Research

A programme built over twelve years across the CAS Institute of Urban Environment, Harbin Institute of Technology (Shenzhen), and Guangzhou University, spanning three connected pillars: the materials themselves, the catalytic chemistry they enable, and the machine learning that increasingly guides both.

Functional porous materials

Design and synthesis of covalent organic frameworks, β-cyclodextrin polymers, magnetic composites and dendritic fibrous nanosilica (DFNS), each engineered so that pore chemistry does the separation work. The current focus is a green, closed-loop route — leaching, selective adsorption, recovery — that treats industrial sludge and wastewater as an ore body rather than a disposal problem.

Extractant-immobilized dendritic fibrous nanosilica for rapid and selective rare-earth separation from complex aqueous matrices Desalination 2026 D-DEHPA on dendritic nanosilica: ~5 min equilibrium, 153.8 mg/g, and a Dy/La separation factor of 17.3 held up in real industrial leachate.
Ultrafast and selective recovery of rare earths from aqueous streams using a P507-scaffolded dendritic nanosilica Journal of Cleaner Production 2026 86.2 mg/g dysprosium capacity with >95% removal inside five minutes, and ~94% retained over five regeneration cycles.
Selective and fast recovery of rare earth elements from industrial wastewater by porous β-cyclodextrin and magnetic β-cyclodextrin polymers Water Research 2020 Porous and magnetic β-cyclodextrin polymers that sequester Nd, Gd, Eu and Y selectively, and regenerate over five cycles.
A closed-loop system to recycle rare earth elements from industrial sludge using green leaching agents and porous β-cyclodextrin polymer composite Resources, Conservation and Recycling 2022 A closed-loop route recovering 76–87% of the rare earths from industrial sludge using green leaching agents.

Heterogeneous catalysis

Core-shell architectures for pollutant conversion, concentrating on the conditions that break real catalysts: wet, sulfur-laden exhaust at temperatures low enough to be practical. Work spans selective catalytic reduction of NOx by methane and the broader problem of activating the C–H bond at low temperature.

Core-shell In/H-Beta@Ce catalyst with enhanced sulfur and water tolerance for selective catalytic reduction of NOx by CH4 Journal of Hazardous Materials 2025 97.5% NOx conversion at 600 °C; the amorphous ceria shell shields the In/O active sites and limits sulfate formation.
Current Progress on Methods and Technologies for Catalytic Methane Activation at Low Temperatures Advanced Science 2022 Catalytic methane activation from 50–500 °C across thermo-, photo- and electrocatalysis and non-thermal plasma.

ML-accelerated discovery

The newest of the three pillars: using machine learning to model how materials and contaminants interact, to apportion pollution sources, and to narrow the experimental search space before anything reaches the bench.

Machine learning approaches for monitoring environmental metal pollutants: Recent advances in source apportionment, detection, quantification, and risk assessment TrAC Trends in Analytical Chemistry 2024 A survey of machine learning for source apportionment, detection, quantification and risk assessment of metal pollutants.

Methods and capabilities

Synthesis: COFs, β-CD polymers, magnetic composites, dendritic fibrous nanosilica, core-shell catalysts, ionic-liquid-grafted mesoporous materials.
Characterisation: XRD, BET, FT-IR, XPS, SEM-EDX, TEM, ICP-MS, TGA, zeta potential.
Process engineering: Flow reactor systems, adsorption columns, leaching–separation–recovery workflows; techno-economic modelling with sensitivity analysis.
Collaboration: Joint projects across more than ten institutions in China, Africa and the United States; co-supervision of MSc candidates.