Amplification of the index of refraction of aqueous immersion fluids with crown ethers: a progress report
Bibliographic record
Abstract
There is a current need for high refractive index (RI) materials that can be used in aqueous systems for improving 193 nm immersion photolighography. Although heavy metal salts such as Ca2+ and Ba2+ have the potential to substantially increase the RI of aqueous solutions, the water solubility of these salts with common anions is often too low to achieve concentrations that significantly increase the RI to the desired values. We have therefore investigated the use of crown ethers to enhance the solubility of these cations. Most of the crown ethers are soluble in water, environmentally benign and commercial and inexpensive materials. Details of the preliminary studies on the proposed model system are presented in this paper. 15-crown-5-ether and 12-crown-4-ether are liquids at room temperature and therefore can be used as neat liquids as immersion fluids without dilution in water. Saturation of crown ethers with inorganic salts do not lead to any increase of the refractive index due to the low solubility of those in such an apolar media. Thus, the use of inorganic salt as refractive index enhancement agent does not seem to be a desirable proposition in the present case. Instead, the use of crown ethers or their derivates can be alternative system since these compounds have properties, such as density, viscosity and boiling point, similar to aqueous media.
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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 | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".