ORCID Profile
0000-0003-1657-1361
Current Organisations
CSIRO
,
CSIRO Queensland Bioscience Precinct
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Publisher: Elsevier BV
Date: 10-2019
Publisher: Elsevier BV
Date: 09-2018
Publisher: Elsevier BV
Date: 11-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Elsevier BV
Date: 10-2020
Publisher: IEEE
Date: 07-2018
Publisher: Elsevier BV
Date: 12-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: MDPI AG
Date: 21-05-2014
DOI: 10.3390/F5050992
Publisher: Elsevier BV
Date: 02-2015
Publisher: IEEE
Date: 07-2019
Publisher: American Chemical Society (ACS)
Date: 30-05-2017
Abstract: Although three-dimensional shaping of metallic nanostructures is an important strategy for control and manipulation of localized surface plasmon resonance (LSPR), its implementation in high-throughput, on-chip fabrication of plasmonic devices remains challenging. Here, we demonstrate nanocontact-based large-area fabrication of a novel, LSPR-active Au architecture consisting of periodic arrays of reduced-symmetry nanoantennas having sub-50 nm, out-of-plane features. Namely, by combining nanosphere and molecular self-assembly processes, we have patterned evaporated polycrystalline Au films for chemical etching of nanocups with controlled aspect ratios (outer diameter d = 100 nm and void volumes = 18 or 39 zL). The resulting nanoantennas were highly ordered, forming a hexagonal lattice structure over centimeter-sized glass substrates, and they displayed characteristic LSPR absorption in the visible/near-infrared spectral range. Theoretical simulations indicated electric field confinement and enhancement patterns located not only around the rims but also inside the nanocups. We also explored how these patterns and the overall spectral characteristics depended on the nanocup aspect ratio as well as on electric field coupling in the arrays. We have successfully tested the fabricated architecture for detection of stepwise immobilization and interactions of proteins, thus demonstrating its potential for both nanoscopic scaffolding and sensing of biomolecular assemblies.
Publisher: Elsevier BV
Date: 02-2016
Publisher: MDPI AG
Date: 08-02-2021
DOI: 10.3390/RS13040603
Abstract: Effective dairy farm management requires the regular estimation and prediction of pasture biomass. This study explored the suitability of high spatio-temporal resolution Sentinel-2 imagery and the applicability of advanced machine learning techniques for estimating aboveground biomass at the paddock level in five dairy farms across northern Tasmania, Australia. A sequential neural network model was developed by integrating Sentinel-2 time-series data, weekly field biomass observations and daily climate variables from 2017 to 2018. Linear least-squares regression was employed for evaluating the results for model calibration and validation. Optimal model performance was realised with an R2 of ≈0.6, a root-mean-square error (RMSE) of ≈356 kg dry matter (DM)/ha and a mean absolute error (MAE) of 262 kg DM/ha. These performance markers indicated the results were within the variability of the pasture biomass measured in the field, and therefore represent a relatively high prediction accuracy. Sensitivity analysis further revealed what impact each farm’s in situ measurement, pasture management and grazing practices have on the model’s predictions. The study demonstrated the potential benefits and feasibility of improving biomass estimation in a cheap and rapid manner over traditional field measurement and commonly used remote-sensing methods. The proposed approach will help farmers and policymakers to estimate the amount of pasture present for optimising grazing management and improving decision-making regarding dairy farming.
Publisher: Elsevier BV
Date: 2020
Publisher: Elsevier BV
Date: 12-2018
Location: Australia
No related grants have been discovered for Yuri Shendryk.