Exploring the microwave absorption properties of Morchella Esculenta: A sustainable biomaterial for electromagnetic interference shielding
Bibliographic record
Abstract
The pursuit of sustainable and efficient materials for microwave absorption has gained significant momentum in recent years. This research article investigates the microwave absorption properties of the Himalayan mushroom Morchella Esculenta as a sustainable biomass material for electromagnetic interference (EMI) shielding applications. The study encompasses morphological, optical, and dielectric characterizations of Morchella Esculenta powder in the frequency range of 2–18 GHz. Morphological analysis reveals the presence of plate-like and wire-like structures along with voids, which contribute to polarization and conductive losses, enhancing microwave absorption. Dielectric measurements indicate significant polarization effects and conductive losses, with a maximum reflection loss of − 27.37 dB at 13.78 GHz for a 7 mm thick sample and an effective absorption bandwidth of 8.15 GHz. The material’s amorphous nature, functional groups, and a low optical band gap of 2 eV further support its microwave absorption capabilities. Simulation studies using CST Microwave Studio confirm near-perfect electromagnetic wave absorption at optimal thickness. The findings demonstrate that Morchella Esculenta, without any chemical or physical treatment, exhibits promising microwave absorption performance, highlighting its potential as an eco-friendly and efficient material for EMI shielding and related technological applications.
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.
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) | 0.000 | 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 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".