Effect of Physical Pretreatments on the Hydrolysis Kinetic, Structural, and Thermal Properties of Pinhão Starch Nanocrystals
Bibliographic record
Abstract
Abstract Starch nanocrystals (SNCs) are insoluble platelet particles with multifunctional properties. SNCs production is mainly based on acid hydrolysis of cornstarch with low yield. This study focuses on investigating the effect of pretreatments (heat‐moisture‐treatment [HMT], annealing [ANN], and sonication [SNT]) on unconventional pinhão starch to produce SNCs by acid hydrolysis to improve the yield and SNCs properties. All starches hydrolysis is described by a first‐order model reaction and shown two phases related at k values. The faster hydrolysis is from SNT (k = 0.61 day−1) and the slower one is at ANN (k = 0.40 day−1). Furthermore, the acid hydrolysis is described by a rapid (0–2 days) phase, followed by a slow phase lasing 3–7 days. The HMT increases the yield of the SNCs (14.7%) but promotes losses in the RC (47.34%) as compared with the native starch (yield 10.23%; RC 52.23%). The ANN improves crystallites perfection, protecting them from acid attack. The pretreatments allow pinhão starch to be used as promising feedstock to produce SNCs with good yield and RC. In addition, ANN can be useful to improve the thermal stability and SNT to speeding up the hydrolysis for SNCs production, while HMT can increase the hydrolysis yield.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".