Carbon-Polyol Coating Using Carbon Produced From Palm Kernel Cake (PKC)
Bibliographic record
Abstract
In our quest to create awareness in using renewable, sustainable natural resources and efficient waste management, palm kernel cake (PKC) which is the waste from the palm industries was used in preparation of Carbon-polyol coating. PKC was subjected to pyrolisis process and the carbon residue obtained was used as a black pigment. In this work modified Polyol was used as the binder for its ideal properties as vehicle to produce good opacity of paint. Various carbon-polyol dispersant with different weight compositions (wt%) of carbon prepared and tested against its respective rheological properties in order to determine ideal paint/ink system. Two different types of paper material (Brown paper B and white paper W) were chosen as a substrate and characterised. Each of the paper was then proofed with Carbon-polyol using palm kernel carbon (PKC) and commercial carbon (PURE_C). Lightfastness test was carried out on the paper specimens and the results on the total colour difference (dE) are obtained. It was found that the total colour change (dE) in specimens using brown paper (B) coated with Carbon polyol coating using carbon derived from palm kernel carbon (PKC_B) and the commercial carbon (PURE_C_B) is within 10%. The other two specimens, using white paper (PKC_W and PURE_C_W), the total colour change (dE) is 17%. It is expected that the coating system has the potential application in paint or ink. Key words: Carbon-Polyol; Palm Kernal Cake (PKC); Colourant; Lightfast; Coating
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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".