Super‐Black Material Created by Plasma Etching Wood
Bibliographic record
Abstract
Abstract A super‐black wood with low reflectivity in the UV/Vis range is created by plasma modifying basswood surfaces. Here the super‐black wood is characterized, the process used to make it is described and its possible practical uses are discussed. Wood samples are exposed to oxygen glow‐discharge plasma. Transverse surfaces exposed to high‐energy plasma have a deep‐black velvety appearance. The reflectance of these surfaces is measured and compared with those of commercial super‐black materials. The reflectivity of samples over a narrower wavelength range is measured with a spectrophotometer and converted into lightness values. The microstructure and surface chemistry of super‐black wood are examined using SEM/X‐ray micro‐CT and FTIR spectroscopy, respectively. Transverse basswood samples modified with high‐energy plasma have reflectivity averaging 0.68% (300–700 nm). The super‐black color of plasma‐modified wood is retained when it is coated with gold/vanadium alloy indicating structural coloration. Plasma creates a low density, lignin‐enriched surface with deep pits, columns and tangled fibrils; features also found in synthetic super‐black materials. In conclusion, this method of creating a super‐black material by plasma‐modification of basswood does not require a lithography pre‐step, generates no liquid waste and, as is demonstrated here, can be used to prototype luxury consumer products.
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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) | 0.002 | 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".