title: The potential of high-entropy alloys as catalyst materials in water-splitting application

authors: Fan, ZY

abstract: High-entropy alloys (HEAs), which are characterized by the inclusion of five or more elements in nearly equiatomic configurations, have garnered increasing attention due to their distinct characteristics, including exceptional physical strength, superior corrosion resistance, outstanding microhardness, and long-lasting durability. The existence of multi-constituent elements in HEAs opens up unique possibilities for the development of compatible and innovative electrocatalytic active sites. Through careful selection of elements in terms of their combination and proportions, these electrocatalytic active sites demonstrate the potential of fine-tuning for numerous technical goals. Current studies have demonstrated the promising activities of HEAs into electrocatalytic areas. However, further enhancements in their activity explore interactions among component elements and require a deeper understanding of electrocatalytic active sites, as well as a deeper comprehension of the underlying electrocatalytic mechanisms. This review aims to provide an analysis of the four core characteristics (the high-entropy effect, the severe lattice distortion effect, the sluggish diffusion effect, and the cocktail effect) associated with electrocatalysts based on HEAs. Additionally, we delve into the various applications of HEAs related to electrochemical energy transformation reactions, which encompass both the hydrogen evolution and oxygen evolution reactions. The purpose of the review is to unravel the inherent complexities associated with electrocatalytic active sites, the interactions among component elements, and the mechanisms governing reactions in HEAs. Lastly, we highlight the urgent challenges and stress the importance of theoretical and experimental research, along with the underlying raison d’être of HEAs in electrocatalysis for supplying future energy needs. It is our expectation that this review will inspire additional investigation and advancement of HEAs in relevant electrocatalysis applications, particularly in the context of water splitting processes.

text: The potential of high-entropy alloys as catalyst materials in water-splitting application High-entropy alloys (HEAs), which are characterized by the inclusion of five or more elements in nearly equiatomic configurations, have garnered increasing attention due to their distinct characteristics, including exceptional physical strength, superior corrosion resistance, outstanding microhardness, and long-lasting durability. The existence of multi-constituent elements in HEAs opens up unique possibilities for the development of compatible and innovative electrocatalytic active sites. Through careful selection of elements in terms of their combination and proportions, these electrocatalytic active sites demonstrate the potential of fine-tuning for numerous technical goals. Current studies have demonstrated the promising activities of HEAs into electrocatalytic areas. However, further enhancements in their activity explore interactions among component elements and require a deeper understanding of electrocatalytic active sites, as well as a deeper comprehension of the underlying electrocatalytic mechanisms. This review aims to provide an analysis of the four core characteristics (the high-entropy effect, the severe lattice distortion effect, the sluggish diffusion effect, and the cocktail effect) associated with electrocatalysts based on HEAs. Additionally, we delve into the various applications of HEAs related to electrochemical energy transformation reactions, which encompass both the hydrogen evolution and oxygen evolution reactions. The purpose of the review is to unravel the inherent complexities associated with electrocatalytic active sites, the interactions among component elements, and the mechanisms governing reactions in HEAs. Lastly, we highlight the urgent challenges and stress the importance of theoretical and experimental research, along with the underlying raison d’être of HEAs in electrocatalysis for supplying future energy needs. It is our expectation that this review will inspire additional investigation and advancement of HEAs in relevant electrocatalysis applications, particularly in the context of water splitting processes.

year: 2025

title: Enhancing spatial resolution of remotely sensed data for mapping freshwater environments

authors: Ashraf, Muhammad Salman

subjects: freshwater mapping

abstract: Freshwater environments are important for ecosystem services and biodiversity. These environments are subject to many natural and anthropogenic changes, which influence their quality; therefore, regular monitoring is required for their effective management. High biotic heterogeneity, elongated land/water interaction zones, and logistic difficulties with access make field based monitoring on a large scale expensive, inconsistent and often impractical. Remote sensing (RS) is an established mapping tool that overcomes these barriers. However, complex and heterogeneous vegetation and spectral variability due to water make freshwater environments challenging to map using remote sensing technology. Satellite images available for New Zealand were reviewed, in terms of cost, and spectral and spatial resolution. Particularly promising image data sets for freshwater mapping include the QuickBird and SPOT-5. However, for mapping freshwater environments a combination of images is required to obtain high spatial, spectral, radiometric, and temporal resolution. Data fusion (DF) is a framework of data processing tools and algorithms that combines images to improve spectral and spatial qualities. A range of DF techniques were reviewed and tested for performance using panchromatic and multispectral QB images of a semi-aquatic environment, on the southern shores of Lake Taupo, New Zealand. In order to discuss the mechanics of different DF techniques a classification consisting of three groups was used - (i) spatially-centric (ii) spectrally-centric and (iii) hybrid. Subtract resolution merge (SRM) is a hybrid technique and this research demonstrated that for a semi aquatic QuickBird image it out performed Brovey transformation (BT), principal component substitution (PCS), local mean and variance matching (LMVM), and optimised high pass filter addition (OHPFA). However some limitations were identified with SRM, which included the requirement for predetermined band weights, and the over-representation of the spatial edges in the NIR bands due to their high spectral variance. This research developed three modifications to the SRM technique that addressed these limitations. These were tested on QuickBird (QB), SPOT-5, and Vexcel aerial digital images, as well as a scanned coloured aerial photograph. A visual qualitative assessment and a range of spectral and spatial quantitative metrics were used to evaluate these modifications. These included spectral correlation and root mean squared error (RMSE), Sobel filter based spatial edges RMSE, and unsupervised classification. The first modification addressed the issue of predetermined spectral weights and explored two alternative regression methods (Least Absolute Deviation, and Ordinary Least Squares) to derive image-specific band weights for use in SRM. Both methods were found equally effective; however, OLS was preferred as it was more efficient in processing band weights compared to LAD. The second modification used a pixel block averaging function on high resolution panchromatic images to derive spatial edges for data fusion. This eliminated the need for spectral band weights, minimised spectral infidelity, and enabled the fusion of multi-platform data. The third modification addressed the issue of over-represented spatial edges by introducing a sophisticated contrast and luminance index to develop a new normalising function. This improved the spatial representation of the NIR band, which is particularly important for mapping vegetation. A combination of the second and third modification of SRM was effective in simultaneously minimising the overall spectral infidelity and undesired spatial errors for the NIR band of the fused image. This new method has been labelled Contrast and Luminance Normalised (CLN) data fusion, and has been demonstrated to make a significant contribution in fusing multi-platform, multi-sensor, multi-resolution, and multi-temporal data. This contributes to improvements in the classification and monitoring of fresh water environments using remote sensing.

text: Enhancing spatial resolution of remotely sensed data for mapping freshwater environments Freshwater environments are important for ecosystem services and biodiversity. These environments are subject to many natural and anthropogenic changes, which influence their quality; therefore, regular monitoring is required for their effective management. High biotic heterogeneity, elongated land/water interaction zones, and logistic difficulties with access make field based monitoring on a large scale expensive, inconsistent and often impractical. Remote sensing (RS) is an established mapping tool that overcomes these barriers. However, complex and heterogeneous vegetation and spectral variability due to water make freshwater environments challenging to map using remote sensing technology. Satellite images available for New Zealand were reviewed, in terms of cost, and spectral and spatial resolution. Particularly promising image data sets for freshwater mapping include the QuickBird and SPOT-5. However, for mapping freshwater environments a combination of images is required to obtain high spatial, spectral, radiometric, and temporal resolution. Data fusion (DF) is a framework of data processing tools and algorithms that combines images to improve spectral and spatial qualities. A range of DF techniques were reviewed and tested for performance using panchromatic and multispectral QB images of a semi-aquatic environment, on the southern shores of Lake Taupo, New Zealand. In order to discuss the mechanics of different DF techniques a classification consisting of three groups was used - (i) spatially-centric (ii) spectrally-centric and (iii) hybrid. Subtract resolution merge (SRM) is a hybrid technique and this research demonstrated that for a semi aquatic QuickBird image it out performed Brovey transformation (BT), principal component substitution (PCS), local mean and variance matching (LMVM), and optimised high pass filter addition (OHPFA). However some limitations were identified with SRM, which included the requirement for predetermined band weights, and the over-representation of the spatial edges in the NIR bands due to their high spectral variance. This research developed three modifications to the SRM technique that addressed these limitations. These were tested on QuickBird (QB), SPOT-5, and Vexcel aerial digital images, as well as a scanned coloured aerial photograph. A visual qualitative assessment and a range of spectral and spatial quantitative metrics were used to evaluate these modifications. These included spectral correlation and root mean squared error (RMSE), Sobel filter based spatial edges RMSE, and unsupervised classification. The first modification addressed the issue of predetermined spectral weights and explored two alternative regression methods (Least Absolute Deviation, and Ordinary Least Squares) to derive image-specific band weights for use in SRM. Both methods were found equally effective; however, OLS was preferred as it was more efficient in processing band weights compared to LAD. The second modification used a pixel block averaging function on high resolution panchromatic images to derive spatial edges for data fusion. This eliminated the need for spectral band weights, minimised spectral infidelity, and enabled the fusion of multi-platform data. The third modification addressed the issue of over-represented spatial edges by introducing a sophisticated contrast and luminance index to develop a new normalising function. This improved the spatial representation of the NIR band, which is particularly important for mapping vegetation. A combination of the second and third modification of SRM was effective in simultaneously minimising the overall spectral infidelity and undesired spatial errors for the NIR band of the fused image. This new method has been labelled Contrast and Luminance Normalised (CLN) data fusion, and has been demonstrated to make a significant contribution in fusing multi-platform, multi-sensor, multi-resolution, and multi-temporal data. This contributes to improvements in the classification and monitoring of fresh water environments using remote sensing.

year: 2011

title: Te Tikanga me nga Kawa

authors: Temara, Pou

subjects: Tikanga

abstract: Ko te tikanga te kaitohu i te huarahi tika e pai ai te whakahaere a te Māori i a ia anō. Ki te kore he tikanga e kore e pai tāna noho, ka mate tōna mauri, te mutunga atu ko tōna toiora ka mate. Ki te kore he tikanga, ka  tūtakarerewa te kāhui. He pērā anō te kawa. Ki te kore he kawa, ko taua āhua anō o te pāihiihi o te whakaaro, ko te noho i roto o te kōpā, te mutunga atu ko te pakanga. Engari he nui ngā pōhēhētanga mō te tikanga me te kawa. Ko ētahi tohunga e kī ana he tapu kē atu te kawa i te tikanga, ko ētahi anō e tohe ana mō te tapu o te tikanga. He tohunga anō e kōkiri ana i te haumanutanga o te tikanga me te tāharaharatanga o te kawa. Kai tēnei tuhinga e takoto ai ngā whakamārama mō te tikanga me te kawa me te rerekētanga o tētahi i tētahi.

text: Te Tikanga me nga Kawa Ko te tikanga te kaitohu i te huarahi tika e pai ai te whakahaere a te Māori i a ia anō. Ki te kore he tikanga e kore e pai tāna noho, ka mate tōna mauri, te mutunga atu ko tōna toiora ka mate. Ki te kore he tikanga, ka  tūtakarerewa te kāhui. He pērā anō te kawa. Ki te kore he kawa, ko taua āhua anō o te pāihiihi o te whakaaro, ko te noho i roto o te kōpā, te mutunga atu ko te pakanga. Engari he nui ngā pōhēhētanga mō te tikanga me te kawa. Ko ētahi tohunga e kī ana he tapu kē atu te kawa i te tikanga, ko ētahi anō e tohe ana mō te tapu o te tikanga. He tohunga anō e kōkiri ana i te haumanutanga o te tikanga me te tāharaharatanga o te kawa. Kai tēnei tuhinga e takoto ai ngā whakamārama mō te tikanga me te kawa me te rerekētanga o tētahi i tētahi.

year: 2011