Synthesis and thermo-oxidative kinetic analysis of cellulose microfibers from palm leaves using ammonia fiber expansion
Bibliographic record
Abstract
Global urbanization is driving high volumes of agricultural and food waste, creating an urgent need for sustainable and effective technologies to convert biomass into valuable products. This study explores the conversion of palm waste into cellulose microfibers (CMF) using Ammonia Fiber Expansion (AFEX) followed by acid hydrolysis, with a focus on structural characterization, thermal stability, and reaction kinetics compared to raw material. The resulting CMF exhibited elongated, uniform fibers with smooth surfaces, with lengths of 0.1–3.0 mm, and diameters of 5–20 μm. X-ray analysis revealed a significant increase in the carbon/oxygen ratio, from 1.8 ± 0.2 in raw palm leaves to 2.7 ± 0.3 in CMF, indicating enhanced carbon content due to dehydration and reduction of carbonyl groups. FTIR spectra confirmed effective removal of lignin and hemicellulose after treatment, further supporting this chemical transformation. Thermal analysis demonstrated that CMF possesses higher heat content than raw leaves, suggesting its potential for energy-related applications. TGA showed that CMF decomposes at slightly higher temperatures, indicating improved thermal stability. Isoconversional kinetic analysis using the Vyazovkin Nonlinear (NLN) and Kissinger-Akahira-Sunose (KAS) methods revealed variable effective activation energies ( E α ), consistent with a complex degradation mechanism. Overall, CMF displayed lower E α values than raw biomass, especially at early and mid-reaction stages. Kinetic modeling at 50% conversion showed a markedly higher pre-exponential factor (A α ) for raw leaves (2.8 × 10¹³ s⁻¹) compared to CMF (7.4 × 10⁹ s⁻¹), reflecting structural alterations from treatment. Both raw and CMF samples exhibited negative activation entropy (Δ S ≠ ) values of − 237.7 and − 240.3 J mol⁻¹ K⁻¹, respectively, suggesting greater molecular order in activated complexes. The enthalpy of activation (Δ H ≠ ) was 149.7 ± 3.9 kJ mol⁻¹ for raw leaves versus 120.4 ± 3.9 kJ mol⁻¹ for CMF, Gibbs free energy of activation (ΔG ≠ ) was slightly higher for raw leaves (297.0 ± 3.9 kJ mol⁻¹) compared to CMF (269.4 ± 3.9 kJ mol⁻¹), primarily due to differences in Δ H ≠ . These kinetic parameters are crucial for any future implementation of palm leaves conversion into CMF at the industrial scale.
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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) | 0.000 | 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) | 0.000 | 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".