Lightweight, flexible, and multifunctional anisotropic nanocellulose-based aerogels for CO2 adsorption
Bibliographic record
Abstract
Abstract CO2 adsorption is a promising strategy to reduce costs and energy use for CO2 separation. In this study, we developed CO2 adsorbents based on lightweight and flexible cellulose nanofiber aerogels with monolithic structures prepared via freeze-casting, and cellulose acetate or acetylated cellulose nanocrystals (a-CNCs) were introduced into the aerogels as functional materials using an impregnation method to provide CO2 affinity. The microstructure of the adsorbent was examined using scanning electron microscopy, and compression tests were performed to analyze the mechanical properties of the adsorbents. The CO2 adsorption behavior was studied by recording the adsorption isotherms and performing column breakthrough experiments. The samples showed excellent mechanical performance and had a CO2 adsorption capacity of up to 1.14 mmol/g at 101 kPa and 273 K. Compared to the adsorbent which contains cellulose acetate, the one impregnated with a-CNCs had better CO2 adsorption capacity and axial mechanical properties owing to the building of a nanoscale scaffold on the surface of the adsorbent. Although the CO2 adsorption capacity could be improved further, this paper reports a potential CO2 adsorbent that uses all cellulose-based materials, which is beneficial for the environment from both resource and function perspectives. Moreover, the interesting impregnation process provides a new method to attach functional materials to aerogels, which have potential for use in many other applications.
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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) | 0.000 | 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 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".