Electrochemical sandwich immunosensor based on porous copper porphyrin hydrogen bond organic framework for accurate quantification of peanut allergen Ara h 1
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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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".