Health and Climate

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Multi-objective Bayesian optimisation of gas sensors

Project Lead: Ransell D’Souza

For the rational design of toxic gas sensors, it is equally important to optimise molecular gas adsorption to the substrate and maximise sensor response. We combine multi-objective Bayesian optimisation with density functional theory and Boltzmann transport equations to compute and balance different functional properties and sensor design considerations.

Partner(s): Prof. Eduard Llobet, Universitat Rovira i Virgili, ES

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Exploring graphene-based biosensing of estradiol hormone

Project Lead: Saara Sippola

Innovative carbon-based electrochemical sensors were proposed to measure blood hormone levels, but little is known about the hormone adsorption mechanisms at the nanoscale. We teamed up with our MHT colleagues for an AI-driven study of 17-Beta-estradiol adsorption on graphene substrates.

Partner(s): Prof. Emilia Peltola, Materials in Health Technology group, UTU

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Biosensors for dopamine based on single-walled carbon nanotubes

Project Lead: Saara Sippola and Matilda Laurila

Dopamine is an important neurotransmitter and recent experimental studies show that it can be detected by carbon nanotubes, depending on their chirality. Our AI-driven adsorption study will shed light on the atomic mechanisms behind this process.

Partner(s): Prof. Emilia Peltola, Materials in Health Technology group, UTU

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Decoding the surface chemistry of atmospheric aerosols

Project Lead: Mandira Das

Aerosol particles in the atmosphere grow by adsorbing layers on water and atmospheric molecules, but their surface chemistry and reactivity is not well understood. Our AI-driven adsorption simulations for atmospheric molecules on aerosol surfaces will help clarify the observations in ambient pressure X-ray photoelectron spectroscopy (AP-XPS) experiments.

Partner(s): Prof. Nønne Prisle, ATMOS group, University of Oulu, FI