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@ARTICLE{Gholami:911113,
      author       = {Gholami, Nahid and Yasari, Elham and Farhadian, Nafiseh and
                      Malek, Kourosh},
      title        = {{A} {D}ata‐{D}riven framework for prediction the cyclic
                      voltammetry and polarization curves of polymer electrolyte
                      fuel cells using artificial neural networks},
      journal      = {International journal of energy research},
      volume       = {46},
      number       = {15},
      issn         = {0363-907X},
      address      = {London [u.a.]},
      publisher    = {Wiley-Intersience},
      reportid     = {FZJ-2022-04439},
      pages        = {20916-20927},
      year         = {2022},
      abstract     = {The primary goal of this research is to predict the cyclic
                      voltammetry and polarization curves of proton exchange
                      membrane fuel cells (PEMFC) without conducting any
                      experiments. For the first time ever, artificial neural
                      network (ANN) is applied to introduce a framework for PEMFC
                      that is composed of various catalyst layers. Carbon-based
                      cathode materials, such as reduced graphene oxide, graphene
                      oxide, graphene nanoplatelets, and carbon black and their
                      hybrids, including various Pt catalyst content, are being
                      investigated. Important properties of cathode materials,
                      such as surface area, Pt percentage, and Pt nanoparticle
                      size were investigated for the classification of various
                      groups. Results showed that total cathode surface area and
                      Pt content are suitable for more precise data classification
                      and are selected as input variables (features), whereas
                      electrochemically active surface area, cyclic voltammetry,
                      and polarization curves are selected as output responses of
                      ANN. In this framework, experimental data for various
                      cathode materials is initially classified using support
                      vector machines and then ANN models are applied to predict
                      the cyclic voltammetry and polarization curves. Results
                      indicate that data are well classified into four main
                      groups, allowing an ANN to achieve the best prediction of
                      curves with a mean square error of less than $0.3\%$ and a
                      relative error of $0.5\%.$ Also, with the help of the
                      polarization curve, the maximum production power vs
                      different voltages can be evaluated. By applying this model,
                      it will be possible to get the necessary electrochemical
                      data for an unknown carbon-based cathode material of a
                      PEMFC. Finally, ANN applications can be proposed as a useful
                      tool for predicting the main cyclic voltammetry and
                      polarization curves of fuel cells.},
      cin          = {IEK-13},
      ddc          = {620},
      cid          = {I:(DE-Juel1)IEK-13-20190226},
      pnm          = {1231 - Electrochemistry for Hydrogen (POF4-123)},
      pid          = {G:(DE-HGF)POF4-1231},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000849660500001},
      doi          = {10.1002/er.8624},
      url          = {https://juser.fz-juelich.de/record/911113},
}