Evidence map›Paper›PMID 34699689›Full record

ArticleAdvanced healthcare materials2022

Computational Modeling and Experimental Characterization of Extrusion Printing into Suspension Baths.

Margaret E Prendergast, Jason A Burdick

Abstract read
In one paragraph

Article in Advanced healthcare materials, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Computational approaches in bioprinting processes.Nature reviews bioengineering · 2026
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  11. Materials today. Bio · 2024
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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.

Margaret E PrendergastDepartment of Bioengineering, University of Pennsylvania, 210 South 33rd Street, Philadelphia, PA, 19104, USA.
Jason A BurdickDepartment of Bioengineering, University of Pennsylvania, 210 South 33rd Street, Philadelphia, PA, 19104, USA.ORCID 0000-0002-2006-332X

Funding

Dynamic Fibrous Scaffolds for Repairing Dense Connective TissuesR01AR056624 · NIAMS · UNIVERSITY OF PENNSYLVANIA · PI Jason A Burdick, Robert L Mauck · 2009 to 2026
$7.5M
NIAMS NIH HHS R01 AR056624
6 · The paper itself

Abstract

The extrusion printing of inks into suspension baths is an exciting tool, as it allows the printing of diverse and soft hydrogel inks into 3D space without the need for layer-by-layer fabrication. However, this printing process is complex and there have been limited studies to experimentally and computationally characterize the process. In this work, hydrogel inks (i.e., gelatin methacrylamide (GelMA)), suspension baths (i.e., agarose, Carbopol), and the printing process are examined via rheological, computational, and experimental analyses. Rheological data on various hydrogel inks and suspension baths is utilized to develop computational printing simulations based on Carreau constitutive viscosity models of the printing of inks within suspension baths. These results are then compared to experimental outcomes using custom print designs where features such as needle translation speed, defined in this work as print speed, are varied and printed filament resolution is quantified. Results are then used to identify print parameters for the printing of a GelMA ink into a unique guest-host hyaluronic acid suspension bath. This work emphasizes the importance of key rheological properties and print parameters for suspension bath printing and provides a computational model and experimental tools that can be used to inform the selection of print settings.

Indexed as

BioprintingBathsComputer SimulationGelatinHydrogelsInkPrinting, Three-DimensionalSuspensionsGelatinHydrogelsSuspensions3D bioprintingbiofabricationextrusion printingsuspension baths

Identifiers

PMID34699689
PMCPMC8986563

What OpenQuestion holds

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Registered trials

None linked

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.