Dielectric characterization of white birch–activated biochar composites: A sustainable alternative to radar-absorbing materials
Bibliographic record
Abstract
Sustainability can be defined as the ability of meet presents needs without compromise the ability of future generations to find their own needs. In this matter, scientists have been warning about natural resource scarcity, and now several researchers are aiming their efforts to develop sustainable technologies. Here, we focus on finding novel uses to biochar, a carbon rich material made from biomass that is usually applied on soil amendment. To expand its applications, biochars were activated using two different methods: a physical activation using CO 2 ; and a chemical one using KOH. We investigate here the dielectric properties of composites made with both activated biochars from white birch, where composites were made using silicone rubber matrix. It was also investigated the influence of composites made with biochar in powder and in flakes. All samples were characterized over the X-band frequency range. Composites made with powder presented a linear permittivity over the frequency range, while composites made with flakes presented oscillations on different frequencies. These oscillations are related to the composite surface, which induces reflection effects because of flakes arrangement in the top surface. Such effects widen opportunities to engineer new materials to be explored as radar-absorbing materials.
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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".