Tudur David
Tudur David
Verified email at bangor.ac.uk
Cited by
Cited by
Using large datasets of organic photovoltaic performance data to elucidate trends in reliability between 2009 and 2019
TW David, H Anizelli, P Tyagi, C Gray, W Teahan, J Kettle
IEEE Journal of Photovoltaics 9 (6), 1768-1773, 2019
Enhancing the stability of organic photovoltaics through machine learning
TW David, H Anizelli, TJ Jacobsson, C Gray, W Teahan, J Kettle
Nano Energy 78, 105342, 2020
Enhancing the stability of perovskite solar cells through functionalisation of metal oxide transport layers with self-assembled monolayers
H Anizelli, TW David, P Tyagi, E Laureto, J Kettle
Solar Energy 203, 157-163, 2020
Multivariate approach for studying the degradation of perovskite solar cells
P Tyagi, TW David, VD Stoichkov, J Kettle
Solar Energy 193, 12-19, 2019
A Novel Computational Model for Organic PV Cells and Modules
G Todeschini, H Huang, N Bristow, TW David, J Kettle
International Journal of Smart Grid-ijSmartGrid 4 (4), 157-163, 2020
Outdoor performance of organic photovoltaics at two different locations: A comparison of degradation and the effect of condensation
GA Soares, TW David, H Anizelli, B Miranda, J Rodrigues, P Lopes, ...
Journal of Renewable and Sustainable Energy 12 (6), 063502, 2020
Development of an improved computer model for organic photovoltaic cells
H Huang, T Coote, N Bristow, TW David, J Kettle, G Todeschini
2020 9th International Conference on Renewable Energy Research and …, 2020
Forecasting OPV outdoor performance, degradation rates and diurnal performances via machine learning
T David, G Amorim, D Bagnis, N Bristow, S Selbach, J Kettle
2020 47th IEEE Photovoltaic Specialists Conference (PVSC), 0412-0418, 2020
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