Use this URL to cite or link to this record in EThOS:
Title: Scalable soft matter patterning from the macro to the nanoscale
Author: Nania, Manuela
ISNI:       0000 0004 6422 8450
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2017
Availability of Full Text:
Access from EThOS:
Access from Institution:
Surface patterning is important for a range of engineering applications, including controlled wetting and spreading of liquids, adhesion and assembly of smart coatings. There is therefore a need of simple, cost-effective and scalable techniques for pattern formation over a wide range of scales. Conventional methods fail to comply with these requirements, as costs and complexity increase in the attempt to impress nm to µm scale features. By contrast, wrinkling of bi-(multi- )layers is inherently inexpensive, scalable and robust, and has the potential for soft matter patterning from the nano- to the macro-scale. This work investigates the controlled multi-layer generation of polydimethylsiloxane (PDMS) glassy skins via surface oxidation using plasma exposure and/or ultraviolet ozonolysis (UVO). Uniaxial mechanical compression is then employed to induce pattern formation via a well-known wrinkling instability. Topographies with wavelengths down to 45 nm are achieved, for the first time, as well as features with characteristic lengthscales of ∼ 10s µm. Moreover, simple design routes for double frequency nested pattern formation, imposed by compression of tri-layer laminate films, are established. The work concludes by exploiting wrinkling as a method for the mechanical characterisation of thin drying films. A time-resolved wrinkling interrogation during film drying is established as a simple and reliable approach to determining evolving mechanical properties of films, overcoming the difficulties associated with handling very thin free-standing films and the limited sensitivity of conventional methods, with potential applications extending to coatings, personal care items, and foods.
Supervisor: Cabral, João ; Matar, Omar Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral