We report the discovery of superconductivity at high pressure in SrFe(2)As(2) and BaFe(2)As(2). The superconducting transition temperatures are up to 27 K in SrFe(2)As(2) and 29 K in BaFe(2)As(2), the highest obtained for materials with pressure-induced superconductivity thus far.

The dyes in dye-sensitized solar cells (DSSCs) require one or more chemical substituents that can act as an anchor, enabling their adsorption onto a metal oxide substrate. This adsorption provides a means for electron injection, which is the process that initiates the electrical circuit in a DSSC. Understanding the structure of various DSSC anchors and the search for new anchors are critical factors for the development of improved DSSCs. Traditionally, carboxylic acid and cyanoacrylic acid groups are employed as dye anchors in DSSCs. In recent years, novel anchor groups have emerged, which make a larger pool of materials available for DSSC dyes, and their associated physical and chemical characteristics offer interesting effects at the interface between dye and metal oxide. This review focuses especially on the structural aspects of these novel dye anchors for TiO2-based DSSCs, including pyridine, phosphonic acid, tetracyanate, perylene dicarboxylic acid anhydride, 2-hydroxylbenzonitrile, 8-hydroxylquinoline, pyridine-N-oxide, hydroxylpyridium, catechol, hydroxamate, sulfonic acid, acetylacetanate, boronic acid, nitro, tetrazole, rhodanine, and salicylic acid substituents. We anticipate that further exploration and understanding of these new types of anchoring groups for TiO2 substrates will not only contribute to the development of advanced DSSCs, but also of quantum dot-sensitized solar cells, water splitting systems, and other self-assembled monolayer-based technologies.

The emergence of "big data" initiatives has led to the need for tools that can automatically extract valuable chemical information from large volumes of unstructured data, such as the scientific literature. Since chemical information can be present in figures, tables, and textual paragraphs, successful information extraction often depends on the ability to interpret all of these domains simultaneously. We present a complete toolkit for the automated extraction of chemical entities and their associated properties, measurements, and relationships from scientific documents that can be used to populate structured chemical databases. Our system provides an extensible, chemistry-aware, natural language processing pipeline for tokenization, part-of-speech tagging, named entity recognition, and phrase parsing. Within this scope, we report improved performance for chemical named entity recognition through the use of unsupervised word clustering based on a massive corpus of chemistry articles. For phrase parsing and information extraction, we present the novel use of multiple rule-based grammars that are tailored for interpreting specific document domains such as textual paragraphs, captions, and tables. We also describe document-level processing to resolve data interdependencies and show that this is particularly necessary for the autogeneration of chemical databases since captions and tables commonly contain chemical identifiers and references that are defined elsewhere in the text. The performance of the toolkit to correctly extract various types of data was evaluated, affording an F-score of 93.4%, 86.8%, and 91.5% for extracting chemical identifiers, spectroscopic attributes, and chemical property attributes, respectively; set against the CHEMDNER chemical name extraction challenge, ChemDataExtractor yields a competitive F-score of 87.8%. All tools have been released under the MIT license and are available to download from http://www.chemdataextractor.org .

A comprehensive review on the recent progress of black silicon research and its applications in solar cell technologies.