Corn cob pyrolysis: A systematic literature review of methods and applications
Bibliographic record
Abstract
Abstractas The agricultural sector is experiencing a surge in waste generation due to population growth, creating an urgent need to convert byproducts into value‐added products. Maize (Zea mays L.), a leading global crop, produces significant byproducts, such as corn cob, which are often undervalued. Biomass, including corn cob, is a promising carbon‐neutral energy source. While combustion is commonly used, pyrolysis is gaining traction due to its ability to generate solid (biochar), liquid (bio‐oil), and gaseous products, each with various applications. This study presents a systematic literature review on the pyrolysis of corn cob, utilizing three databases: Scopus, Web of Science, and Springer. This search resulted in a portfolio composed of 409 research papers. The Normalized Index for Ranking Papers (NIRP) method was employed to rank the selected papers, followed by bibliometric and systematic analyses. The review identifies key trends, publication dynamics, and influential works in the field. It highlights China's leading role in research output and citation impact and provides insights into the evolution of research topics and methodologies. The review emphasizes the significance of biomass pyrolysis as a viable alternative to fossil fuels, commercial adsorbents, catalyst supports, and fertilizers, providing both economic and environmental benefits. By examining the current state of research, this work seeks to inform future studies and promote the advancement of efficient and eco‐friendly waste conversion technologies. The findings aim to encourage further exploration of biomass pyrolysis, highlighting its potential to contribute to sustainable resource management and reduce reliance on non‐renewable resources.
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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.030 | 0.029 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".