Reece Dillon

Background

Reece is a PhD research student with a broad background in biological science and a focus on molceular biology through a BSc (Hons) and a MSc by research respectively. Reece is currently working towards a PhD at Cranfield University with focus on advancing real-time detection and characterisation of bioaerosols through utilisation of UV-LIF based technologies. His undergraduate studies have included a wide background of biological studies around human biology, environmental science, forensic studies, plant biology, and much more, with an end project on Zinc-alpha2-glycoproteins (ZAG) binding potential to the ligand Phosphatidylcholine (PC) during his BSc (Hons) final year. He then completed a MSc by research based around the effects of 'front end' sampling variation and how it impacts downstream assay results such as qPCR and LAMP. This MSc reinforced his passion for studies around viral and bacterial detection and safe handling, which lead him to his current bioaerosol research project.

Current activities

My PhD research project aims to better understand the bioaerosol characteristics in residential environments in the UK by utilising continuous and real-time measurements with an advanced UVLIF-based bioaerosol sensor (Spectral Intensity Bioaerosol Sensor). Specifically focussing on advancing knowledge on bioaerosol fluorescence characteristics, developing new data analytic systems for bioaerosol analysis and elucidating the size segregated temporal profiles of bioaerosols. This will be achieved by both lab-based and real-world experiments to generate new data which will allow the development and validation of spectral profiles for bioaerosols with a focus on indoor bacteria and mould. Thereafter, allowing the development and validation of optimised discrimination and classification methods for single for single particle multichannel UV-LIF measurements. This new quantitative evidence will advance our knowledge on how various environmental factors such as building designs, construction material, damp, temperature, humidity, ventilation, outdoor environments, along with human activities determines indoor bioaerosol concentrations and compositions. This will inform policies, practises, and solutions (Structural, environmental, or behavioural) to manage bioaerosol risks in residential environments, as well as developing and validating artificial intelligence (AI) based numerical prediction models to reduce or eliminate exposure to harmful bioaerosols while balancing needs for energy efficiency and indoor air quality of residential environments. Additionally, the findings will inform the design and development of new portable, cost-effective, high-resolution bioaerosol sensors.