Identifying cheap, yet effective, oxygen evolution catalysts is critical to the advancement of water splitting. Using liquid exfoliated Co(OH)2 nanosheets as a model system, we developed a simple procedure to maximise the activity of any OER nano-catalyst. We first confirmed the nanosheet edges as the active areas by analysing the catalytic activity as a function of nanosheet size. This allowed us to select the smallest nanosheets (length~50 nm) as the best performing catalysts. While the number of active sites per unit electrode area can be increased via the electrode thickness, we found this to be impossible beyond ~10 m due to mechanical instabilities. However, adding carbon nanotubes increased both toughness and conductivity significantly.These enhancements meant that composite electrodes consisting of small Co(OH)2 nanosheets and 10wt% nanotubes could be made into free-standing films with thickness of up to 120 m with no apparent electrical limitations. The presence of diffusion limitations resulted in an optimum electrode thickness of 70 m, yielding a current density of 50 mA cm -2 at an overpotential of 235 mV, close to the state of the art in the field. Applying this procedure to a high performance catalyst such as NiFeOx should significantly surpass the state-of-the-art.Keywords: nano-catalyst, layered material, exfoliation, oxygen evolution reaction, sizedependence 2 ToC figToC text: Liquid exfoliation of Co(OH)2 yields suspensions of nanosheets which are easily processed and so optimised for OER catalysis. This processability has allowed the variation of nanosheet size and the production of catalytic electrodes with controlled thickness as well as the addition of carbon nanotubes to enhance electrode conductivity and strength. This has resulted in an optimised electrode design with near record performance.

With sharp increasing in biological sequences, the traditional sequence alignment methods become unsuitable and infeasible. It motivates a surge of fast alignment-free techniques for sequence analysis. Among these methods, many sorts of feature vector methods are established and applied to reconstruction of species phylogeny. The vectors basically consist of some typical numerical features for certain biological problems. The features may come from the primary sequences, secondary or three dimensional structures of macromolecules. In this study, we propose a novel numerical vector based on only primary sequences of organism to build their phylogeny. Three chemical and physical properties of primary sequences: purine, pyrimidine and keto are also incorporated to the vector. Using each property, we convert the nucleotide sequence into a new sequence consisting of only two kinds of letters. Therefore, three sequences are constructed according to the three properties. For each letter of each sequence we calculate the number of the letter, the average position of the letter and the variation of the position of the letter appearing in the sequence. Tested on several datasets related to mammals, viruses and bacteria, this new tool is fast in speed and accurate for inferring the phylogeny of organisms.

Classification of DNA sequences is an important issue in the bioinformatics study, yet most existing methods for phylogenetic analysis including Multiple Sequence Alignment (MSA) are time-consuming and computationally expensive. The alignment-free methods are popular nowadays, whereas the manual intervention in those methods usually decreases the accuracy. Also, the interactions among nucleotides are neglected in most methods. Here we propose a new Accumulated Natural Vector (ANV) method which represents each DNA sequence by a point in ℝ 18 . By calculating the Accumulated Indicator Functions of nucleotides, we can further find an Accumulated Natural Vector for each sequence. This new Accumulated Natural Vector not only can capture the distribution of each nucleotide, but also provide the covariance among nucleotides. Thus global comparison of DNA sequences or genomes can be done easily in ℝ 18 . The tests of ANV of datasets of different sizes and types have proved the accuracy and time-efficiency of the new proposed ANV method.

Zika virus (ZIKV) is a mosquito-borne flavivirus. It was first isolated from Uganda in 1947 and has become an emergent event since 2007. However, because of the inconsistency of alignment methods, the evolution of ZIKV remains poorly understood. In this study, we first use the complete protein and an alignment-free method to build a phylogenetic tree of 87 Zika strains in which Asian, East African, and West African lineages are characterized. We also use the NS5 protein to construct the genetic relationship among 44 Zika strains. For the first time, these strains are divided into two clades: African 1 and African 2. This result suggests that ZIKV originates from Africa, then spread to Asia, Pacific islands, and throughout the Americas. We also perform the phylogeny analysis for 53 viruses in genus Flavivirus to which ZIKV belongs using complete proteins. Our conclusion is consistent with the classification by the hosts and transmission vectors.