Assembling Native Elementary Cellulose Nanofibrils via a Dynamic and Spatially Confined Functionalization
Bibliographic record
Abstract
Selective surface modification of bio-sourced colloids affords effective fractionation and functionalization of polysaccharide-based nanomaterials, as shown by the classic TEMPO-mediated oxidation. However, such route leads to changes of the native surface chemistry, affecting interparticle interactions and limiting the full exploitation of the supermaterial properties associated with such nanomaterial assemblies. Here we introduce a methodology to extract elementary cellulose fibrils by treatment of biomass with N-succinylimidazole, achieving spatially confined (92% regioselectivity towards primary C6-OH) and dynamic surface functionalization, as elucidated by nuclear magnetic resonance, infrared spectroscopy, and gel permeation chromatography. No polymer degradation or crosslinking nor changes in crystallinity occur under the mild conditions of the process yielding elementary fibrils. The structure of the fibrils was validated by cross-corelating solid-state NMR, chromatographic analysis, and atomic force microscopy imaging. We demonstrate the fully reversible nature of the dynamic modification, which offers a significant opportunity for the reconstitution of the interfaces back to the native states, chemically and structurally. Consequently, access to 3D structuring of native elementary cellulose I fibrils is made possible, reproducing the supramolecular features of the native cellulosic supermaterials. Overall, we propose the reversible and regioselective surface succinylation as a suitable route to overcome current limitations in the production of cellulose nanomaterials, which is required to unlock the full potential of cellulose as a sustainable building block.
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 | 0.000 | 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".