scholarly journals Nanocavity crossbar arrays for parallel electrochemical sensing on a chip

2014 ◽  
Vol 5 ◽  
pp. 1137-1143 ◽  
Author(s):  
Enno Kätelhön ◽  
Dirk Mayer ◽  
Marko Banzet ◽  
Andreas Offenhäusser ◽  
Bernhard Wolfrum
Keyword(s):  
High Spatial Resolution ◽  
Redox Cycling ◽  
Electrochemical Sensing ◽  
Wafer Surface ◽  
Novel Device ◽  
Redox Active ◽  
Parallel Data ◽  
Acquisition Mode ◽  
Parallel Band ◽  
Very High

We introduce a novel device for the mapping of redox-active compounds at high spatial resolution based on a crossbar electrode architecture. The sensor array is formed by two sets of 16 parallel band electrodes that are arranged perpendicular to each other on the wafer surface. At each intersection, the crossing bars are separated by a ca. 65 nm high nanocavity, which is stabilized by the surrounding passivation layer. During operation, perpendicular bar electrodes are biased to potentials above and below the redox potential of species under investigation, thus, enabling repeated subsequent reactions at the two electrodes. By this means, a redox cycling current is formed across the gap that can be measured externally. As the nanocavity devices feature a very high current amplification in redox cycling mode, individual sensing spots can be addressed in parallel, enabling high-throughput electrochemical imaging. This paper introduces the design of the device, discusses the fabrication process and demonstrates its capabilities in sequential and parallel data acquisition mode by using a hexacyanoferrate probe.

scholarly journals High spatial resolution photo mosaicking for the monitoring of coralligenous reefs

Coral Reefs ◽  
2021 ◽  
Author(s):  
E. Casoli ◽  
D. Ventura ◽  
G. Mancini ◽  
D. S. Pace ◽  
A. Belluscio ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Low Cost ◽  
Pixel Resolution ◽  
Recording Apparatus ◽  
Assemblage Composition ◽  
Very High Spatial Resolution ◽  
Vertical Walls ◽  
Non Destructive ◽  
Very High

AbstractCoralligenous reefs are characterized by large bathymetric and spatial distribution, as well as heterogeneity; in shallow environments, they develop mainly on vertical and sub-vertical rocky walls. Mainly diver-based techniques are carried out to gain detailed information on such habitats. Here, we propose a non-destructive and multi-purpose photo mosaicking method to study and monitor coralligenous reefs developing on vertical walls. High-pixel resolution images using three different commercial cameras were acquired on a 10 m2 reef, to compare the effectiveness of photomosaic method to the traditional photoquadrats technique in quantifying the coralligenous assemblage. Results showed very high spatial resolution and accuracy among the photomosaic acquired with different cameras and no significant differences with photoquadrats in assessing the assemblage composition. Despite the large difference in costs of each recording apparatus, little differences emerged from the assemblage characterization: through the analysis of the three photomosaics twelve taxa/morphological categories covered 97–99% of the sampled surface. Photo mosaicking represents a low-cost method that minimizes the time spent underwater by divers and capable of providing new opportunities for further studies on shallow coralligenous reefs.

scholarly journals Mapping Urban Functional Zones by Integrating Very High Spatial Resolution Remote Sensing Imagery and Points of Interest: A Case Study of Xiamen, China

2018 ◽  
Vol 10 (11) ◽  
pp. 1737 ◽  
Author(s):  
Jinchao Song ◽  
Tao Lin ◽  
Xinhu Li ◽  
Alexander V. Prishchepov
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Images ◽  
Remote Sensing Imagery ◽  
Points Of Interest ◽  
Very High Spatial Resolution ◽  
Functional Zones ◽  
Very High

Fine-scale, accurate intra-urban functional zones (urban land use) are important for applications that rely on exploring urban dynamic and complexity. However, current methods of mapping functional zones in built-up areas with high spatial resolution remote sensing images are incomplete due to a lack of social attributes. To address this issue, this paper explores a novel approach to mapping urban functional zones by integrating points of interest (POIs) with social properties and very high spatial resolution remote sensing imagery with natural attributes, and classifying urban function as residence zones, transportation zones, convenience shops, shopping centers, factory zones, companies, and public service zones. First, non-built and built-up areas were classified using high spatial resolution remote sensing images. Second, the built-up areas were segmented using an object-based approach by utilizing building rooftop characteristics (reflectance and shapes). At the same time, the functional POIs of the segments were identified to determine the functional attributes of the segmented polygon. Third, the functional values—the mean priority of the functions in a road-based parcel—were calculated by functional segments and segmental weight coefficients. This method was demonstrated on Xiamen Island, China with an overall accuracy of 78.47% and with a kappa coefficient of 74.52%. The proposed approach could be easily applied in other parts of the world where social data and high spatial resolution imagery are available and improve accuracy when automatically mapping urban functional zones using remote sensing imagery. It will also potentially provide large-scale land-use information.

Ratiometric electrochemical sensing and biosensing based on multiple redox-active state COFDHTA-TTA

2019 ◽  
Vol 281 ◽  
pp. 1009-1015 ◽  
Author(s):  
Mengli Xu ◽  
Linyu Wang ◽  
Yi Xie ◽  
Yonghai Song ◽  
Li Wang
Keyword(s):  
Active State ◽  
Electrochemical Sensing ◽  
Redox Active

Classification of multi-temporal spectral indices for crop type mapping: a case study in Coalville, UK

2018 ◽  
Vol 156 (1) ◽  
pp. 24-36 ◽  
Author(s):  
Y. Palchowdhuri ◽  
R. Valcarce-Diñeiro ◽  
P. King ◽  
M. Sanabria-Soto
Keyword(s):  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Reference Data ◽  
High Spatial Resolution ◽  
Classification Algorithms ◽  
Type Mapping ◽  
Crop Type ◽  
Multi Temporal ◽  
Very High

AbstractRemote sensing (RS) offers an efficient and reliable means to map features on Earth. Crop type mapping using RS at various temporal and spatial resolutions plays an important role spanning from environmental to economical. The main objective of the current study was to evaluate the significance of optical data in a multi-temporal crop type classification-based on very high spatial resolution and high spatial resolution imagery. With this aim, three images from WorldView-3 and Sentinel-2 were acquired over Coalville (UK) between April and July 2016. Three vegetation indices (VIs); the normalized difference vegetation index, the green normalized difference vegetation index and soil adjusted vegetation index were generated using red, green and near-infrared spectral bands; then a supervised classification was performed using ground reference data collected from field surveys, Random forest (RF) and decision tree (DT) classification algorithms. Accuracy assessment was undertaken by comparing the classified output with the reference data. An overall accuracy of 91% and κ coefficient of 0·90 were estimated using the combination of RF and DT classification algorithms. Therefore, it can be concluded that integrating very high- and high-resolution imagery with different VIs can be implemented effectively to produce large-scale crop maps even with a limited temporal-dataset.

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