Expanding the Applications of High-amylose Fava Bean Starch: Optimization Strategies for Sustainable Valorization Into Aerogels
Bibliographic record
Abstract
Fava bean starch (FBS), an underutilized starch with naturally high amylose content, offers promising functionality for aerogel development but remains largely unexplored. This study explores the sustainable valorization of high-amylose fava bean starch (FBS) into aerogels, focusing on optimization strategies. Isolated FBS (via alkaline wet washing) and air-classified FBS were used to fabricate aerogels, with key parameters─drying time, retrogradation time, and tunicate cellulose nanocrystal (t-CNC) loading─optimized using Box-Behnken Design and response surface methodology (RSM). A Box–Behnken Design combined with response surface methodology (RSM) identified optimal fabrication conditions (45 h drying, 5–5.5 h retrogradation, and varying t-CNC loadings). Aerogels from isolated FBS exhibited more uniform pore architecture, higher compressive strength (up to 0.20 MPa vs 0.12 MPa for nonpurified), and improved thermal stability (up to 340.87 °C vs 323.78 °C), while both retained high porosity (95.52–98.44%) and low density (0.05–0.06 g/cm 3 ). Synchrotron-based microcomputed tomography (SR-μCT) revealed a well-organized honeycomb structure, supporting potential applications in thermal insulation and reactive fluid transport
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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".