Valorization of date palm residues for biochar production: Assessing biochar characteristics for agricultural application
Bibliographic record
Abstract
Date palm residues are abundant in many arid and semi-arid regions, presenting both an environmental challenge and an opportunity for sustainable resource management. This study evaluates the valorization of date palm residues through slow pyrolysis at 500 °C to produce biochar suitable for agricultural soil enhancement. The date palm residues, which consist of 41.95% cellulose, 28.49% hemicellulose, and 26.56% lignin, were processed to yield biochar with a production efficiency of 44.95%. The biochar's physicochemical properties were extensively analyzed, showing a fixed carbon content of 70.74%, a pH of 9.19, and a cation exchange capacity (CEC) of 68.05 cmol/kg. Elemental analysis revealed high carbon (71.9%) and low nitrogen (0.74%) content, indicating its stability and potential for long-term carbon sequestration. Thermogravimetric analysis (TGA) indicated thermal stability, while scanning electron microscopy (SEM) revealed a highly porous structure, beneficial for water retention and microbial colonization. These findings demonstrate that the slow pyrolysis process yields biochar with favorable properties, making it a promising amendment for soil fertilization. This study highlights the potential of biochar production in transforming date palm waste into valuable resources while mitigating the environmental impacts and costs associated with its disposal.
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 | 0.001 | 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".