Fabrication of plasmonic Au nanostructures on dielectric supports using 10 keV electron beam lithography and tests for SERS biodetection
Bibliographic record
Abstract
Plasmonic nanostructures have received an increasing attention due to their unique ability to mediate conversion of energy of light into different useful forms. This opens pathways for numerous applications from ultrasensitive surface-enhanced Raman scattering (SERS) characterization of materials to heterogeneous photocatalysis and green energy harvesting. However, plasmonic nanostructures should meet a number of requirements for their potential could be realized. In addition to nanoscale dimensions, a high uniformity and compatibility with existing microelectronic settings are required. Electron beam lithography (EBL) offers an unmatched control over nanoscale geometries and also a flexibility to allow for various designs. However, careful co-optimization of EBL exposure and development is required to fabricate periodic patterns with deep nanoscale dimensions. The usage of dielectric substrates is particularly challenging due to the accumulation of charge during EBL exposures. In this work, we have optimized a 10 keV EBL process to fabricate periodic arrays of 50 nm pitch dots on fused silica (FS) supports. To avoid distortions due to charging, a layer of conductive polymer was applied on the surface of the EBL resist, PMMA. In addition, we have investigated the impact of the conductive layer on the PMMA’s exposure by numerical modeling. Despite the predicted significant broadening of the 10 keV electron beam that reaches PMMA after traveling through the conductive layer, quality arrays of dots were successfully fabricated. We used the patterned PMMA as a mask to fabricate 50 nm pitch arrays of Au dots on FS. In order to verify the performance of these Au/FS structures, we used them for SERS biodetection. For this purpose, the samples were biofunctionalized with thiolated DNA aptamers that bind specifically to an important biomarker, protein interleukin 6 (IL-6). The samples were loaded with IL-6 from a solution and characterized by SERS. The results suggest that the fabricated Au/FS plasmonic nanostructures produce an efficient SERS effect. Anticipated multifunctional applications of the plasmonic nanostructures beyond the demonstrated SERS biodetection are discussed.
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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 | 0.001 |
| 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) | 0.001 | 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".