Direct imaging of the alternating disordered and crystalline structure of cellulose fibrils via super-resolution fluorescence microscopy
Bibliographic record
Abstract
Cellulose, the primary component of the plant cell wall, has fueled the wood, textile, pulp and paper industries for centuries, and has recently been used for the production of renewable nanomaterials. The tight crystalline packing of glucan chains within cellulose fibrils is responsible for its superior mechanical properties but renders this material recalcitrant to biochemical and chemical breakdown and limits its use as a green resource. The presence of nanoscale dislocations within cellulose fibrils has been postulated for decades and is thought to be responsible for the production and size of cellulose nanocrystals (CNCs) following acid hydrolysis. However, dislocations have never been directly visualized and their prevalence and size have remained elusive. In this study, we have used super-resolution (SR) fluorescence microscopy to directly visualize and measure alternating crystalline and disordered regions within individual fluorescently labelled bacterial cellulose fibrils. The measured size of the crystalline regions ranges from 40 – 400 nm and shows striking overlap with the length distribution of bacterial CNCs produced through sulfuric acid hydrolysis, supporting the fringed micellar model for the supramolecular structure of cellulose fibrils. The disordered regions were found to be 20 – 120 nm in length and show heterogeneous accessibility, which directs fibril cleavage during the initial stages of cellulose acid hydrolysis. Two-colour SR imaging of cellulose fibrils and bound exoglucanases (Cel7A), in combination with degree of crystallinity measurements suggest that these dislocations are nanoscale in size, and do not result in amorphous cellulose pockets large enough to accommodate enhanced enzyme binding. Through characterization of disordered regions in cellulose fibrils, we have gained insight into the role of cellulose nanostructure in its breakdown by chemical and enzymatic means.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".