ArticleACS omega2024
Controlling Mechanical Properties of Medical-Grade Scaffolds through Electrospinning Parameter Selection.
Article in ACS omega, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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.
Who cites it
3 citing papers in PubMed.
- Hybrid SES-MEW Scaffold Strategies: A Narrative Review of Multi-Scale Fiber Architectures for Soft and Hard Tissue Engineering.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Electrostatic Patterning of Nanofibrous Microcapsules for Three-Dimensional Cell Culture.Journal of functional biomaterials · 2026Article
- Development of a novel small diameter vascular graft based on an electrospun blend PET/PU scaffold: from fabrication to structural, mechanical, and in vitro evaluation.Journal of materials science. Materials in medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electrospinning (ES) is a versatile process mode for creating fibrous materials with various structures that have broad applications ranging from regenerative medicine to tissue engineering and surgical mesh implants. The recent commercialization of this technology for implant use has driven the use of resorbable electrospun products. Resorbable electrospun meshes offer great promise as temporary implants that can utilize the layer upon layer method of additive manufacturing to incorporate porosity as a function of process parameters into a scaffold structure. The interconnected porosity and feature size known to ES have previously been observed to hold great potential for simulating the natural cellular environment of soft tissue. This microstructure, proper degradation kinetics, and mechanical properties combine to provide the design basis for artificial tissue structures that could aid in not only wound healing but also true tissue engineering and regenerative medicine. While current advancement in the field is understood to be limited by material properties, the importance of optimizing mechanical properties with currently available materials should not be overlooked. This work investigated the process parameter effects and interactions that control the structure-property relationship for a range of medical-grade aliphatic polyester materials with a range of intrinsic properties. An ε-caprolactone homopolymer (PCL), l-lactide homopolymer (PLLA), and Lactoflex, a copolymer with intermediate properties relative to the homopolymers, were characterized before, during, and after the additive manufacturing process. The interacting effects of process parameters, distance to collector, and dispensing rate were shown to produce variable-density, nonwoven scaffold structures. The resorbable mesh scaffolds of PLLA, PCL, and Lactoflex demonstrated a broad range of mechanical properties (approximately 1-10 MPa ultimate tensile strength and 5-390 MPa tensile modulus). Postprocessing of scaffolds demonstrated removal of solvents and preservation of micrometer-sized features. Resorbable polymers and electrospinning can produce scaffold materials with excellent features and offer tremendous potential in the field of implantable resorbable devices.
Identifiers
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Registered trials
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.