ReviewBiophysics reviews2020
Physicochemical parameters that underlie inkjet printing for medical applications.
Review in Biophysics reviews, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- AI-driven 3D bioprinting for regenerative medicine: From bench to bedside.Bioactive materials · 2025Review
- Advancements in 3D skin bioprinting: processes, bioinks, applications and sensor integration.International journal of extreme manufacturing · 2025Review
- Bioinks and biofabrication techniques for biosensors development: A review.Materials today. Bio · 2024Review
- Predicting pharmaceutical inkjet printing outcomes using machine learning.International journal of pharmaceutics: X · 2023Article
- Evolution of Transdermal Drug Delivery Devices and Novel Microneedle Technologies: A Historical Perspective and Review.JID innovations : skin science from molecules to population health · 2023Review
- Printing Technologies as an Emerging Approach in Gas Sensors: Survey of Literature.Sensors (Basel, Switzerland) · 2022Review
- Recent Advances in 3D Printing for Parenteral Applications.The AAPS journal · 2021Review
- Welcome toBiophysics reviews · 2020Article
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
One of the most common types of 3D printing technologies is inkjet printing due to its numerous advantages, including low cost, programmability, high resolution, throughput, and speed. Inkjet printers are also capable of fabricating artificial tissues with physiological characteristics similar to those of living tissues. These artificial tissues are used for disease modeling, drug discovery, drug screening, and replacements for diseased or damaged tissues. This paper reviews recent advancements in one of the most common 3D printing technologies, inkjet dispensing. We briefly consider common printing techniques, including fused deposition modeling (FDM), stereolithography (STL), and inkjet printing. We briefly discuss various steps in inkjet printing, including droplet generation, droplet ejection, interaction of droplets on substrates, drying, and solidification. We also discuss various parameters that affect the printing process, including ink properties (e.g., viscosity and surface tension), physical parameters (e.g., internal diameter of printheads), and actuation mechanisms (e.g., piezoelectric actuation and thermal actuation). Through better understanding of common 3D printing technologies and the parameters that influence the printing processes, new types of artificial tissues, disease models, and structures for drug discovery and drug screening may be prepared. This review considers future directions in inkjet printing research that are focused on enhancing the resolution, printability, and uniformity of printed structures.
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.