Green Band Research Articles

An On-Orbit Relative Sensor Normalization for Unbalance Images from the Ice Pathfinder Satellite (BNU-1)

The Ice Pathfinder satellite (code: BNU-1) is the first Chinese microsatellite, designed for monitoring polar climate and environmental changes. The major payload of BNU-1 is the wide-field camera which provides multispectral satellite images with a 73.69 m spatial resolution and a 739 km swath width. However, the color misrepresentation issue can be observed as the BUN-1 image appears yellowish as it gets farther towards the center field of view (FOV). The blue band of the image appears to be higher near the center FOV and declines generously towards both the edge areas of the image, which may cause the color misrepresentation issue. In this study, we develop a relative sensor normalization method to reduce the radiance errors of the blue band of BNU-1 images. This method uses the radiometric probability density distribution of the BNU-1 panchromatic band as a reference, correcting the probability density distribution of the blue band radiance first. Then, the mean adjustment is used to correct the mean of the blue band radiance after probability density function (PDF) correction, obtaining the corrected radiance in the blue band. Comparisons with the ground measurements and the Landsat8 image reveal the following: (1) The radiances of snow surfaces also have good consistency with ground observations and Landsat-8 images in the red, green, and blue bands. (2) The radiance errors of the uncorrected BNU-1 images are eliminated. The RMSE decreases from 80.30 to 32.54 W/m2/μm/sr. All these results indicate that the on-orbit relative correction method proposed in this study can effectively reduce the radiance errors of the BNU-1 images.

Characterization of sol gel derived spin coated copper incorporation of CdS films for nonlinear optical applications

The sol-gel derived spin-coated Copper (Cu) doped cadmium sulphide (CdS) thin films were deposited on glass substrates. The structural, optical, electrical, and non-linear properties of the films have been investigated in detail. The crystal structure of the Cu-doped CdS films was found to be the hexagonal phase, and an improvement in the crystallinity was observed. The optical absorbance was found to change with deposition parameters, and the bandgap of the films was found to be 2.3 eV, which varied with doping and annealing. The photoluminescent properties of the films showed green and near band edge emission. The electrical characteristics of the Cu-doped CdS films were investigated by Hall effect measurements. The copper doped CdS thin films exhibited prominent NLO properties. The NLA coefficients β obtained are of the order of 10−9m/W. The nonlinear refractive index n2 and nonlinear susceptibility χ (3) values were found to be of the order of 10−11 esu.

OGBN P37 Improving the Accuracy of Endoscopic Measurements of the Size of Gastrointestinal Lesions by a Guidewire Scale

Abstract Background Endoscopic measurement of lesion size is important for the assessment of the healing process, treatment strategies, and cancer staging. Consequently, accurate measurement of the size of the lesion is critical for proper candidate selection for endoscopic resection of early gastric cancer. A visual estimation of the size of the lesion has been widely used, but inaccuracy has been found. Hence, several endoscopic measurement methods have been proposed, but no reliable method has been used thus far. Therefore, we developed a guidewire scale for measuring the size of the lesion. This study focused on improving the accuracy on a practical level. Methods The guidewire scale consisted of green and yellow bands alternating on the wire, which were 5 mm for each band and a total of 30 mm in length. To assess the procedure’s validity and reliability, endoscopists estimated the size of the artificial lesions in the model by visually comparing them with the guidewire measurements. We found measurement error of the guidewire by 0.036±0.25 mm. The endoscopists determined the size of the lesion by the guidewire scale and used a numeral rating scale (NRS) to assess the satisfaction after the procedure. Results We enrolled 52 patients who were evaluated for the size of the lesion by the guidewire scale. The rate of completion of the procedure was 100% without any complications. Overall, the endoscopists’ satisfaction was good (NRS ≥ 8), but satisfaction was significantly higher in the dyspepsia group (P = 0.03). The mean of the size of the lesion was 10.7±8.9 mm, and small ulcer size (size ≤ 1 cm) displayed more satisfaction. The average time of measuring the lesion was 39.4±13.6 seconds, and the procedure time ≤ 30 seconds was highly satisfactory. Conclusions Our guidewire scale enhanced the accuracy of the endoscopic measurement of the size of the lesion. Thus, this guidewire scale could be used to estimate the sizes easily and safely in a clinical study.

Unveiling the effect of strain engineering on the electrochemical properties of hydrothermally grown nanostructured indium doped ZnSeO3 for photoanode applications

The crucial role of In as a dopant on the structural, optical, and thermogravimetric characteristics of the zinc selenite (ZnSeO3) nanopowders has been investigated in detail using X-Ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), Energy Dispersive Spectroscopy (EDS), Raman spectroscopy, diffuse reflectance spectroscopy (DRS), photoluminescence (PL) spectroscopy, and Thermogravimetric Analysis (TGA). The structural analysis indicates that all patterns are assigned to the ZnSeO3 orthorhombic structure. Also, XRD analysis shows that In3+ ions may have replaced Zn2+ ions, which causes lattice expansion. Both the Debye–Scherrer method, and the Williamson–Hall method have also been applied to study the influence of strain on the calculation of the crystallite size. The crystallite size was observed to increase with an increase in dopant concentration. The FE-SEM corroborated that the prepared samples are orthorhombic, with the EDS and mapping confirming the presence of In as a dopant. Raman spectroscopy results corroborated the XRD results indicating an expansion in the crystal structure of ZnSeO3 with the introduction of dopants. Based on DRS data, the introduction of In decreases the energy band gap of the synthesized ZnSeO3 nanopowder samples from 3.305 to 3.276. PL spectra confirm the presence of indium with the green emission band attributed to dopants dominating the emission. The TGA investigation shows an improvement in the mass loss with the introduction of dopants. EIS results indicated an improvement in the conductivity as the charge transfer resistance decreased from 525.04 to 21.95 kΩ for the undoped ZnSeO3 and 0.75% In–ZnSeO3 thin films showing improvement in charge mobility.

Effects of surface conditions on the visible luminescence of ZnO

The effects of surface conditions on photo-induced-carrier (PIC) diffusion are studied by solving one-dimensional continuity equation. Using various forms of ZnO samples with different surface conditions, the PICs lost at sample surface are substantiated to have important contributions to the visible luminescence. For unpassivated ZnO nanorods and single-crystal samples that undergo a high-temperature annealing in air, the visible luminescence originating from the PICs diffused to the surface is estimated more than 99.8 % and 70 % at room temperature, respectively, and more than 99.99 % and 97 % at low temperature. The structureless luminescence caused by PICs, such as the green, yellow, and orange luminescence bands are well fitted under the framework of tail-state luminescence, while the structured green band is suggested to be in association with a specific type of surface states formed during high-temperature annealing in oxidizing atmosphere and can be fitted with Huang-Rhys model by taking into account tail-state luminescence.

Individual Tree Species Identification and Crown Parameters Extraction Based on Mask R-CNN: Assessing the Applicability of Unmanned Aerial Vehicle Optical Images

Automatic, efficient, and accurate individual tree species identification and crown parameters extraction is of great significance for biodiversity conservation and ecosystem function assessment. UAV multispectral data have the advantage of low cost and easy access, and hyperspectral data can finely characterize spatial and spectral features. As such, they have attracted extensive attention in the field of forest resource investigation, but their applicability for end-to-end individual tree species identification is unclear. Based on the Mask R-CNN instance segmentation model, this study utilized UAV hyperspectral images to generate spectral thinning data, spectral dimensionality reduction data, and simulated multispectral data, thereby evaluating the importance of high-resolution spectral information, the effectiveness of PCA dimensionality reduction processing of hyperspectral data, and the feasibility of multispectral data for individual tree identification. The results showed that the individual tree species identification accuracy of spectral thinning data was positively correlated with the number of bands, and full-band hyperspectral data were better than other hyperspectral thinning data and PCA dimensionality reduction data, with Precision, Recall, and F1-score of 0.785, 0.825, and 0.802, respectively. The simulated multispectral data are also effective in identifying individual tree species, among which the best result is realized through the combination of Green, Red, and NIR bands, with Precision, Recall, and F1-score of 0.797, 0.836, and 0.814, respectively. Furthermore, by using Green–Red–NIR data as input, the tree crown area and width are predicted with an RMSE of 3.16m2 and 0.51m, respectively, along with an rRMSE of 0.26 and 0.12. This study indicates that the Mask R-CNN model with UAV optical images is a novel solution for identifying individual tree species and extracting crown parameters, which can provide practical technical support for sustainable forest management and ecological diversity monitoring.

Boosting Semantic Segmentation of Remote Sensing Images by Introducing Edge Extraction Network and Spectral Indices

Deep convolutional neural networks have greatly enhanced the semantic segmentation of remote sensing images. However, most networks are primarily designed to process imagery with red, green, and blue bands. Although it is feasible to directly utilize established networks and pre-trained models for remotely sensed images, they suffer from imprecise land object contour localization and unsatisfactory segmentation results. These networks still need to explore the domain knowledge embedded in images. Therefore, we boost the segmentation performance of remote sensing images by augmenting the network input with multiple nonlinear spectral indices, such as vegetation and water indices, and introducing a novel holistic attention edge detection network (HAE-RNet). Experiments were conducted on the GID and Vaihingen datasets. The results showed that the NIR-NDWI/DSM-GNDVI-R-G-B (6C-2) band combination produced the best segmentation results for both datasets. The edge extraction block benefits better contour localization. The proposed network achieved a state-of-the-art performance in both the quantitative evaluation and visual inspection.

Assessing soil moisture levels using visible UAV imagery and machine learning models

The estimation of soil moisture (SM) as an important variable in the hydrological cycle of nature is necessary for the optimal management of water and soil resources. One of the indirect methods to estimate SM is using visible imagery with unmanned aerial vehicles (UAVs). This study aims to evaluate the potential of visible UAV imagery for estimating SM in a bare soil field in Iran. In this study, M5 tree (M5P), random forest (RF), sequential minimal optimization regression (SMOreg), and multilayer perceptron (MLP) methods have been used for SM modeling from RGB (Red, Green and Blue) bands and brightness and intensity indices of aerial imagery. Three evaluation methods were used to assess the accuracy of the models, including the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). Four different indices, including difference index (DI), ratio index (RI), normalized difference index (NDI), and perpendicular index (PI), were used to estimate SM. The green and red bands pair were found to be the optimal bands for SM estimation. The findings showed that the PI index provided the most accurate SM estimates (R2 = 0.51). The RF model predicted SM most accurately among the machine learning models tested (R2 = 0.67). However, all models underestimated SM content in high-moisture areas and overestimated it in low-moisture areas, with the MLP model showing the most significant overestimation. All the indices were saturated beyond 25% SM. In general, this study highlighted the potential of aerial RGB imagery and associated indices for assessing SM levels within bare soil fields. However, it should be noted that the use of individual bands and indices alone is not sufficient to make an accurate estimate of SM.

Towards tree-based systems disturbance monitoring of tropical mosaic landscape using a time series ensemble learning approach

Tree clearing and degradation inside and outside forest ecosystems in Africa are important contributors to global carbon budget and emissions. Part of the uncertainties in emission estimates, among other things, is related to the non-inclusion of disturbances across all tree-based systems and limitations in the capacity of existing methodologies to detect subtle changes or degradation. Here, we present a tree-based system (TBS) disturbance monitoring approach in a mosaic landscape in East Africa. For this purpose, high- and low-intensity disturbance detection time series algorithms, namely Breaks for Additive Season and Trend monitor (BFASTmonitor), Continuous Degradation Detection (CODED), and Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr), were used. These algorithms were tested in three approaches based on reference canopy cover change data from repeated airborne laser scanning. Data processing was performed using Collection 2 Landsat (5, 7, and 8) time series in Google Earth Engine. The approaches were based on testing the accuracy of 1) the algorithms' magnitude of change outputs using multispectral information (i.e., several vegetation indices and bands); 2) multispectral ensemble learning approach applied at each of the three algorithms; and 3) combining the multiple algorithms in an ensemble learning approach. Our results showed an improvement in the accuracy of detecting a TBS disturbance from approach 1 to 3. The best result (44% omission error and 31% commission error against reference airborne laser scanning data) was obtained when BFASTmonitor and CODED were combined in an ensemble learning approach. The shortwave infrared bands were more important for detecting TBS disturbance than green, red, and near-infrared bands. The application of the best model in TBS disturbance monitoring in the region revealed that disturbances were more dominant outside forest ecosystems than inside. Improved disturbance monitoring in TBS can contribute to more accurate emission estimates in Africa and elsewhere in the tropics.

Using Multi-Spectral Remote Sensing for Flood Mapping: A Case Study in Lake Vembanad, India

Water is an essential natural resource, but increasingly water also forms a threat to the human population, with floods being the most common natural disaster worldwide. Earth Observation has the potential for developing cost-effective methods to monitor risk, with free and open data available at the global scale. In this study, we present the application of remote sensing observations to map flooded areas, using the Vembanad-Kol-Wetland system in the southwest of India as a case study. In August 2018, this region experienced an extremely heavy monsoon season, which caused once-in-a-century floods that led to nearly 500 deaths and the displacement of over a million people. We review the use of existing algorithms to map flooded areas in the Lake Vembanad region using the spectral reflectances of the green, red and near-infrared bands from the MSI sensor on board Sentinel-2. Although the MSI sensor has no cloud-penetrating capability, we show that the Modified Normalised Difference Water Index and the Automated Water Extraction Index can be used to generate flood maps from multi-spectral visible remote sensing observations to complement commonly used SAR-based techniques to enhance temporal coverage (from 12 to 5 days). We also show that local knowledge of paddy cultivation practices can be used to map the manoeuvring of water levels and exclude inundated paddy fields to improve the accuracy of flood maps in the study region. The flood mapping addressed here has the potential to become part of a solution package based on multi-spectral visible remote sensing with capabilities to simultaneously monitor water quality and risk of human pathogens in the environment, providing additional important services during natural disasters.

Examining the potential for early detection of spruce bark beetle attacks using multi-temporal Sentinel-2 and harvester data

Forests are invaluable terrestrial ecosystems with considerable economic, ecological, and environmental benefits. Bark beetles have been recognized as one of the major causes of forest disturbance, and climate change can exacerbate their impact, leading to more tree mortality. Early detection of bark beetle attacks is vital to reduce forest loss and devastating consequences. This study examines the potential for early detection of European spruce bark beetle (Ips typographus L.) attacks in southeastern Sweden using comprehensive harvester data and time series of Sentinel-2 images, 2015–2021. Specifically, it aims at 1) determining the most pronounced wavelength bands and vegetation indices (VIs) of Sentinel-2 for early detection, 2) determining the number of attacked trees in a Sentinel-2 pixel required to enable detection, 3) testing three change detection approaches, Detecting Breakpoints and Estimating Segments in Trend (DBEST), Mean-Level-Shift (MLS), and Cumulative Sum (CUSUM) to investigate the potential for early detection of bark beetle attacks. The greatest separation in reflectance between healthy and attacked pixels, from first swarming peak (May 2018) till harvesting (April 2019), was observed in the SWIR1 (0.018) and SWIR2 (0.011) bands followed by red-edge (0.008), red (0.007), NIR (0.005), and the green band (0.004). The blue band showed the least separation (0.003). All VIs showed a change in their base level after the swarming and this was more prominent for NDRS with an increase of 0.14, followed by NDWI (-0.13), CCI (-0.11) and NDVI (-0.09), all with decreasing values. The observed responses of VIs in relation to the number of attacked trees in a Sentinel-2 pixel increased gradually for pixels having one to ten infested trees, with the strongest response observed for pixels with 9 to 14 attacked trees. Pixels including more than 14 attacked trees did not show any further substantial change in VIs. DBEST, on average, indicated that the infestation impact on VIs is detectable one month after the swarming peak with a 15–31 days precision. MLS and CUSUM with up to two months' accuracies were ranked next. NDRS and CCI showed superior detection performance compared to NDVI and NDWI. The detection was based on smoothed time series of Sentinel-2 data to reduce the influence of noise and missing data and cannot be directly applied to a near real-time detection method.

Assessing Spectral Band, Elevation, and Collection Date Combinations for Classifying Salt Marsh Vegetation with Unoccupied Aerial Vehicle (UAV)-Acquired Imagery

New England salt marshes provide many services to humans and the environment, but these landscapes are threatened by drivers such as sea level rise. Mapping the distribution of salt marsh plant species can help resource managers better monitor these ecosystems. Because salt marsh species often have spatial distributions that change over horizontal distances of less than a meter, accurately mapping this type of vegetation requires the use of high-spatial-resolution data. Previous work has proven that unoccupied aerial vehicle (UAV)-acquired imagery can provide this level of spatial resolution. However, despite many advances in remote sensing mapping methods over the last few decades, limited research focuses on which spectral band, elevation layer, and acquisition date combinations produce the most accurate species classification mappings from UAV imagery within salt marsh landscapes. Thus, our work classified and assessed various combinations of these characteristics of UAV imagery for mapping the distribution of plant species within these ecosystems. The results revealed that red, green, and near-infrared camera image band composites produced more accurate image classifications than true-color camera-band composites. The addition of an elevation layer within image composites further improved classification accuracies, particularly between species with similar spectral characteristics, such as two forms of dominant salt marsh cord grasses (Spartina alterniflora) that live at different elevations from each other. Finer assessments of misclassifications between other plant species pairs provided us with additional insights into the dynamics of why classification total accuracies differed between assessed image composites. The results also suggest that seasonality can significantly affect classification accuracies. The methods and findings utilized in this study may provide resource managers with increased precision in detecting otherwise subtle changes in vegetation patterns over time that can inform future management strategies.

Background noise model of spaceborne photon-counting lidars over oceans and aerosol optical depth retrieval from ICESat-2 noise data

The ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) photon-counting lidar is a revolutionary active remote sensing device and is extremely sensitive to both laser signal and solar background induced noise photons. The weak laser signals, such as aerosol and water subsurface backscattered laser photons, may be buried in noise in the daytime. A noise model for spaceborne photon-counting lidars is of vital importance in understanding and evaluating the performance of a lidar system, as well as in data processing and applications. From the reciprocity perspective, the main noise source for photon-counting lidars is solar background radiation, which in turn is the signal source of ocean color sensors. In the absence of clouds, it is possible to estimate the aerosol information using background noise data of ICESat-2 if the background noise is accurately modeled. In this study, a background noise model of photon-counting lidars over oceans is proposed that carefully considers the contributions of gas molecules, aerosol, water surface, subsurface, and detector dark noise. As the background rate from clouds are much larger than the sum of other factors, in this study, we only consider the cloudless atmosphere by using a cloud screening process. By inputting the system and environmental parameters, the theoretical predictions of noise level in six study areas have an average MAPE of ∼5% compared with ICESat-2 measured noise data. The results also indicate that without clouds, the variation of aerosols dominates the variation of ICESat-2 total background noise in open ocean areas. Then, based on this noise model, a method to estimate aerosol optical depths (AODs) over oceans is proposed solely on the ICESat-2 product including background noise rate and environmental data. Validation against MODIS (Moderate Resolution Imaging Spectroradiometer) daily AODs indicates that the average MAPE is <20% and the average correlation coefficient is 0.95 in six study areas. This method may provide a new AOD retrieval way over oceans in the daytime, when it is difficult to directly calculate AODs using lidar backscattered signal under strong background noise. In addition, the background noise of the ICESat-2 photon-counting lidar in its six channels (or pixels) has the potential to be regarded as the signal of an “ocean color camera” with only a single narrow green band. Based on this consideration, future works may be conducted to combine ocean color remote sensing with ICESat-2 photon-counting lidar.

Understanding the effect of Mn2+ on Yb3+/Er3+ co-doped NaYF4 upconversion and obtaining the optimal combination of these tridoping

In this work, we investigated in detail the upconversion properties of several types of nanoparticles, including NaYF4:5%Yb3+/30%Mn2+, NaYF4:40%Mn2+/x%Yb3+ (x% = 1, 5, 10, 20, 30, and 40), NaYF4:2%Er3+/x%Mn2+ (x% = 20, 30, 40, 50, 60, and 70), NaYF4:40%Mn2+/x%Er3+ (x% = 1, 2, 5, and 10), and NaYF4:40%Mn2+/1%Yb3+/x%Er3+ (x% = 0, 2, 5, and 10). We studied their upconversion emission under 980 nm excitation in both pulsed and continuous wave modes at different synthesis temperatures. The nanoparticles were characterized using transmission electron microscopy (TEM), X-ray diffraction (XRD), and photoluminescence (PL) spectroscopy. The doping of Yb3+ and Mn2+ ions resulted in the nanoparticles assuming cubic and hexagonal crystal structures. The emission intensity increased (106.4 (a.u.*103) to 334.4(a.u.*103)) with increasing synthesis temperature from 120 to 140 °C, while a sharp decrease was observed when the synthesis temperature was increased to 200 °C. The gradual decrease in peak intensity with increasing Mn2+ concentration from 20 to 70% was attributed to energy transfer from Mn2+ to Yb3+. In NaYF4:Mn2+/Yb3+/Er3+ UCNPs, increasing the Er3+ concentration from 0 to 10% led to the disappearance of the blue, orange, and green emission bands. The intense upconversion luminescence pattern with high spatial resolution indicates excellent potential for applications in displays, biological sensors, photodetectors, and solar energy converters.

Mechanistic Insight into the Defect-Engineered White Light Emission from the Single-Phase Orthovanadate Phosphor Synthesized by a Facile Rapid Microwave-Assisted Synthesis.

The synthesis of single-phase barium orthovanadate phosphors by a one-pot microwave-assisted hydrothermal approach has been reported, wherein the homogeneous thermal zone generated at the molecular level by microwave radiation gives rise to tunable distortion in the tetrahedral VO4-3 and oxygen vacancies, eventually enabling intrinsic white light emission with CIE of 0.31,0.38, high photoluminescence internal quantum efficiency of 35%, and external quantum efficiency of 28% whereas phosphor synthesized by the hydrothermal route exhibits only bluish-green emission (PLQE: 0.5%). The Rietveld refinement confirms the formation of a single trigonal phase having dissimilar V-O bond lengths and bond angles, implying the formation of a distorted phosphor under optimized conditions, and corroborates with Raman and Fourier transform infrared analyses. The X-ray photoelectron spectroscopy and electron paramagnetic resonance analysis reveal that the origin of white light emission is due to short- and long-range defects, in particular the oxygen vacancies, which eventually form an intermediate energy level in the forbidden region between the valence and conduction bands. Lifetime spectra show triexponential fitting, corresponding to two charge transfer blue and green emission bands (3T2, 3T1 to 1A1) and one oxygen vacancy-related red emission at RT. Furthermore, these phosphors are thermally stable, as no change in the structure or emission characteristics are observed. A prototype fabricated using a 365 nm chip exhibits white-light-emission CIE of 0.353,0.392, correlated color temperature of 4867 K, color rendering index of 85, and high luminous efficacy of 102 lm/W at 140 mA operating current, portentous for practical applications.

Probing analysis of Cu-doping on the structural, optical, morphological and magnetic properties of hematite nanoparticles and their antibacterial activity

The present study describes the synthesis of pure and Cu doped α-Fe2O3nanoparticles (with various concentrations of Copper 1, 3, 6, and 9 wt%) by conventional chemical precipitation technique and examines their structural, morphological, optical, magnetic, and antibacterial capabilities. The XRD pattern of pure and Cu-doped α-Fe2O3 nanoparticles exhibit rhombohedral structure and the estimated crystalline sizes were ranged from 39 to 58 nm. It is discovered that the estimated density dislocations linked to the agglomeration/cluster formations diminish when interstitial vacancies are filled with copper. The obtained bandgap from Tauc’s plot, 2.07 eV of pure α-Fe2O3 is found to less than Cu doped α-Fe2O3 nanoparticles (2.9–3.4 eV), due to the structural changes and the tailing of localised states into deep bandgap energy levels. The intense blue emission bands (410–490 nm) arised due to the movement of trapped electrons from the donor level to the valance band and broad green emission bands (522–560 nm) are due to deep level CuO defect to the Fe2O3. The fundamental stretching of Fe-O vibrations and the presence of Cu in prepared samples were identified in FTIR and Raman spectra. SEM micrograph shows the uniform distribution of spherical nanoparticles with size ranged from 39 to 61 nm, which is in good accord with XRD studies. Further, the magnetic characteristics of the pure and Cu-doped α-Fe2O3 samples were assessed using a vibrating sample magnetometer (VSM); the ensuing hysteresis loop of the Cu-doped α-Fe2O3 displays weaker ferromagnetic behaviour. In the present investigations, the disc diffusion technique has been used to examine the antibacterial activity. Thus, the results of antibacterial activities demonstrated that at concentrations of 200 and 500 μg/ml of pure and Cu-doped α-Fe2O3 NPs, the highest zone of inhibition was found against gram (+ve) positive bacteria and was followed by the gram (–ve) negative bacteria’s.

Assessing coastal bathymetry and climate change impacts on coastal ecosystems using Landsat 8 and Sentinel-2 satellite imagery

Coastal ecosystems are facing heightened risks due to human-induced climate change, including rising water levels and intensified storm events. Accurate bathymetry data is crucial for assessing the impacts of these threats. Traditional data collection methods can be cost-prohibitive. This study investigates the feasibility of using freely accessible Landsat and Sentinel satellite imagery to estimate bathymetry and its correlation with hydrographic chart soundings in Port Klang, Malaysia. Through analysis of the blue and green spectral bands from the Landsat 8 and Sentinel 2 datasets, a bathymetry map of Port Klang's seabed is generated. The precision of this derived bathymetry is evaluated using statistical metrics like Root Mean Square Error (RMSE) and the coefficient of determination. The results reveal a strong statistical connection (R2 = 0.9411) and correlation (R2 = 0.7958) between bathymetry data derived from hydrographic chart soundings and satellite imagery. This research not only advances our understanding of employing Landsat imagery for bathymetry assessment but also underscores the significance of such assessments in the context of climate change's impact on coastal ecosystems. The primary goal of this research is to contribute to the comprehension of Landsat imagery's utility in bathymetry evaluation, with the potential to enhance safety protocols in seaport terminals and provide valuable insights for decision-making concerning the management of coastal ecosystems amidst climate-related challenges. The findings of this research have practical implications for a wide range of stakeholders involved in coastal management, environmental protection, climate adaptation and disaster preparedness.

Non-invasive thermal sensing and improved recoverable energy storage density of Bi0.5Na0.5TiO3: Er3+ doped multifunctional ferroelectric ceramic

In this work, Er3+ substituted bismuth sodium titanate ceramics with the chemical composition Bi0.5-xErxNa0.5TiO3 (x = 0.00, 0.01, 0.02, 0.03, 0.04, and 0.05) were synthesized using conventional solid state technique. The influence of Er3+ ions on structural, optical, ferroelectric, and temperature sensing properties have been investigated. The prepared ceramic powders were initially heated at a calcination temperature 850 °C to form single phase Bi0.5-xErxNa0.5TiO3 and finally sintered at temperature 1050 °C. Formation of pure phase compositions with rhombohedral crystal structure is confirmed through X-ray diffraction studies. The typical FTIR bands near 540, 860, 910 cm−1 confirmed the presence of Ti–O stretching of octahedral groups in the perovskite structure. The decent squared shaped saturated P-E hysteresis are obtained under an electric field of 60 ≤ E ≤ 70 kV/cm, and the loops become slimmer at higher Er3+ concentrations (x = 0.04). The efficiency of energy storage density increases with Er3+ doping and an improved recoverable energy storage (Wr = 2.73 J/cm3) and a higher efficiency (η = 70.77%) are obtained for Er content, x = 0.04. The photoluminescence spectra were recorded at two excitation wavelengths (488 nm and 980 nm). Two distinct green emission bands (529 nm and 550 nm) and one weak red emission band (670 nm) were observed at both excitation wavelengths. Increasing Er3+ content beyond (x > 0.03) leads to significant quenching of light emission due to cross relaxation process and non-radiative relaxations. The pump power dependency revealed that two photons were involved in the light upconversion process. The time-resolved fluorescence spectroscopy confirmed the decrease in lifetime with increasing Er3+ concentration. The absolute and relative sensitivity of the prepared ceramic at Er3+ concentration (x = 0.03) were found to be 0.47% K−1 at 523 K and 1.1% K−1 at 303 K, respectively. These optical and electrical properties open the possibility of realizing multifunctionality in the field of energy storage and opto-electronic applications.

Efficient bright green phosphor for white light-emitting diode using in backlight application

The (Ba,Ca)ScO2F:Bi3+,K+ phosphor with narrow emission band and green emission color peaking at 510 nm is demonstrated in the study. This phosphor is prepared using a solid-state reaction at high temperatures. Based on characterizing data, the increasing concentration of Ca2+ induces the shrinkage of the unit cell and the narrower spacing, leading to the increase in the photoluminescence of the phosphor. This also cause a red shift in the emitting range from 504 nm to 510 nm. The phosphor shows good absorption peaking at 415 nm, matching the excitation wavelength of the used nearultraviolet (UV) light emitting diode (LED) chip. When being applied in a white light‐emitting diodes (WLED) with 4,000 K, the phosphor could increase the luminous flux as its doping concentration increase. Besides, the WLED doping (Ba, Ca) ScO2F: Bi3+, K+ shows emission regions in blue, green, and red wavelength bands. The data from the study indicates that the phosphor is potential for developing the white-LED for backlight purposes.

ZnO nanocomposites containing Cd are synthesized with high photodegradation potential for wastewater treatment

The zinc oxide (ZnO) nanoparticle on a two - electron oxygen reduction reaction is a particularly promising candidate for in-situ application. Herein, we prepared the Cd doped ZnO nanocomposites through a simple hydrothermal method. The X-ray diffraction (XRD) analysis has been carried out to confirm the structure and size of the nanomaterial. The optical absorption and energy gap of the prepared nanoparticles have been identified by UV–vis-NIR studies. Photoluminescence (PL) studies on the synthesized nanomaterials confirm the characteristic existence of blue and green emission bands. Fourier transform infrared (FTIR) spectroscopy has been studied on the ZnO nanoparticles to analyze the -OH and CO-O vibrations. The structural and surface morphology analysis was inferred by scanning electron microscopy (SEM), transmission electron microscopy (TEM), and energy dispersive X-ray (EDX) microanalysis. Cd doped ZnO nanocomposites are environmentally friendly and can be utilized to quickly remove contaminants from wastewater. The findings from the adsorption equilibrium fit the Brunauer-Emmett-Teller (BET) model very well, showing multi-layer adsorption. The photodegradation studies on Cd doped ZnO nanocomposites were carried out, which is an advanced oxidation process widely used for methylene blue removal. Therefore, the higher photocatalytic performance revealed that Cd doped ZnO nanocomposites could be a promising photocatalyst for environmental remediation applications.