Bibliographic record
Abstract
Metal-organic frameworks, MOFs, are a very important class of porous materials, many of which being already commercial. Concurrently porphyrin-based MOFs, PMOFs, are one sub-class, but the porphyrin unit provides multiple features rendering them quite special. Indeed, the PMOFs are semi-conducting, deeply colored and strongly fluorescent materials, all at the same time, and can easily be prepared in the nano-sized scale. These features can cleverly be exploited in the field of food security. The various manners by which PMOFs contribute to food security is surveyed and critically described. Detection of toxins and mycotoxins, and preventions against microbes, mainly fungi and bacteria, are the two main current research topics in this area. The development of sensors and biosensor (including aptasensors) is clearly the most flourishing topic as colorimetric, fluorescence, electrochemistry, and electroluminescence techniques have successfully been developed. Prevention is another successful approach. Despite it is quite important, sanitary prevention and correcting techniques have not been fully explored so far. Two strategies have been assessed, 1) antibacterial food packaging and 2) extraction of contaminants, when possible. This presentation surveys the various techniques and applications developed so far in food security hazards using PMOFs.
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".