Loss Mechanisms in Thick‐Film Low‐Bandgap Polymer Solar Cells

Polymer bulk heterojunction solar cells based on low bandgap polymer:fullerene blends are promising for next generation low‐cost photovoltaics. While these solution‐processed solar cells are compatible with large‐scale roll‐to‐roll processing, active layers used for typical laboratory‐scale devices are too thin to ensure high manufacturing yields. Furthermore, due to the limited light absorption and optical interference within the thin active layer, the external quantum efficiencies (EQEs) of bulk heterojunction polymer solar cells are severely limited. In order to produce polymer solar cells with high yields, efficient solar cells with a thick active layer must be demonstrated. In this work, the performance of thick‐film solar cells employing the low‐bandgap polymer poly(dithienogermole‐thienopyrrolodione) (PDTG‐TPD) was demonstrated. Power conversion efficiencies over 8.0% were obtained for devices with an active layer thickness of 200 nm, illustrating the potential of this polymer for large‐scale manufacturing. Although an average EQE > 65% was obtained for devices with active layer thicknesses > 200 nm, the cell performance could not be maintained due to a reduction in fill factor. By comparing our results for PDTG‐TPD solar cells with similar P3HT‐based devices, we investigated the loss mechanisms associated with the limited device performance observed for thick‐film low‐bandgap polymer solar cells.     

STEPS: A grid search methodology for optimized peptide identification filtering of MS/MS database search results

For bottom‐up proteomics, there are wide variety of database‐searching algorithms in use for matching peptide sequences to tandem MS spectra. Likewise, there are numerous strategies being employed to produce a confident list of peptide identifications from the different search algorithm outputs. Here we introduce a grid‐search approach for determining optimal database filtering criteria in shotgun proteomics data analyses that is easily adaptable to any search. Systematic Trial and Error Parameter Selection‐–referred to as STEPS‐–utilizes user‐defined parameter ranges to test a wide array of parameter combinations to arrive at an optimal “parameter set” for data filtering, thus maximizing confident identifications. The benefits of this approach in terms of numbers of true‐positive identifications are demonstrated using datasets derived from immunoaffinity‐depleted blood serum and a bacterial cell lysate, two common proteomics sample types.