Deposition and characterization of gold black coatings for thermal infrared detectors
Bibliographic record
Abstract
High absorptivity and low thermal mass are two important requirements for coatings applied to thermal infrared detectors. Gold black coatings are very good candidates to ensure these characteristics in the broadband infrared spectral range. A specific deposition system was designed and built at INO in order to provide gold-black coatings for different broadband detection applications including the broadband radiometer (BBR) instrument for the European Space Agency (ESA) EarthCARE satellite. A parametric study targeting uniform optical absorptance within the spectral range from 0.2 μm to 50 μm was conducted. Specular reflectance lower than 10% was obtained for extended wavelength range up to 100 μm. The coating thickness ranges typically between 20 μm and 35 μm, with uniformity of about ± 3 μm over a sample surface of 10x10 mm2. The deposit density was typically ~0.3% of the bulk density of gold. To singulate the blackened infrared detector pixels, a laser micromachining process was developed. The setup exhibits a 1μm positioning accuracy and allows for ablation of 3 μm to 12 μm wide channels through the gold-black thickness, while preserving the pixel and gold-black deposit integrity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".