An Automated Analysis of Homocoupling Defects Using MALDI-MS and Open-Source Computer Software.

Conjugated organic polymers have substantial potential for multiple applications but their properties are strongly influenced by structural defects such as homocoupling of monomer units and unexpected end-groups. Detecting and/or quantifying these defects requires complex experimental techniques, which hinder the optimization of synthesis protocols and fundamental studies on the influence of structural defects. Mass spectrometry offers a simple way to detect these defects but a manual analysis of many complex spectra is tedious and provides only approximate results.

On the critical competition between singlet exciton decay and free charge generation in non-fullerene based organic solar cells with low energetic offsets.

Reducing voltage losses while maintaining high photocurrents is the holy grail of current research on non-fullerene acceptor (NFA) based organic solar cell. Recent focus lies in understanding the various fundamental mechanisms in organic blends with minimal energy offsets - particularly the relationship between ionization energy offset (ΔIE) and free charge generation. Here, we quantitatively probe this relationship in multiple NFA-based blends by mixing Y-series NFAs with PM6 of different molecular weights, covering a broad power conversion efficiency (PCE) range: from 15% down to 1%.

Single-component organic solar cells-Perspective on the importance of chemical precision in conjugated block copolymers.

Organic photovoltaics (OPV) present a promising thin-film solar cell technology with particular benefits in terms of weight, aesthetics, transparency, and cost. However, despite being studied intensively since the mid 90's, OPV has not entered the mass consumer market yet. Although the efficiency gap with other thin-film photovoltaics has largely been overcome, active layer stability and performance reproducibility issues have not been fully resolved.

Ultra-high-resolution UAV-imaging and supervised deep learning for accurate detection of in potato fields.

is the second most devastating foliar pathogen of potato crops worldwide, causing premature defoliation of the plants. This disease is currently prevented through the regular application of detrimental crop protection products and is guided by early warnings based on weather predictions and visual observations by farmers. To reduce the use of crop protection products, without additional production losses, it would be beneficial to be able to automatically detect in potato fields.