Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 272

The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.

Selective Enzymatic Degradations of Poly(l-lactide) and Poly(ε-caprolactone) Blend Films

Liu, Lijian, Li, Suming, Garreau, Henri, Vert, Michel·2000·Biomacromolecules

Abstract

Solution cast films were prepared from poly(L-lactide) (PLLA) and poly(epsilon-caprolactone) (PCL) as well as from three blends, namely B75, B50, and B25 with PLLA/PCL proportions of 75/25, 50/50, and 25/75, respectively. The enzymatic degradation of square samples (10 x 10 x 0.2 mm) cut from the films was investigated at 37 degrees C in a pH = 8.6 Tris buffer containing proteinase K or in a pH = 7.0 phosphate buffer containing Pseudomonas lipase. It was confirmed that proteinase K can degrade amorphous domains of PLLA, but cannot degrade crystalline PLLA or PCL. In contrast, Pseudomonas lipase can degrade both amorphous and crystalline PCL but cannot degrade PLLA. The two faces of solution cast films showed different morphologies due to the solvent evaporation process. The lower face appeared more crystalline than the upper face because of the plasticizing effect of solvent entrapped inside which allowed crystallization to proceed. Therefore, the lower face was more resistant to enzymatic attack by proteinase K in the cases of PLLA and the blends. The two polymers in the blends exhibited well separated crystalline domains. PCL seemed to constitute the continuous phase of the blends with formation of large size spherulites when the PCL content was over 50%. The selective degradation of PCL or PLLA components revealed the inner morphology of the blends where microspherelike or islandlike patterns were observed.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1021/bm000046k

Cites

20 of 20 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Cited by

See all 272

Metadata

Authors
Liu, L., Li, S., Garreau, H., & Vert, M.
Venue
Biomacromolecules
Published
2000-08-12
Volume
1
Issue
3
Pages
350-359
ISSN
  • 1525-7797 (ISSN-L)
  • 1526-4602
OpenAlex
W1979899054
Cited by
272
References
20
FWCI
3.47
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.