Dr Ruishan Liu

Lecturer
Faculty:
Faculty of Engineering, Agri-tech and the Environment
Location:
ARU Peterborough
Areas of Expertise:
Engineering , Mechanical Engineering

Ruishan is an experienced lecturer in mechanical engineering. She was awarded a First-Class Honours MEng in Chemical Engineering from the University of Nottingham in 2019, a PhD in Engineering from the University of Cambridge in 2024, Fellowship of the Higher Education Academy (FHEA) in 2024, and is a member of the Institution of Engineering and Technology (MIET).

Email: [email protected]

Background

Ruishan began her academic career as a teaching assistant at the University of Cambridge, where she taught over 1,000 undergraduates across eight laboratory modules and supervised one group project within the Mechanical Engineering division over four years. The modules included 2D and 3D drawing (Solidworks), characterizing materials’ mechanical properties using over 10 destructive and non-destructive testing methods, heat treatment of steels, and Python-based digital circuit projects. This experience has equipped Ruishan with a breadth of skills and depth of knowledge required to effectively teach challenging subjects such as engineering mechanics, computer-aided design, engineering mathematics, fluid mechanics, thermodynamics, and heat transfer. Beyond the technical aspects, Ruishan has worked with students with ADHD, autism, and other disabilities, as well as students from over 50 countries and diverse backgrounds. Ruishan is currently pursuing Senior Fellowship and aims to design and deliver inclusive and practical engineering knowledge to a broader audience, including apprentices.

Spoken Languages
  • English
  • Chinese Mandarin
Research interests

Bioengineering: Fabrication of artificial human tissues to study diseases and explore effective treatment methods. Ruishan is currently working on publishing a paper on fabricating artificial neuronal axons using PEDOT:PSS to study demyelination diseases such as multiple sclerosis.

Areas of research supervision

Ruishan supervised a group of four students on selecting suitable biocomposites for designing a sustainable surfboard, contributing to the sustainability goals of the 21st century.

Teaching
  • Foundation Year Mathematics
  • Engineering Mathematics
  • Computer-Aided Design (Solidworks)
  • Fluid Mechanics and Thermodynamics
Qualifications
  • Fellowship of the Higher Education Academy (FHEA) (2023–2024): Obtained through the Advancing Educational Practice Programme at the Cambridge Centre for Teaching and Learning.
  • PhD in Engineering (2019–2024): University of Cambridge. Thesis: Conductive PEDOT:PSS Fibres for Modelling and Assessing Oligodendrocyte Ensheathment.
  • Integrated Master’s Degree in Chemical Engineering (MEng, Hons: First-Class) (2014–2019): University of Nottingham. Foundation year and first year completed in Ningbo, China; second, third, and fourth years in Nottingham, UK.
Memberships, editorial boards
  • Member of the Institution of Engineering and Technology – MIET.
Research grants, consultancy, knowledge exchange

Ruishan collaborated with neuroscientists at the Cambridge Stem Cell Institute, gaining fundamental knowledge of neuroscience, particularly myelination

Selected recent publications
  • Liu, R. (2023). Conductive PEDOT:PSS fibres for modelling and assessing oligodendrocyte ensheathment [Apollo - University of Cambridge Repository]. https://doi.org/10.17863/CAM.106977
  • Elisabeth L. Gill, Wenyu Wang, Ruishan Liu, Yan Yan Shery Huang, Additive batch electrospinning patterning of tethered gelatin hydrogel fibres with swelling-induced fibre curling, Additive Manufacturing, Volume 36, 2020, 101456, ISSN 2214-8604, https://doi.org/10.1016/j.addma.2020.101456
Recent presentations and conferences

Materials Research Society (MRS) Fall Meeting 2022 Boston, USA

Presentation History:

  • Dec 1, 2022, 10:30 AM Adaptive Fibre Organic Electrochemical Transistor for Volumetric Embedded Sensing in Organ-on-a-Chip
  • Nov 29, 2022, 11:30 AM Towards Green, Mass Customization of Electronic Fabrics
  • Nov 28, 2022, 8:00 PM Machine Learning Enabled Biofabrication