Ultralow Broadband Reflectivity in Black Silicon via Synergy between Hierarchical Texture and Specific‐Size Au Nanoparticles
Bibliographic record
Abstract
Abstract Antireflectivity is one of the critical factors defining the performance of black silicon in optical, photothermal, photochemical, and optoelectronic applications. The photonic applications under visible light illumination are commonly tuned through surface texturing; however, their promising performance under longer wavelength (>1100 nm) requires either intrinsic lattice modifications or additional substance enhancement. Recent advances in microfabrication and material engineering have enabled in‐depth exploration into the synergy between surface texturing and material reinforcement. In this study, black silicon with novel chimney‐like hierarchical micro/nanostructures is fabricated via two‐step reactive ion etching, and subsequently gold nanoparticles (Au NPs) are loaded on the black silicon by magnetron sputtering deposition. The micro/nanostructures result in synergy effect with the Au NPs on suppression of light reflection. An ultralow broadband reflection (<1%, wavelength 220–2600 nm) is achieved from the Au‐loaded black silicon substrates. The impact of Au NPs and structural design such as size, spacing, shape, and etching duration on antireflectivity of black silicon is investigated. This study opens up new avenue for high‐efficiency applications of black silicon in the fields of sustainable energy and photonics/microelectronics.
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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) | 0.001 | 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".