Global insights into biomass pyrolysis mechanisms: A scientometric and mechanistic approach
Bibliographic record
Abstract
• Research on biomass pyrolysis mechanism grew rapidly since 2019. • Leading contributors to biomass pyrolysis mechanism were identified. • Five thematic clusters were identified through keyword analysis. • Biomass components exhibit different thermal decomposition patterns. This review provides a comprehensive analysis of biomass pyrolysis by combining scientometric evaluation with mechanistic insight. The scientometric analysis, based on 174 articles retrieved from Scopus database, traced the evolution of biomass pyrolysis mechanism research from 1989, with significant growth observed from 2019 onwards. China, United States, and United Kingdom emerged as leading contributors in publication output, while China, United States, and Italy led in citation impact. Influential researchers such as Chen Hanping and Yang Haiping, along with key journals including Journal of Analytical and Applied Pyrolysis, Fuel , and Energy & Fuels , have significantly shaped the field. Keyword co-occurrence analysis identified five major research themes: thermal decomposition and analytical techniques; co-pyrolysis and synergistic effects; catalytic pyrolysis and product analysis; component chemistry in biomass conversion; and reactor design and performance. The mechanistic analysis focused on the distinct thermal degradation behaviors of all the six biomass components, which underwent characteristic reactions such as dehydration, depolymerization, and decarboxylation, influencing the yield of pyrolysis products. By combining scientometric trends with mechanistic understanding, this study clarifies research evolution, key contributors, dominant themes, and reaction mechanisms in biomass pyrolysis. This review offers valuable guidance for researchers, industries, and policymakers working toward efficient biomass conversion, sustainable energy production, and environmental management.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.079 | 0.097 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".