Outline

Overview

The application of science and technology at the nanoscale for biomedical problems promises to revolutionise medicine. Recent years have witnessed unprecedented advances in the diagnosis and treatment of diseases by applying nanotechnology to medicine. This course focuses on explaining the fundamentals of nanomedicine, and highlighting the special properties and application of nanomaterials in medicine. This course also reviews the most significant biomedical applications of nanomaterials including the recent breakthroughs in drug delivery, medical imaging, gene therapy, biosensors and cancer treatment.

Unit details and rules

Academic unit	Biomedical Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	AMME5931
Assumed knowledge ?	Knowledge in calculus, linear differential equations, basic mechanics and electromagnetism
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Gurvinder Singh, gurvinder.singh@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final examination Examination on the content covered during the semester	40%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Presentation	In class presentation Group presentation from week 9 to week 11	15%	Multiple weeks	12 min	AI allowed
Outcomes assessed:
In-person practical, skills, or performance task or test	Mid Semester Quiz Live mid-semester quiz based on the lecture content from wk1 to wk5	7.5%	Week 06 Due date: 02 Apr 2026 at 13:10 Closing date: 02 Apr 2026	50 minutes	AI prohibited
Outcomes assessed:
In-person practical, skills, or performance task or test	Final Semester Quiz-2 In Class final semester quiz based on the lecture content from wk 6 to wk11	7.5%	Week 12 Due date: 21 May 2026 at 13:10 Closing date: 21 May 2026	50 minutes	AI prohibited
Outcomes assessed:
Written work	Lab Report Lab activities from week 4 to week 7	20%	Week 12 Due date: 22 May 2026 at 23:59	Report Submission: Week 12	AI allowed
Outcomes assessed:
Practical skill	Weekly Assignments Individual in-class assessments designed to evaluate learning outcomes for each lecture from weekly tutorial activities	10%	Weekly	Week 1-13 Due date: each week Friday	AI allowed
Outcomes assessed:
= group assignment ?

Assessment summary

Assessment Summary:

Two quizzes (15%; 7.5% each): Mid semester (wk6)/end of semester (wk12)

Tutorial activities participation: Weekly In class assignment (10%)

Group presentation: Class assignment (15%) (wk 9 to wk 11)

Lab report submission based on lab work (20%) (Submission due on wk12)

Final examination based on lecture content (40%)

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2021 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an exceptional standard as defined by grade descriptors or exemplars established by the faculty.
Distinction	75 - 84	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a very high standard as defined by grade descriptors or exemplars established by the faculty.
Credit	65 - 74	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a good standard as defined by grade descriptors or exemplars established by the faculty.
Pass	50 - 64	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an acceptable standard as defined by grade descriptors or exemplars established by the faculty.
Fail	0 - 49	To be awarded to students who, in their performance in assessment tasks, fail to demonstrate the learning outcomes for the unit at an acceptable standard established by the faculty. This grade, with corresponding mark, should also be used in cases where a student fails to achieve a mandated standard in a compulsory assessment, thereby failing to demonstrate the learning outcomes to a satisfactory standard. In such cases the student will receive the mark awarded by the faculty up to a maximum of 49.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to nanomaterials in medicine	Lecture (2 hr)
Week 01	Nanomaterials properties	Tutorial (2 hr)
Week 02	Synthesis of nanoparticles	Lecture (2 hr)
Week 02	Synthesis of nanomaterials	Tutorial (2 hr)
Week 03	Nanocharacterization tools	Lecture (2 hr)
Week 03	Nanocharacterization tools	Tutorial (2 hr)
Week 04	Protein-nanoparticle interaction	Lecture (2 hr)
	Protein-nanoparticle interaction	Tutorial (2 hr)
	Lab1: Synthesis of nanomaterials	Practical (2 hr)
Week 05	Cell-nanoparticle interaction	Lecture (2 hr)
	Cell-nanoparticle interaction	Tutorial (2 hr)
	Lab2: Functionalization and characterization of nanomaterials	Practical (2 hr)
Week 06	Nanotoxicity	Lecture (2 hr)
Week 06	Formulation and characterization of drug loaded nanoparticles	Tutorial (2 hr)
Week 07	Lipid nanoparticles for biomedical applications	Lecture (2 hr)
	Nanotoxicity	Tutorial (2 hr)
	Lab 3: Cell Culture: Incubation of functionalized nanoparticles to cell	Practical (2 hr)
Week 08	Gold nanoparticles for biomedical applications	Lecture (2 hr)
	Lipid nanoparticles for biomedical applications	Tutorial (2 hr)
	Lab 4: Cell Culture: - Live-dead assays after seven days	Practical (2 hr)
Week 09	Magnetic nanoparticles for imaging contrast and therapeutic agents	Lecture (2 hr)
Week 09	Gold nanoparticles for biomedical applications	Tutorial (2 hr)
Week 10	Quantum dots: A promising tool for bioimaging and biodiagnostic applications	Lecture (2 hr)
Week 10	Magnetic nanoparticles for imaging contrast and therapeutic agents	Tutorial (2 hr)
Week 11	Intelligent micro-and nanorobots for biomedical applications	Lecture (2 hr)
Week 11	Quantum dots: A promising tool for bioimaging and biodiagnostic applications	Tutorial (2 hr)
Week 12	Nanoporous materials for biomedical applications and revision	Lecture (2 hr)
Week 12	Intelligent micro-and nanorobots for biomedical applications	Tutorial (2 hr)
Week 13	Revision of unit content	Lecture (2 hr)
Week 13	Nanoporous materials for biomedical applications	Tutorial (2 hr)

Attendance and class requirements

Class attendance is required.

The University attendance policy can be found here (see clause 68): https://www.sydney.edu.au/policies/showdoc.aspx?recnum=PDOC2014/378&RendNum=0

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Critically discuss the current state and recent advances in nanomaterials in medicine, and evaluate the current challenges in nanomedicine
LO2. Apply and integrate core engineering principles in the design and analysis of nanomedicine
LO3. Design and formulate novel nanoscale material–based solutions to address unmet clinical needs and overcome limitations of existing biomedical technologies
LO4. Devise solutions that account for technical and financial feasibility, as well as practical surgical considerations
LO5. Appreciate and recognize the interdisciplinary nature of the field of nanomedicine, whereby concepts from a wide range of areas including materials science, human biology, chemistry and physics are brought together
LO6. Employ techniques for effective oral and written communication of the concepts and knowledge underlining the background science and engineering applications of nanomaterials in medicine
LO7. Identify, obtain, and analyse information using appropriate search strategies to gain in-depth knowledge and current advances in nanomaterials used in medicine
LO8. Employ professional techniques and activities such as assigning tasks, managing time, meeting deadlines, and communicating with your colleagues in a professional manner.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

Based on the student feedback, we have not made any changes in lecture topic and assessment structure. However, we will make changes in each lecture content and tutorial activities (creative task based questions, mentimeter).

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

BMET5931: Nanomaterials in Medicine

Semester 1, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links