Cellulose nanocrystals constructed chevaux-de-frise: effect of surface topography on mechano-bactericidal activity
Bibliographic record
Abstract
Nature offers much inspiration in the quest for antibacterial surfaces. Chevaux-de-frise nanostructures can be formed using needle-like cellulose nanocrystals (CNCs) to mechanically eliminate foodborne bacteria upon contact. To further reveal the relationship between surface topography and mechano-bactericidal activity, the chevaux-de-frise structures constructed by two types of CNCs with different aspect ratios, namely shorter CNCs (HCNCs, aspect ratio ∼9) from waste textile and longer ones (t-CNCs, aspect ratio ∼55) from tunicate cellulose. These nanostructured surfaces were tested for antibacterial efficacy against Gram-positive and Gram-negative bacteria. The results demonstrated that the nanostructure composed of t-CNCs was relatively more effective in killing the bacteria due to their larger aspect ratio than HCNCs, while rod-shaped Gram-negative E. coli suffered most (>0.8 log reduction) from the chevaux-de-frise structures compared to the other types of bacteria. The surface topography was changed by mechanical pressing and the CNC surfaces lost the antibacterial activity as the surface roughness decreased. The fast bactericidal activity (3-min contact) was not caused by the rupture nor changing the biochemical compositions of the cells. Therefore, this study confirms that the topography of the chevaux-de-frise structures is the key factor to enable the mechano-bactericidal activity of CNC-based sustainable antibacterial surfaces, but the mechanisms still need further investigation.
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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".