Nanoscale Chemical Imaging of Supported Lipid Monolayers using Tip‐Enhanced Raman Spectroscopy
Bibliographic record
Abstract
Visualizing the molecular organization of lipid membranes is essential to comprehend their biological functions. However, current analytical techniques fail to provide a non-destructive and label-free characterization of lipid films under ambient conditions at nanometer length scales. In this work, we demonstrate the capability of tip-enhanced Raman spectroscopy (TERS) to probe the molecular organization of supported DPPC monolayers on Au (111), prepared using the Langmuir-Blodgett (LB) technique. High-quality TERS spectra were obtained, that permitted a direct correlation of the topography of the lipid monolayer with its TERS image for the first time. Furthermore, hyperspectral TERS imaging revealed the presence of nanometer-sized holes within a continuous DPPC monolayer structure. This shows that a homogeneously transferred LB monolayer is heterogeneous at the nanoscale. Finally, the high sensitivity and spatial resolution down to 20 nm of TERS imaging enabled reproducible, hyperspectral visualization of molecular disorder in the DPPC monolayers, demonstrating that TERS is a promising nanoanalytical tool to investigate the molecular organization of lipid membranes.
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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".