Optimizing Activation Temperature of Sustainable Porous Materials Derived from Forestry Residues: Applications in Radar-Absorbing Technologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Biochar, a carbon-rich material derived from the thermochemical conversion of biomass under oxygen-free conditions, has emerged as a sustainable resource for radar-absorbing technologies. This study explores the production of activated biochars from end-of-life wood panels using a scalable and sustainable physical activation method with CO2 at different temperatures, avoiding the extensive use of corrosive chemicals and complex procedures associated with chemical or vacuum activation. Compared to conventional chemically or vacuum-activated biochars, the physically activated biochar demonstrated competitive performance while minimizing environmental impact, operational complexity, and energy consumption. Furthermore, activation at 750°C reduces energy consumption by 14% and 28% compared to activations at 850°C and 950°C, respectively, emphasizing the cost-effectiveness of this method for large-scale applications. The composite with 15% of biochar embedded in silicon rubber presented good electromagnetic performance, achieving a measured reflection loss (RL) of −37.2 dB at 11.3 GHz with an 8.4 mm thickness and an effective absorption bandwidth (EAB) of 1.25 GHz. These results highlight the potential of biochar-silicone rubber composites as flexible radar-absorbing materials (RAMs) for applications in electromagnetic shielding, anechoic chambers, and Internet of Things (IoT) devices. This study also shows the importance of forestry residues as sustainable precursors for producing low-cost porous carbon materials, aligning with circular economy principles and the United Nations’ 2030 Agenda for Sustainable Development. This work establishes a framework for scalable, cost-effective, and sustainable biochar production, addressing critical challenges in electromagnetic interference (EMI) mitigation and advancing the global adoption of green technologies.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it