Evidence map›Paper›PMID 36556644›Full record

ArticleMaterials (Basel, Switzerland)2022

Temperature and Infill Density Effects on Thermal, Mechanical and Shape Memory Properties of Polylactic Acid/Poly(ε-caprolactone) Blends for 4D Printing.

Ang Li, Xin-Gang Chen, Lan-Ying Zhang, Yang-Fei Zhang

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ang LiSchool of Materials Science and Engineering, Peking University, Beijing 100871, China.
Xin-Gang ChenSchool of Materials Science and Engineering, Peking University, Beijing 100871, China.
Lan-Ying ZhangSchool of Materials Science and Engineering, Peking University, Beijing 100871, China.ORCID 0000-0003-1994-8531
Yang-Fei ZhangSchool of Materials Science and Engineering, Peking University, Beijing 100871, China.ORCID 0000-0001-6571-9946

Funding

Ministry of Education 6141A02033206
6 · The paper itself

Abstract

Polylactic acid (PLA)/poly(ε-caprolactone) (PCL) blends have exhibited good shape memory properties and degradable characteristics in various 4D printing fields such as biomedicine, flexible electronics, and soft robotics, where the service temperature fluctuates easily by environment temperature and polymer characteristics. In this work, printed PLA/PCL 4D samples with different infill densities were prepared by material extrusion printing of pre-extruded filaments and characterized under different temperatures. The results show that the microstructures of printed samples are not influenced by printing process and have similar unique orientation as that of filaments. The thermal properties are stable and show obvious phase transition temperatures, while the mechanical properties decrease slightly in low temperature region and then decrease rapidly when temperature is over 60 °C. The increase in infill density can further improve the storage modulus more than 40% and have no significant influence on the thermal properties. The printed samples also exhibit good shape memory performances with fast recovery speeds less than 22 s. Furthermore, a two-step model is provided to predict the effective modulus of printed PLA/PCL samples and agrees well with experimental data. The results prove that temperature and infill density have different influences on the thermal, mechanical and shape memory properties of PLA/PCL blends.

Indexed as

4D printingmodelpolylactic acidpoly(ε-caprolactone)shape memory

Identifiers

PMID36556644
PMCPMC9783479

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.