The Application of Artificial Intelligence for Cleaning Surfaces of the Solar Cells to Improve the Voltage Output

2017 ◽  
pp. 577-589 ◽  
Author(s):  
Ahmad Ridho’i
Keyword(s):  
Artificial Intelligence ◽  
Solar Cells ◽  
Voltage Output

scholarly journals Application of Artificial Intelligence for Optimization of Organic Solar Cells Production Process

2020 ◽  
Vol 12 (2) ◽  
pp. 34
Author(s):  
Grazia Lo Sciuto
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Solar Cells ◽  
Computer Science ◽  
Organic Solar Cells ◽  
Full Potential ◽  
Production Chain ◽  
Lecture Notes ◽  
Occurrence Matrix ◽  
Conversion Efficiencies

The study of organic solar cells (OSCs) has been rapidly developed in recent years. Organic solar cell technology is sought after mainly due to the ease of manufacture and their exclusive properties such as mechanical flexibility, light-weight, and transparency. These properties of OSCs are well-suited for unconventional applications with power conversion efficiencies more high than 10%. The flexibility of the used substrates and the thinness of the devices make OSCs ideal for roll-to-roll production. However the organic solar cells still have very low conversion efficiencies due to degradation and stability of the technology. In order to extract their full potential, OSCs have to be optimized. On the other hand the production chain of the organic solar cells (OSC) can take advantage of the use of artificial intelligence (AI). In fact the integration into the production workflow makes solar cells more competitive and efficient. This paper presents some applications of the AI for optimization of OSCs production processes Full Text: PDF ReferencesLo Sciuto, G., Capizzi, G., Coco, S., Shikler, R., "Geometric shape optimization of organic solar cells for efficiency enhancement by neural networks." (2017) Lecture Notes in Mechanical Engineering, pp. 789-796. CrossRef Barnea, S.N., Lo Sciuto, G., Hai, N., Shikler, R., Capizzi, G., Wozniak, M., Polap, D., "Photo-electro characterization and modeling of organic light-emitting diodes by using a radial basis neural network." (2017) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10246 LNAI, pp. 378-389. CrossRef Ye, L.; Hu, H.; Ghasemi, M.; Wang, T.; Collins, B.A.; Kim, J.H.; Jiang, K.; Carpenter, J.H.; Li, H.; Li, Z.; et al. "Quantitative relations between interaction parameter, miscibility and function in organic solar cells." Nat. Mater. 2018, 17, 253-260. CrossRef Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC-3(6), 610-621 (1973) CrossRef Capizzi, G., Sciuto, G.L., Napoli, C., Tramontana, E., Wozniak, M.: Automatic classification of fruit defects based on co-occurrence matrix and neural networks. In: 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 861-867, September 2015. CrossRef

Voltage output of efficient perovskite solar cells with high open-circuit voltage and fill factor

2014 ◽  
Vol 7 (8) ◽  
pp. 2614-2618 ◽  
Author(s):  
Seungchan Ryu ◽  
Jun Hong Noh ◽  
Nam Joong Jeon ◽  
Young Chan Kim ◽  
Woon Seok Yang ◽  
...  
Keyword(s):  
Solar Cells ◽  
Energy Level ◽  
Fill Factor ◽  
Open Circuit Voltage ◽  
Perovskite Solar Cells ◽  
Open Circuit ◽  
Solar Absorber ◽  
Voltage Output ◽  
Hole Transporting ◽  
Hole Transporting Materials

The voltage output of perovskite solar cells is found to be dependent on both the energy level of perovskite itself as a solar absorber and hole transporting materials.

In silico investigation of Cu(In,Ga)Se2-based solar cells

2020 ◽  
Vol 22 (46) ◽  
pp. 26682-26701
Author(s):  
Hossein Mirhosseini ◽  
Ramya Kormath Madam Raghupathy ◽  
Sudhir K. Sahoo ◽  
Hendrik Wiebeler ◽  
Manjusha Chugh ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Solar Cells ◽  
In Silico ◽  
Materials Science ◽  
State Of The Art ◽  
Data Driven ◽  
Photovoltaic Materials ◽  
Art Methods

State-of-the-art methods in materials science such as artificial intelligence and data-driven techniques advance the investigation of photovoltaic materials.

scholarly journals Barbiturate end-capped non-fullerene acceptors for organic solar cells: tuning acceptor energetics to suppress geminate recombination losses

2018 ◽  
Vol 54 (24) ◽  
pp. 2966-2969 ◽  
Author(s):  
Ching-Hong Tan ◽  
Jeffrey Gorman ◽  
Andrew Wadsworth ◽  
Sarah Holliday ◽  
Selvam Subramaniyan ◽  
...  
Keyword(s):  
Solar Cells ◽  
High Voltage ◽  
Organic Solar Cells ◽  
Geminate Recombination ◽  
Voltage Output

We report the synthesis of two barbiturate end-capped non-fullerene acceptors and demonstrate their efficient function in high voltage output organic solar cells.

A thiourea additive-based quadruple cation lead halide perovskite with an ultra-large grain size for efficient perovskite solar cells

Nanoscale ◽  
2019 ◽  
Vol 11 (45) ◽  
pp. 21824-21833 ◽  
Author(s):  
Jyoti V. Patil ◽  
Sawanta S. Mali ◽  
Chang Kook Hong
Keyword(s):  
Thin Films ◽  
Grain Size ◽  
Solar Cells ◽  
Power Conversion Efficiency ◽  
Conversion Efficiency ◽  
Power Conversion ◽  
Perovskite Solar Cells ◽  
Inorganic Perovskite ◽  
Halide Perovskite ◽  
Lead Halide

Controlling the grain size of the organic–inorganic perovskite thin films using thiourea additives now crossing 2 μm size with >20% power conversion efficiency.

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