Wood Reconfiguration Enables Broadband Blackbody in Large‐Area, Modular, Optically Welded Carbon Constructs
Bibliographic record
Abstract
Abstract A broadband blackbody requires both perfect anti‐reflective characteristics and effective light entrapment spanning wavelengths from the mid‐infrared (MIR) to the ultraviolet (UV) range. This ideal combination has not been achieved in several artificial superblack systems or even in naturally occurring superblack structures. A broadband blackbody is created by carbonizing delignified wood infused with lignin particles (LPs), forming a reconfigured wood (cRW) system. The LPs enhance the dimensional fidelity of cRW and promote the development of sparse, highly aligned fibrillar microstructures, achieving super‐absorbance levels spanning from the MIR to the UV wavelengths, reaching over 99.8% absorption. This performance is further amplified in large‐area light traps constructed from tiled cRW, which are optically welded, modular and customizable in size and shape. The tiled cRW configuration effectively eliminates thermal ghost reflections and outperforms individual cRW units. This system is demonstrated as a perfect broadband blackbody, which can act as promising reference infrared radiator in IR thermography that benefit from precise sensor calibration. Altogether, this optically welded superabsorber trap introduces a wood‐based solution for broadband blackbody materials, opening new opportunities across diverse applications.
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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".