Photochromic Paper Indicators for Acidic Food Spoilage Detection
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide A photoresponsive microstructured composite is fabricated through the impregnation of cellulosic filter paper (FP) with a spiropyran-modified acrylic polymer. The polymer enwraps uniformly each individual cellulose fiber, increases the thermal stability of cellulose, and ensures the preservation of the composite functionalities even upon removal of the surface layers through mechanical scratching. The photochromic spiropyran moieties of the polymer, even while embedded in the cellulosic sheet, can reversibly interconvert between the colorless spiropyran and the pink merocyanine isomeric states upon irradiation with UV and visible light, respectively. Moreover, the photochromic polymer presents a faster photochromic response and a higher resistance to photodegradation, with an outstanding reusability for more than 100 switching cycles when it is incorporated in the cellulose network. Most importantly, the acidochromism of the modified FP, attributed to the spiropyran molecules after UV activation, allows the real-time optical and visual detection of acidity changes and spoilage in food products, such as wine and milk. Spoilage due to bacterial degradation and oxidation processes generates acidic vapors that induce the protonation of the merocyanine. This results in a visually detectable chromic transition from pink to white of the treated cellulose fibers, corresponding to a blue shift in the absorption spectrum. The developed photoresponsive cellulose composite can serve as cost-effective robust optical component in integrated functional platforms and consumer-friendly indicators for smart food packaging, as well as portable on demand acidoresponsive interfaces for gas monitoring in industrial and environmental applications.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".