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Cited by 380

The DOI matches its Crossref record; 177 of the 177 references it cites were found in a registry or an index.

Electrospinning: Processes, Structures, and Materials

Open access
Ahmadi Bonakdar, Mahboubeh, Rodrigue, Denis·2024·Macromol

Abstract

Electrospinning is a simple and affordable method of producing nanofibers, offering a large specific surface area and highly porous structures with diameters ranging from nanometers to micrometers. This process relies on an electrostatic field, providing precise control over the fiber dimensions and morphologies through parameter optimization and the use of specialized spinnerets and collectors. The paper extensively covers the electrospinning process and parameters, shedding light on the factors influencing electrospinning. It addresses the morphological and structural aspects of electrospun fibers that are used in different applications. Additionally, this paper explores various polymeric and non-polymeric materials used in electrospinning. Furthermore, it investigates the incorporation of fillers during electrospinning, using an electric field to enhance properties and functionality. The review concludes by offering insights into upscaling electrospinning production.

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Registry

Crossref · 10.3390/macromol4010004

Cites

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Citations

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Metadata

Authors
Ahmadi Bonakdar, M., & Rodrigue, D.
Venue
Macromol
Publisher
MDPI AG
Published
2024-02-11
Volume
4
Issue
1
Pages
58-103
ISSN
2673-6209 (ISSN-L)
OpenAlex
W4391754737
Cited by
380
References
177
FWCI
31.76
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Electrospinning: Processes, Structures, and Materials. https://app.cochat.ai/papers/10.3390/macromol4010004
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Ahmadi Bonakdar, M., & Rodrigue, D. (2024). Electrospinning: Processes, Structures, and Materials. Macromol. https://doi.org/10.3390/macromol4010004

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.