Advances in Surface Plasmon Resonance Imaging Enable Quantitative Tracking of Nanoscale Changes in Thickness and Roughness
Bibliographic record
Abstract
To date, detailed studies of the thickness of coatings using surface plasmon resonance have been limited to samples that are very uniform in thickness, and this technique has not been applied quantitatively to samples that are inherently rough or undergo instabilities with time. Our manuscript describes a significant improvement to surface plasmon resonance imaging (SPRi) that allows this sensitive technique to be used for quantitative tracking of the thickness and roughness of surface coatings that are rough on the scale of tens of nanometers. We tested this approach by studying samples with an idealized, one-dimensional roughness: patterned channels in a thin polymer film. We find that a novel analysis of the SPRi data collected with the plane of incidence parallel to the patterned channels allows the determination of the thickness profile of the channels in the polymer film, which is in agreement with that measured using atomic force microscopy. We have further validated our approach by performing SPRi measurements perpendicular to the patterned channels, for which the measured SPR curve agrees well with the single SPR curve calculated using the average thickness determined from the thickness profile as determined using AFM. We applied this analysis technique to track the average thickness and RMS roughness of cellulose microfibrils upon exposure to cellulolytic enzymes, providing quantitative determinations of the times of action of the enzymes that are of direct interest to the cellulosic ethanol industry.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".