Ultra‐Strong and Tough Bio‐Based Polyester Elastomer with Excellent Photothermal Shape Memory Effect and Degradation Performance
Bibliographic record
Abstract
Abstract The exploitation of bio‐based materials derived from renewable resources represents a pivotal strategic approach in addressing environmental pollution and alleviating the scarcity of fossil resources. 2,5‐Furandicarboxylic acid (FDCA) is the most potential substitute for terephthalic acid. Lignin is the most abundant aromatic biomass resource. However, the preparation of high‐performance lignin/FDCA‐based bio‐polyesters remains a formidable challenge. Herein, a multifunctional lignin‐modified polyester elastomer (LFPEe) is designed using FDCA‐based polyester oligomer (PPeF) and lignin (AOH) as building blocks. The LFPEe exhibits superior mechanical properties with the optimum tensile strength, fracture strain, and elastic recovery ratio up to 58.9 MPa, 610% and 88.9%, respectively, attributing to the formation of dual cross‐linking network with nanophase separation structure. Furthermore, leveraging the inherent characteristics of lignin, the LFPEe demonstrates excellent light‐controlled shape memory and excellent UV shielding performance. This innovative work not only breaks the performance dependence of FDCA‐based polyester on high molecular weight but also highlights a novel paradigm for value‐added utilization of lignin in sustainable bio‐polyesters.
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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) | 0.002 | 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".