A sustainable thermochemical conversion of animal biomass to N-heterocycles
Bibliographic record
Abstract
Abstract The production of high-valued organonitrogen chemicals, especially N-heterocycles, requires artificial N 2 fixation accompanied by the consumption of fossil resources. To avoid the use of these energy- and resource-intensive processes, we develop a sustainable strategy to convert nitrogen-rich animal biomass into N-heterocycles through a thermochemical conversion process (TCP) under atmospheric pressure. A high percentage of N-heterocycles (87.51%) were obtained after the TCP of bovine skin due to the abundance of nitrogen-containing amino acids (e.g., glycine, proline, and l -hydroxyproline). Animal biomass with more diverse amino acid composition (e.g., muscles) yielded higher concentrations of amines/amides and nitriles after TCP. In addition, by introducing catalysts (KOH for pyrrole and Al 2 O 3 for cyclo-Gly–Pro) to TCP, the production quantities of pyrrole and cyclo-Gly–Pro increased to 30.79 mg g −1 and 38.88 mg g −1 , respectively. This approach can be used to convert the significant animal biomass waste generated annually from animal culls into valued organonitrogen chemicals while circumventing NH 3 -dependent and petrochemical-dependent synthesis routes. Graphical Abstract
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) | 0.002 | 0.001 |
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".