ArticlebioRxiv : the preprint server for biology2026
3D Microscale Mechanical Simulations of Hydrogel Coated Electrospun Meshes.
Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Electrospun fiber meshes have long served as biomaterials in a wide range of biomedical applications due to their functional similarities to extracellular matrix and highly tunable properties. Altering the mechanical behaviors of individual fibers and their microarchitecture (e.g.; diameter, crimp, orientation, density) can in principle be used to control bulk level behaviors. Moreover, electrospun meshes are often combined with softer coatings and hydrogels to control surface interactions with body tissues. Yet, fully optimizing their behaviors for specific applications remains an elusive target due to a continued lack of understanding of the micromechanical mechanisms and their relation to bulk mechanical behaviors. Our goal herein was to understand how actual nanoCT-generated 3D microfiber geometry can be used to predict bulk mechanical properties of hydrogel-mesh composites. Electrospun polyurethane meshes were fabricated with a random fiber orientation and coated with a PEG-based hydrogel. The fiber-hydrogel composite was then imaged with a nanoCT scanner at a voxel resolution of 180 nm. From these images, custom Python programs were written to segment, refine, and tesselate a high-resolution finite element of the fiber mesh and hydrogel volumes into a single integrated bi-material finite element model. The resulting mesh was used to run simulations of the planar biaxial mechanical tests used to characterize the bulk mechanical behaviors. Our framework thus enabled systematic investigations of both the macroscopic bulk mechanical response of the overall fiber mesh and the microscopic localized mechanical response of fibers under various stages of loading. The resultant simulations were accurate and predictive of the bulk mechanical responses. It is interesting to note that the fiber-hydrogel composite material experienced the largest stresses within the fiber phase and the largest strains within the hydrogel. This key result underscores that while the previous analytical model assumed local affine deformations, at the microscale this assumption does not hold. We also found very different effective fiber stress-strain responses in each model. It is likely these differences are due to the substantial heterogeneous non-affine local deformations present in the actual fiber-hydrogel composite. This finding further reveals the need for more rigorous approaches to better understand how electrospun-based materials function in order to improve their use in modern medical devices and implants.
Indexed as
Identifiers
What OpenQuestion holds
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.