DrXue Ning

My key research interests include anaerobic digestion, electrode-assisted biological processes, microbial electrosynthesis, biogenic CO₂ utilisation, biochar-derived functional materials, and bio- and photoelectrochemical systems. A central theme of my work is the engineering of material–microbe and electrode–microbe interfaces to enhance electron transfer, microbial activity and resource recovery.

Topic 1: Biogenic CO₂ utilisation from microbial electrosynthesis

Microbial electrosynthesis or MES is an integrated electrocatalytic and biocatalytic platform that employs microorganisms as biocatalysts to facilitate the reduction of CO₂ into value-added organic compounds using electrons supplied by a cathode. My research includes bioelectrode design and optimisation (such as gas diffusion electrode or biomass-derived electrode), bioprotein and bio-alcohol production, Multiphysics simulation (including concentration and electrical field).

Topic 2: Contaminant removal through hybrid bio-electrochemical engineering

Electroactive bacteria (such as Shewanella oneidensis, a model exoelectrogen) has the capability to degrade contaminants by using them as electron donors, then transfer these electrons to the anode in a bio-electrochemical system. My research focus on engineered living materials (3D bioprinting of exoelectrogens within synthetic abiotic electrode architectures) design, kinetic modelling on microbial-electrode interface (integrating secondary current distribution and concentration diffusion) and emerging antibiotics removal.

Topic 3: Artificial intelligence (AI)-driven bio-electrochemical system design and performance predictions

The design and optimisation of bio-electrochemical systems involve multiple variables, including electrode materials, reactant and product concentrations, operating conditions such as electrode potential, applied voltage and current density. Machine learning (ML) provides a powerful tool to investigate this multi-dimensional complexity, outperforming traditional time-consuming statistical approaches. I am particularly interested in collaborative research focused on identifying optimal materials and operating parameters by ML to maximise biochemical production in bio-electrochemical systems, such as microbial electrosynthesis.