Facile Synthesis of Cu<sub>2</sub>O@TiO<sub>2</sub>-PtCu Nanocomposites as a Signal Amplification Strategy for the Insulin Detection
Bibliographic record
Abstract
Novel ultrasensitive sandwich-type electrochemical immunosensor was proposed for the quantitative detection of insulin, a representative biomarker for diabetes. To this end, molybdenum disulfide nanosheet-loaded gold nanoparticles (MoS2/Au NPs) were used as substrates to modify bare glassy carbon electrodes. MoS2/Au NPs not only present superior biocompatible and large specific surface area to enhance the loading capacity of primary antibody (Ab1) but also present good electrical conductivity to accelerate electron transfer rate. Moreover, the amino-functionalized cuprous oxide decorated with titanium dioxide octahedral composites (Cu2O@TiO2-NH2) were prepared to load dendritic platinum–copper nanoparticles (PtCu NPs) to realize signal amplification strategy. The resultant nanocomposites (cuprous oxide decorated with titanium dioxide octahedral loaded dendritic platinum–copper nanoparticles) demonstrate uniform octahedral morphology and size, which effectively increases the catalytically active sites and specific surface area to load the secondary antibody (Ab2), even increases conductivity. Most importantly, the resultant nanocomposites possess superior electrocatalytic activity for hydrogen peroxide (H2O2) reduction, which present the signal amplification strategy. Under the optimal conditions, the proposed immunosensor exhibited a linear relationship between logarithm of insulin antigen concentration and amperometric response within a broad range from 0.1 pg/mL to 100 ng/mL and a limit detection of 0.024 pg/mL. Meanwhile, the immunosensor was employed to detect insulin in human serum with satisfactory results. Furthermore, it also presents good reproducibility, selectivity, and stability, which exhibits broad application prospects in biometric analysis.
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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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".