Periodate oxidation-mediated nanocelluloses: Preparation, functionalization, structural design, and applications
Bibliographic record
Abstract
In recent years, the remarkable progress in nanotechnology has ignited considerable interest in investigating nanocelluloses, an environmentally friendly and sustainable nanomaterial derived from cellulosic feedstocks. Current research primarily focuses on the preparation and applications of nanocelluloses. However, to enhance the efficiency of nanofibrillation, reduce energy consumption, and expand nanocellulose applications, chemical pre-treatments of cellulose fibers have attracted substantial interest and extensive exploration. Various chemical pre-treatment methods yield nanocelluloses with diverse functional groups. Among these methods, periodate oxidation has garnered significant attention recently, due to the formation of dialdehyde cellulose derived nanocellulose, which exhibits great promise for further modification with various functional groups. This review seeks to provide a comprehensive and in-depth examination of periodate oxidation-mediated nanocelluloses (PONCs), including their preparation, functionalization, hierarchical structural design, and applications. We believe that PONCs stand as highly promising candidates for the development of novel nano-cellulosic materials.
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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".