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Liu, Yinghui

Publications and source records attributed to Liu, Yinghui.

Chapter 14: Surface plasmon resonance enhanced artificial photosynthesis of chemical fuels for energy storage

Nanostructured noble metals such as Au, Ag, and Cu have interesting optical properties because of the oscillation motions of their surface electrons upon strong coupling with light under resonance conditions. This resonant oscillation motion of conduction electrons refers to surface plasmon resonance (SPR) and localized SPR (LSPR) when localized near the surface of a nanoparticle. The extinction spectrum of a solution of plasmonic nanoparticles has tunable wavelength responses from UV to NIR due to strong light scattering and absorption which are highly sensitive to the permittivity of the nanoparticles, their sizes and shapes, and chemical environment. Strong light scattering due to the LSPR of plasmonic nanoparticles creates a strong localized and far-field intensity capable of enhancing light absorption characteristics of a chromophore near a plasmonic surface. Engineering the chromophores’ radiative decay dynamics can be done by 1) increasing its radiative decay rate to increase its photoluminescence intensity and 2) increasing its nonradiative decay rates associated to direct charge transfer to the metal surface. Such interesting photophysical properties of a chromophore can be extended to other light-absorbing materials such as semiconductor thin films and nanostructures. This plasmonic effect on the photophysics of a light-absorbing material can be theoretically and experimentally validated. The phenomenon has also been applied to advanced optoelectronic devices such as organic light-emitting diodes (OLED)1 and organic photovoltaics (OPV).2-4 The local field created by the SPR can provide an intense EM field to enhance photoluminescence emission of an organic chromophore5-8 and Raman scattering of an organic molecule, and single-molecule Raman9-10 can be detected on specially designed LSPR substrate. (Figure 1 on SPR for energy) Recent studies suggest that LSPR can be incorporated in light-harvesting and conversion systems to increase energy conversion in a solar cell and photoelectrochemical cell and chemical transformations of CO2 to chemical fuels.11-12 Plasmonic active metals naturally exhibit catalytic activities for electrochemical fuel conversion that can be enhanced by engineering their structures to form unique catalytic structures such as symmetry-broken Au-Cu Janus nanocrystals.13 These studies are critical to addressing the global challenges of energy14-15 and CO2 emission from nonrenewable sources such as coal, petroleum, and natural gas.16-17 Electrochemical systems comprised of unique photonic structures and functions that enable efficient and affordable energy harvesting/conversion/storage are highly desired for providing safe and environment-friendly energy sources. This chapter reviews our recent work of LSPR enabled photoelectrochemical water splitting and recent advances in LSPR-enabled CO2 reduction and photochemical reactions reported in the literature. Scientific and technical challenges of applying LSPR to enhance these energy conversion and storage systems are discussed at the conclusion of this chapter.

Pan, Shanlin↗

Impacts of active satellite sensors' low-level cloud detection limitations on cloud radiative forcing in the Arctic

Previous studies revealed that satellites sensors with the best detection capability identify 25 %–40 % and 0 %–25 % fewer clouds below 0.5 and between 0.5–1.0 km, respectively, over the Arctic. Quantifying the impacts of cloud detection limitations on the radiation flux are critical especially over the Arctic Ocean considering the dramatic changes in Arctic sea ice. In this study, the proxies of the space-based radar, CloudSat, and lidar, CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations), cloud masks are derived based on simulated radar reflectivity with QuickBeam and cloud optical thickness using retrieved cloud properties from surface-based radar and lidar during the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment. Limitations in low-level cloud detection by the space-based active sensors, and the impact of these limitations on the radiation fluxes at the surface and the top of the atmosphere (TOA), are estimated with radiative transfer model Streamer. The results show that the combined CloudSat and CALIPSO product generally detects all clouds above 1 km, while detecting 25 % (9 %) fewer in absolute values below 600 m (600 m to 1 km) than surface observations. These detection limitations lead to uncertainties in the monthly mean cloud radiative forcing (CRF), with maximum absolute monthly mean values of 2.5 and 3.4 Wm –2 at the surface and TOA, respectively. Cloud information from only CALIPSO or CloudSat lead to larger cloud detection differences compared to the surface observations and larger CRF uncertainties with absolute monthly means larger than 10.0 Wm –2 at the surface and TOA. The uncertainties for individual cases are larger – up to 30 Wm –2 . These uncertainties need to be considered when radiation flux products from CloudSat and CALIPSO are used in climate and weather studies.

54 ENVIRONMENTAL SCIENCES↗

Operational Implementation of Sea Ice Concentration Estimates from the AMSR2 Sensor

An operation implementation of a passive microwave sea ice concentration algorithm to support NOAA's operational mission is presented. The NASA team 2 algorithm, previously developed for the NASA advanced microwave scanning radiometer for the Earth observing system (AMSR-E) product suite, is adapted for operational use with the JAXA AMSR2 sensor through several enhancements. First, the algorithm is modified to process individual swaths and provide concentration from the most recent swaths instead of a 24-hour average. A latency (time since observation) field and a 24-hour concentration range (maximum-minimum) are included to provide indications of data timeliness and variability. Concentration from the Bootstrap algorithm is a secondary field to provide complementary sea ice information. A quality flag is implemented to provide information on interpolation, filtering, and other quality control steps. The AMSR2 concentration fields are compared with a different AMSR2 passive microwave product, and then validated via comparison with sea ice concentration from the Suomi visible and infrared imaging radiometer suite. This validation indicates the AMSR2 concentrations have a bias of 3.9% and an RMSE of 11.0% in the Arctic, and a bias of 4.45% and RMSE of 8.8% in the Antarctic. In most cases, the NOAA operational requirements for accuracy are met. However, in low-concentration regimes, such as during melt and near the ice edge, errors are higher because of the limitations of passive microwave sensors and the algorithm retrieval.

Arctic↗