Plasmon-Induced Photoluminescence Immunoassay for Tuberculosis Monitoring Using Gold-Nanoparticle-Decorated Graphene
Bibliographic record
Abstract
Metal-nanoparticle-functionalized graphene, in particular, graphene sheets containing Au nanoparticles (Au NPs), have generated considerable interest because of their unique optical and electrical characteristics. In this study, we successfully produced graphene sheets decorated with Au NPs (AuGrp) using phytochemicals as reducing agents. During this reaction, Au ions intercalated into the layered graphene flakes and were then reduced into NPs, exfoliating the graphene sheets. The physicochemical properties of the AuGrp nanocomposites were characterized, and the exfoliation process was investigated using a molecular dynamics simulation of Au NPs between graphene sheets. Our proposed technique is advantageous because the phytochemicals are mild reducing agents that preserve the graphene structure during exfoliation and NP decoration. The dispersity of the NPs on the graphene sheets was drastically improved due to the use of metal-ion intercalation. Moreover, the electrical conductivity was 6-30 times higher than that of bare graphene and reduced graphene oxide. Using antibody (Ab) modified AuGrp sheets and quantum dots, a plasmonic-induced photoluminescence immunoassay of tuberculosis (TB) antigen (aG) CFP-10 was demonstrated for a potential application of these materials. The enhancement of photoluminescence (PL) response was monitored depending on the various TB aG concentrations from 5.1 pg/mL to 51 μg/mL, and the detection limit for CFP-10 was 4.5 pg/mL. Furthermore, the selectivity was demonstrated with Ag85 as the other TB aG, and PL enhancement was not observed in this case. Therefore, AuGrp-based immunoassay showed the potential for biosensor application.
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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 | 0.000 | 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".