🎓 Discover PhD and Master's programmes at leading universities worldwide — Sign up free to save searches and get email alerts
AU

Optimising Extrusion and Printability for 3D-Printed Oral Solid Dosage Forms

Aston University College of Health and Life Sciences
Partially Funded 🎓 Health Sciences 🎓 Materials Science rheology 3d printing process optimisation extrusion hot-melt extrusion semi-solid extrusion oral dosage forms pharmaceutical engineering

Explore extrusion and formulation strategies to improve 3D printing of personalised oral medicines. Investigate hardware and material controls to enhance print quality and manufacturing scalability for pharmaceutical applications.

AI-generated overview

🌍
Why This Research Matters

This research improves the reliability and scalability of 3D printing for personalised oral medicines, potentially transforming pharmaceutical manufacturing by enabling higher throughput and consistent drug dosing. It supports the advancement of GMP-compliant personalised drug production, enhancing treatment outcomes.

Additive Manufacture Extrusion Control Pharmaceutical Formulation Process Analytics Personalised Medicine

Project Description

Project Overview

Extrusion-based 3D printing is promising for personalised oral medicines but faces challenges in throughput, dose consistency, and print reliability. This project aims to address these by optimising feedstock formulations and extrusion process parameters.

What You Will Do

Engineer hot-melt extrusion (HME) filaments and semi-solid extrusion (SSE) pastes with tailored rheological properties to ensure stable, continuous deposition. Formulation will involve polymer-based systems, control of active pharmaceutical ingredient (API) particle size, and flow modifiers for shear-thinning behaviour and structural recovery. Physicochemical characterisation techniques such as HPLC, DSC, TGA, XRPD, and FTIR will be used to understand formulations. Hardware investigations will optimize nozzle design, thermal management, and integrate pressure, torque, and temperature dynamics with a DOE framework to link formulation and hardware to quality attributes.

Expected Outcomes

Validated operating parameters to enhance throughput and quality of the extrusion materials and prints. Development of practical guidelines for feedstock preparation and process control contributing to reliable, GMP-compatible 3D printing of personalised oral solid dosage forms.

Why This Matters

This research underpins advances in personalised medicine manufacturing, addressing current limitations in 3D printing of drugs to improve patient-specific treatments with consistent quality and scalability.

Entry Requirements

Applicants should have either: a First or Upper Second Class undergraduate degree in a relevant subject, OR a First or Upper Second Class undergraduate degree plus a Merit or Distinction in a relevant Masters degree. Overseas qualifications will be considered for equivalence.

How to Apply

Submit a complete application with transcripts, research and personal statements, CV, two academic references, English language evidence, and passport copy. Contact Dr Daniel Kirby at D.J.KIRBY1@aston.ac.uk for discussions on consumables and queries. Upload copies of correspondence with supervisor as part of application.

Eligibility

UK/Home
EU
International

Supervisor Profile

DD
Dr Daniel Kirby
Aston University, College of Health and Life Sciences

Dr Daniel Kirby specializes in pharmaceutical engineering with a focus on additive manufacturing and extrusion processes for drug delivery systems. He applies materials science and process analytics to develop scalable, consistent pharmaceutical manufacturing technologies. His work bridges formulation science and hardware optimisation to advance personalised medicine production.

Related Opportunities

PhD Research on Advanced Infrastructure Materials and Cementitious Mixtures
University of Miami Ali Ghahremaninezhad 🎓 Civil Engineering 🎓 Materials Science

Explore the advanced mechanical and durability properties of cementitious materials modified with innovative additives. Investigate failure mechanisms in metals and contribute to sustainable infrastructure material deve…

This research enhances the sustainability and performance of construction materials critical to infrastructure longevity. Innovations in ce…

Infrastructure Materials
PhD on Materials, Manufacturing, and Recycling of Electrochemical Energy Storage Systems
University of Oklahoma Dr. Manoj Jangid 🎓 Chemical Engineering 🎓 Materials Science

Explore the science of next-generation batteries focusing on materials and recycling techniques. Investigate coatings and stress dynamics to boost battery durability and efficiency in real applications.

This research is critical for developing longer-lasting, safer, and more sustainable batteries essential for electric vehicles and renewabl…

1050+ citations · h20
Electrochemistry Materials Engineering Coating Interfaces Li-ion Batteries
PhD Research on Advanced Materials for Energy, Aerospace, Space, and Nuclear Applications
The University of Texas at El Paso Dr. Md Ariful Ahsan 🎓 Chemistry 🎓 Materials Science

Explore AI and physics-based methods to predict and design materials for extreme environments. Conduct experimental and computational research on material failure, additive manufacturing, and electrochemical techniques …

This research addresses critical challenges in developing durable materials for extreme aerospace, space, and nuclear environments. It also…

2907+ citations · h30
Advanced Materials
PhD in AI-Driven Soft Materials Design for Energy and Circular Economy
University of South Carolina Dr. Shengli (Bruce) Jiang 🎓 Chemical Engineering 🎓 Computer Science

Explore AI-driven approaches combining molecular simulation and generative deep learning to design next-generation soft materials. Develop sustainable polymers and electrolytes with enhanced stability and scalability ta…

This research supports environmental sustainability through designing advanced soft materials that enable plastic waste upcycling and impro…

785+ citations · h14
machine learning molecular simulations soft materials chemical engineering