Surface Characterization and Classification of Slow and Fast Pyrolyzed Biochar Using Novel Methods of Pycnometry and Hyperspectral Imaging
Bibliographic record
Abstract
The birchwood biochar produced by slow as well as fast pyrolysis was analyzed and compared according to physical characteristics of porosity and reflectance. A relation between char porosity and the reflectance of the biochar structure was found wherein porosity was found to be inversely proportional to reflectance. The wavelengths providing images with maximum clarity were established for classification through hyperspectral imaging technique. The wavelengths that were found to be optimum for both slow and fast pyrolysis biochars were 947 nm and 1685 nm, which fall in the near-IR and short-IR wavelength ranges, respectively. The results of the hyperspectral imaging support the findings of the porosity evaluations from pycnometric analysis, which showed that the biochar sample treated at 350°C for slow pyrolysis and 400°C for fast pyrolysis for a holding time of 20 min had the highest porosity and in turn showed the lowest reflectance mean values. The theory that reflectance of biochar samples decrease with increasing porosity of the char structure was substantiated through this qualitative study.
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 | 0.001 | 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".