industrial interest over the last few years. Cellulose nanocrystals are shards of a very common polymer and possess a number of interesting properties including a high aspect ratio and large tension modulus. Cellulose nanocrystal structure can be manipulated during the extraction procedure to control size, degree of crystallinity and surface charge. Furthermore the crystals can be functionalized with surface functional groups, including sulfate esters, and successfully incorporated into polymer matrices. This article will explore physical and chemical extraction procedures, and characterization techniques including atomic force microscopy, transmission electron microscopy and x-ray diffraction. Finally, the future promise of cellulose nanocrystals will be discussed including potential applications in electronics, materials and medical industries.
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.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".