Article3D printing and additive manufacturing2024
Factorial Design of Experiment Method to Characterize Bioprinting Process Parameters to Obtain the Targeted Scaffold Porosity.
Article in 3D printing and additive manufacturing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Integrating Decision Trees and Clustering for Efficient Optimization of Bioink Rheology and 3D Bioprinted Construct Microenvironments.Journal of manufacturing science and engineering · 2025Article
- Characterization and Machine Learning-Driven Property Prediction of a Novel Hybrid Hydrogel Bioink Considering Extrusion-Based 3D Bioprinting.Gels (Basel, Switzerland) · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Due to its inbuilt ability to release biocompatible materials encapsulating living cells in a predefined location, 3D bioprinting is a promising technique for regenerating patient-specific tissues and organs. Among various 3D bioprinting techniques, extrusion-based 3D bioprinting ensures a higher percentage of cell release, ensuring suitable external and internal scaffold architectures. Scaffold architecture is mainly defined by filament geometry and width. A systematic selection of a set of process parameters, such as nozzle diameter, print speed, print distance, extrusion pressure, and material viscosity, can control the filament geometry and width, eventually confirming the user-defined scaffold porosity. For example, carefully selecting two sets of process parameters can result in a similar filament width (FW). However, the lack of availability of sufficient analytical relationships between printing process parameters and FW creates a barrier to achieving defined scaffold architectures with available resources. In this article, the factorial design of experiment (DoE) method has been adopted to obtain a relationship among scaffold properties that is, FW with 3D printing process parameters. The FW was determined using an image processing technique and an analytical relationship was developed, including various process parameters to maintain defined FW variation for different hydrogels within an acceptable range to confirm the overall geometric fidelity of the scaffold. The validation experiment results showed that our analytical relationship obtained from the DoE effectively predicts the scaffold's architectural property. Furthermore, the proposed analytical relationships can help achieve defined scaffold architectures with available resources.
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.