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Presentation on "Atomistic modelling on the detection of gas-phase radicals by functionalized silicon nanowires" for the XXXV Panhellenic conference.

Authors: Kaltsas, Dimitrios; Tsetseris, Leonidas;

Presentation on "Atomistic modelling on the detection of gas-phase radicals by functionalized silicon nanowires" for the XXXV Panhellenic conference.

Abstract

Abstract: Air pollution is one of the biggest environmental threats that society faces today, directly affecting human health and the earth’s climate. Atmospheric radicals, in particular hydroxyl (•OH) and nitrate (•NO3) are known to play a crucial role in transforming the air and have been coined as “the detergents of the atmosphere”. On the one hand these radicals help cleanse the troposphere by reacting with different pollutants, on the other hand they may also lead to formation of secondary pollutants like ground level ozone and acid rain. The concentration of atmospheric radicals varies greatly depending on environmental conditions (such as cloudiness, humidity and presence of pollutants), with typical concentrations less than 1 ppt due to their short lifetimes. Detection therefore proves to be challenging, relying on expensive and complex techniques that can only be performed in specialized labs. A major research goal in atmospheric science is to be able to detect these gas-phase radicals at different times and places on earth in order to better understand their behavior and impact on the atmosphere. One possibility is to develop an electronic device based on functionalized silicon nanowires that can act as sensors for the electrical detection of OH and NO3. To this end, we used quantum-mechanical first-principles calculations based on density-functional theory (DFT) to study the atomic-scale details of the interactions between the target radicals and the sensing molecules. We first examined the interactions between radicals and different functionalization molecules (alkenes, gallic acid, β-pinene and others) in the gas-phase in order to determine suitable candidates. The main goal was to identify molecules that produce a significant change in the dipole moment (as this can induce a signal on the surface of the nanowire), while also being selective towards the detection of radicals rather than to other abundant atmospheric species (e.g. O3, NO2, H2O). Two main types of reactions were investigated, radical adduct formation (RAF) and hydrogen abstraction (HA). In the first type (RAF) the radicals are bound to unsaturated C atoms of the sensing molecule, while in the second type (HA) a hydrogen atom from the sensing molecule is transferred to the free radical. We then studied how these sensing molecules may form self-assembled organic adlayers on Si and SiO2 surfaces. Through DFT and ab initio molecular dynamics simulations we probed the stability and morphology of the functionalized surfaces and how the length and packing of the molecules may affect detection. In addition, the sensing molecules were also tested for their selectivity, by probing their interactions with ozone, both as free molecules and on silicon surfaces. Overall, the study showcased atomic-scale processes that could play a decisive role in the successful detection of the OH and NO3 radicals. Additional Information: Dimitrios Kaltsas delivered this presentation for the 35th Panhellenic Conference on Solid State Physics and Materials Science, an interdisciplinary event that brings together physicists, material scientists, chemists and engineers to meet and discuss their state-of-the-art research activities on solid state physics and materials science. RADICAL represents a 'Fundamental Breakthrough in Detection of Atmospheric Free Radicals'. Find out more on the RADICAL project website: radical-air.eu The RADICAL project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 899282.

Keywords

Atmospheric radicals, Radical, Air Quality Monitoring, Environmental monitoring, Gas sensor, DFT, Density Functional Theory

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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