Electrochemiluminescence Immunosensor Based on CdSe Nanocomposites
Bibliographic record
Abstract
A novel strategy for the enhancement of electrochemiluminescence (ECL) was developed by combining CdSe nanocrystals (NCs), carbon nanotube-chitosan (CNT-CHIT), and 3-aminopropyl-triethoxysilane (APS). A label-free ECL immunosensor for the sensitive detection of human IgG (HIgG) was fabricated. The colloidal solution containing CdSe NCs/CNT-CHIT composite was first covered on the Au electrode surface to form a robust film, which showed high ECL intensity and good biocompatibility. After APS as a cross-linker was covalently conjugated to the CdSe NCs/CNT-CHIT film, the ECL intensity was greatly enhanced. And, an intensity about 20-fold higher than that of the CdSe NCs/CNT-CHIT film was observed. After antibody was bound to the functionalized film via glutaric dialdehyde (GLD), the modified electrode could be used as an ECL immunosensor for the detection of HIgG. The specific immunoreaction between HIgG and antibody resulted in the decrease in ECL intensity. The ECL intensity decreased linearly with HIgG concentration in the range of 0.02-200 ng mL(-1), and the detection limit was 0.001 ng mL(-1). The immunosensor has the advantages of high sensitivity, speed, specificity, and stability and could become a promising technique for protein detection.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".