Fabrication and Characterization of Citric Acid-Modified Starch Nanoparticles/Plasticized-Starch Composites
Bibliographic record
Abstract
Starch nanoparticles (SN) were prepared by delivering ethanol as the precipitant into starch-paste solution dropwise. Citric acid (CA) modified SN (CASN) were fabricated with the dry preparation technique. According to the characterization of CASN with Fourier transform infrared, X-ray diffraction, rapid visco analyzer, and scanning electron microscopy (SEM), amorphous CASN could not be gelatinized in hot water because of the cross-linking, and most of CASN ranged in size from about 50 to 100 nm. The nanocomposites were also prepared using CASN as the filler in glycerol plasticized-pea starch (GPS) matrix by the casting process. SEM revealed that CASN was dispersed evenly in the GPS matrix. As shown in dynamic mechanical thermal analysis, the introduction of CASN could improve the storage modulus and the glass transition temperature of CASN/GPS composites. The tensile yield strength and Young's modulus increased from 3.94 to 8.12 MPa and from 49.8 to 125.1 MPa, respectively, when the CASN contents varied from 0 to 4 wt %. Moreover, the values of water vapor permeability decreased from 4.76 x 10(-10) to 2.72 x 10(-10) g m(-1) s(-1) Pa(-1). The improvement of these properties could be attributed to the good interaction between CASN filler and GPS matrix. The comprehensive application of green chemistry principles were demonstrated in the preparation of CASN and CASN/GPS composites.
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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".