Electrochemical sandwich immunosensor based on porous copper porphyrin hydrogen bond organic framework for accurate quantification of peanut allergen Ara h 1
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
• A novel sandwich immunosensor was constructed using porous Cu - HOF for Ara h 1 detection. • Cu-HOF has excellent electrocatalytic and conductive properties. • This sensor provides low detection limit (1.92 ng/mL) and wide linear range (80∼8000 ng/mL). • Method was used to test actual samples and standard addition experiments with good accuracy. Peanut allergy is a well-known and potentially life-threatening condition, driving the search for reliable methods for peanut allergens detection. In this study, a novel sandwich-type electrochemical immunosensor was developed using a copper-porphyrin hydrogen-bonded organic framework (Cu-HOF) with outstanding electrochemical performance, enabling highly accurate and sensitive detection of the major peanut allergen Ara h 1. Cu-HOF was utilized as an efficient electrocatalyst toward acetaminophen oxidation to generate a significantly enhanced electrochemical signal. The antibody-modified Cu-HOF forms an immune sandwich structure with the Ara h 1 aptamer electrode in the presence of Ara h 1, triggering the Ara h 1-specific electrochemical detection. The biosensor delivered a broad linear detection range (80∼8000 ng/mL) and a low detection limit of 1.92 ng/mL for Ara h 1. The electrochemical method that was developed was also validated using actual samples and exhibited good consistency with the results from a commercial ELISA kit. This suggests that the developed Ara h 1 biosensor is a valuable tool for the peanut allergy prevention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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