Speaker
Description
Heavy metal contamination of water bodies caused by artisanal and small-scale gold mining, locally known as galamsey, remains a major environmental and public health challenge in Ghana. Mercury, lead and arsenic-related species are of particular concern because of their toxicity, persistence and potential for bioaccumulation. Metal–organic frameworks are promising adsorbents for heavy metal sequestration due to their tuneable pore structures, functional linkers and chemically specific active sites. However, conventional material discovery often relies on experimental trial-and-error and classical screening, which may not fully resolve the electronic origin of metal–framework binding.
This work presents a quantum machine learning-oriented computational framework for designing metal–organic frameworks optimised for heavy metal sequestration in galamsey-polluted waters. The study focuses on thiolated zirconium-based MOFs, especially UiO-66-SH, for Hg(^{2+}) capture. The local binding process is reduced to a chemically meaningful Hg–S active-site model, where sulfur donor orbitals interact with mercury acceptor orbitals. Density functional theory is used to screen adsorption energy, bond distance, charge transfer and selectivity descriptors. The selected active-site model is then converted into a second-quantised Hamiltonian, mapped to qubits using Jordan–Wigner transformation and solved using the Variational Quantum Eigensolver.
The proposed DFT–VQE workflow provides a rational route for ranking MOF candidates before synthesis and experimental validation. This work demonstrates how quantum algorithms can support environmental materials design by linking molecular electronic structure, qubit Hamiltonians and adsorption selectivity for sustainable water remediation.
| Topic | Quantum variational circuits and their applications |
|---|---|
| Additional Topic | Hybrid quantum-classical approaches for learning and optimization |