In the three-phase (pure donor, pure acceptor, and mixed phases) morphologies of organic solar cells, the mixed phases produce an energy cascade that promotes the generation of free carriers. However, how to optimize the content of the mixed phases is a challenging problem. The authors proposed to control different content of mixed phases in DRTB-T and IDIC blends by additive and solvent vapor annealing (SVA). The authors first formed the largest extent amount of mixed phases by the additive cinene (2%) to inhibit the crystallization of DRTB-T and IDIC. And then, different amounts of mixed phases were achieved by further SVA for different times (from 0 to 50 s) to increase the content of pure DRTB-T and IDIC phases. The energetic offsets (ΔE) of pure and mixed phases gradually decrease from 0.529 to 0.477 eV for different content of mixed phases. When ΔE was 0.498 eV, the highest photocurrent density (Jsc) was obtained. The power conversion efficiency was increased from 3.23% (without any treatment) to 8.54%. Therefore, the authors demonstrated that the optimized content of the mixed phases is critical to device performance.

A linkage–linkage disequilibrium map that describes the pattern and extent of linkage dis-equilibrium (LD) decay with genomic distance has now emerged as a viable tool to unravel the genetic structure of population differentiation and fine-map genes for complex traits. The prerequisite for constructing such a map is the simultaneous estimation of the linkage and LD between different loci. Here, we develop a computational algorithm for simultaneously estimating the recombination fraction and LD in a natural outcrossing population with multilocus marker data, which are often estimated separately in most molecular genetic studies. The algorithm is founded on a commonly used progeny test with open-pollinated offspring sampled from a natural population. The information about LD is reflected in the co-segregation of alleles at different loci among parents in the population. Open mating of parents will reveal the genetic linkage of alleles during meiosis. The algorithm was constructed within the polynomial-based mixture framework and implemented with the Expectation–Maximization (EM) algorithm. The by-product of the derivation of this algorithm is the estimation of outcrossing rate, a parameter useful to explore the genetic diversity of the population. We performed computer simulation to investigate the influences of different sampling strategies and different values of parameters on parameter estimation. By providing a number of testable hypotheses about population genetic parameters, this algorithmic model will open a broad gateway to understand the genetic structure and dynamics of an outcrossing population under natural selection.