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Chemical polymerization of a 3,4-ethylenedioxythiophene derivative bearing a sulfonate group (EDOT-S) is reported. The polymer, PEDOT-S, is fully water-soluble and has been produced by polymerizing EDOT-S in water, using Na2S2O8 and a catalytic amount of FeCl3. Elemental analysis and XPS measurements indicate that PEDOT-S is a material with a substantial degree of self-doping, but also contains free sulfate ions as charge-balancing counterions of the oxidized polymer. Apart from self-doping PEDOT-S, the side chains enable full water solubility of the material; DLS studies show an average cluster size of only 2 nm. Importantly, the solvation properties of the PEDOT-S are reflected in spin-coated films, which show a surface roughness of 1.2 nm and good conductivity (12 S/cm) in ambient conditions. The electro-optical properties of this material are shown with cyclic voltammetry and spectroelectrochemical experiment reveals an electrochromic contrast (∼48% at λmax = 606 nm).

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Phylogenetic inference is widely used to investigate the relationships between homologous sequences. RNA molecules have played a key role in these studies because they are present throughout life and tend to evolve slowly. Phylogenetic inference has been shown to be dependent on the substitution model used. A wide range of models have been developed to describe RNA evolution, either with 16 states describing all possible canonical base pairs or with 7 states where the 10 mismatched nucleotides are reduced to a single state. Formal model selection has become a standard practice for choosing an inferential model and works well for comparing models of a specific type, such as comparisons within nucleotide models or within amino acid models. Model selection cannot function across different sized state spaces because the likelihoods are conditioned on different data. Here, we introduce statistical state-space projection methods that allow the direct comparison of likelihoods between nucleotide models and 7-state and 16-state RNA models. To demonstrate the general applicability of our new methods, we extract 287 RNA families from genomic alignments and perform model selection. We find that in 281/287 families, RNA models are selected in preference to nucleotide models, with simple 7-state RNA models selected for more conserved families with shorter stems and more complex 16-state RNA models selected for more divergent families with longer stems. Other factors, such as the function of the RNA molecule or the GC-content, have limited impact on model selection. Our models and model selection methods are freely available in the open-source PHASE 3.0 software.

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