High-Density Au Anchored to Ti<sub>3</sub>C<sub>2</sub>-Based Colorimetric-Fluorescence Dual-Mode Lateral Flow Immunoassay for All-Domain-Enhanced Performance and Signal Intercalibration
Bibliographic record
Abstract
In this work, a novel MXene-Au nanoparticle (Ti 3 C 2 @Au) was synthesized with a high molar extinction coefficient, strong fluorescence quenching ability, ultrahigh antibody affinity, high stability, and good dispersibility, and it was used to develop a colorimetric-fluorescence dual-mode lateral flow immunoassay (LFIA). The detection limits of this method for the detection of dexamethasone in milk, beef, and pork were 0.0018, 0.12, and 0.084 μg/kg in the “turn-off” mode (colorimetric signal), and 0.0013, 0.080, and 0.070 μg/kg in the “turn-on” mode (fluorescent signal), respectively, which was up to 231-fold more sensitive compared with that of the reported LFIAs. The recovery rates ranged from 81.1–113.7%, and 89.2–115.4%, with the coefficients of variation ranging from 1.4–15.0%, and 1.9–14.8%, respectively. The results of the LC-MS/MS confirmation test on 30 real samples had a good correlation with that of our established method ( R 2 > 0.97). This work not only developed novel nanocarriers for antibody-based LFIA but also ensured high-performance 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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".