(Digital Presentation) Fabricating SERS-Active Nanofibers Covered with Au Nanoparticles for SERS Optophysiology
Bibliographic record
Abstract
Nanofibers as biosensors have attracted great attention due to its facile removal from the biosystem after a period of intra- or extracellular measurements and site-specific measurement among others. The application of nanofibers covered with Au nanoparticles as SERS sensor circumvents the aggregation and accumulation of Au nanoparticles in vitro or in vivo, in addition to the greater Raman enhancement of signal than on planar surface. We report here on a strategy using block copolymer brush-layer templating and ligand exchange for fabricating highly reproducible and stable SERS-active nanofibers with tip diameters down to 60 nm and covered with well-dispersed and uniformly distributed branched AuNPs, which have intrinsic hotspots favoring inherently high plasmonic sensitivity. In addition, AuNPs with tunable morphology and adjacent spacing on the nanofibers can be adjusted using an in situ growth technique, thereby enhanced SERS sensitivity was obtained due to the asymmetric structure and coupling between the adjacent AuNPs. Tunable AuNP morphologies and hence the optical characteristics of the AuNPs on the nanofibers can be easily controlled by choice of experimental parameters, particularly the growth time. Besides, finite difference time domain (FDTD) simulations were performed to gain more insight into the electric-field enhancement of AuNPs on the high-curvature substrates. Furthermore, SERS application of these nanosensors in pH sensing and Hg2+ detection is demonstrated here, offering appealing and promising candidates for real time monitoring of extra/intra-cellular species in vitro or in vivo. In addition to SERS sensing, these highly uniform nanosensors have other far-reaching implications, including medical diagnostics, therapeutics and so on.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.191 | 0.056 |
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".