Evidence map›Paper›PMID 37687451›Full record

ArticleMaterials (Basel, Switzerland)2023

Effect of Polydopamine and Curcumin on Physicochemical and Mechanical Properties of Polymeric Blends.

Muhammad Tahir, Alina Sionkowska

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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

2 authors.

Muhammad TahirDepartment of Biomaterials and Cosmetic Chemistry, Faculty of Chemistry, Nicolaus Copernicus University in Torun, Gagarina 7, 87-100 Torun, Poland.
Alina SionkowskaDepartment of Biomaterials and Cosmetic Chemistry, Faculty of Chemistry, Nicolaus Copernicus University in Torun, Gagarina 7, 87-100 Torun, Poland.ORCID 0000-0002-1551-2725

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, we prepared composites made from polyvinyl alcohol (PVA), sodium alginate (SA), curcumin (Cur), and polydopamine (PD). The film-forming properties of the composites were researched for potential wound-healing applications. The structures of the polymer blends and composites were studied by FTIR spectroscopy and microscopic observations (AFM and SEM). The mechanical properties were measured using a Zwick Roell testing machine. It was observed that the formation of a polymeric film based on the blend of polyvinyl alcohol and sodium alginate led to the generation of pores. The presence of curcumin in the composite resulted in the alteration of the blend properties. After solvent evaporation, the polymeric blend of PVA, SA, and curcumin formed a stable polymeric film, but the film showed poor mechanical properties. The addition of polydopamine led to an improvement in the mechanical strength of the film and an increase in its surface roughness. A polymeric film of sodium alginate presented the highest surface roughness value among all the studied specimens (66.6 nm), whereas polyvinyl alcohol showed the lowest value (1.60 nm). The roughness of the composites made of PVA/SA/Cur and PVA/SA/Cur/PD showed a value of about 25 nm. Sodium alginate showed the highest values of Young's modulus (4.10 GPa), stress (32.73 N), and tensile strength (98.48 MPa). The addition of PD to PVA/SA/Cur led to an improvement in the mechanical properties. Improved mechanical properties and appropriate surface roughness may suggest that prepared blends can be used for the preparation of wound-healing materials.

Indexed as

curcuminpolydopaminepolymeric compositepolyvinyl alcoholsodium alginate

Identifiers

PMID37687451
PMCPMC10488858

What OpenQuestion holds

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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.