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Adeleke Maradesa

Hong Kong University of Science & Technology (HKUST)

  • Hong Kong University of Science & Technology (HKUST)

SCHOLARLY PAPERS

3

DOWNLOADS

235

TOTAL CITATIONS

14

Scholarly Papers (3)

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1.

Deconvolution of Electrochemical Impedance Spectroscopy Data Using the Deep-Neural-Network-Enhanced Distribution of Relaxation Times

Number of pages: 45 Posted: 28 Jul 2022
Emanuele Quattrocchi, Baptiste Py, Adeleke Maradesa, Quentin Meyer, Chuan Zhao and Francesco Ciucci
Hong Kong University of Science & Technology (HKUST), Hong Kong University of Science & Technology (HKUST), Hong Kong University of Science & Technology (HKUST), University of New South Wales (UNSW), University of New South Wales (UNSW) and Hong Kong University of Science & Technology (HKUST) - Department of Mechanical and Aerospace Engineering
Downloads 105 (669,616)
Citation 2

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Electrochemical Impedance Spectroscopy, Distribution of relaxation times, Neural networks, Lithium-ion batteries, Fuel cells

2.

Gaussian Processes for the Analysis of Electrochemical Impedance Spectroscopy Data: Prediction, Filtering, and Active Learning

Number of pages: 39 Posted: 11 Jul 2022
Baptiste Py, Adeleke Maradesa and Francesco Ciucci
Hong Kong University of Science & Technology (HKUST), Hong Kong University of Science & Technology (HKUST) and Hong Kong University of Science & Technology (HKUST) - Department of Mechanical and Aerospace Engineering
Downloads 100 (704,534)
Citation 12

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Electrochemical Impedance Spectroscopy, Gaussian Processes, Filtering, Polarization Resistance, Active Learning

3.

Probabilistic Deconvolution of the Distribution of Relaxation Times from Multiple Electrochemical Impedance Spectra

Number of pages: 38 Posted: 06 Jul 2024
Adeleke Maradesa, Baptiste Py and Francesco Ciucci
Hong Kong University of Science & Technology (HKUST), Hong Kong University of Science & Technology (HKUST) and University of Bayreuth
Downloads 30 (1,345,526)

Abstract:

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Electrochemical impedance spectroscopy, Distribution of relaxation times, Gaussian processes, quasi-GP model, Fuel cells, Batteries