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Sökning: WFRF:(Andersson S) > RISE

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  • Andersson, P, et al. (författare)
  • Microlubrication effect by laser-textured steel surfaces
  • 2007
  • Ingår i: Wear. - 0043-1648 .- 1873-2577. ; 262, s. 369-379
  • Tidskriftsartikel (refereegranskat)abstract
    • The beneficial effect on lubrication achieved through microtexturing by laser ablation was investigated. The investigation was based on two independent experimental approaches with oil-lubricated smooth and laser-textured steel surfaces in oscillating sliding contact with a steel ball. Two types of laser-textured patterns of microcavities were studied. It was found that, in comparison with smooth steel surfaces, the laser texturing significantly reduces friction and wear. The most significant improvement in the tribological performance was achieved with an oil of high viscosity combined with a texture comprising a low density of deep microcavities.
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  • Hakonen, Aron, 1970, et al. (författare)
  • Hand-Held Femtogram Detection of Hazardous Picric Acid with Hydrophobic Ag Nanopillar SERS Substrates and Mechanism of Elasto-Capillarity
  • 2017
  • Ingår i: ACS Sensors. - : American Chemical Society (ACS). - 2379-3694. ; 2:2, s. 198-202
  • Tidskriftsartikel (refereegranskat)abstract
    • Picric acid (PA) is a severe environmental and security risk due to its unstab e, toxic, and explosive properties. It is also challenging to detect in trace amounts and in situ because of its highly acidic and anionic character. Here, we assess sensing of PA under nonlaboratory conditions using surface-enhanced Raman scattering (SERS) silver nanopillar substrates and handheld Raman spectroscopy equipment. The advancing elasto-capillarity effects are explained by molecular dynamics simulations. We obtain a SERS PA detection limit on the order of 20 ppt, corresponding attomole amounts, which together with the simple analysis methodology demonstrates that the presented approach is highly competitive for ultrasensitive analysis in the field.
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  • Kaiser, M., et al. (författare)
  • VEDLIoT: Very Efficient Deep Learning in IoT
  • 2022
  • Ingår i: Proceedings of the 2022 Design, Automation and Test in Europe Conference and Exhibition, DATE 2022. - : IEEE. - 9783981926361
  • Konferensbidrag (refereegranskat)abstract
    • The VEDLIoT project targets the development of energy-efficient Deep Learning for distributed AIoT applications. A holistic approach is used to optimize algorithms while also dealing with safety and security challenges. The approach is based on a modular and scalable cognitive IoT hardware platform. Using modular microserver technology enables the user to configure the hardware to satisfy a wide range of applications. VEDLIoT offers a complete design flow for Next-Generation IoT devices required for collaboratively solving complex Deep Learning applications across distributed systems. The methods are tested on various use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. VEDLIoT is an H2020 EU project which started in November 2020. It is currently in an intermediate stage with the first results available.
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