Developing surfAce enhaNCEd infrared absorption photonics Quantum Devices for multiple vibrational modes imaging (DANCE-QD)

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Existing in situ nano biomedical imaging techniques are highly labour and time intensive, often requiring specialist training (e.g. cryo-microscopy), and focus on the analysis of bulk samples due to the scarcity and nanometre scale of intra- or extracellular cell microstructures (e.g. vesicles). The inability of cells and proteins to maintain their structure in dehydrated states also adds further challenges and often results in artefacts. An in situ and rapid technology is therefore required for the in- depth analysis of biological tissues in their native state. Recently, photo-induced force microscopy (PiFM), which provides an external electromagnetic field as a driving force to map 3D chemical and topographical characterisation as a function of phonon energies, has been suggested as a turn-key solution for rapid in situ bio-medical imaging (socially desirable). However, the environmental phonon distribution in liquids creates high background noise due to water molecule vibrations. Vibrational modes can be selectively enhanced using tailored micro-structures, that act as antennas to create local electric field enhancements and near-field coupling to significantly increase near infrared signals. The aim of this studentship is to develop microfabricated antennas as a substrate for in situ aqueous imaging and image reconstruction algorithm development. We will use colloidal semiconductor nanocrystals, quantum materials, two-dimensional materials, metals and photo and electron beam lithography to fabricate micro and nano-photonic structures to enhance IR signals via a PiFM imaging system at Cardiff (unique in UK). Applicants should hold, or expect to receive, a First Class or high Upper Second Class UK Honours degree (or the equivalent) in Electronic Engineering, Physics, and computer science and should have an interest in nano and micro fabrication, clean room processing, and computer vision. A master's level qualification with strong education and research background/experience in electron microscopy, computer vision and image processing and reconstruction would be advantageous.