Portable sensor devices based on multifunctional framework materials: Recent advances for food safety assurance by on-site detection
Bibliographic record
Abstract
: Food contamination has emerged as a critical issue in the realm of global public health. Although traditional detection techniques provide high sensitivity for identifying contaminants in food, their complexity, time-consuming, and reliance on specialized personnel restrict the practical application. Consequently, there is an urgent need to develop rapid, smart, and portable on-site detection techniques to safeguard food safety. Framework material-mediated sensing platforms have gained significant prominence in the analysis of food contaminants. By integrating them with portable devices, on-site detection of food safety becomes possible. In this review, we for the first time thoroughly summarized the recent advances in framework materials sensor platforms integrating with portable analytical devices for the on-site detection of food contaminants. To start with, the common functions of structurally diverse framework materials in sensors were introduced and discussed. Subsequently, attention was focused on the research progress on integrating multifunctional framework materials with portable sensing devices (e.g., test strips, hydrogels, non-invasive smart labels, and microfluidic chips) for point-of-care testing (POCT) detection. Ultimately, the applications of portable sensing devices based on framework materials in monitoring various food contaminants were summarized in detail, pointing to the direction for on-site POCT detection in food safety. More importantly, the challenges and opportunities of framework materials in food safety monitoring were also considered. Emerging analysis technology based on framework materials provides a highly sensitive, cost-effective, and fast detection platform for food contaminants. Notably, the integration of portable devices with framework materials can address the demand for on-site detection of food safety. We anticipate that the ongoing design and optimization of tunable framework materials will create expanded opportunities for POCT portable detection in food safety. • Multifunctionality of framework materials in sensors was systematically presented. • On-site portable detection devices based on framework materials were discussed. • Applications of portable devices for food safety on-site detection were outlined. • Portable sensors based on framework materials will be smarter and more digital.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".