Dual-porous cellulose nanofibril aerogels <i>via</i> modular drying and cross-linking
Bibliographic record
Abstract
Nanofibrillar foams and aerogels are traditionally either macroporous with low surface area and high mechanical strength, or mesoporous with high surface area and low mechanical strength. In this work, an anionic cellulose nanofibril (CNF)-based dual-porous aerogel with BET specific surface area up to 430 m2 g-1 was prepared via a modular process combining directional freeze-thawing (creating macro-pores, ca. 50-200 μm) and supercritical drying (creating meso-pores, ca. 2-50 nm). Furthermore, by optionally utilizing both physical and chemical cross-linking strategies, aerogels with a Young's modulus of up to 711 kPa and good stability in aqueous conditions were demonstrated. By altering cross-linking strategies, the properties of resulting aerogels, such as hydrophilicity, mechanical strength and stability in water, can be precisely controlled for different applications. As a result, cationic methylene blue (MB) and metal ions (Ag+) were chosen as model species to investigate the absorption properties of the physically cross-linked aerogels in water. The aerogels showed a maximum adsorption of MB up to 234 mg g-1 and of Ag+ up to 116 mg g-1 as a result of the high specific surface area of the aerogels and their strong electrostatic interaction with the model species. Importantly, the hierarchical dual porosity of the aerogels enabled fast adsorption kinetics combined with a considerable adsorption capacity overall. Finally, it was shown that the adsorbed Ag+ could be converted to metallic Ag, demonstrating the additional functionality of these dual porous hybrid aerogels for antibacterial or catalytic applications.
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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 | 0.001 |
| 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".