Butterfly Pea Flower Anthocyanin Immobilized pH Sensitive Intelligent Nanocomposite Films of Starch/Chitin with Improved Thermal and Mechanical Properties
Bibliographic record
Abstract
Abstract Thin films with pH sensitivity have been a trendsetter in the current research scenario. Edible films for packaging are comparatively newer ones, where the study uses conventional food materials as a source or precursor to fabricate the films. Starch(S) is one such material that is not much exploited in packaging and pH sensing. Here, the study presents the preparation of a new type of sensitive, intelligent film from starch‐chitin(CHN) composites and adds the anthocyanin pigment extracted from the Butterfly Pea flower (BPE). The combination films are much more sensitive to ammonia and formalin and thus suggested for the detection of adulteration in fish. All the films (S, S/BPE, S/CHN, and S/BPE/CHN) are fabricated by the solution casting method. The addition of 10 wt% CHN results in the enhancement of the thermal and mechanical properties of pH indicator films. Owing to the presence of anthocyanin pigment, the films can change their color changes in pH. The developed starch‐based intelligent films display wide color differences from bright red to green over the 1–12 pH range, which is discriminated by the naked eye.
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".