Unifying Charge Generation,Recombination, and Extraction in Low‐Offset Non‐Fullerene Acceptor Organic Solar Cells

Even though significant breakthroughs with over 18% power conversion efficiencies (PCEs) in polymer:non‐fullerene acceptor (NFA) bulk heterojunction organic solar cells (OSCs) have been achieved, not many studies have focused on acquiring a comprehensive understanding of the underlying mechanisms governing these systems. This is because it can be challenging to delineate device photophysics in polymer:NFA blends comprehensively, and even more complicated to trace the origins of the differences in device photophysics to the subtle differences in energetics and morphology. Here, a systematic study of a series of polymer:NFA blends is conducted to unify and correlate the cumulative effects of i) voltage losses, ii) charge generation efficiencies, iii) non‐geminate recombination and extraction dynamics, and iv) nuanced morphological differences with device performances. Most importantly, a deconvolution of the major loss processes in polymer:NFA blends and their connections to the complex BHJ morphology and energetics are established. An extension to advanced morphological techniques, such as solid‐state NMR (for atomic level insights on the local ordering and donor:acceptor π? π interactions) and resonant soft X‐ray scattering (for donor and acceptor interfacial area and domain spacings), provide detailed insights on how efficient charge generation, transport, and extraction processes can outweigh increased voltage losses to yield high PCEs.     

<Emphasis Type="Italic">Scoredist</Emphasis>: A simple and robust protein sequence distance estimator

Background  

Distance-based methods are popular for reconstructing evolutionary trees thanks to their speed and generality. A number of methods exist for estimating distances from sequence alignments, which often involves some sort of correction for multiple substitutions. The problem is to accurately estimate the number of true substitutions given an observed alignment. So far, the most accurate protein distance estimators have looked for the optimal matrix in a series of transition probability matrices, e.g. the Dayhoff series. The evolutionary distance between two aligned sequences is here estimated as the evolutionary distance of the optimal matrix. The optimal matrix can be found either by an iterative search for the Maximum Likelihood matrix, or by integration to find the Expected Distance. As a consequence, these methods are more complex to implement and computationally heavier than correction-based methods. Another problem is that the result may vary substantially depending on the evolutionary model used for the matrices. An ideal distance estimator should produce consistent and accurate distances independent of the evolutionary model used.     

Improved profile HMM performance by assessment of critical algorithmic features in SAM and HMMER

Background  

Profile hidden Markov model (HMM) techniques are among the most powerful methods for protein homology detection. Yet, the critical features for successful modelling are not fully known. In the present work we approached this by using two of the most popular HMM packages: SAM and HMMER. The programs' abilities to build models and score sequences were compared on a SCOP/Pfam based test set. The comparison was done separately for local and global HMM scoring.