Physical Pretreatment on Common Bean Starch at Acid Hydrolyzed Nanocrystals Structure and Properties
Bibliographic record
Abstract
Abstract Starch nanocrystals (SNC) are insoluble platelets with crystalline structures produced by acid hydrolysis. Pretreatments, including heat–moisture treatment (HMT), annealing (ANN), and sonication (SNT) can be used to improve SNC properties. They investigate the impact of these pretreatments on SNC structure and properties, including hydrolysis kinetics and yield, molecular structure, infrared spectroscopy, crystallinity (Xc), and thermal stability. Hydrolysis of native and pretreated starches followed a two‐phase first‐order model with an initial rapid stage and a slower second stage based on the k‐values. SNC yield is improved by at least 180% than previously reported. HMT SNC yield is 42.3% while native SNC is 35.2%. Structural analysis reveals that SNC displayed an A‐type structure with increased Xc. However, prolonged acid hydrolysis (7 days) reduces Xc by breaking long molecular chains into shorter glucose ones, reducing SNC yield. Melting temperatures (Tp) of pretreated SNC increase after 5 days of hydrolysis. Pretreated carioca bean starch shows advantages for SNC production after 5 days of hydrolysis, reaching good yield and Xc. HMT and SNT prove effective in improving hydrolysis yield and thermal stability, while ANN slightly accelerates SNC production. Their findings provide valuable insights into optimizing pretreatments for enhancing SNC properties and expanding their 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) | 0.001 | 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".