Waste to a Value-Added Material: Production of Biochar from Young Coconut Waste
Bibliographic record
Abstract
Young coconut waste (YCW) holds considerable potential as a lignocellulosic biomass feedstock for sustainable biochar production.Indonesia has substantial potential for utilizing young coconut biomass waste, yet its use to date remains largely confined to small-scale and specific applications.This study aims to investigate the thermal decomposition behavior, functional group transformations, surface characteristics, and adsorption potential of biochar derived from YCW through pyrolysis at 300, 350, and 400℃, temperatures selected to capture the transition between initial devolatilization and the onset of aromatic structure formation.TG-DTG analysis was conducted to assess thermal stability within the temperature range of 25-700℃ at heating rates of 10, 15, and 20℃/min, while FTIR spectroscopy and nitrogen adsorption-desorption isotherms were employed to characterize chemical functional groups and SBET, respectively.TG-DTG curves showed that the heating rate significantly affected the thermal stability of YCW, with a heating rate of 10℃/min resulting in more controlled decomposition and a higher biochar yield.FTIR spectroscopy analysis indicated the degradation of C=O, -OH, and C-H groups, along with the formation of aromatic C=C bonds, particularly at 350℃.Biochar produced at 350℃ exhibited the most favorable pore development and surface chemistry.The highest BET surface area was recorded for YCW350 (1.298 m² /g), followed by YCW300 (1.266 m² /g), with a substantial decrease observed for YCW400 (0.127 m² /g) due to pore collapse.These findings provide an initial physicochemical characterization of YCW biochar, with enhanced thermal stability and chemical reactivity, offering potential applications in energy systems and agricultural waste utilization.
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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 | 0.001 | 0.001 |
| 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".