High-Fidelity 3D Nanoprinting of Plasmonic Gold Nanoantennas.

The direct-write fabrication of freestanding nanoantennas for plasmonic applications is a challenging task, as demands for overall morphologies, nanoscale features, and material qualities are very high. Within the small pool of capable technologies, three-dimensional (3D) nanoprinting via focused electron beam-induced deposition (FEBID) is a promising candidate due to its design flexibility. As FEBID materials notoriously suffer from high carbon contents, the chemical postgrowth transfer into pure metals is indispensably needed, which can severely harm or even destroy FEBID-based 3D nanoarchitectures. Following this challenge, we first dissect FEBID growth characteristics and then combine individual advantages by an advanced patterning approach. This allows the direct-write fabrication of high-fidelity shapes with nanoscale features in the sub-10 nm range, which allow a shape-stable chemical transfer into plasmonically active Au nanoantennas. The here-introduced strategy is a generic approach toward more complex 3D architectures for future applications in the field of 3D plasmonics.

Uncertainty in Soil Moisture Retrievals: an Ensemble Approach using SMOS L-Band Microwave Data.

The uncertainty of surface soil moisture (SM) retrievals from satellite brightness temperature (TB) observations depends primarily on the choice of radiative transfer model (RTM) parameters, prior SM information and TB inputs. This paper studies the sensitivity of several established and experimental SM retrieval products from the Soil Moisture Ocean Salinity (SMOS) mission to these choices at 11 reference sites, located in 7 watersheds across the United States (US). Different RTM parameter sets cause large biases between retrievals. Whereas typical RTM parameter sets are calibrated for SM retrievals, it is shown that a parameter set carefully optimized for TB forward modeling can also be used for retrieving SM. It is also shown that the inclusion of dynamic prior SM estimates in a Bayesian retrieval scheme can strongly improve SM retrievals, regardless of the choice of RTM parameters. The second part of this paper evaluates the ensemble uncertainty metrics for SM retrievals obtained by propagating a wide range of RTM parameters through the RTM, and the relationship with time series metrics obtained by comparing SM retrievals with in situ data. As expected for bounded variables, the total spread in the ensemble SM retrievals is smallest for wet and dry SM values and highest for intermediate SM values. After removal of the strong long-term SM bias associated with the RTM parameter values for individual ensemble members, the remaining anomaly ensemble SM spread shows higher values when SM deviates further from its long-term mean SM. This reveals higher-order biases (e.g. differences in variances) in the retrieval error, which should be considered when characterizing retrieval error. The time-average anomaly ensemble SM spread of 0.037 m3/m3 approximates the actual time series unbiased root-mean-square-difference of 0.042 m3/m3 between ensemble mean retrievals and in situ data across the reference sites.