Carboxylated Chitosan Nanocrystals: A Synthetic Route and Application as Superior Support for Gold-Catalyzed Reactions
Bibliographic record
Abstract
In this study, we demonstrate for the first time the fabrication of carboxylated chitosan nanocrystals (ChsNC) with high degree of deacetylation (DDA) at >80% and narrow size distribution. We also studied its application as a sustainable support material for metal-based catalysts. Carboxylated chitin nanocrystals (ChNCs) were initially prepared through partial cleavage of glycosidic bonds in chitin by ammonium persulfate, with concurrent oxidation of chitin C6 primary alcohols to produce carboxylate groups on the surface of the ChNCs. ChsNCs were subsequently prepared using an alkaline deacetylation procedure in the presence of NaBH4 to preserve the nanorod structure of the biomaterial. The resulting nanocrystals feature both carboxyl and amino functional groups. Transmission electron microscopy (TEM), X-ray diffraction (XRD), and Fourier-transform infrared (FTIR) spectroscopy were used to determine the morphology and composition of these carboxylated ChNCs and ChsNCs. Subsequently, we tested the ability of the as-made ChsNCs as a biomass-based catalyst support for Au nanoparticles (NPs) using the 4-nitrophenol reduction and the aldehyde–amine–alkyne (A3) coupling reactions to demonstrate its capabilities in regard to the ones of cellulose nanocrystals (CNCs). In particular, Au NPs over ChsNCs featured the highest turnover frequency (TOF) value for the 4-nitrophenol reduction reported for all Au-based catalysts supported on carbon-based systems. Spectroscopic and imaging techniques confirmed the importance of precisely controlling the redox state of Au as it is being deposited to afford a highly disperse active site on the bionano-support.
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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) | 0.000 | 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) | 0.000 | 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".