The conversion of chicken manure to biooil by fast pyrolysis I. Analyses of chicken manure, biooils and char by<sup>13</sup>C and<sup>1</sup>H NMR and FTIR spectrophotometry
Bibliographic record
Abstract
Fast pyrolysis of chicken manure produced two biooils (Fractions I and II) and a residual char. All four materials were analyzed by chemical methods, 13C and 1H Nuclear Magnetic Resonance Spectrometry (13C and 1H NMR), and Fourier Transform Infrared Spectrosphotometry (FTIR). The char showed the highest C content and the highest aromaticity. Of the two biooils Fraction II was higher in C, yield and calorific value but lower in N than Fraction I. The S and ash content of the two biooil fractions were low. The Cross Polarization Magic Angle Spinning (CP-MAS) 13C NMR spectrum of the initial chicken manure showed it to be rich in cellulose, which was a major component of sawdust used as bedding material. Nuclear Magnetic Resonance (NMR) spectra of the two biooils indicated that Fraction I was less aromatic than Fraction II. Among the aromatics in the two biooils, we were able to tentatively identify N-heterocyclics like indoles, pyridines, and pyrazines. FTIR spectra were generally in agreement with the NMR data. FTIR spectra of both biooils showed the presence of both primary and secondary amides and primary amines as well as N-heterocyclics such as pyridines, quinolines, and pyrimidines. The FTIR spectrum of the char resembled that of the initial chicken manure except that the concentration of carbohydrates was lower.
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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) | 0.001 | 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".