A new fabrication strategy of the graphene-coated solid-phase microextraction (SPME) fiber is developed. Graphite oxide was first used as starting coating material that covalently bonded to the fused-silica substrate using 3-aminopropyltriethoxysilane (APTES) as cross-linking agent and subsequently deoxidized by hydrazine to give the graphene coating in situ. The chemical bonding between graphene and the silica fiber improve its chemical stability, and the obtained fiber was stable enough for more than 150 replicate extraction cycles. The graphene coating was wrinkled and folded, like the morphology of the rough tree bark. Its performance is tested by headspace (HS) SPME of polycyclic aromatic hydrocarbons (PAHs) followed by GC/MS analysis. The results showed that the graphene-coated fiber exhibited higher enrichment factors (EFs) from 2-fold for naphthalene to 17-fold for B(b)FL as compared to the commercial polydimethylsioxane (PDMS) fiber, and the EFs increased with the number of condensed rings of PAHs. The strong adsorption affinity was believed to be mostly due to the dominant role of π-π stacking interaction and hydrophobic effect, according to the results of selectivity study for a variety of organic compounds including PAHs, the aromatic compounds with different substituent groups, and some aliphatic hydrocarbons. For PAHs analysis, the graphene-coated fiber showed good precision (<11%), low detection limits (1.52-2.72 ng/L), and wide linearity (5-500 ng/L) under the optimized conditions. The repeatability of fiber-to-fiber was 4.0-10.8%. The method was applied to simultaneous analysis of eight PAHs with satisfactory recoveries, which were 84-102% for water samples and 72-95% for soil samples, respectively.

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Rapid socioeconomic development in earthquake-prone areas can cause rapid changes in seismic loss risks. These changes make it difficult to ensure that risk reduction strategies are realistic, practical and effective over time. To overcome this difficulty, ongoing changes in risk should be captured timely, definitively, and accurately and then specific and well-timed adjustments of the relevant strategies should be made. However, methods for rapidly characterizing such seismic disaster risks over a large area have not been sufficiently developed. By focusing on building loss risks, this paper presents the development of an integrated method that combines remote sensing data and local OPEN ACCESSRemote Sens. 2015, 7 2544 knowledge to resolve this problem. This method includes two key interdependent steps.(1) To extract the heights and footprint areas of a large number of buildings accurately and quickly from single high-resolution optical remote sensing images; (2) To estimate the floor areas, identify structural types, develop damage probability matrixes, and determine economic parameters for calculating monetary losses due to seismic damage to the buildings by reviewing building-relevant local knowledge based on these two parameters (i.e., the building heights and footprint areas). This method is demonstrated in the Tangshan area of China. Based on the integrated method, the total floor area of the residential and public office buildings in central Tangshan in 2009 was 3.99% lower than the corresponding area number obtained by a conventional earthquake loss estimation project. Our field-based verification indicated that the mean relative error of the method for estimating the floor areas of the assessed buildings was 2.99%. A simulation of the impacts of the 1976 Ms 7.8 Tangshan earthquake using this method indicated that the total damaged floor area of the residential and public office buildings and the associated direct monetary loses in the study area could have been 8.00 and 28.73 times greater, respectively, than in 1976 if this earthquake had recurred in 2009, which is a strong warning to the local people regarding the increasing challenges they may face.

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