Depositing Highly Uniform Au Nanoparticles on the Surface of High-Curvature Nanofibers 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. To achieve this, our group has reported a block copolymer-template method to deposit Au nanoparticles on the highly curved surface of nanofibers.[1] Here the versatility of this strategy is demonstrated by depositing nanoparticles with the different dimensions or shapes and varying the density of Au nanoparticles on the surface of the nanofibers. To the best of our knowledge, we believe this is the first work to show how to obtain such highly uniform distribution of anistropic nanoparticles on the surface of high-curvature nanofibers. Among all the nanosensors studied in this work, the label-free nanofibers covered with Au nanostars with shorter branches shows the biggest enhancement, which could detect neurotransmitter secretion such as dopamine with moderate laser power and low integration time. Furthermore, measurement of pH in the cell microenvironment and pH gradient in the cell medium can be realized by our biosensors as well. The rapid pH response to the external environment makes it very promising for the application of the dynamic pH measurement 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. [1] Zhu, H., Lussier, F., Ducrot, C., Bourque, M. J., Spatz, J. P., Cui, W., Yu, L., Peng, W., Trudeau, L. E., Bazuin, C. G. and Masson, J. F., ACS Appl. Mater. Interfaces, 2019, DOI: 10.1021/acsami.8b19161. Figure 1
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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 | 0.000 | 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".