Anja Aarva

Aalto University — Finland

Materials Science · Physical Sciences

11h-index719citations19works0.02yr avg
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Research Topics

Machine Learning in Materials Science(9), Electron and X-Ray Spectroscopy Techniques(5), Advancements in Solid Oxide Fuel Cells(4), Diamond and Carbon-based Materials Research(4), X-ray Spectroscopy and Fluorescence Analysis(4)

Publications19 total

Accurate Computational Prediction of Core-Electron Binding Energies in Carbon-Based Materials: A Machine-Learning Model Combining Density-Functional Theory and <i>GW</i>
Chemistry of Materials·2022· 58 citedOpen Access
X-ray Spectroscopy Fingerprints of Pristine and Functionalized Graphene
The Journal of Physical Chemistry C·2021· 24 citedOpen Access
Connection between the physicochemical characteristics of amorphous carbon thin films and their electrochemical properties
Journal of Physics Condensed Matter·2021· 8 citedOpen Access
Data from Understanding X-ray spectroscopy of carbonaceous materials by combining experiments, density functional theory and machine learning. Parts I and II.
Zenodo (CERN European Organization for Nuclear Research)·2021Open Access
Data from Understanding X-ray spectroscopy of carbonaceous materials by combining experiments, density functional theory and machine learning. Parts I and II.
Figshare·2021Open Access
Trends in Carbon, Oxygen, and Nitrogen Core in the X-ray Absorption Spectroscopy of Carbon Nanomaterials: A Guide for the Perplexed
The Journal of Physical Chemistry C·2020· 65 citedOpen Access
Undoped Tetrahedral Amorphous Carbon (ta-C) Thin Films for Biosensing
2020
Trends in Carbon, Oxygen, and Nitrogen Core in the X-ray Absorption Spectroscopy of Carbon Nanomaterials: A Guide for the Perplexed
2020
Biofouling affects the redox kinetics of outer and inner sphere probes on carbon surfaces drastically differently – implications to biosensing
Physical Chemistry Chemical Physics·2020· 23 citedOpen Access
Understanding X-ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part II: Quantitative Fitting of Spectra
Chemistry of Materials·2019· 76 citedOpen Access
Understanding X-ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part I: Fingerprint Spectra
Chemistry of Materials·2019· 104 citedOpen Access
Reactivity of Amorphous Carbon Surfaces: Rationalizing the Role of Structural Motifs in Functionalization Using Machine Learning
Chemistry of Materials·2018· 105 citedOpen Access
Computational Surface Chemistry of Tetrahedral Amorphous Carbon by Combining Machine Learning and Density Functional Theory
Chemistry of Materials·2018· 96 citedOpen Access
Doping as a means to probe the potential dependence of dopamine adsorption on carbon-based surfaces: A first-principles study
The Journal of Chemical Physics·2017· 13 citedOpen Access
SU-8 based pyrolytic carbon for the electrochemical detection of dopamine
Journal of Materials Chemistry B·2017· 37 citedOpen Access
SOFC and MCFC: Commonalities and opportunities for integrated research
International Journal of Hydrogen Energy·2010· 107 cited
From Energy Policies to Active Components in Solid Oxide Fuel Cells: State-of-the-Art and the Way Ahead
ECS Transactions·2009· 2 cited
From Energy Policies to Active Components in Solid Oxide Fuel Cells: State-of-the-Art and the Way Ahead
ECS Meeting Abstracts·2009Open Access
Effect of Ethene Impurity on Performance of Solid Oxide Fuel Cell
ECS Transactions·2007· 1 cited

Frequent Co-authors

Tomi Laurila(13), A. Miguel(10), Sami Sainio(8), Volker L. Deringer(5), Stephen J. McPhail(3), Angelo Moreno(3), Niklas Wester(3), Elli Leppänen(3), Gábor Cśanyi(2), Charles J. Titus(2), Dennis Nordlund(2), Esko I. Kauppinen(2), Tommi Palomäki(2), Jessica E. Koehne(2), Olli Pitkänen(2), Krisztián Kordás(2), Maria Kim(2), Harri Lipsanen(2), Miran Mozetič(2), M. Meyyappan(2)