A Leaf out of Nature’s Book: Hairy Nanocelluloses for Bioinspired Mineralization
Bibliographic record
Abstract
The quest for designing new materials to unravel and mimic the biogenic mechanisms behind the formation of superior natural structures through biomineralization has stimulated interest in a broad range of disciplines. Here, we show that cellulose, the basic structural material of trees, and the most abundant yet inactive biopolymer in the world, can be chemically engineered to yield a new class of nanocelluloses with a ppm-level biomimetic effect. We introduce hairy nanocelluloses, namely, electrosterically stabilized nanocrystalline cellulose (ENCC) and dicarboxylated cellulose (DCC), as the first polysaccharide-based materials to address key biomimetic material design concerns, involving (i) an all-natural backbone, (ii) no anthropogenic effects such as eutrophication due to the N-, P-, and/or S-bearing groups, (iii) capability for macroscale mineralization, (iv) no extreme and/or controlled reaction condition requirements, (v) a high efficiency at extremely low concentrations, and (vi) a strong polymorph selectivity. In a model system under ambient conditions, the bioinspired mineralization of calcium carbonate with ENCC/DCC resulted in macroscale nacre-like sheets of vaterite, the least thermodynamically stable polymorph of CaCO 3, which were then decorated with stabilized microscale lenticular vaterite to unveil the biomimetic mineralization mechanism. The emergence of these advanced sustainable nanomaterials may open new horizons in the field of bioinspired nanoengineering for designing inorganic nanostructures and hybrid inorganic–organic nanocomposites.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".