A Label-Free Photoelectrochemical Immunosensor Based on Water-Soluble CdS Quantum Dots
Bibliographic record
Abstract
We demonstrate herein a newly developed lable-free photoelectrochemical immunosensor using a CdS quantum dots (QDs) multilayer film coupled with a biospecific interaction. The CdS QDs multilayer film was prepared by layer-by-layer assembling positively charged poly(dimethyldiallylammonium chloride) (PDDA) and thioglycolic acid (TGA)-capped water-soluble CdS QDs with negative charges on the surface of an indium−tin oxide (ITO) electrode. Ascorbic acid (AA) was exploited as an efficient and nontoxic electron donor for scavenging photogenerated holes under mild solution medium. The photoexcitation of CdS QDs modified electrode potentiostated at 0 V (vs. Ag/AgCl) in the presence of 0.1 M AA led to a stable anodic photocurrent. To perform the immunoassay, goat antimouse IgG was conjugated onto CdS QDs modified electrode by using the classic EDC coupling reactions between COOH groups on the surfaces of the TGA capped CdS QDs and NH<sub>2</sub> groups of the antibody. The concentrations of mouse IgG were measured through the decrease in photocurrent intensity resulting from the increase in steric hindrances due to the formation of the immunocomplex. The synthetic conditions (different Cd/S ratio and different pH) of CdS QDs and the number of PDDA/CdS bilayers could influence the photoelectrochemical properties of CdS QDs modified electrodes used for immunosensor construction. Under the optimal conditions, a linear relationship between photocurrent decrease and mouse IgG concentration was obtained in the range of 10 pg/mL to 100 ng/mL with a detection limit of 8.0 pg/mL. This strategy opens a new perspective for the application of QDs, which might be of great significance for QDs in photoelectrochemical bioanalysis in the future.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.595 | 0.010 |
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; both teacher heads agree on what is shown here.
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".