<i>In Situ</i> Growth of AuNPs on Glass Nanofibers for SERS Sensors
Bibliographic record
Abstract
It is challenging to fabricate plasmonic nanosensors on high-curvature surfaces with high sensitivity and reproducibility at low cost. Here, we report a facile and straightforward strategy, based on an in situ growth technique, for fabricating glass nanofibers covered by asymmetric gold nanoparticles (AuNPs) with tunable morphologies and adjustable spacings, leading to much improved surface-enhanced Raman scattering (SERS) sensitivity because of hotspots generated by the AuNP surface irregularities and adjacent AuNP coupling. First, nanosensors covered with uniform and well-dispersed citrate-capped spherical AuNPs were constructed using a polystyrene-b-poly(4-vinylpyridine) (PS-P4VP, with 33 mol % P4VP content and 61 kg/mol total molecular weight) block copolymer brush-layer templating method, and then, the deposited AuNPs were grown to asymmetric AuNPs. AuNP morphologies and hence the optical characteristics of AuNP-covered glass nanofibers were easily controlled by the choice of experimental parameters, such as the growth time and growth solution composition. In particular, tunable AuNP average diameters between about 40 and 80 nm with AuNP spacings between about 50 and 1 nm were achieved within 15 min of growth. The SERS sensitivity of branched AuNP-covered nanofibers (3 min growth time) was demonstrated to be more than threefold more intense than that of the original spherical AuNP-covered nanofibers using a 633 nm laser. Finite-difference time-domain simulations were performed, showing that the electric field enhancement is highest for intermediate AuNP diameters. Furthermore, SERS applications of these nanosensors for H2O2 detection and pH sensing were demonstrated, offering appealing and promising candidates for real-time monitoring of extra/intracellular species in vitro and in vivo.
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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".