Large-area Mapping Research Articles

Large-area Mapping of the Mass Mortality and Subsequent Regeneration of <i>Abies mariesii </i>Forests in the Zao Mountains in Northern Japan

Large-area Mapping of the Mass Mortality and Subsequent Regeneration of <i>Abies mariesii </i>Forests in the Zao Mountains in Northern Japan

Chemical vapor transport synthesis, characterization and compositional tuning of ZrSxSe2−x for optoelectronic applications

ZrS2, ZrSe2 and mixed alloy ZrSxSe2−x materials were achieved through chemical vapor transport. The incongruent melting system of Zr-S-Se formed crystalline layered flakes as a transport product that grew up to 2 cm in lateral size with cm-scale flakes consistently obtained for the entire compositional range exhibiting visible hexagonal features. Bulk flakes of the series ZrSxSe2−x (x = 0, 0.15, 0.3, 0.6, 1.05, 1.14, 1.51, 1.8 and 2) were analyzed through Raman spectroscopy revealing significant convolution of primary bonding modes and shifting of Raman features as a function of increasing sulfur composition. Additionally, activation of new modes not present in the pure compounds are observed as effects which result from disorder introduced into the crystal due to the random mixing of S-Se in the alloying process. Further structural characterization was performed via x-ray diffraction (XRD) on the layered flakes to evaluate the progression of layer spacing function of alloy composition which was found to range between 6.24 Å for ZrSe2 and 5.85 Å for ZrS2. Estimation of the compositional ratios of the alloy flakes through energy dispersive spectroscopy (EDS) large-area mapping verified the relation of the targeted source stoichiometry represented in the layered flakes. Atomic-resolution high angle annular dark field (HAADF)-scanning transmission electron microscopy (STEM) imaging was performed on the representative Zr(S0.5Se0.5)2 alloy to validate the 1T atomic structure and observe the arrangement of the chalcogenide columns stacks. Additionally, selected area diffraction pattern generated from the [0 0 0 1] zone axis revealed the in-plane lattice parameter to be approximately 3.715 Å.

Operational Large-Area Land-Cover Mapping: An Ethiopia Case Study

Knowledge of land cover and land use nationally is a prerequisite of many studies on drivers of land change, impacts on climate, carbon storage and other ecosystem services, and allows for sufficient planning and management. Despite this, many regions globally do not have accurate and consistent coverage at the national scale. This is certainly true for Ethiopia. Large-area land-cover characterization (LALCC), at a national scale is thus an essential first step in many studies of land-cover change, and yet is itself problematic. Such LALCC based on remote-sensing image classification is associated with a spectrum of technical challenges such as data availability, radiometric inconsistencies within/between images, and big data processing. Radiometric inconsistencies could be exacerbated for areas, such as Ethiopia, with a high frequency of cloud cover, diverse ecosystem and climate patterns, and large variations in elevation and topography. Obtaining explanatory variables that are more robust can improve classification accuracy. To create a base map for the future study of large-scale agricultural land transactions, we produced a recent land-cover map of Ethiopia. Of key importance was the creation of a methodology that was accurate and repeatable and, as such, could be used to create earlier, comparable land-cover classifications in the future for the same region. We examined the effects of band normalization and different time-series image compositing methods on classification accuracy. Both top of atmosphere and surface reflectance products from the Landsat 8 Operational Land Imager (OLI) were tested for single-time classification independently, where the latter resulted in 1.1% greater classification overall accuracy. Substitution of the original spectral bands with normalized difference spectral indices resulted in an additional improvement of 1.0% in overall accuracy. Three approaches for multi-temporal image compositing, using Landsat 8 OLI and Moderate Resolution Imaging Spectroradiometer (MODIS) data, were tested including sequential compositing, i.e., per-pixel summary measures based on predefined periods, probability density function compositing, i.e., per-pixel characterization of distribution of spectral values, and per-pixel sinusoidal models. Multi-temporal composites improved classification overall accuracy up to 4.1%, with respect to single-time classification with an advantage of the Landsat OLI-driven composites over MODIS-driven composites. Additionally, night-time light and elevation data were used to improve the classification. The elevation data and its derivatives improved classification accuracy by 1.7%. The night-time light data improve producer’s accuracy of the Urban/Built class with the cost of decreasing its user’s accuracy. Results from this research can aid map producers with decisions related to operational large-area land-cover mapping, especially with selecting input explanatory variables and multi-temporal image compositing, to allow for the creation of accurate and repeatable national-level land-cover products in a timely fashion.

Towards standardisation of contact and contactless electrical measurements of CVD graphene at the macro-, micro- and nano-scale

Graphene has become the focus of extensive research efforts and it can now be produced in wafer-scale. For the development of next generation graphene-based electronic components, electrical characterization of graphene is imperative and requires the measurement of work function, sheet resistance, carrier concentration and mobility in both macro-, micro- and nano-scale. Moreover, commercial applications of graphene require fast and large-area mapping of electrical properties, rather than obtaining a single point value, which should be ideally achieved by a contactless measurement technique. We demonstrate a comprehensive methodology for measurements of the electrical properties of graphene that ranges from nano- to macro- scales, while balancing the acquisition time and maintaining the robust quality control and reproducibility between contact and contactless methods. The electrical characterisation is achieved by using a combination of techniques, including magneto-transport in the van der Pauw geometry, THz time-domain spectroscopy mapping and calibrated Kelvin probe force microscopy. The results exhibit excellent agreement between the different techniques. Moreover, we highlight the need for standardized electrical measurements in highly controlled environmental conditions and the application of appropriate weighting functions.

Slope Failure Prediction Using Random Forest Machine Learning and LiDAR in an Eroded Folded Mountain Belt

The probabilistic mapping of landslide occurrence at a high spatial resolution and over a large geographic extent is explored using random forests (RF) machine learning; light detection and ranging (LiDAR)-derived terrain variables; additional variables relating to lithology, soils, distance to roads and streams and cost distance to roads and streams; and training data interpreted from high spatial resolution LiDAR-derivatives. Using a large training set and all predictor variables, an area under the receiver operating characteristic (ROC) curve (AUC) of 0.946 is obtained. Our findings highlight the value of a large training dataset, the incorporation of a variety of terrain variables and the use of variable window sizes to characterize the landscape at different spatial scales. We also document important variables for mapping slope failures. Our results suggest that feature selection is not required to improve the RF modeling results and that incorporating multiple models using different pseudo absence samples is not necessary. From our findings and based on a review of prior studies, we make recommendations for high spatial resolution, large-area slope failure probabilistic mapping.

Airborne Electromagnetic and Radiometric Peat Thickness Mapping of a Bog in Northwest Germany (Ahlen-Falkenberger Moor)

Knowledge on peat volumes is essential to estimate carbon stocks accurately and to facilitate appropriate peatland management. This study used airborne electromagnetic and radiometric data to estimate the volume of a bog. Airborne methods provide an alternative to ground-based methods, which are labor intensive and unfeasible to capture large-scale (>10 km2) spatial information. An airborne geophysical survey conducted in 2004 covered large parts of the Ahlen-Falkenberger Moor, an Atlantic peat bog (39 km2) close to the German North Sea coast. The lateral extent of the bog was derived from low radiometric and elevated surface data. The vertical extent resulted from smooth resistivity models derived from 1D inversion of airborne electromagnetic data, in combination with a steepest gradient approach, which indicated the base of the less resistive peat. Relative peat thicknesses were also derived from decreasing radiation over peatlands. The scaling factor (µa = 0.28 m−1) required to transform the exposure rates (negative log-values) to thicknesses was calculated using the electromagnetic results as reference. The mean difference of combined airborne results and peat thicknesses of about 100 boreholes is very small (0.0 ± 1.1 m). Although locally some (5%) deviations (>2 m) from the borehole results do occur, the approach presented here enables fast peat volume mapping of large areas without an imperative necessity of borehole data.

Efficient Optical Quantification of Heterogeneous Emitter Ensembles

Defect-based quantum emitters in solid-state materials offer a promising platform for quantum communication and sensing. Confocal fluorescence microscopy techniques have revealed quantum emitters in a multitude of host materials. The ability to quickly and accurately survey emitter ensembles is important for characterizing these new quantum emitter systems. In some materials, however, optical properties vary widely among emitters, even within the same sample. In these cases, traditional ensemble fluorescence measurements are confounded by heterogeneity, whereas individual defect-by-defect studies are impractical. Here we describe a method to quantitatively and systematically analyze the properties of heterogeneous emitter ensembles using large-area photoluminescence maps. We apply this method to study the effects of sample treatments on emitters in hexagonal boron nitride, and we find that low-energy (3 keV) electron irradiation creates emitters, whereas high-temperature (850 °C) annealing in an inert gas environment brightens emitters.

Cartografia do Invisível: Revelando a Agricultura de Pequena Escala com Imagens Rapideye na Região do Baixo Tocantins, Pa.

Dados derivados de sensoriamento remoto aliados ao uso de técnicas de classificação digital de imagens fornecem uma visão sinóptica e informações sobre a dimensão temporal dos fenômenos espaciais, possibilitando gerar informações sobre a dinâmica e os padrões espaciais da paisagem em áreas de grandes extensões territoriais como a Amazônia. Nos mapeamentos extensivos, como os realizados com imagens de satélite pelo INPE para a Amazônia Legal, é comum a definição e descrição de classes de uso e cobertura da terra em função da resolução espacial e radiométrica dos sensores utilizados. Por essa razão, pequenas áreas que não atingem o tamanho mínimo da área de mapeamento e/ou com usos diversificados são, em geral, agregadas em uma única classe. Esse é o exemplo da classe denominada Mosaico de Ocupação presente na legenda do sistema de monitoramento do uso e cobertura da terra da Amazônia-TerraClass. Esta classe procura representar, em parte, a agricultura familiar, no entanto, como o mapeamento é feito pelo sensor TM ou OLI dos satélites da série Landsat, com resolução espacial de 30m e como o TerraClass define uma área mínima de mapeamento de 6,25 hectares (ha), a identificação de pequenas áreas agrícolas é comprometida, pois essas categorias são agregadas em classes mistas e não são mais distinguíveis. Para estudos que procuram dar visibilidade aos padrões espaciais de atividades de produção de pequena escala torna-se necessário realizar o refinamento dessas classes com dados de resolução espacial com melhor definição. Neste contexto, o objetivo desse trabalho é testar três algoritmos semiautomáticos de classificação, baseados em pixel e em regiões: os algoritmos MAXVER, Bhattacharya e K-Vizinho Mais Próximo (KNN), para avaliar a capacidade de refinamento da classe Mosaico de Ocupação do dado produzido pelo TerraClass-2014. A área de estudo compreende parte dos municípios de Cametá, Mocajuba e Baião, localizados na região Nordeste do Pará, onde a produção de mandioca, pimenta-do-reino, cacau e açaí têm grande importância econômica para população local. Para o mapeamento das categorias contidas na classe Mosaico de Ocupação, foram utilizadas imagens do RapidEye, sensor REIS, ortoimagens com 5m de resolução espacial. Foi estimada a exatidão global dos algoritmos testados obtendo-se índices de 26%, 38% e 78%, para os algoritmos MAXVER, Bhattacharya e K-Vizinho Mais Próximo, respectivamente. Além da maior exatidão Global (78%), o algoritmo K-Vizinho Mais Próximo apresentou melhores resultados relativo às classes de vegetação secundária, hidrografia, e pasto sujo, com mais de 90% de acerto. A classe agricultura anual de pequena escala apresentou acerto de 62%, enquanto o índice de acerto dos outros dois algoritmos testados não passou de 8%. A abordagem metodológica desenvolvida demonstrou a viabilidade do uso das imagens de alta resolução espacial e de métodos semiautomáticos para a classificação de classes de uso e cobertura da terra associadas à classe Mosaico de Ocupação do TerraClass. A metodologia pode ser utilizada para complementar às atuais bases de dados existentes para a Amazônia (TerraClass, MapBiomas e IBGE), explicitando as categorias agrícolas de produção em pequena escala, dando visibilidade a esses sistemas de produção, frequentemente negligenciados nos mapeamentos que abrangem grandes extensões territoriais.

THE USE OF DECONVOLUTION TECHNIQUE FOR THE ANALYSIS OF GAMMA SPECTROMETRY DATA FROM FIELD MONITORING USING UNMANNED AERIAL VEHICLES.

Airborne gamma spectrometry is an effective tool for prompt monitoring and mapping of large areas contaminated after NPP accident, radionuclides leakage cases, an impact of uranium ore mining and processing, etc. Airborne spectrometry data analysis using deconvolution technique enables to calculate air kerma rates and/or radionuclides concentrations as well as identification of radionuclides. Application of this technique on the airborne data (from manned as well as an unmanned survey using drones) is rather specific due to the requirements for short time of one scan data acquisition, a relatively long distance from the source and small detector size, due to the limited payload of the usually used drones. Application of deconvolution techniques for analysis of spectra with very poor statistics, methods and possibilities to improve the processing of such spectra are discussed.

Discriminating treed and non-treed wetlands in boreal ecosystems using time series Sentinel-1 data

Wetlands are recognized for their importance to a range of ecosystem goods and services; however, detailed information on wetland presence, type, extent, and persistence is challenging to attain over large areas and/or long time periods due to the spatial complexity and temporal dynamism of wetlands. In this study we explored the potential for within-year time series of C-band Synthetic Aperture Radar (SAR) observations from the free and open Sentinel-1 data archive to improve discrimination of treed and non-treed wetlands and non-wetlands in a boreal forest environment. Through a set of 3843 classification experiments for the year 2017, we tested the influence of three factors on classification accuracy: (i) input features (two backscatter coefficients in VV and VH polarization (σVV and σVH) and four quantitative measures derived from the Stokes vector); (ii) the temporal form of features (i.e. using all within-year observations versus generalized measures such as monthly/seasonal means or annualized statistics); and (iii) missing observations in Sentinel-1 time series due to varying observation availability across space. Among the tested features, we found the greatest utility in σVV and σVH. Directly using all within-year observations yielded higher accuracy than using generalized temporal forms. Moreover, the temporal form of the features had a greater impact on classification accuracy than the features themselves. The highest overall accuracy (0.860 ± 0.002) was achieved using σVV and σVH from all within-year observations. The majority of class confusion occurred between treed wetlands and non-wetlands. We found no significant reduction in the overall accuracy by simulated missing observations in time series when using all within-year observations. With the increasing availability of free and open data from the Sentinel-1 archive, new opportunities are emerging to readily integrate within-year time series into large-area land cover mapping, particularly if analysis-ready SAR data products further reduce preprocessing requirements for end users.

Twist Angle mapping in layered WS2 by Polarization-Resolved Second Harmonic Generation

Stacked atomically thin transition metal dichalcogenides (TMDs) exhibit fundamentally new physical properties compared to those of the individual layers. The twist angle between the layers plays a crucial role in tuning these properties. Having a tool that provides high-resolution, large area mapping of the twist angle, would be of great importance in the characterization of such 2D structures. Here we use polarization-resolved second harmonic generation (P-SHG) imaging microscopy to rapidly map the twist angle in large areas of overlapping WS2 stacked layers. The robustness of our methodology lies in the combination of both intensity and polarization measurements of SHG in the overlapping region. This allows the accurate measurement and consequent pixel-by-pixel mapping of the twist angle in this area. For the specific case of 30° twist angle, P-SHG enables imaging of individual layers.

Hyperspectral infrared laser polarimetry for single-shot phase-amplitude imaging of thin films.

We recently presented a novel laser-based infrared (IR) spectroscopic phase-amplitude polarimeter for sub-decisecond and sub-mm2 measurements of organic thin films [Opt. Lett.44, 4387 (2019)OPLEDP0146-959210.1364/OL.44.004387]. Here we report on the hyperspectral-imaging capabilities of this device. The single-shot polarimeter employs a broadly tunable mid-IR (1318-1765 cm-1) quantum cascade laser (QCL) and a four-channel beam-division design for simultaneous phase and amplitude measurements. Fast QCL tuning speeds of up to 1500 cm-1/s enable hyperspectral mapping of large sample areas (50×50 mm2) within several tens of minutes, achieving 120 μm spatial and <0.5 cm-1 spectral resolution. We apply the instrument for imaging both the heterogeneous chemical and structural properties of sub-100 nm thin polymer and fatty-acid films. Our polarimeter opens up new applications regarding laterally resolved IR analyses of complex thin films.

A DATA CAPTURE FRAMEWORK FOR IMPROVING THE QUALITY OF SUBSURFACE UTILITY INFORMATION

Abstract. To plan, develop, and manage underground space and make informed decisions leading to desirable outcomes, planners, land administrators, and engineers need to make sense of the underground. A reliable digital twin of the underground – a realistic, digital representation of the physical world below the surface – is required. Utilities, consisting of the pipes, ducts, cables, manholes and other assets that provide electricity, gas, water, sewerage, and telecommunication services, make up a significant portion of the shallow layers of the subsurface. A lack of reliable information on subsurface utilities may potentially lead to poor decisions leading to undesirable outcomes, lengthy and costly planning and land administration processes, and lengthy, costly, and hazardous development processes. The advent of mobile 3D ground penetrating radar technology offers the promise of non-destructive mapping of existing subsurface utilities in a large area in a relatively short amount of time and could potentially be used to improve the reliability of available information. In this work, a small number of cases studies are described at evaluating the feasibility of a large area mapping approach for subsurface utilities. Based on the results of the studies, a data capture framework for the gradual improvement of the quality of information on existing subsurface utilities is proposed.

Three-Dimensional Cooperative Mapping for Connected and Automated Vehicles

To improve the efficiency of three-dimensional (3-D) LIDAR mapping, multiple robots cooperation has been considered in mapping of large areas. In order to merge the local maps acquired by independent robots, it is crucial to identify common areas among the local maps. In this article, we propose a novel 3-D cooperative mapping approach for connected and automated vehicles that use only 3-D LIDAR data to cooperatively create globally consistent maps among multiple robots. Assuming each individual vehicle is able to create its own local map, the cooperative mapping process is divided into two main tasks: The common area detection task and the map merging task. We first significantly reduce the time cost of common area detection task by utilizing ground information as features. Further, the accuracy of map merging is guaranteed by whole frame matching of the common area frame pairs generated in the previous step. The proposed method is tested and validated by experiments in real-world environment for various scenarios; results show that the efficiency and accuracy of mapping are both improved compared to the individual robot mapping approach.

A comparison of Landsat 8, RapidEye and Pleiades products for improving empirical predictions of satellite-derived bathymetry

Satellite derived bathymetry (SDB) enables rapid mapping of large coastal areas through measurement of optical penetration of the water column. The resolution of bathymetric mapping and achievable horizontal and vertical accuracies vary but generally, all SDB outputs are constrained by sensor type, water quality and other environmental conditions. Efforts to improve accuracy include physics-based methods (similar to radiative transfer models e.g. for atmospheric/vegetation studies) or detailed in-situ sampling of the seabed and water column, but the spatial component of SDB measurements is often under-utilised in SDB workflows despite promising results suggesting potential to improve accuracy significantly. In this study, a selection of satellite datasets (Landsat 8, RapidEye and Pleiades) at different spatial and spectral resolutions were tested using a log ratio transform to derive bathymetry in an Atlantic coastal embayment. A series of non-spatial and spatial linear analyses were then conducted and their influence on SDB prediction accuracy was assessed in addition to the significance of each model's parameters. Landsat 8 (30 m pixel size) performed relatively weak with the non-spatial model, but showed the best results with the spatial model. However, the highest spatial resolution imagery used – Pleiades (2 m pixel size) showed good results across both non-spatial and spatial models which suggests a suitability for SDB prediction at a higher spatial resolution than the others. In all cases, the spatial models were able to constrain the prediction differences at increased water depths.

CVD growth of ultrapure diamond, generation of NV centers by ion implantation, and their spectroscopic characterization for quantum technological applications

Abstract Applications of nitrogen-vacancy (NV) centers in diamond in quantum technology have attracted considerable attention in recent years. Deterministic generation of ensembles of NV centers can advance the research on quantum sensing, many-body quantum systems, multipartite entanglement and so on. Here we report the complete process of controlled generation of NV centers in diamond as well as their characterisation: growing diamond films through chemical vapor deposition (CVD), ion implantation and spectroscopic characterization of the defect centers using a confocal microscope. A microwave-assisted CVD set-up is presented which we constructed for the preparation of single-crystalline homoepitaxial diamond films. The films were prepared with minimized nitrogen concentration, which is confirmed through photoluminescence measurements. We demonstrate an in situ ultra high vacuum (UHV) implantation and heating process for creation of NV centers using a novel experimental set-up. For the first time hot implantation has been shown which prevents surface charging effects. We do not observe graphitization due to UHV heating. By optimizing the implantation parameters it has been possible to implant NV centers in a precise way. We present large area mapping of the samples to determine the distribution of the centers and describe the characterization of the centers by spectroscopic techniques. Reducing the decoherence caused by environmental noise is of primary importance for many applications in quantum technology. We demonstrate improvement on coherence time T_{2} of the NV spins by suppression of their interaction with the surrounding spin-bath using robust dynamical decoupling sequences.

A low-cost portable system for elemental mapping by XRF aiming in situ analyses

This work presents the development of a portable system that allows 2D elemental mapping of large areas (maximum of 35.0 × 35.0 cm2) by XRF. A measuring head, composed of an x-ray source and a detector, is mounted on a 3-axis stage with movement reproducibility better that 0.03 mm. The final elemental map resolution of 1.4 mm was experimentally determined in the best condition of collimation, and the same experiments indicate that the resolution is fully dominated by the x-ray beam spot size. The main goal of this new setup is to perform analyses of historical, archaeological and geological objects. Because it's lightweight, our setup has the potential to be of great utility for in situ analyses, given the great difficulty and high costs transporting the artifacts from the museum to a laboratory. The importance of this instrumentation is related to the need to identify the distribution of the chemical elements in large surface areas of cultural heritage objects by a nondestructive method.

Exploring intra-annual variation in cropland classification accuracy using monthly, seasonal, and yearly sample set

ABSTRACT Monitoring cropland dynamics is critical for water management and crop-yield estimation but cropland mapping is still a challenge because of its seasonality and human intervention. Both large-area mapping and change detection studies use images from certain or the same seasons; however, the uneven distribution of Landsat data within a year may affect accuracy. This study aimed to explore the accuracy and uncertainty of cropland mapping performance in different season and month by analyzing all available images in three Landsat footprints in Egypt, Ethiopia, and South Africa from 1984 to 2016. Three different combinations of training models (using yearly, seasonal, and monthly sample set) were used to compare the effect on cropland classification in different month and season through 10-fold cross-validation. There was a tendency to achieve better cropland mapping performance in the dry season and peak growth cycle in all three sites. We showed that monthly and seasonal cropland mapping accuracy was only slightly (~2%) increased using training data in corresponded month. Therefore, we recommended transfer seasonal/yearly training data to fine temporal resolution (i.e., monthly and seasonal) cropland mapping for time and labor efficiency.

Synthesis and Metrology for Large-Area Applications of Single-Wall Carbon Nanotube Forests

Commercial applications of vertically aligned single-wall carbon nanotube (SWCNT) “forests” are on the horizon, with the emergence of their use in nanocomposite membranes,1 electronic interconnects, thermal interface materials, among other promising demonstrations. To realize these potential applications, we sought to not only design reproducible wafer-scale synthesis of high-density SWCNT forests but also to establish advanced metrology to enable quantitative, large-area mapping of several key forest characteristics. This detailed information will unlock better understanding of interdependent correlations among the characteristics and how these correlations inform SWCNT forest growth mechanisms toward enhanced synthetic control. We performed low-pressure chemical vapor deposition (CVD) on 4-in. Si wafers in an AIXTRON® Black Magic cold-wall furnace. The combination of a low flux of carbon precursors with sub-nm Fe/Mo catalyst films on alumina-coated Si wafers maintained small-diameter SWCNTs (mean as small at 1.87 nm) with number densities up to 2.2 x1012 cm-2. Although removing Mo resulted in categorically larger CNT diameters and lower densities (< 0.9 x1012 cm-2), the mass conversion rate from C2H2 to SWCNT product was essentially constant (~1.2 x106 % g catalyst-1) for all catalyst compositions and CNT densities. These results corroborate the notion that hydrocarbon dissociation may be thermally driven without assistance from the catalyst under these conditions, and that the role of Mo was primarily a physical one in creating smaller, denser SWCNTs. Furthermore, we leveraged a suite of advanced characterization techniques to generate area maps across a 4-in wafer to visualize and evaluate the uniformity of various forest properties, including Raman microscopy (forest height, graphitization level, diameters), synchrotron X-ray scattering (mean diameter),2 and Rutherford backscattering spectroscopy (forest density). The uniformity and coupled correlations among different structural characteristics (e.g., diameter-density, rate-density) within a single wafer were consistent with those observed for multiple wafers grown by sequential runs (up to 5 in a day). This validates the reproducibility of our process and suggests that these correlations may be more universal phenomena. Lastly, our developments in synthesis represent to our knowledge best-in-class co-optimization of small-diameter, high-density, and large-area SWCNT forest growth. We will briefly discuss growth benchmarks in context of membranes, electrochemical devices, and antireflective surfaces as examples. Bui, N.; Meshot, E. R.; Kim, S.; Peña, J.; Gibson, P. W.; Wu, K. J.; Fornasiero, F. Advanced Materials 2016, 28 (28), 5871-5877.Meshot, E.; Zwissler, D. W.; Bui, N.; Kuykendall, T. R.; Wang, C.; Hexemer, A.; Wu, K. J. J.; Fornasiero, F. ACS Nano 2017, 11 (6), 5405-5416.

High definition large area mapping of geological samples using a Maia detector array in the Nuclear Microprobe

A Maia detector array has been adapted and installed on the CSIRO-MARC Nuclear Microprobe. Maia uses an annular array of 384 silicon detectors to provide a solid-angle of 1.3 sr to improve the collection of X-rays induced by Particle Induced X-ray Emission (PIXE) using a micron focussed MeV proton beam. It features event-by-event data acquisition with dead-time correction and pileup rejection and a total count-rate capacity above 10 M/s, versatile stage and beam scanning modes (coupled to the DAQ-36 system described previously) to cater for a variety of sample configuration and experimental needs, precise measurement of transit time and integration of beam fluence per pixel, and spectral deconvolution of event data in real-time using the Dynamic Analysis method to provide element images during data collection. Here we demonstrate the system on thin sections made from geological samples extracted from active seafloor hydrothermal vents in the PACMANUS hydrothermal field in Papua New Guinea.