Full Spectroscopic Tip-Enhanced Raman Imaging of Single Nanotapes Formed from β-Amyloid(1–40) Peptide Fragments
Bibliographic record
Abstract
This study demonstrates that spectral fingerprint patterns for a weakly scattering biological sample can be obtained reproducibly and reliably with tip-enhanced Raman spectroscopy (TERS) that correspond well with the conventional confocal Raman spectra collected for the bulk substance. These provided the basis for obtaining TERS images of individual self-assembled peptide nanotapes using an automated, objective procedure that correlate with the simultaneously obtained scanning tunneling microscopy (STM) images. TERS and STM images (64 × 64 pixels, 3 × 3 μm²) of peptide nanotapes are presented that rely on marker bands in the Raman fingerprint region. Full spectroscopic information in every pixel was obtained, allowing post-processing of data and identification of species of interest. Experimentally, the "gap-mode" TERS configuration was used with a solid metal (Ag) tip in feedback with a metal substrate (Au). Confocal Raman data of bulk nanotapes, TERS point measurements with longer acquisition time, atomic force microscopy images, and an infrared absorption spectrum of bulk nanotapes were recorded for comparison. It is shown that the unique combination of topographic and spectroscopic data that TERS imaging provides reveals differences between the STM and TERS images, for example, nanotapes that are only weakly visible in the STM images, a coverage of the surface with an unknown substance, and the identification of a patch as a protein assembly that could not be unambiguously assigned based on the STM image alone.
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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) | 0.001 | 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".