Energetic characterization and flash pyrolysis of different elephant grass cultivars ( <scp> <i>Pennisetum purpureum</i> Schum </scp> .)
Bibliographic record
Abstract
Abstract Limited fossil fuel reserves combined with greenhouse gas intensification due to CO 2 emissions has encouraged research in renewable fuels. In this work, a flash pyrolysis study of elephant grass cultivars— Pennisetum purpureum Schum cultivar Mott (MEG), P. purpureum Schum cultivar Roxo (REG), and P. purpureum Schum cultivar Capiaçu (CEG)—was carried out. The biomasses were evaluated in terms of energy characterization by proximate and ultimate analysis, thermogravimetric analyses (TG/DTG), X‐ray diffraction (XRD), Fourier transform infrared (FTIR), and analytical pyrolysis (Py‐GC/MS) at 600°C. The characterization results showed that these biomasses have potential for energy applications and to produce valuable chemicals. The pyrolysis products produced were mainly oxygenated, including short‐chain organic acids (C 1 ‐C 4 ), furans, esters, aldehydes, ketone, and phenols. Although the obtained results were similar for the three biomasses with small variations in the yields of the pyrolysis products, the study reveals an important difference in terms of energy density. CEG was proven as the most promising elephant grass cultivar to be applied for fast pyrolysis to obtain bio‐oil due to its higher dry matter production (2115 t km −2 ), power generated (9529 MWh km −2 ), HHV (16.22 MJ kg −1 ), lower ash content (6.75%), higher volatile content (74.84%), and higher carbon content (42.57%).
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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".