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Sökning: WFRF:(Wang L) > Högskolan i Gävle

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  • Chen, R., et al. (författare)
  • A low cost surface plasmon resonance biosensor using a laser line generator
  • 2015
  • Ingår i: Optics Communications. - : Elsevier BV. - 0030-4018 .- 1873-0310. ; 349, s. 83-88
  • Tidskriftsartikel (refereegranskat)abstract
    • Due to the instrument designed by using a common surface plasmon resonance biosensor is extremely expensive, we established a portable and cost-effective surface plasmon resonance biosensing system. It is mainly composed of laser line generator, P-polarizer, customized prism, microfluidic cell, and line Charge Coupled Device (CCD) array. Microprocessor PIC24FJ128GA006 with embedded A/D converter, communication interface circuit and photoelectric signal amplifier circuit are used to obtain the weak signals from the biosensing system. Moreover, the line CCD module is checked and optimized on the number of pixels, pixels dimension, output amplifier and the timing diagram. The micro-flow cell is made of stainless steel with a high thermal conductivity, and the microprocessor based Proportional-Integral-Derivative (PID) temperature-controlled algorithm was designed to keep the constant temperature (25 °C) of the sample solutions. Correspondingly, the data algorithms designed especially to this biosensing system including amplitude-limiting filtering algorithm, data normalization and curve plotting were programmed efficiently. To validate the performance of the biosensor, ethanol solution samples at the concentrations of 5%, 7.5%, 10%, 12.5% and 15% in volumetric fractions were used, respectively. The fitting equation ΔRU=-752987.265+570237.348×RI with the R-Square of 0.97344 was established by delta response units (ΔRUs) to refractive indexes (RI). The maximum relative standard deviation (RSD) of 4.8% was obtained. 
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  • Hu, J., et al. (författare)
  • Detection of clenbuterol hydrochloride residuals in pork liver using a customized surface plasmon resonance bioanalyzer
  • 2015
  • Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 10:3
  • Tidskriftsartikel (refereegranskat)abstract
    • A surface plasmon resonance (SPR) immunoassay with an immobilization of self-assembled molecular identification membrane for the detection of residual Clenbuterol Hydrochloride (CLB) in pork liver was systematically investigated and experimentally validated for its high performance. SPR immunoassay with a regular competitive inhibition assay cannot be directly verified to detect CLB residuals. In this study, the binding of Au film with mercaptopropionic acid was investigated using the known form of the strong S-Au covalent bonds formed by the chemical radical of the mercaptopropionic acid and the Au film. After that, the immunoglobulin IgG of swine (SwIgG-CLB) was bonded with the mercaptopropionic acid by covalent -CO-NH- amide bonding. The modified comprehensive analysis of how the membrane structure works was introduced together with the customized SPR bioanalyzer. In order to evaluate the performance of this biomembrane structure, the concentrations of CLBcontained solutions of 0 ng•mL-1, 10 ng•mL-1, 20 ng•mL-1, 33.3 ng•mL-1, and 40 ng•mL-1 were prepared by adding CLB reagents into the solutions of CLB antibody (Clenbuterol Hydrochloride Antibody, CLB-Ab), successively and then the response unit (RU) was measured individually. Using the data collected from the linear CCD array, the fitting curve was established with the R-Square value of 0.9929. Correspondingly, the recovery rate ranged from 88.48% to 103.21% was experimented and the limit of detection of CLB in 1.26 ng•mL-1 was obtained efficiently. It was concluded that the detection method associated with biomembrane properties is expected to contribute much to the determination of residual CLB in pork liver quantitatively by using the customized SPR bioanalyzer. © 2015 Hu et al.
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  • Biurrun, Idoia, et al. (författare)
  • Benchmarking plant diversity of Palaearctic grasslands and other open habitats
  • 2021
  • Ingår i: Journal of Vegetation Science. - Oxford : John Wiley & Sons. - 1100-9233 .- 1654-1103. ; 32:4
  • Tidskriftsartikel (refereegranskat)abstract
    • Journal of Vegetation Science published by John Wiley & Sons Ltd on behalf of International Association for Vegetation Science.Aims: Understanding fine-grain diversity patterns across large spatial extents is fundamental for macroecological research and biodiversity conservation. Using the GrassPlot database, we provide benchmarks of fine-grain richness values of Palaearctic open habitats for vascular plants, bryophytes, lichens and complete vegetation (i.e., the sum of the former three groups). Location: Palaearctic biogeographic realm. Methods: We used 126,524 plots of eight standard grain sizes from the GrassPlot database: 0.0001, 0.001, 0.01, 0.1, 1, 10, 100 and 1,000 m2 and calculated the mean richness and standard deviations, as well as maximum, minimum, median, and first and third quartiles for each combination of grain size, taxonomic group, biome, region, vegetation type and phytosociological class. Results: Patterns of plant diversity in vegetation types and biomes differ across grain sizes and taxonomic groups. Overall, secondary (mostly semi-natural) grasslands and natural grasslands are the richest vegetation type. The open-access file ”GrassPlot Diversity Benchmarks” and the web tool “GrassPlot Diversity Explorer” are now available online (https://edgg.org/databases/GrasslandDiversityExplorer) and provide more insights into species richness patterns in the Palaearctic open habitats. Conclusions: The GrassPlot Diversity Benchmarks provide high-quality data on species richness in open habitat types across the Palaearctic. These benchmark data can be used in vegetation ecology, macroecology, biodiversity conservation and data quality checking. While the amount of data in the underlying GrassPlot database and their spatial coverage are smaller than in other extensive vegetation-plot databases, species recordings in GrassPlot are on average more complete, making it a valuable complementary data source in macroecology. © 2021 The Authors.
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