Integrated Sensing Platform Based on MOF@COF Nanocomposites for the Ultrasensitive Biosensing of Sesame Allergens Ses i 2
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
• Design and synthesize novel composite structural materials UIO-66-NH2@TA-COF. • UIO-66-NH2@TA-COF exhibits excellent electroactive catalytic performance. • A composite aptamer sensor sensitively spots sesame allergen Ses i 2. • This provides an enhanced and efficient method for rapid detection of Ses i 2. Sesame allergy has emerged as a public health issue due to its persistent occurrence, severe symptomatic manifestations, and widespread incidence globally. Among sesame allergens, 2S albumins (Ses i 2) is one of the widely recognized allergens, and its precise detection is urgently needed to prevent sesame-related allergies. In this work, a composite structure was designed and synthesized by combining metal ion self-assembled metal organic frameworks (MOFs) and imine constructed covalent organic frameworks (COFs) (represented by MOF@COF), which constructing a high-sensitivity sensing system for identification and detection of Ses i 2. A novel hybrid material, comprising a strategically assembled combination of UIO-66-NH 2 and TA-COF, was crafted to yield a functionalized UIO-66-NH 2 @TA-COF composite, demonstrating remarkable electrochemical performance. As a result, a significant quantity of aptamer strands can adhere to the UIO-66-NH 2 @TA-COF surface resulting from robust π-π stacking interactions. With the change in the relative concentration of Ses i 2 from 5 to 650 nM, the UIO-66-NH 2 @TA-COF sensor exhibited a low detection limit of 0.13 nM, which demonstrates its extremely high sensitivity. In addition, the UIO-66-NH 2 @TA-COF sensor exhibited excellent reproducibility, stability and specific recognition ability to Ses i 2. Therefore, this innovative sensor exhibits great potential for real-world applications in food safety.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it