Mechanically Self-Assembled, Three-Dimensional Graphene–Gold Hybrid Nanostructures for Advanced Nanoplasmonic Sensors
Bibliographic record
Abstract
Hybrid structures of graphene and metal nanoparticles (NPs) have been actively investigated as higher quality surface enhanced Raman spectroscopy (SERS) substrates. Compared with SERS substrates, which only contain metal NPs, the additional graphene layer provides structural, chemical, and optical advantages. However, the two-dimensional (2D) nature of graphene limits the fabrication of the hybrid structure of graphene and NPs to 2D. Introducing three-dimensionality to the hybrid structure would allow higher detection sensitivity of target analytes by utilizing the three-dimensional (3D) focal volume. Here, we report a mechanical self-assembly strategy to enable a new class of 3D crumpled graphene-gold (Au) NPs hybrid nanoplasmonic structures for SERS applications. We achieve a 3D crumpled graphene-Au NPs hybrid structure by the delamination and buckling of graphene on a thermally activated, shrinking polymer substrate. We also show the precise control and optimization of the size and spacing of integrated Au NPs on crumpled graphene and demonstrate the optimized NPs' size and spacing for higher SERS enhancement. The 3D crumpled graphene-Au NPs exhibits at least 1 order of magnitude higher SERS detection sensitivity than that of conventional, flat graphene-Au NPs. The hybrid structure is further adapted to arbitrary curvilinear structures for advanced, in situ, nonconventional, nanoplasmonic sensing applications. We believe that our approach shows a promising material platform for universally adaptable SERS substrate with high sensitivity.
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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 | 0.000 | 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 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".