Public defence in Electronics Integration and Reliability, M.Sc.(Tech.) Anja Aarva

M.Sc.(Tech.) Anja Aarva will defend the thesis "Understanding the chemistry of customized carbonaceous nanomaterials" on 15 December 2022 at 12 (EET) in Aalto University School of Electrical Engineering, Department of Electrical Engineering and Automation, in lecture hall TU1, Maarintie 8, Espoo.

Opponent: Dr. Annika Bande, Helmholtz-Zentrum Berlin fur Materialien und Energie, Germany
Custos: Prof. Tomi Laurila, Aalto University School of Electrical Engineering, Department of Electrical Engineering and Automation

Thesis available for public display 10 days prior to the defence at: https://aaltodoc.aalto.fi/doc_public/eonly/riiputus/
Doctoral theses in the School of Electrical Engineering: https://aaltodoc.aalto.fi/handle/123456789/53

Public defence announcement:

Carbon-based materials can be used in several high technology applications. In our group the focus is set on sensors for monitoring neurotransmitter levels or drug concentrations. This can provide better means for diagnostics of several neurological disorders, for instance Parkinson’s disease and Alzheimer's disease, or to offer more accurate pain management, respectively. In addition to medical applications, carbonaceous materials are used in batteries, solar cells and biomedical coatings, to name few.

In this work we first investigated electrochemical properties of carbon surfaces, which is important with respect to sensor development. In electrochemical detection of neurotransmitters, the interaction between the surface and the molecule plays an important role. We studied this interaction with density functional theory in order to understand how strongly the molecule in question is connected with the surface. Three types of carbonaceous surfaces were compared: graphene, diamond and amorphous carbon. These steps that were taken in order to aid sensor development provided motivation for further investigation of carbon-based structures.

Carbonaceous materials usually contain also other elements than carbon, such as hydrogen and oxygen. Their presence alters the properties of the material. In order to obtain desired properties for certain application, atomic-level knowledge of the carbon structure is required. Again, we compared previously mentioned forms of carbon, now both plain and functionalized. First we carried out a detailed study of the structure of amorphous carbon and rationalized it via machine learning based clustering. Then we moved on to characterization.

X-ray spectroscopy provides detailed information about the atomic structure of a sample. However, interpretation of the results can be challenging. In this thesis a new computational method that can be used to analyze the spectra is introduced. In this methodology we combined density functional theory with machine learning in order to study experimentally measured spectra. This reveals local chemical environments that are present in the sample.

In conclusion, this thesis combines computational data with experimental measurements in order to develop tailor made carbon-based nanomaterials which can be utilized in wide range of applications.

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