<title>Optically induced mass transport generated in near fields</title>
Bibliographic record
Abstract
In the last few years a range of techniques for opto-mechanical manipulations of organic films and small structures has been developed and significantly improved. Among these techniques a very promising candidate turned out to be the optically induced mass transport. Not only that the physical mechanisms underlying this phenomenon is not yet been fully understood, but in addition, the lateral dimensions of structures created in that way have been limited by the used light wavelength. In order to gain deeper insight into the physical fundamentals of this phenomenon and to open possibilities for applications (lithography, data storage, manipulation of molecules, ...) it is necessary to create and study reproducible, sharply defined single structures not only in a macroscopic but also in nanometer range. SNOM (Scaning Nearfield Optical Microscopy) seemed to us an intriguing method to approach this goal. We report here novel experimental results about the generation of ultra-small structures by optically driven mass transport. We have investigated different ways to generate localized mass transport in azobenzene-containing films by using focused light in far and nearfields. Thus, the dimensions of optically created structures range to 5 μm (lens focusing) and even down to 100 nm (SNOM nearfield). These experiments offer new expectations to manipulate ultra small objects on surfaces by optical means without mechanically touching them.
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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) | 0.005 | 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".