Integrasi Filler Berbasis Limbah Pertanian terhadap Sifat Material Bioplastik Polylactic Acid (PLA): Tinjauan Literatur Terstruktur
Bibliographic record
Abstract
Krisis lingkungan akibat akumulasi plastik berbasis minyak bumi mendorong pengembangan material alternatif yang lebih ramah lingkungan. Polylactic Acid (PLA), sebagai bioplastik biodegradable dari sumber terbarukan, menjanjikan solusi pengganti plastik konvensional, namun sifatnya yang rapuh dan produksinya yang mahal membatasi penggunaannya. Kajian ini bertujuan untuk menganalisis pengaruh penambahan filler berbasis limbah pertanian terhadap peningkatan sifat fisik dan mekanik PLA. Studi ini merupakan tinjauan literatur terstruktur dengan menganalisis 32 artikel ilmiah dari 2017–2025. Hasil kajian menunjukkan bahwa filler dari limbah seperti sekam padi (5% berat) dapat meningkatkan tensile strength hingga 55%, yield stress sebesar 88%, dan menurunkan laju transmisi oksigen (OTR) hingga 52%. Kulit delima (15% berat) memperkuat komposit dengan peningkatan modulus tarik sebesar 42% dan impact strength sebesar 41%. Biochar dari biomassa karbonisasi juga meningkatkan kekakuan dan stabilitas termal hingga suhu degradasi awal naik 15–18°C. Sebaliknya, filler dari tapioka dan cangkang telur menunjukkan penurunan kekuatan mekanik pada konsentrasi tinggi (>20–30%). Pemanfaatan limbah pertanian sebagai filler dalam PLA berpotensi meningkatkan performa material sekaligus mendukung ekonomi sirkular. Namun, keberhasilannya sangat tergantung pada jenis filler, komposisi, dan kesesuaian interaksi antarfasa. Kajian lanjutan diperlukan untuk mengoptimalkan formulasi komposit.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".