Optical tests of nanoengineered liquid mirrors
Bibliographic record
Abstract
We describe a new technology for the fabrication of inexpensive high-quality mirrors. We begin by chemically producing a large number of metallic nanoparticles coated with organic ligands. The partides are then spread on a liquid substrate where they self-assemble to give optical quality reflective surfaces. Since liquid surface can be modified by a variety of means (e.g., rotation, electromagnetic fields), this opens the possibility of making a new class of versatile and inexpensive optical elements that can have complex shapes and that can be modified within short time scales. Interferommetric measurements show optical quality surfaces. We have obtained reflectivity curves that show 80% peak reflectivities. We are confident that we can improve the reflectivity curves because theoretical models predict higher values. We expect nanoengineered liquid mirrors to be useful for scientific and engineering applications. The technology is interesting for large optics, such as large rotating parabolic mirrors, because of its low cost. Furthermore, because the surfaces of of ferrofluids can be shaped with magnetic fields, one can generate complex, time-varying surfaces that are difficult to make with conventional techniques.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".