Office of Assessment, Accreditation, and Academic Program Planning

UNCG Student Learning Outcomes

Nanoscience (PhD)


The mission of the Nanoscience department is to prepare students from a variety of disciplinary backgrounds to conduct basic and advanced research in Nanoscience in industrial, governmental or academic settings. Within this context, the Nanoscience Department provides a graduate level trans-disciplinary educational and discovery research experience in the nanosciences, sustainable nanosystems, innovation and community engaged outreach. It also works collaboratively with the Nanoengineering Department to achieve an integrated foundational research program in emerging high impact areas.

Learning Outcomes

Understand the Scope and Basis of Nanoscience Research
Students completing their first year of study in the program will understand the interdisciplinary foundations of nanoscience research and know how to interpret nanoscience-based research literature.

Creativity, Critical Thinking and Communication Skills
Graduate students will demonstrate graduate-level creativity, critical thinking and communication skills.

Knowledge integration, synthesis and dissertation proposal.

Hypothesis-driven research and dissertation defense.
Qualified PhD students write, present and defend a dissertation research proposal, evaluated by a committee consisting of at least four graduate committee members. Students are evaluated separately for writing, speaking, transdisciplinary creativity, intellectual merit, broader impacts, enhanced critical thinking / problem-solving skills, and hands-on aptitude. Successful students will apply and demonstrate a mastery of the scientific method for hypothesis driven research, i.e., Understand and identify a foundational question or challenge within the context of the state-of-the-art in the field; define a clear and testable hypothesis; design and execute relevant research experiments; analyze the data and interpret the results; test the hypothesis and explain any discrepancies between predictions and observations.

Click here to visit the website for this department.