The Development of Poly(lactic acid) (PLA)-Based Blends and Modification Strategies: Methods of Improving Key Properties towards Technical Applications—Review
Bibliographic record
Abstract
The widespread use of poly(lactic acid) (PLA) from packaging to engineering applications seems to follow the current global trend. The development of high-performance PLA-based blends has led to the commercial introduction of various PLA-based resins with excellent thermomechanical properties. The reason for this is the progress in the field of major PLA limitations such as low thermal resistance and poor impact strength. The main purpose of using biobased polymers in polymer blends is to increase the share of renewable raw materials in the final product rather than its possible biodegradation. However, in the case of engineering applications, the focus is on achieving the required properties rather than maximizing the percentage of biopolymer. The presented review article discusses the current strategies to optimize the balance of the key features such as stiffness, toughness, and heat resistance of PLA-based blends. Improving of these properties requires molecular structural changes, which together with morphology, crystallinity, and the influence of the processing conditions are the main subjects of this article. The latest research in this field clearly indicates the high potential of using PLA-based materials in highly demanding applications. In the case of impact strength modification, it is possible to obtain values close to 800 J/m, which is a value comparable to polycarbonate. Significant improvement can also be confirmed for thermal resistance results, where heat deflection temperatures for selected types of PLA blends can reach even 130 °C after modification. The modification strategies discussed in this article confirm that a properly conducted process of selecting the blend components and the conditions of the processing technique allows for revealing the potential of PLA as an engineering plastic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".