Nanostructured materials have greatly improved the performance of electrochemical energy storage devices because of the increased activity and surface area. However, nanomaterials (e.g. nanocarbons) normally possess low packing density, thus occupy more space which restricts their suitability for making electrochemical devices as compact as possible. This has resulted in their low volumetric performance (capacitance, energy density, and power density), which is a practical obstacle for the application of nanomaterials in mobile and on-board energy storage devices. While rating electrode materials for supercapacitors, their volumetric performance is equally important as the gravimetric metrics and more reliable in particular for systems with limited space. However, the adopted criteria for measuring the volumetric performance of supercapacitors vary in the literature.Identifying the appropriate performance criteria for the volumetric values will set a universal ground for valid comparison. Here, the authors discuss the rationale for quantifying the volumetric performance metrics of supercapacitors from the three progressive levels of materials, electrodes and devices. It is hoped that these thoughts will be of value for the general community in energy storage research.

Siberian apricot (Prunus sibirica L.), an ecologically and economically important tree species with a high degree of tolerance to a variety of extreme environmental conditions, is widely distributed across the mountains of northeastern and northern China, eastern and southeastern regions of Mongolia, Eastern Siberia, and the Maritime Territory of Russia. However, few studies have examined the genetic diversity and population structure of this species. Using 31 nuclear microsatellites, we investigated the level of genetic diversity and population structure of Siberian apricot sampled from 22 populations across China. The number of alleles per locus ranged from 5 to 33, with an average of 19.323 alleles. The observed heterozygosity and expected heterozygosity ranged from 0.037 to 0.874 and 0.040 to 0.924 with average values of 0.639 and 0.774, respectively. A STRUCTURE-based analysis clustered all of the populations into four genetic clusters. Significant genetic differentiation was observed between all population pairs. A hierarchical analysis of molecular variance attributed about 94% of the variation to within populations. No significant difference was detected between the wild and semi-wild groups, indicating that recent cultivation practices have had little impact on the genetic diversity of Siberian apricot. The Mantel test showed that the genetic distance among the populations was not significantly correlated with geographic distance (r = 0.4651, p = 0.9940). Our study represents the most comprehensive investigation of the genetic diversity and population structure of Siberian apricot in China to date, and it provides valuable information for the collection of genetic resources for the breeding of Siberian apricot and related species.

Chinese jujube (Ziziphus jujuba Mill, 2n = 2× = 24, Rhamnaceae) is an economically important Chinese native species. It has high nutritional value, and its medicinal properties have led to extensive use in traditional oriental medicine. The characterization of genotypes using molecular markers is important for genetic studies and plant breeding. However, few simple sequence repeat (SSR) markers are available for this species. In this study, 1,488 unique SSR clones were isolated from Z. jujuba ‘Dongzao’ using enriched genomic libraries coupled with a three-primer colony PCR screening strategy, yielding a high enrichment rate of 73.3%. Finally, 1,188 (80.87%) primer pairs were amplified successfully in the size expected for ‘Dongzao’. A total of 350 primer pairs were further selected and evaluated for their ability to detect polymorphisms across a panel of six diverse cultivars; among these, 301 primer pairs detected polymorphisms, and the polymorphism information content (PIC) value across all loci ranged from 0.15 to 0.82, with an average of 0.52. An analysis of 76 major cultivars employed in Chinese jujube production using 31 primer pairs revealed comparatively high genetic diversity among these cultivars. Within-population differences among individuals accounted for 98.2% of the observed genetic variation. Neighbor-joining clustering divided the cultivars into three main groups, none of which correspond to major geographic regions, suggesting that the genetics and geographical origin of modern Chinese jujube cultivars might not be linked. The current work firstly reports the large-scale development of Chinese jujube SSR markers. The development of these markers and their polymorphic information represent a significant improvement in the available Chinese jujube genomic resources and will facilitate both genetic and breeding applications, further accelerating the development of new cultivars.

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Taro (Colocasia esculenta) is an important crop with a long history of cultivation. In this study 5278 SSRs were identified in taro transcriptome data. A total of 2858 primer pairs were designed for marker development. 100 primers were randomly selected and synthesized. Among them, 72 primer pairs were successfully amplified and 62 were polymorphic in taro accessions. The number of alleles ranged from 2 to 14 for each different polymorphic locus and the polymorphism information content valued ranged from 0.01 to 0.82. The phylogenetic tree was also constructed to analyse the genetic diversity in 68 taro accessions. The large number of taro SSR markers developed in the present study will be useful in the researches of genetic diversity, germplasm characterization and molecular breeding etc.

The vibration signals of rolling bearing are often highly nonstationary and nonlinear, and consequently it is not accurate to extract and identify the characteristics of these signals by the traditional methods. In order to improve the performance on the feature extraction from bearing signals and the accuracy of the diagnosis, it requires effective signal processing and diagnose algorithms. In this paper, a new fault diagnosis algorithm which combines complementary ensemble empirical mode decomposition (CEEMD), probabilistic neural network (PNN) and particle swarm optimization (PSO) algorithm optimized by improved linear decreasing weight (LDW) algorithm is proposed. In this method, firstly the vibration signals are decomposed into a number of Intrinsic Mode Functions (IMFs) by the CEEMD algorithm since it has good adaptive ability to nonstable signals and can effectively extract fault features. Then the improved LDWPSO algorithm is introduced to solve the problem that the selection of smoothing factor in PNN model is arbitrary and uncertain. Finally, train and diagnose the fault types of rolling bearing using the LDWPSO-PNN model. The proposed method is verified by the experimental datasets. The results indicate that the method can extract the feature vectors of the vibration signals and distinguish them effectively.

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