Ring geometric effect on the performance of AlGaN-based deep-ultraviolet light-emitting diodes.

In this study, we fabricated and characterized various parallel flip-chip AlGaN-based deep-ultraviolet (DUV) micro-ring LEDs, including changes in ring dimensions as well as the p-GaN-removed region widths at the outer micro-ring, respectively (PRM LEDs). It is revealed that the LED chips with smaller mesa withstand higher current density and deliver considerably higher light output power density (LOPD), under the same proportion of the hole to the entire mesa column (before it is etched into ring) within the limits of dimensions. However, as the ring-shaped mesa decreases, the LOPD begins to decline because of etching damage. Subsequently, at the same external diameter, the optical performance of micro-ring LEDs with varied internal diameters is studied. Meanwhile, the influence of different structures on light extraction efficiency (LEE) is studied by employing a two-dimensional (2D)-finite-difference time-domain (FDTD) method. In addition, the expand of the p-GaN-removed region at the outer micro-ring as well as the corresponding effective light emission region have some influence to LOPD. The PRM-23 LED (with an external diameter of 90 µm, an internal diameter of 22 µm, and a p-GaN-removed region width of 8 µm) has an LOPD of 53.36 W/cm2 with a current density of 650 A/cm2, and an external quantum efficiency (EQE) of 6.17% at 5 A/cm2. These experimental observations provide a comprehensive understanding of the optical and electrical performance of DUV micro-LEDs for future applications.

Phytoplankton Image Segmentation and Annotation Method Based on Microscopic Fluorescence.

Microscopic phytoplankton segmentation is an important part of water quality assessment. The segmentation of microscopic phytoplankton still faces challenges for computer vision, such as being affected by background impurities and requiring a large number of manual annotation. In this paper, the characteristics of phytoplankton emitting fluorescence under excitation light were utilized to segment and annotate phytoplankton contours by fusing fluorescence images and bright field images. Morphological operations were used to process microscopic fluorescence images to obtain the initial contours of phytoplankton. Then, microscopic bright field images were processed by Active Contour to fine tune the contours. Seven algae species were selected as the experimental objects. Compared with manually labeling the contour in LabelMe, the recall, precision, FI score and IOU of the proposed segmentation method are 85.3%, 84.5%, 84.7%, and 74.6%, respectively. Mask-RCNN was used to verify the correctness of labels annotated by the proposed method. The average recall, precision, F1 score and IOU are 97.0%, 86.5%, 91.1%, and 84.2%, respectively, when the Mask-RCNN is trained with the proposed automatic labeling method. And the results corresponding to manual labeling are 95.3%, 86.1%, 90.3%, and 82.8% respectively. The experimental results show that the proposed method can segment the phytoplankton microscopic image accurately, and the automatically annotated contour data has the same effect as the manually annotated contour data in Mask-RCNN, which greatly reduces the manual annotation workload.

Study on Calibration Method for the Count of Living Algal Cells Detection Based on Variable Fluorescence in Ballast Water.

In the monitoring the discharge of ballast water, the count of living algal cells is of utmost significant. Variable fluorescence, denoted as Fv, stands as an optimal parameter for photosynthetic fluorescence, efficiently charactering the living algal cells count, unaffected by the ballast waters' complex background fluorescence environment. This study deeply investigates the quantitative relationship between Fv and the count of living algal cells. Observations indicate that single cell fluorescence yield (abbreviated as SCF) varies significantly across different algae species, leading to considerable errors in quantifying living algal cell count in ballast water with unknown components using the calibration relationship between Fv and the cell count. Thus, correcting SCF prior to calibration becomes necessary. The paper proposes an innovative SCF correction method based on cell cross-sectional area and an eµ factor (where µ is the expected value of the functional absorption cross-section of PSII) This method mitigates the influence of cell size and species differences on quantifying the living algal cell count. Correction operation trials revealed that dividing the SCF measurement by cell cross-sectional area and multiplying by eµ enhanced the correction effect. Comparative experiments demonstrated marked improvement: Relative errors (REs) for Chlorella pyrenoidosa and Chlorella marine, both belonging to the Chlorophyta group, fell from 92.1% and 90.6% to 37.2% and 9.5% respectively post-correction. Similarly, REs for Thalassiosira weissflogii and Nitzschia closterium minutissima, from the Bacillariophyta group, decreased from 74.7% and 68.1% to 14.3% and 19.1% respectively. The RE of Peridinium from the Pyrrophyta group dropped from 28.4% to 12.1%. The results underscore the effectiveness of cell cross-sectional area and eµ in correcting SCF, thus offering a novel correction method for swift and precise measurement of living algal cell count in ballast water, based on variable fluorescence.

Quantitative Representation of Water Quality Biotoxicity by Algal Photosynthetic Inhibition.

The method based on the photosynthetic inhibition effect of algae offers the advantages of swift response and straightforward measurement. Nonetheless, this effect is influenced by both the environment and the state of the algae themselves. Additionally, a single parameter is vulnerable to uncertainties, rendering the measurement accuracy and stability inadequate. This paper employed currently utilized photosynthetic fluorescence parameters, including Fv/Fm(maximum photochemical quantum yield), Performance Indicator (PIabs), Comprehensive Parameter Index (CPI) and Performance Index of Comprehensive Toxicity Effect (PIcte), as quantitative toxicity characteristic parameters. The paper compared the univariate curve fitting results with the multivariate data-driven model results and investigated the effectiveness of Back Propagation(BP) Neural Network and Support Vector Machine for Regression (SVR) models to enhance the accuracy and stability of toxicity detection. Using Dichlorophenyl Dimethylurea (DCMU) samples as an example, the mean Relative Root Mean Square Error (RRMSE) corresponding to the optimal parameter PIcte for the dose-effect curve fitting was 1.246 in the concentration range of 1.25-200 µg/L. On the other hand, the mean RRMSEs corresponding to the results of the BP neural network and SVR models were 0.506 and 0.474, respectively. Notably, BP neural network exhibited excellent prediction accuracy in the medium-high concentration range of 7.5-200 µg/L, with a mean RRSME of only 0.056. Regarding the stability of the results, the mean Relative Standard Deviation (RSD) of the univariate dose-effect curve results was 15.1% within the concentration range of 50-200 µg/L. In contrast, the mean RSDs for both BP neural network and SVR results were less than 5%. In the concentration range of 1.25-200 µg/L, the mean RSDs were 6.1% and 16.5%, with the BP neural network performing well. The experimental results of Atrazine were analyzed to further validate the effectiveness of the BP neural network in improving the accuracy and stability of results. These findings provided valuable insights for the development of biotoxicity detection by using the algae photosynthetic inhibition method.