The Impact of Dust Particles on the Function of Screen-Printed Triple-Mesoscopic Perovskite Solar Cells
Abstract
Screen-printed mesoporous carbon perovskite solar cells (m-CPSCs) offer a low-cost manufacturing approach that could significantly reduce upscaling costs, providing an accessible pathway to green energy production opening new opportunities in countries where this is currently cost-prohibitive. Clean rooms represent a very expensive investment and overhead, presenting a significant barrier to reducing production cost. Despite this, very little research has been carried out to determine whether a clean room is essential to PSC manufacture. This study examines how organic dust particle contaminants (1-10 µm) impact screen-printed m-CPSCs. Unexpectedly, cells with contamination between layers showed no significant difference in PCE compared to pristine controls. Dust in pastes affected print quality more severely, negatively impacting the quality of subsequent layers and leading to inconsistencies in print quality, compounding problems further along in the manufacturing process if screens and pastes are not kept free from such contamination. These findings suggest that screen-printed solar cells can be produced outside of clean rooms if high cleanliness standards are maintained during storage and print runs. This outcome is significant for low-cost PSC printing, indicating that it may be possible to produce high performing modules in industrial setups without expensive clean room installation and management.
Read the paper
Related studies
- The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices
- The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
- In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
- In silico prediction of splice-altering single nucleotide variants in the human genome
- In silico clinical trials: how computer simulation will transform the biomedical industry
- In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.