Insight on the Ultrastructure, Physicochemical, Thermal Characteristics and Applications of Palm Kernel Shells
Bibliographic record
Abstract
The ultrastructure and physicochemical and thermal properties of Palm Kernel Shells (PKS) in comparison with Coconut Kernel Shells (CKS) were investigated herein. Powder samples were prepared and characterized using Surface Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM). Chemical and elemental constituents, as well as thermal performance were assessed by Van Soest Method, TEM/EDXA and SEM/EDS techniques. Differential Scanning Calorimetry (DSC) and Thermogravimetric Analysis (TGA) were also performed for thermal characterization. SEM/EDS and TEM/EDXA revealed that most of the PKS and CKS materials are composed of particles with irregular morphology; these are mainly amorphous phases of carbon/oxygen with small amounts of K, Ca and Mg. The DSC data permitted to derive the materials’ thermal transition phases and the relevant characteristic temperatures and physical properties. Thermal Transition phases of PKS observed herein are consistent with the chemical composition obtained and are similar to those of CKS. Nonetheless, TGA/DTG showed that the combustion characteristics of PKS are higher than those of CKS. Taken together, our results reveal that PKS have nanopores and can be efficiently used for 3D printing and membrane filtration applications. Moreover, the chemical constituents found in PKS samples are in agreement with those reported in the literature for material structural applications and thus, present potential use of PKS in these applications.
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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) | 0.001 | 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".