Electrochemical immunoassay of carcinoembryonic antigen based on TiO<sub>2</sub>–graphene / thionine / gold nanoparticles composite
Bibliographic record
Abstract
A novel multilayer film based on gold nanoparticles (AuNPs), thionine (Thi), and TiO2–graphene (TiO2–Gr) was exploited to develop a highly sensitive amperometric immunosensor for detecting carcinoembryonic antigen (CEA). Firstly, Nafion–TiO2–Gr homogeneous composite was dropped on the surface of a glassy carbon electrode (GCE). Then Thi was chemisorbed by the TiO2–Gr–Nafion composite. Furthermore, the negative ly charged AuNPs were chemisorbed onto Thi film through the electrostatic force with the amino groups of Thi. Cyclic voltammetry (CV) was employed to characterize the assembly process and the performance of the immunosensor. Because of the synergistic effect of the AuNPs, Thi, and the unique properties of TiO2–Gr, the obtained immunosensor exhibited a wide linear response to CEA in two ranges from 0.1 to 10.0 ng mL−1 and from 10.0 to 120.0 ng mL−1 with a relatively low detection limit of 0.01 ng mL−1 (S/N = 3), as well as good stability and repeatability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".