Current international research into cellulose as a functional nanomaterial for advanced applications
Bibliographic record
Abstract
Abstract This review paper provides a recent overview of current international research that is being conducted into the functional properties of cellulose as a nanomaterial. A particular emphasis is placed on fundamental and applied research that is being undertaken to generate applications, which are now becoming a real prospect given the developments in the field over the last 20 years. A short introduction covers the context of the work, and definitions of the different forms of cellulose nanomaterials (CNMs) that are most widely studied. We also address the terminology used for CNMs, suggesting a standard way to classify these materials. The reviews are separated out into theme areas, namely healthcare, water purification, biocomposites, and energy. Each section contains a short review of the field within the theme and summarizes recent work being undertaken by the groups represented. Topics that are covered include cellulose nanocrystals for directed growth of tissues, bacterial cellulose in healthcare, nanocellulose for drug delivery, nanocellulose for water purification, nanocellulose for thermoplastic composites, nanocellulose for structurally colored materials, transparent wood biocomposites, supercapacitors and batteries.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".