Impact of Weak Organic Acids as Coagulants on Tailoringthe Properties of Cellulose Aerogel Beads
Bibliographic record
Abstract
Cellulose is the most abundant polysaccharide on Earth and is well known for its renewability, biodegradability and chemical stability.1-2 We have been investigating the use of weak organic acids to tailor the properties of cellulose aerogel beads.3 The cellulose solution was prepared by using commercial cellulose in a mixture of NaOH, urea, and water as solvent.4 Three weak acids, acetic acid, lactic acid, and citric acid, and a strong acid, hydrochloric acid, were chosen as regeneration baths. The production of aerogel beads by conventional dropping technique was studied and optimized for each acid. The produced cellulose aerogels were characterized by nitrogen adsorption-desorption isotherm, BJH pore data analyses, density analyses, IR spectroscopy, scanning electron microscopy, and X-ray powder diffractometry, and their properties were compared. In common, all the aerogel beads showed interconnected nanofibrillar network. The pore size distribution was highly influenced by the acids employed for regeneration. High concentration of weak acids contributed to low shrinkage, high specific surface area and high pore volume. In conclusion, this study showed an alternate path way to tailor the properties of cellulose aerogel beads. Furthermore, cellulose from biomass waste can be used for such products too. Thus, hemp and flax from Canadian suppliers were used as starting material. Within the presentation an overview on the production of cellulose aerogels from various raw materials working under different conditions will be presented. Reference 1. Wang S., Lu A., and Zhang L., Prog. Polym. Sci., 53, 169-206, 2016. 2. Wong L. C., Leh C. P., and Goh C. F., Carbohydr. Polym., 264, 118036, 2021. 3. Costa D., Milow B., and Ganesan K., Chem. Eur. J., 30(51), e202401794, 2024. 4. Budtova T., and Navard P., Cellulose, 23(1), 5-55, 2015.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.021 |
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; both teacher heads agree on what is shown here.
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".