Substructure imaging of heterogeneous nanomaterials with enhanced refractive index contrast by using a functionalized tip in photoinduced force microscopy
Bibliographic record
Abstract
The opto-mechanical force response from light-illuminated nanoscale materials has been exploited in many tip-based imaging applications to characterize various heterogeneous nanostructures. Such a force can have two origins: thermal expansion and induced dipoles. The thermal expansion reflects the absorption of the material, which enables one to chemically characterize a material at the absorption resonance. The induced dipole interaction reflects the local refractive indices of the material underneath the tip, which is useful to characterize a material in the spectral region where no absorption resonance occurs, as in the infrared (IR)-inactive region. Unfortunately, the dipole force is relatively small, and the contrast is rarely discernible for most organic materials and biomaterials, which only show a small difference in refractive indices for their components. In this letter, we demonstrate that refractive index contrast can be greatly enhanced with the assistance of a functionalized tip. With the enhanced contrast, we can visualize the substructure of heterogeneous biomaterials, such as a polyacrylonitrile-nanocrystalline cellulose (PAN-NCC) nanofiber. From substructural visualization, we address the issue of the tensile strength of PAN-NCC fibers fabricated by several different mixing methods. Our understanding from the present study will open up a new opportunity to provide enhanced sensitivity for substructure mapping of nanobiomaterials, as well as local field mapping of photonic devices, such as surface polaritons on semiconductors, metals and van der Waals materials.
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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 | 0.001 | 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".