New Approach to Shape Memory Polymer Composite Production Using Alkaline Lignin-Reinforced Epoxy-Based Shape Memory Polymers
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide In the past few decades, there has been continued interest in shape memory polymers (SMPs), and tremendous efforts have been made to develop multifunctional composites of these SMPs to enhance the existing properties of SMPs. Although fossil-based sources are widely used in the production of shape memory polymer composites (SMPCs), the depletion of fossil-based resources and associated environmental problems increase interest toward renewable biobased products synthesized from natural resources. This study aims to produce alkaline lignin-reinforced SMPCs by using alkaline lignin in the SMP matrix. Thermo-mechanical, morphological, and shape memory tests are performed in order to reveal the effect of alkaline lignin usage in the SMP matrix on SMPC production. Differential scanning calorimetry analysis results show that adding alkaline lignin into the SMP matrix with 1 and 3% ratios led to an increase in T g values, while raising the alkaline lignin ratio to 5% decreased the T g value. According to the DMA results, increasing the alkaline lignin ratios caused an increase in the storage modulus of SMPCs, and the best storage modulus value was obtained at the 5% alkaline lignin ratio. The results of the three-point bending test also confirmed the results obtained from the DMA analysis, showing that an increasing alkaline lignin ratio caused an increase in the bending modulus. Scanning electron microscopy analysis showed a rough structure in 1 and 3% alkaline lignin supplementation, while a smoother structure was observed in 5% alkaline lignin supplementation. The smoother structure of the sample containing 5% alkaline lignin indicates that alkaline lignin supplementation exhibits a smoother surface by showing a plasticizing effect. As a result, it was observed that increasing the lignin ratio increased the polymer/alkaline lignin interaction, resulting in a harder structure and an increase in the flexural modulus value.
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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 | 0.000 | 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 | 0.001 |
| 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".