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Keywords = multi-view surface array

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17 pages, 37771 KiB  
Article
Research and Development of a Multi-Point High-Precision Displacement Measuring System for the Installation Space of Vibration Isolation on Submarine Raft Structures
by Yunqing Yu, Shuisheng Xu, Mei Wang and Qiang Xie
Appl. Sci. 2023, 13(21), 12024; https://doi.org/10.3390/app132112024 - 3 Nov 2023
Viewed by 1029
Abstract
The well installed status of raft vibration isolation is undoubtedly of great significance in marine engineering, especially for submarines. To achieve this, the accurate measurement of the installation space of the vibration isolation is necessary. The traditional measuring technique has many drawbacks. Therefore, [...] Read more.
The well installed status of raft vibration isolation is undoubtedly of great significance in marine engineering, especially for submarines. To achieve this, the accurate measurement of the installation space of the vibration isolation is necessary. The traditional measuring technique has many drawbacks. Therefore, simultaneously measuring the multi-point spacing with high precision between two metal surfaces is the focus of this work. Based on eddy current sensing principle, a multi-point spacing measuring system with a simple structure and good measurement accuracy has been developed and reported. The system includes a sensor array component, an integrated controlling component, and a calibration platform. The measured data from multiple points are obtained at the same time through the sensor array and are uploaded to the host computer and a corresponding LabVIEW program was exploited to display, process, and store the spacing results. Furthermore, the least square algorithm has been employed to calculate the flatness of the measured metal surfaces, and the GUM (guide to the expression of uncertainty in measurement) method has been applied to evaluate the flatness error uncertainty. The experimental tests show that each measuring duration only lasts for seconds to get results and the error uncertainty of the measured surface flatness could reduce to less than 1.0 μm. The developed measuring system has better efficiency and higher precision compared to traditionally manual operations. The measuring and analysis method could also be applied to other related situations. Full article
(This article belongs to the Special Issue Ships and Offshore Structures: Design and Mechanical Behavior)
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Figure 1
<p>The schematic diagram of vibration isolators for floating rafts in submarines.</p>
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<p>The installation of a vibration isolator between rafts and supporting base.</p>
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<p>Schematic diagram of an eddy current sensor.</p>
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<p>Schematic diagram of clearance measurement.</p>
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<p>General framework of the system.</p>
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<p>A photograph of an eddy current sensor applied in this work.</p>
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<p>The designed supporting frame. (<b>a</b>) Model of the sensor array component; (<b>b</b>) The meshed geometry being analyzed; (<b>c</b>) The deformation status under horizontal and vertical positions (<b>d</b>).</p>
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<p>Photographs of the sensor array component (<b>a</b>) and the integrated controlling component (<b>b</b>).</p>
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<p>The calibration platform of the measuring system.</p>
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<p>The measurement processing workflow.</p>
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<p>The formal measurement in the test platform.</p>
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17 pages, 2640 KiB  
Article
Bacterial Outer Membrane Vesicles Promote Lung Inflammatory Responses and Macrophage Activation via Multi-Signaling Pathways
by Sunhyo Ryu, Kareemah Ni, Chenghao Wang, Ayyanar Sivanantham, Jonathan M. Carnino, Hong-Long Ji and Yang Jin
Biomedicines 2023, 11(2), 568; https://doi.org/10.3390/biomedicines11020568 - 15 Feb 2023
Cited by 7 | Viewed by 2284
Abstract
Emerging evidence suggests that Gram-negative bacteria release bacterial outer membrane vesicles (OMVs) and that these play an important role in the pathogenesis of bacterial infection-mediated inflammatory responses and organ damage. Despite the fact that scattered reports have shown that OMVs released from Gram-negative [...] Read more.
Emerging evidence suggests that Gram-negative bacteria release bacterial outer membrane vesicles (OMVs) and that these play an important role in the pathogenesis of bacterial infection-mediated inflammatory responses and organ damage. Despite the fact that scattered reports have shown that OMVs released from Gram-negative bacteria may function via the TLR2/4-signaling pathway or induce pyroptosis in macrophages, our study reveals a more complex role of OMVs in the development of inflammatory lung responses and macrophage pro-inflammatory activation. We first confirmed that various types of Gram-negative bacteria release similar OMVs which prompt pro-inflammatory activation in both bone marrow-derived macrophages and lung alveolar macrophages. We further demonstrated that mice treated with OMVs via intratracheal instillation developed significant inflammatory lung responses. Using mouse inflammation and autoimmune arrays, we identified multiple altered cytokine/chemokines in both bone marrow-derived macrophages and alveolar macrophages, suggesting that OMVs have a broader spectrum of function compared to LPS. Using TLR4 knock-out cells, we found that OMVs exert more robust effects on activating macrophages compared to LPS. We next examined multiple signaling pathways, including not only cell surface antigens, but also intracellular receptors. Our results confirmed that bacterial OMVs trigger both surface protein-mediated signaling and intracellular signaling pathways, such as the S100-A8 protein-mediated pathway. In summary, our studies confirm that bacterial OMVs strongly induced macrophage pro-inflammatory activation and inflammatory lung responses via multi-signaling pathways. Bacterial OMVs should be viewed as a repertoire of pathogen-associated molecular patterns (PAMPs), exerting more robust effects than Gram-negative bacteria-derived LPS. Full article
(This article belongs to the Special Issue Pathophysiological Mechanisms of Leukocyte Activation and Recruitment)
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<p>Effects of OMVs on MHS cells. Effects of OMVs on MHS cells. (<b>A</b>) TEM images of OMV and EXOs. Size similarity between OMV and EXO is shown. (<b>B</b>) Representative images of BALF cells collected from PBS, EXO-treated, and OMV-treated mice. 60 µL PBS, 2 µg of EXO or OMV with 60 µL PBS were instilled intra-tracheally (i.t.) into WT mice. (<b>C</b>,<b>D</b>) TNF-α and IL-1β levels in cells treated with control (elution buffer), exosomes (control), and OMVs in MHS cells (<b>C</b>) or BMDM (<b>D</b>). 24 h after stimulation, the above two cytokines were determined using ELISA (<span class="html-italic">n</span> = 2). Results were expressed as mean ± SD. Statistical Analysis was performed non-parametrically using the One-way Analysis of variance (ANOVA) with Tukey’s multiple comparison tests to determine significant differences between the experimental groups. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.005, and **** <span class="html-italic">p</span> &lt; 0.001 set as Statistical significance.</p>
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<p>Effects of OMVs on BMDMs. (<b>A</b>) Effects of OMVs (0.01 µg/mL) on the cytokine and iNOS production in BMDMs (time course). (<b>B</b>) Effects of OMVs (24 h) on the cytokine and iNOS production in BMDMs (dose response). (<b>A</b>,<b>B</b>) Expression levels of TNFα, IL-1β, IL-10, CXCL1, and iNOS were analyzed using real-time PCR. <span class="html-italic">n</span> = 3 or 4. TBP is used as the housekeeping gene. (<b>C</b>) Effect of OMVs on ROS production in BMDM. BMDM were treated with OMVs for 3 h with the indicated doses. Data represents <span class="html-italic">n</span> = 6. (<b>D</b>) Migration assay of OMV-treated BMDM. BMDMs were treated with OMVs (0.01 µg/mL or 0.1 µg/mL respectively). Migration assays were analyzed. (<b>E</b>) Effect of OMVs on Phagocytosis in BMDMs. (<b>D</b>,<b>E</b>) BMDMs were treated with the indicated amounts of OMVs for 24 h (<span class="html-italic">n</span> = 4). (<b>F</b>–<b>G</b>) Time and dose dependent effect of OMVs on the production of proinflammatory mediators in BMDM using ELISA. (<b>F</b>) Cells were treated for 0.5 h, 3 h, 12 h or 24 h in in 0.01 µg/mL OMVs and (<b>G</b>) Cells were treated for 24 h in different concentrations of OMVs (0.01, 0.03, 0.1, 0.3 µg/mL). The sandwich-ELISA method was used to assess the secretory levels of proinflammatory mediators TNF-α and CXCL-1 in the culture supernatant. Results were expressed as mean ± SD. Statistical Analysis was performed using two-tailed unpaired Student’s <span class="html-italic">t</span>-test. Mean  ±  SD is plotted (<span class="html-italic">n</span> = 2 or 3). * <span class="html-italic">p</span> ≤ 0.005, ** <span class="html-italic">p</span> ≤ 0.001, *** <span class="html-italic">p</span> ≤ 0.0005, **** <span class="html-italic">p</span> ≤ 0.001 versus the control (0 hrs. in A, EB treated in B, C, D); EB—Elution Buffer, hrs.—Hour and ns—non-significant.</p>
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<p>OMVs induced inflammatory lung responses. (<b>A</b>) Mice were exposed to different doses of OMVs. After 24 h, BALF were collected, and cells in BALF were stained using H&amp;E staining. The total cell counts (left, lower panel), neutrophil counts (middle, lower panel), and macrophage counts (right, lower panel) were shown. (<b>B</b>,<b>C</b>) The TNF-α (<b>B</b>) and IL-1β (<b>C</b>) were determined in BALF using ELISA. (<b>D</b>) Effects of OMVs (25 µg/mL) on total cell counts infiltrating the lung tissues (left), on neutrophil counts (middle), and macrophage counts (right). (<b>E</b>,<b>F</b>) Mice were treated with OMVs (25 µg/mL) i.t., after 24 h, cells infiltrating lung tissue (<b>E</b>) were stained using H&amp;E staining; Lung tissue (<b>F</b>) was also stained using H&amp;E staining. Results were expressed as mean (<span class="html-italic">n</span> = 2 individual experiments) ± SD. Statistical Analysis was performed non-parametrically using the One-way Analysis of variance (ANOVA) with Tukey’s multiple comparison tests to determine significant differences between the experimental groups. * <span class="html-italic">p</span> ≤ 0.005 ** <span class="html-italic">p</span> ≤ 0.001, *** <span class="html-italic">p</span> ≤ 0.0005, **** <span class="html-italic">p</span> ≤ 0.001 vs. the control (PBS treated) or 12.5 µg OMV treatment group.</p>
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<p>OMVs treatment in macrophages induces pro-inflammatory cytokines and chemokines. (<b>A</b>,<b>B</b>) BMDM were treated with OMVs at the indicated doses, after 24 h, mouse inflammation and autoimmune arrays were used to screen for candidates involved in OMV-stimulated inflammatory responses. Panel (<b>A</b>) shows upregulated chemokines and panel (<b>B</b>) shows downregulated chemokines. To visually present the up- and down-regulation resulted from OMV treatment, control groups were normalized to 1. (<b>C</b>) A mouse chemokine array was performed to screen for alterations in chemokine levels resulted from equal amounts of EXO or OMV treatment in MHS. Cells were treated with 0.01 µg/mL OMV or EXO for 24 h. (<b>C</b>) Representative graph showing 7 of the 25 chemokines profiled by the chemokine array. Data points were analyzed using two-tailed unpaired Student’s <span class="html-italic">t</span>-test to compare a treatment group to the control group; Mean  ±  SD; * <span class="html-italic">p</span> ≤ 0.005, ** <span class="html-italic">p</span> ≤ 0.001, *** <span class="html-italic">p</span> ≤ 0.0005, **** <span class="html-italic">p</span> ≤ 0.001 versus the control (Negative control EB treated); # <span class="html-italic">p</span> ≤ 0.005, ## <span class="html-italic">p</span> ≤ 0.001, #### <span class="html-italic">p</span> ≤ 0.001 vs. Exosome-treated.</p>
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<p>OMVs induce macrophage activation via multi-signaling pathways. (<b>A</b>,<b>B</b>) BMDMs were obtained from TLR4 knockout mice and then treated with OMVs (10–25 µg/mL) or LPS (1–10 µg/mL). After 24 h, TNF-α (<b>A</b>) and IL-1β (<b>B</b>) levels in the cell culture supernatant were determined using ELISA. Results are presented using mean  ±  SD from at least two independent experiments (* <span class="html-italic">p</span> ≤ 0.01). (<b>C</b>,<b>D</b>) BMDMs were treated with OMVs in the dose as indicated. After 24 h, mouse inflammation and autoimmune arrays were used to screen for the candidates involved in OMV-stimulated inflammatory responses. The upregulated genes were shown in (<b>C</b>) and downregulated genes were shown in (<b>D</b>). Control groups were normalized to “1” and folds of increase/decrease were presented for the indicated candidates. Data points were analyzed using two-tailed unpaired Student’s <span class="html-italic">t</span>-test to compare with LPS treated or EB treated group; Mean (<span class="html-italic">n</span> = 2)  ± SD; * <span class="html-italic">p</span> ≤ 0.005 ** <span class="html-italic">p</span> ≤ 0.001, *** <span class="html-italic">p</span> ≤ 0.0005, **** <span class="html-italic">p</span> ≤ 0.001 vs. the control (EB treated) or 10 µg LPS treatment group.</p>
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<p>Expression profile of mouse inflammatory genes. (<b>A</b>,<b>B</b>) The top panel shows results from mouse inflammation and autoimmune array of BMDM treated with the indicated amounts of OMVs for 24 h. In the bottom panel, the profile of mRNA expression of inflammatory target genes was analyzed by M96 PrimePCR array plates utilizing the OMV or exosome-treated samples and plotted against those of their controls. Mouse BMDM were treated with 10 µg/mL OMV or EXO for 24 h. (<b>A</b>) A summary of the upregulated genes. (<b>B</b>) A summary of the downregulated genes. (<b>C</b>,<b>D</b>) The validated mRNA expression of S100a8 in BMDM (C-left), MHS (C-right), and TLR4 K/O BMDM (<b>D</b>), after treatment with control vehicle, and the indicated concentration of exosome and OMV. Results were expressed as mean (<span class="html-italic">n</span> = 2) ± SD. Statistical Analysis was performed non-parametrically using the One-way Analysis of variance (ANOVA) with Tukey’s multiple comparison tests to determine significant differences between the experimental groups. * <span class="html-italic">p</span> ≤ 0.005, ** <span class="html-italic">p</span> ≤ 0.001, *** <span class="html-italic">p</span> ≤ 0.0005, **** <span class="html-italic">p</span> ≤ 0.001 versus the control (EB treated); ### <span class="html-italic">p</span> ≤ 0.0005 vs. Exosome.</p>
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26 pages, 4970 KiB  
Article
Layout Design of Strapdown Array Seeker and Extraction Method of Guidance Information
by Hao Yang, Xibin Bai and Shifeng Zhang
Aerospace 2022, 9(7), 373; https://doi.org/10.3390/aerospace9070373 - 11 Jul 2022
Cited by 3 | Viewed by 2117
Abstract
This paper proposed a multi-view surface array to enlarge the field-of-view (FOV) from 45° × 45° to 72° × 75° and improve the estimation precision of guidance information. First, based on circular and rectangular FOV sensors, the superposition characteristics of FOV in the [...] Read more.
This paper proposed a multi-view surface array to enlarge the field-of-view (FOV) from 45° × 45° to 72° × 75° and improve the estimation precision of guidance information. First, based on circular and rectangular FOV sensors, the superposition characteristics of FOV in the +-shaped layout and the X-shaped layout were explored and analyzed. Secondly, normalization processing was applied to obtain the equivalent measurement value of the central sensor and the corresponding error distribution. Based on such measurement value and error distribution, the filtering model could be constructed, which could effectively solve the problem of observation number variation caused by multiple sensors. Thirdly, a multivariate iterated extended Kalman filter (MIEKF) was proposed to make full use of multiple measurements. By iterating multiple unequal observations and making full use of the known error distribution information, the noise of filtered data was found to be effectively reduced and the estimation precision of guidance information was improved. Finally, based on a 6-DOF trajectory simulation, the correctness and effectiveness of the proposed method were verified. Simulation results show that MIEKF can improve the estimation accuracy of the line-of-sight (LOS) angle by at least 30% and the estimation accuracy of the LOS angle rate by nearly 80% compared with EKF. Full article
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<p>Sensor layout diagrams. (<b>a</b>) Left view of +-shaped layout. (<b>b</b>) Front view of +-shaped layout. (<b>c</b>) Left view of X-shaped layout. (<b>d</b>) Front view of X-shaped layout.</p>
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<p>Back vision project images. (<b>a</b>) +-shaped layout. (<b>b</b>) X-shaped layout.</p>
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<p>Transformation relationship between coordinate systems.</p>
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<p>Sensor coordinate system.</p>
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<p>Circular FOV sensor array overlay diagrams. (<b>a</b>) +-shaped layout. (<b>b</b>) X-shaped layout.</p>
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<p>Rectangular FOV sensors array overlay diagrams. (<b>a</b>) +-shaped layout. (<b>b</b>) X-shaped layout.</p>
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<p>Workflow of Kalman filter.</p>
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<p>MIEKF filtering process.</p>
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<p>The result of sensors measurement. (<b>a</b>) Sensor 1. (<b>b</b>) Sensor 2. (<b>c</b>) Sensor 3. (<b>d</b>) Sensor 4. (<b>e</b>) Sensor 5.</p>
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<p>The results of EKF &amp; IEKF. (<b>a</b>) Elevation of line-of-sight. (<b>b</b>) The rate of elevation of line-of-sight. (<b>c</b>) Azimuth of line-of-sight. (<b>d</b>) The rate of azimuth of line-of-sight.</p>
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<p>The error of EKF and IEKF. (<b>a</b>) The mean error. (<b>b</b>) The standard deviation.</p>
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<p>The results of EKF &amp; MIEKF. (<b>a</b>) Elevation of line-of-sight. (<b>b</b>) The rate of elevation of line-of-sight. (<b>c</b>) Azimuth of line-of-sight. (<b>d</b>) The rate of azimuth of line-of-sight.</p>
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<p>The error of EKF1, EKF5 and MIEKF. (<b>a</b>) The mean error. (<b>b</b>) The standard deviation.</p>
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<p>The number of sensors which could detect the target.</p>
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<p>The variation range of the field angle.</p>
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12 pages, 3882 KiB  
Article
Fabrication and Characterization of Curved Compound Eyes Based on Multifocal Microlenses
by Gaoge Lian, Yongshun Liu, KeKai Tao, Huaming Xing, Ruxia Huang, Mingbo Chi, Wenchao Zhou and Yihui Wu
Micromachines 2020, 11(9), 854; https://doi.org/10.3390/mi11090854 - 16 Sep 2020
Cited by 18 | Viewed by 3577
Abstract
Curved compound eyes have generated great interest owing to the wide field of view but the application of devices is hindered for the lack of proper detectors. One-lens curved compound eyes with multi-focal microlenses provide a solution for wide field imaging integrated in [...] Read more.
Curved compound eyes have generated great interest owing to the wide field of view but the application of devices is hindered for the lack of proper detectors. One-lens curved compound eyes with multi-focal microlenses provide a solution for wide field imaging integrated in a commercial photo-detector. However, it is still a challenge for manufacturing this kind of compound eye. In this paper, a rapid and accurate method is proposed by a combination of photolithography, hot embossing, soft photolithography, and gas-assisted deformation techniques. Microlens arrays with different focal lengths were firstly obtained on a polymer, and then the planar structure was converted to the curved surface. A total of 581 compound eyes with diameters ranging from 152.8 µm to 240.9 µm were successfully obtained on one curved surface within a few hours, and the field of view of the compound eyes exceeded 108°. To verify the characteristics of the fabricated compound eyes, morphology deviation was measured by a probe profile and a scanning electron microscope. The optical performance and imaging capability were also tested and analyzed. As a result, the ommatidia made up of microlenses showed not only high accuracy in morphology, but also imaging uniformity on a focal plane. This flexible massive fabrication of compound eyes indicates great potential for miniaturized imaging systems. Full article
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<p>(<b>a</b>) Design of microlenses with multiple focal lengths on a curved surface; (<b>b</b>) image of curved compound eyes captured by optical stereoscopic microscope; (<b>c</b>) lateral view of curved compound eyes captured by digital microscope with large depth of field.</p>
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<p>Schematic of manufacturing curved compound eyes: (<b>a</b>) AZ5214 photoresist spin-coated on silicon substrate and exposed; (<b>b</b>) developed and etched of silicon wafers; (<b>c</b>) patterned silicon substrate bonded to borosilicate glass; (<b>d</b>) passivation layer grown on the surface; (<b>e</b>) polymer heated and pressed; (<b>f</b>) polymer peeled off to get microlens array (MLA) pattern; (<b>g</b>) structure replicated on polymer with polydimethylsiloxane (PDMS); (<b>h</b>) PDMS membrane deformed and packaged in a sealed cavity under pressure difference; (<b>i</b>) photosensitive adhesive poured and cured; (<b>j</b>) curved compound eyes peeled off.</p>
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<p>Three-dimensional diagram of the home-built metal mold structure: (<b>a</b>) grooved tray for relieving excess pressure in the hot embossing process; (<b>b</b>) sealed cavity for package of the PDMS intermediate die with MLA structure.</p>
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<p>Experimental setup used to measure altitudes of MLAs with different diameters and sag height of PMDS deformation under different pressure in real time.</p>
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<p>Result of morphology measurement experiment: (<b>a</b>) altitudes of pressed polymeric MLAs with different diameters from 0th to 16th cycles; (<b>b</b>) deformation of PDMS membranes with different thickness under pressure from 4–24 Kpa.</p>
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<p>Three-dimensional (3D) profile of the compound eye: (<b>a</b>) SEMimage of the multifocal compound eye at side view; (<b>b</b>) the measured profile of microlenses in center of the multifocal compound eye.</p>
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<p>Experimental installation for optical performance: (<b>a</b>) schematic diagram of the setup; (<b>b</b>) actual platform comprised of He-Ne laser source, microscopic objective lens, pin hole, convex lens, MLAs, optical density filters, microscopic objective lens, and photodetector.</p>
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<p>Focal spots of compound eyes with single and multiple focal lengths: (<b>a</b>) focal spots of single focal length microlenses on dome area; (<b>b</b>) normalized intensity distribution of single focal length microlenses; (<b>c</b>) focal spots of multiple focal length microlenses on the middle area; (<b>d</b>) normalized intensity distribution of multiple focal length microlenses.</p>
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<p>Images captured by the compound eyes: (<b>a</b>) calibration plate captured by the multi-focal compound eyes; (<b>b</b>) image of Lenna in contrast to the single focal compound eyes; (<b>c</b>) image of Lenna captured by the multifocal compound eyes.</p>
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<p>Experiment of field of view (FOV) testing: (<b>a</b>) experimental setup of the testing system; (<b>b</b>) numbers and letters captured by multifocal compound eyes; (<b>c</b>) close look at the upper-left portion of the image; (<b>d</b>) close look at the lower-right portion of the image.</p>
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12 pages, 4110 KiB  
Article
Design of an Aluminum/Polymer Plasmonic 2D Crystal for Label-Free Optical Biosensing
by Luca Tramarin and Carlos Angulo Barrios
Sensors 2018, 18(10), 3335; https://doi.org/10.3390/s18103335 - 5 Oct 2018
Cited by 1 | Viewed by 3108
Abstract
A design study of a nanostructured two-dimensional plasmonic crystal based on aluminum and polymeric material for label-free optical biosensing is presented. The structure is formed of Al nanohole and nanodisk array layers physically separated by a polymeric film. The photonic configuration was analyzed [...] Read more.
A design study of a nanostructured two-dimensional plasmonic crystal based on aluminum and polymeric material for label-free optical biosensing is presented. The structure is formed of Al nanohole and nanodisk array layers physically separated by a polymeric film. The photonic configuration was analyzed through finite-difference time-domain (FDTD) simulations. The calculated spectral reflectance of the device exhibits a surface plasmon polariton (SPP) resonance feature sensitive to the presence of a modeled biolayer adhered onto the metal surfaces. Simulations also reveal that the Al disks suppress an undesired SPP resonance, improving the device performance in terms of resolution as compared to that of a similar configuration without Al disks. On the basis of manufacturability issues, nanohole diameter and depth were considered as design parameters, and a multi-objective optimization process was employed to determine the optimum dimensional values from both performance and fabrication points of view. The effect of Al oxidation, which is expected to occur in an actual device, was also studied. Full article
(This article belongs to the Special Issue Label-free Optical Nanobiosensors)
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<p>(<b>a</b>) Schematics of the modeled Al/polymer multilayered square lattice plasmonic crystal label-free biosensor; (<b>b</b>) Cross-sectional view along AA´ including a biolayer of thickness <span class="html-italic">t</span><sub>bio</sub> adhered on Al metal surfaces. Light along the −<span class="html-italic">z</span> axis impinges on the device, and the spectral distribution of normally reflected light is computed.</p>
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<p>Reflectance spectra of a 500 nm period Al/polymer plasmonic 2D crystal (inset) for <span class="html-italic">d</span> = 150 nm and <span class="html-italic">h</span> = 150 nm. Al metal thickness is 100 nm. Blue and red (dotted) lines correspond to <span class="html-italic">t</span><sub>bio</sub> = 0 and <span class="html-italic">t</span><sub>bio</sub> = 20 nm, respectively. A and B indicate minima (dips) of the curve for t<sub>bio</sub> = 0.</p>
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<p>Cross-sectional electric field distributions of the studied configuration for <span class="html-italic">d</span> = 150 nm and <span class="html-italic">h</span> = 150 nm. (<b>a</b>) E<sub>x</sub> at resonance (<span class="html-italic">λ</span> = 516 nm), (<b>b</b>) E<sub>x</sub> out-of-resonance (<span class="html-italic">λ</span> = 600 nm), (<b>c</b>) E<sub>z</sub> at resonance (<span class="html-italic">λ</span> = 516 nm), (<b>d</b>) E<sub>z</sub> out-of-resonance (<span class="html-italic">λ</span> = 600 nm).</p>
Full article ">Figure 3 Cont.
<p>Cross-sectional electric field distributions of the studied configuration for <span class="html-italic">d</span> = 150 nm and <span class="html-italic">h</span> = 150 nm. (<b>a</b>) E<sub>x</sub> at resonance (<span class="html-italic">λ</span> = 516 nm), (<b>b</b>) E<sub>x</sub> out-of-resonance (<span class="html-italic">λ</span> = 600 nm), (<b>c</b>) E<sub>z</sub> at resonance (<span class="html-italic">λ</span> = 516 nm), (<b>d</b>) E<sub>z</sub> out-of-resonance (<span class="html-italic">λ</span> = 600 nm).</p>
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<p>Reflectance spectra of a 500 nm period Al/polymer plasmonic 2D crystal without Al disks (inset) for <span class="html-italic">d</span> = 150 nm and <span class="html-italic">h</span> = 150 nm. Al metal thickness is 100 nm. Blue and red (dotted) lines correspond to <span class="html-italic">t</span><sub>bio</sub> = 0 and <span class="html-italic">t</span><sub>bio</sub> = 20 nm, respectively. A, B, and C indicate minima (dips) of the curve for <span class="html-italic">t</span><sub>bio</sub> = 0.</p>
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<p>Cross-sectional electric (E<sub>z</sub>) field distributions of (<b>a</b>) the original configuration and (<b>b</b>) no-disk configuration for <span class="html-italic">d</span> = 150 nm and <span class="html-italic">h</span> = 150 nm at <span class="html-italic">λ</span> = 545 nm.</p>
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<p>(<b>a</b>) Surface sensitivity (S<sub>S</sub>) in nm/nm; (<b>b</b>) figure of merit (FOM) in nm<sup>−1</sup>; (<b>c</b>) reflectance resonance amplitude and (<b>d</b>) desirability function (F) for <span class="html-italic">w</span><sub>1</sub> = 0.3, <span class="html-italic">w</span><sub>2</sub> = 0.5, and <span class="html-italic">w</span><sub>3</sub> = 0.3 of the studied Al/polymer 2D plasmonic crystal as a function of the design parameters <span class="html-italic">d</span> and <span class="html-italic">h</span>.</p>
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<p>Reflectance spectra of a 500 nm period Al/polymer plasmonic 2D crystal (<span class="html-italic">d</span> = 150 nm, <span class="html-italic">h</span> = 150 nm) without (blue curves) and with (red curves) a 5 nm thick Al oxide layer. Solid and dashed lines correspond to the absence or presence of a 20 nm thick biolayer, respectively.</p>
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3500 KiB  
Article
Determination of Optimum Viewing Angles for the Angular Normalization of Land Surface Temperature over Vegetated Surface
by Huazhong Ren, Guangjian Yan, Rongyuan Liu, Zhao-Liang Li, Qiming Qin, Françoise Nerry and Qiang Liu
Sensors 2015, 15(4), 7537-7570; https://doi.org/10.3390/s150407537 - 27 Mar 2015
Cited by 17 | Viewed by 6640
Abstract
Multi-angular observation of land surface thermal radiation is considered to be a promising method of performing the angular normalization of land surface temperature (LST) retrieved from remote sensing data. This paper focuses on an investigation of the minimum requirements of viewing angles to [...] Read more.
Multi-angular observation of land surface thermal radiation is considered to be a promising method of performing the angular normalization of land surface temperature (LST) retrieved from remote sensing data. This paper focuses on an investigation of the minimum requirements of viewing angles to perform such normalizations on LST. The normally kernel-driven bi-directional reflectance distribution function (BRDF) is first extended to the thermal infrared (TIR) domain as TIR-BRDF model, and its uncertainty is shown to be less than 0.3 K when used to fit the hemispheric directional thermal radiation. A local optimum three-angle combination is found and verified using the TIR-BRDF model based on two patterns: the single-point pattern and the linear-array pattern. The TIR-BRDF is applied to an airborne multi-angular dataset to retrieve LST at nadir (Te-nadir) from different viewing directions, and the results show that this model can obtain reliable Te-nadir from 3 to 4 directional observations with large angle intervals, thus corresponding to large temperature angular variations. The Te-nadir is generally larger than temperature of the slant direction, with a difference of approximately 0.5~2.0 K for vegetated pixels and up to several Kelvins for non-vegetated pixels. The findings of this paper will facilitate the future development of multi-angular thermal infrared sensors. Full article
(This article belongs to the Section Remote Sensors)
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Graphical abstract

Graphical abstract
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<p>The hemispheric distribution of the simulated DBT from Equation (1) (the first column), the fitted DBT from the TIR-BRDF model (the second column) and their temperature difference (the third column) at (<b>a</b>) LAI = 0.5; (<b>b</b>) LAI = 1.0 and (<b>c</b>) LAI = 2.0, respectively.</p>
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<p>The influence of LAI on RMSE and maximum temperature difference from the TIR-BRDF model.</p>
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<p>Histograms of the temperature RMSE caused by the three-angle TIR-BRDF model with respect to temperature noise about (<b>a</b>) [−0.5, 0.5] K and (<b>b</b>) [−1.0, 1.0] K included in the input DBT data, respectively.</p>
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<p>The illustration of three linear-array patterns. The “pixel series number” is the number of each pixel corresponding with <a href="#sensors-15-07537-f005" class="html-fig">Figure 5</a>; the pixels marked with “PA”, “PB” and “PC” will be used for analysis.</p>
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<p>(<b>a</b>) Viewing zenith angles and (<b>b</b>) viewing azimuth angles of the three linear-array detectors. The pixel series number can be found in <a href="#sensors-15-07537-f004" class="html-fig">Figure 4</a>.</p>
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<p>RMSE histograms of DBT difference for different pixels and SZAs. (<b>a</b>) is for all pixels in <a href="#sensors-15-07537-f004" class="html-fig">Figure 4</a>; (<b>b</b>) is for the pixel PC ; (<b>c</b>) is for the pixel PB ;(<b>d</b>) is for the pixel PA.</p>
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<p>The cumulative percentages of RMSE in [0.0, 1.0] K for different pixels in Nadir array and solar zenith angle (SZA).</p>
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<p>(<b>a</b>) The histograms of DBT difference at nadir; and (<b>b</b>) the cumulative percentages of DBT difference in [−1.0, 1.0] K for different viewing angles and SZAs.</p>
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<p>RMSE histograms of DBT difference for different pixels and SAAs.</p>
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<p>RMSE histograms of DBT difference for different pixels and SAAs.</p>
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<p>The cumulative percentages of RMSE in [0.0, 1.0] K for different pixels in Nadir array and SAA.</p>
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<p>(<b>a</b>) The histograms of DBT difference at nadir for different SAAs; and (<b>b</b>) the cumulative percentages of nadir DBT difference in [−1.0, 1.0] K for different viewing angles and SAAs.</p>
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<p>RMSE histograms of DBT difference for different pixels and LAIs. (<b>a</b>) is for all pixels in <a href="#sensors-15-07537-f004" class="html-fig">Figure 4</a>; (<b>b</b>) is for the pixel PC ; (<b>c</b>) is for the pixel PB ;(<b>d</b>) is for the pixel PA.</p>
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<p>The cumulative percentages of RMSE in [0.0, 1.0] K for different pixels in Nadir array and LAIs.</p>
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<p>(<b>a</b>) The histograms of DBT difference at nadir for different LAIs; and (<b>b</b>) the cumulative percentages of DBT difference in [−1.0, 1.0] K for different viewing angles and LAIs.</p>
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<p>(<b>a</b>) Study region (38.84 °N–38.87 °N, 100.39 °E–100.43 °E) VNIR image from the CCD camera; (<b>b</b>) The measured brightness temperature of one TIR image collected in the study area; while (<b>c</b>) and (<b>d</b>) are the viewing azimuth angle and viewing zenith angle, respectively; (<b>e</b>) the distribution of viewing direction (VAA: 0°~360° and VZA: 0°~60°) and brightness temperature for the points A, B and C marked in (<b>b</b>).</p>
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<p>(<b>a</b>) Study region (38.84 °N–38.87 °N, 100.39 °E–100.43 °E) VNIR image from the CCD camera; (<b>b</b>) The measured brightness temperature of one TIR image collected in the study area; while (<b>c</b>) and (<b>d</b>) are the viewing azimuth angle and viewing zenith angle, respectively; (<b>e</b>) the distribution of viewing direction (VAA: 0°~360° and VZA: 0°~60°) and brightness temperature for the points A, B and C marked in (<b>b</b>).</p>
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<p>(<b>a</b>–<b>c</b>) are the effective temperature at nadir (<span class="html-italic">T<sub>e</sub>-nadir</span>) calculated from all viewing directions, from the directions with adjacent angle difference larger than 8° (denoted as <span class="html-italic">delta_angle</span> = 8°), and from the directions with adjacent angle difference larger than 15° (denoted as <span class="html-italic">delta_angle</span> = 15°), respectively; (<b>d</b>–<b>f</b>) are temperature RMSE in Equation (9) from all directions, <span class="html-italic">delta_angle</span> = 8° and 15°, respectively; (<b>g</b>) is the maximum viewing angle difference in the study area; (<b>h</b>) is the temperature difference between (<b>a</b>) and (<b>b</b>), and between (<b>a</b>) and (<b>c</b>); (<b>i</b>) is the RMSE distribution of figure (<b>d</b>–<b>f</b>).</p>
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<p>(<b>a</b>–<b>c</b>) are the effective temperature at nadir (<span class="html-italic">T<sub>e</sub>-nadir</span>) calculated from all viewing directions, from the directions with adjacent angle difference larger than 8° (denoted as <span class="html-italic">delta_angle</span> = 8°), and from the directions with adjacent angle difference larger than 15° (denoted as <span class="html-italic">delta_angle</span> = 15°), respectively; (<b>d</b>–<b>f</b>) are temperature RMSE in Equation (9) from all directions, <span class="html-italic">delta_angle</span> = 8° and 15°, respectively; (<b>g</b>) is the maximum viewing angle difference in the study area; (<b>h</b>) is the temperature difference between (<b>a</b>) and (<b>b</b>), and between (<b>a</b>) and (<b>c</b>); (<b>i</b>) is the RMSE distribution of figure (<b>d</b>–<b>f</b>).</p>
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<p>(<b>a</b>) The difference of the nadir temperature and the minimum effective temperature of observations; (<b>b</b>) is the temperature difference histogram. The case of <span class="html-italic">delta_angle</span> = 15° was used for example.</p>
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<p>(<b>a</b>–<b>c</b>) are the kernel coefficients <span class="html-italic">f<sub>iso</sub></span>, <span class="html-italic">f<sub>vol</sub></span> and <span class="html-italic">f<sub>geo</sub></span> in Equation (4), respectively; (<b>d</b>–<b>f</b>) are the variation of the three kernel coefficients with NDVI in the study area.</p>
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<p>(<b>a</b>–<b>c</b>) are the kernel coefficients <span class="html-italic">f<sub>iso</sub></span>, <span class="html-italic">f<sub>vol</sub></span> and <span class="html-italic">f<sub>geo</sub></span> in Equation (4), respectively; (<b>d</b>–<b>f</b>) are the variation of the three kernel coefficients with NDVI in the study area.</p>
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2036 KiB  
Article
System-Level Biochip for Impedance Sensing and Programmable Manipulation of Bladder Cancer Cells
by Cheng-Hsin Chuang, Yao-Wei Huang and Yao-Tung Wu
Sensors 2011, 11(11), 11021-11035; https://doi.org/10.3390/s111111021 - 23 Nov 2011
Cited by 30 | Viewed by 8600
Abstract
This paper develops a dielectrophoretic (DEP) chip with multi-layer electrodes and a micro-cavity array for programmable manipulations of cells and impedance measurement. The DEP chip consists of an ITO top electrode, flow chamber, middle electrode on an SU-8 surface, micro-cavity arrays of SU-8 [...] Read more.
This paper develops a dielectrophoretic (DEP) chip with multi-layer electrodes and a micro-cavity array for programmable manipulations of cells and impedance measurement. The DEP chip consists of an ITO top electrode, flow chamber, middle electrode on an SU-8 surface, micro-cavity arrays of SU-8 and distributed electrodes at the bottom of the micro-cavity. Impedance sensing of single cells could be performed as follows: firstly, cells were trapped in a micro-cavity array by negative DEP force provided by top and middle electrodes; then, the impedance measurement for discrimination of different stage of bladder cancer cells was accomplished by the middle and bottom electrodes. After impedance sensing, the individual releasing of trapped cells was achieved by negative DEP force using the top and bottom electrodes in order to collect the identified cells once more. Both cell manipulations and impedance measurement had been integrated within a system controlled by a PC-based LabVIEW program. In the experiments, two different stages of bladder cancer cell lines (grade III: T24 and grade II: TSGH8301) were utilized for the demonstration of programmable manipulation and impedance sensing; as the results show, the lower-grade bladder cancer cells (TSGH8301) possess higher impedance than the higher-grade ones (T24). In general, the multi-step manipulations of cells can be easily programmed by controlling the electrical signal in our design, which provides an excellent platform technology for lab-on-a-chip (LOC) or a micro-total-analysis-system (Micro TAS). Full article
(This article belongs to the Special Issue Biochips)
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<p>The 2D and 3D model of multilayer electrodes DEP chip; the height of the flow chamber is 90 μm, the thickness of SU-8 layer is 10 μm and the diameter of the cavity is 20 μm.</p>
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<p><b>(a)</b> The contour of electric field as applied the AC signal to the middle and top electrodes for trapping cells; <b>(b)</b> The contour of electric field as applied the AC signal to the bottom and top electrodes for releasing cells.</p>
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<p>The vertical component of ∇E<sup>2</sup>: <b>(a)</b> Trapping cells by applied AC signal to top and middle electrodes; <b>(b)</b> Releasing cells by applied AC signal to top and bottom electrodes.</p>
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<p>The distribution of current density as impedance measurement by applying voltage to middle and bottom electrodes; <b>(a)</b> without particle in the SU-8 cavity; <b>(b)</b> and <b>(c)</b> with 15 μm and 20 μm cell in the SU-8 cavity, respectively.</p>
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<p>The variation of total current density as changing the cell membrane conductivity and permittivity.</p>
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<p>The microfabrication processes of DEP chip.</p>
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<p><b>(a)</b> the SEM image of the 3 × 3 micro-cavity array; <b>(b)</b> the photograph of the entire DEP chip; <b>(c)</b> optical image of bottom electrodes (resealing electrode) under micro-cavity array.</p>
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<p>The experimental setup for DEP trapping, releasing and impendence sensing of cells.</p>
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<p>Optical micrographs demonstrating the trapping and programmable releasing of single cells: <b>(a)</b> LabVIEW program for manipulation of cells, <b>(b)</b> cells suspended in the micro-cavity array, <b>(c)</b> trapping cells in the micro-cavity array by switch on No. 10 with an AC signal in the negative-DEP range, <b>(d)</b> show the target (arrow) cell released by switch on No.1 button, <b>(e)</b> all other trapped cells were released by switch on No. 10 button with an AC signal in the positive-DEP frequency range, <b>(f)</b> LabVIEW program for impedance sensing of cells.<span class="inline_supplementary_material" href="sensors-11-11021-s001.mov" type="simple" style="display:none;"> </span></p>
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