PhD Thesis Award 2023

The Graduate School establishes the PhD Thesis Award to recognise, reward and promote the distinguished research achievements by graduating PhD students. There are two award classes, namely Outstanding and Merit, recognising students’ performance of different levels.

PolyU PhD Thesis Award - Outstanding Award
PolyU PhD Thesis Award - Merit Award

Outstanding Award

These findings hold promise for thousands of individuals affected by debilitating conditions, offering renewed hope and an improved quality of life.

School of Optometry

Faculty of Health and Social Sciences

Dr PAN Li

Thesis Title: Neuroprotection through pharmacological targeting retinal immune microenvironment in retinal neurodegenerative diseases

- For contribution to retinal neuroinflammation and neuroprotection

Chief Supervisor: Dr DO Chi-wai

Retinal neurodegeneration is a complex process involving the structural and functional loss of neurons and, ultimately, cell death. It commonly occurs in many ocular diseases, leading to severe vision impairment and even blindness. However, current available treatment options for neurodegenerative diseases are scarce. Neuroinflammation triggered by prolonged microglia activation is increasingly reported as a key driver in the progression of neurodegeneration. Emerging evidence suggests that the suppression of neuroinflammation may present a potential therapy for these diseases. In our studies, we discovered that insulin-like growth factor binding protein-like protein 1 (IGFBPL1) is a key regulator of pro-homeostatic microglia through in-depth RNA-sequencing and single-cell RNA sequencing analyses. IGFBPL1 exerted remarkably potent therapeutic effects by inhibiting neuroinflammation and microglial activation via IGF-1R signalling. In addition, we found that baicalein, a natural flavonoid extracted from the root of Scutellaria baocalensis Georgi (SB), effectively suppressed microglia activation via inhibiting IL-17 pathway, as demonstrated by the proteomic study. Therapeutic administration of IGFBPL1 or baicalein not only suppressed microglia-dependent inflammatory cascades but also exhibited neuroprotective effects and improved visual functions post-retinal I/R injury. These results provide new promising alternatives for treating retinal neurodegenerative diseases by suppressing neuroinflammation.

Merit Award

Overall, this research has shed new light on understanding metal nucleation and growth for stable metal batteries, contributing to the development of sustainable energy storage systems.

Department of Applied Physics

Faculty of Science

Dr HOU Zhen

Thesis Title: Boosting zinc metal anodes performance via interface engineering: reaction kinetics, morphology control and electrochemical reversibility

- For contribution to understanding Zn nucleation and growth towards stable Zn metal batteries

Chief Supervisor: Dr ZHANG Biao

The thesis is devoted to reversible Zn metal anodes through regulating the Zn nucleation and growth. We first demonstrate that the slow desolvation kinetics elevates the nucleation overpotential for improving the nucleation sites through introducing acetonitrile co-solvent into the electrolyte. The electrolyte recipe is further tailored to produce abundant Zn nucleation seeds and smooth Zn growth. The designed oligomer co-solvent enables preferential surface adsorption and the optimized solvation sheath. Furthermore, we propose a facile pulsed cycling protocol where an initial high current density (J) is leveraged to form sufficient nuclei for guiding even metal deposition at standard J in the subsequent process, realizing high-performance Zn, Li and K metal batteries. Besides modulating the Zn nucleation, we also control the Zn growth process by constructing a metallic tin-coated separator. Specifically, its decent electrical conductivity and zincophilicity can eliminate the inevitably formed Zn dendrites via face-to-face Zn growth, thus enabling improved cycle life even at high J and cycling capacity.

This research provides a revolutionary way to understand the properties and behaviours of granular materials and sheds light on how to develop digital twins for structures and infrastructures.

Department of Civil and Environmental Engineering

Faculty of Construction and Environment

Dr ZHANG Pin

Thesis Title: Data-driven modelling of soil properties and behaviours with geotechnical applications

- For contribution to intelligent geomechanics and geoengineering

Chief Supervisor: Professor YIN Zhenyu

Construction in civil engineering relies on case-specific laboratory and in-situ experiments to determine the characterizations of materials and responses of infrastructure. Thus, generated project data can be plentiful but tends to be underused due to limitations of human interpretation. Data-driven modelling (DDM) is a viable and highly effective solution to extract maximum value from these data. Whilst DDM has brought many benefits in a wide range of disciplines, its generalisability and robustness in the civil engineering domain remain uncertain. Addressing these uncertainties is key for the civil engineering community to adopt new DDM towards achieving global targets of net zero by 2050. This area is also a priority in many countries (National AI Strategy). To this end, my thesis elaborated data-driven models combined with existing physics for predicting soil properties and mechanical behaviours merely based on their micro computed-tomography (μCT) images, and revolutionizing mechanics and civil engineering.