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Applications of Digital Materials Manufacturing (CENG0065)

Key information

Faculty
Faculty of Engineering Sciences
Teaching department
Chemical Engineering
Credit value
15
Restrictions
This module is only available to students enrolled on MSc Digital Manufacturing of Advanced Materials
Timetable

Alternative credit options

There are no alternative credit options available for this module.

Description

Aim:

  • To provide students a contextual understanding of various target applications for data-driven materials engineering, e.g. for energy, environment and healthcare.
  • To form a new generation of students with a holistic knowledge on the design and management of automated systems for materials synthesis.听听

Synopsis:

Data-driven accelerated optimisation enables the bespoke development of materials for a range of target applications. This module will provide a thematic context centered around the grand challenges of energy, environment, and healthcare. Practical case studies will serve to demonstrate how automated synthesis, high throughput characterization, data science form the core of the 鈥渄esign 鈥 make 鈥 test鈥 cycle to solve current challenges of materials optimization.

The module will focus on the integration of hardware, i.e. the experimental system, and software, i.e. computational and data analysis modules, for the design, management and optimization of automated synthesis. Selected themes will span from energy to pharma/biopharma applications and will be presented in close collaboration with industry partners and cover aspects of materials and manufacturing demands across multiple length scales.听听

Learning Outcomes:

Upon successful completion, students will be able to:听听

  • Explain and exemplify how the holistic workflow of data-driven materials optimization (i.e. automated synthesis, high throughput characterization, data science) and how it is implemented in various industrial settings and for a diversity of target applications.听听

  • Analyse practical challenges of data-driven materials optimization in manufacturing settings and find workable and sustainable solutions to address these.听

  • Implement and evaluate principles of the design 鈥 make 鈥 test cycle in a focus theme of choice, e.g. production in pharma/biopharma (small molecule-, peptide-, protein-based materials).听

  • Use automation and data-driven optimization concepts to design and characterize innovative systems in industrial sectors such as energy, environment and healthcare.听听

Module deliveries for 2024/25 academic year

Intended teaching term: Term 1 听听听 Postgraduate (FHEQ Level 7)

Teaching and assessment

Mode of study
In person
Intended teaching location
香港六合彩 East
Methods of assessment
30% Coursework
70% Dissertations, extended projects and projects
Mark scheme
Numeric Marks

Other information

Number of students on module in previous year
6
Module leader
Dr Max Besenhard
Who to contact for more information
chemeng.teaching.admin@ucl.ac.uk

Last updated

This module description was last updated on 8th April 2024.