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17 pages, 685 KiB  
Article
Advanced Quantification of Receptor–Ligand Interaction Lifetimes via Single-Molecule FRET Microscopy
by Lukas Schrangl, Vanessa Mühlgrabner, René Platzer, Florian Kellner, Josephine Wieland, Reinhard Obst, José L. Toca-Herrera, Johannes B. Huppa, Gerhard J. Schütz and Janett Göhring
Biomolecules 2024, 14(8), 1001; https://doi.org/10.3390/biom14081001 (registering DOI) - 13 Aug 2024
Viewed by 264
Abstract
Receptor–ligand interactions at cell interfaces initiate signaling cascades essential for cellular communication and effector functions. Specifically, T cell receptor (TCR) interactions with pathogen-derived peptides presented by the major histocompatibility complex (pMHC) molecules on antigen-presenting cells are crucial for T cell activation. The binding [...] Read more.
Receptor–ligand interactions at cell interfaces initiate signaling cascades essential for cellular communication and effector functions. Specifically, T cell receptor (TCR) interactions with pathogen-derived peptides presented by the major histocompatibility complex (pMHC) molecules on antigen-presenting cells are crucial for T cell activation. The binding duration, or dwell time, of TCR–pMHC interactions correlates with downstream signaling efficacy, with strong agonists exhibiting longer lifetimes compared to weak agonists. Traditional surface plasmon resonance (SPR) methods quantify 3D affinity but lack cellular context and fail to account for factors like membrane fluctuations. In the recent years, single-molecule Förster resonance energy transfer (smFRET) has been applied to measure 2D binding kinetics of TCR–pMHC interactions in a cellular context. Here, we introduce a rigorous mathematical model based on survival analysis to determine exponentially distributed receptor–ligand interaction lifetimes, verified through simulated data. Additionally, we developed a comprehensive analysis pipeline to extract interaction lifetimes from raw microscopy images, demonstrating the model’s accuracy and robustness across multiple TCR–pMHC pairs. Our new software suite automates data processing to enhance throughput and reduce bias. This methodology provides a refined tool for investigating T cell activation mechanisms, offering insights into immune response modulation. Full article
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Figure 1
<p>Experimental method. (<b>a</b>) Measurement of TCR–pMHC interaction times. T cell receptors are labeled using an H57 scF<sub>V</sub> carrying the FRET donor fluorophore (Alexa Fluor (AF) 555). A functionalized SLB carrying adhesion proteins (ICAM-1), co-stimulatory molecules (B7-1, not shown), and pMHC acts as an antigen presenting cell surrogate. The pMHC presents a stimulatory peptide labeled with the FRET acceptor fluorophore (Alexa Fluor (AF) 647). Only when a ligand is bound to a receptor, fluorophores are close enough (separated by about their Förster radius <math display="inline"><semantics> <msub> <mi>R</mi> <mn>0</mn> </msub> </semantics></math>) to enable FRET. (<b>b</b>) Resulting microscopy image data. Left: Emission of donor fluorophores (TCR labels) upon donor excitation (beginning of the recording). Cell contours were determined via adaptive thresholding. Center: Acceptor fluorophores labeling SLB-bound pMHC upon acceptor excitation (beginning of the recording). Right: FRET signals (acceptor emission upon donor excitation), indicating TCR–pMHC bond, are pointed out by the arrows (77th frame of the recording). (<b>c</b>) Exemplary single-molecule FRET time trace. The time trace of the rightmost signal from (<b>b</b>) appears and vanishes in single, discrete steps and exhibits a plateau, suggesting single-molecular origin.</p>
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<p>Illustration of variables defined for survival analysis. The green line indicates a potential smFRET trace. Within the observation window, microscopy images are recorded repeatedly at intervals <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>t</mi> </mrow> </semantics></math>, depicted by purple vertical lines. A binding event takes place some time <math display="inline"><semantics> <msub> <mi>t</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>pre</mi> </mrow> </msub> </semantics></math> before being recorded in a microscopy frame. After its apparent lifetime, which we interpret as a realization of the (exponentially distributed) random variable <math display="inline"><semantics> <msub> <mi>T</mi> <mi>app</mi> </msub> </semantics></math>, FRET is terminated as a result of unbinding or photobleaching. The measured duration <math display="inline"><semantics> <msub> <mi>t</mi> <mi>i</mi> </msub> </semantics></math> is derived from the number of frames <math display="inline"><semantics> <msub> <mi>n</mi> <mi>i</mi> </msub> </semantics></math> in which the smFRET signal was detectable.</p>
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<p>Proof of concept using simulated data. (<b>a</b>) Simulation of time traces (green). FRET signals are switched on and off with exponentially distributed lifetimes. Microscopy image acquisitions correspond to sampling the time trace at time points separated by an interval <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>t</mi> </mrow> </semantics></math>, illustrated by the vertical lines. Possible scenarios taken into account via survival analysis (<a href="#sec3dot1-biomolecules-14-01001" class="html-sec">Section 3.1</a>) are indicated by the annotated arrows. (<b>b</b>) Inference of <math display="inline"><semantics> <msub> <mi>τ</mi> <mi>app</mi> </msub> </semantics></math>. The histogram depicts simulated track lengths for fixed <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>t</mi> <mo>=</mo> <mn>3</mn> <mspace width="3.33333pt"/> <mi mathvariant="normal">s</mi> </mrow> </semantics></math> for three scenarios: (i) signals are present at the start of the recording window, (ii) at the end of the recording window, (iii) they lie fully within the recording window. The probability density function (PDF) derived using survival analysis (green line) is virtually indistinguishable from the true PDF (red dotted line). Analysis utilizing conventional maximum likelihood estimation (MLE, see also <a href="#sec2dot7-biomolecules-14-01001" class="html-sec">Section 2.7</a>) yields a clear deviation in the PDF (purple line) and a value for <math display="inline"><semantics> <msub> <mi>τ</mi> <mi>app</mi> </msub> </semantics></math> which is too low. (<b>c</b>) Determination of the binding lifetime <math display="inline"><semantics> <msub> <mi>τ</mi> <mi>lt</mi> </msub> </semantics></math>. Datasets as in (<b>b</b>) were simulated and analyzed for different <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>t</mi> </mrow> </semantics></math>. Red crosses mark the simulated values for <math display="inline"><semantics> <msub> <mi>τ</mi> <mi>app</mi> </msub> </semantics></math>, green dots indicate values determined using survival analysis, and purple dots denote values inferred via conventional MLE. Equation (<a href="#FD1-biomolecules-14-01001" class="html-disp-formula">1</a>) was fit to the resulting <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mi>app</mi> </msub> <mrow> <mo>(</mo> <mo>Δ</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, of which results are shown as dotted red line, green line, and purple line, respectively. The shaded areas indicate corresponding error margins.</p>
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<p>Method robustness characterized using simulated data. (<b>a</b>) Choice of recording intervals <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>t</mi> </mrow> </semantics></math>. Three exemplary sets of recording intervals were chosen. Their location on the <math display="inline"><semantics> <msub> <mi>τ</mi> <mi>app</mi> </msub> </semantics></math> vs. <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>t</mi> </mrow> </semantics></math> curve (Equation (<a href="#FD1-biomolecules-14-01001" class="html-disp-formula">1</a>)) is shown in the left panel. The boxplots in the right panel summarize the bond lifetimes <math display="inline"><semantics> <msub> <mi>τ</mi> <mi>lt</mi> </msub> </semantics></math> as determined from 500 simulated experiments with respective <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>t</mi> </mrow> </semantics></math> sets. Analysis was performed using both our survival analysis-based method and conventional MLE. The ground truth <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mi>lt</mi> </msub> <mo>=</mo> <mn>10</mn> <mspace width="3.33333pt"/> <mi mathvariant="normal">s</mi> </mrow> </semantics></math> is indicated by the dashed line. (<b>b</b>) Dataset size. Using the medium <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>t</mi> </mrow> </semantics></math> set from (<b>a</b>), experiments yielding varying numbers of FRET time traces were simulated (500 experiments per size category). The mean numbers of traces per recording interval are indicated in the left panel (error bars: standard deviations). Bond lifetimes <math display="inline"><semantics> <msub> <mi>τ</mi> <mi>lt</mi> </msub> </semantics></math> inferred from the datasets are charted in the right panel. As in (<b>a</b>), our new method is compared to conventional MLE. The ground truth <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mi>lt</mi> </msub> <mo>=</mo> <mn>10</mn> <mspace width="3.33333pt"/> <mi mathvariant="normal">s</mi> </mrow> </semantics></math> is plotted as a dashed line.</p>
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<p>Lifetime measurements for different TCR–pMHC pairs. Apparent lifetimes <math display="inline"><semantics> <msub> <mi>τ</mi> <mi>app</mi> </msub> </semantics></math> for respective recording intervals <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>t</mi> </mrow> </semantics></math> are displayed as dots (maximum likelihood estimate via survival analysis) with error bars (standard error of the estimate). The solid line shows the result of fitting Equation (<a href="#FD1-biomolecules-14-01001" class="html-disp-formula">1</a>), the shaded area indicates the uncertainty. (<b>left</b>): 5c.c7 TCR, IE<sup>k</sup>/MCC pMHC; (<b>right</b>): AND TCR, IE<sup>k</sup>/MCC pMHC.</p>
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16 pages, 4440 KiB  
Article
High Stability and Low Power Nanometric Bio-Objects Trapping through Dielectric–Plasmonic Hybrid Nanobowtie
by Paola Colapietro, Giuseppe Brunetti, Annarita di Toma, Francesco Ferrara, Maria Serena Chiriacò and Caterina Ciminelli
Biosensors 2024, 14(8), 390; https://doi.org/10.3390/bios14080390 - 13 Aug 2024
Viewed by 256
Abstract
Micro and nano-scale manipulation of living matter is crucial in biomedical applications for diagnostics and pharmaceuticals, facilitating disease study, drug assessment, and biomarker identification. Despite advancements, trapping biological nanoparticles remains challenging. Nanotweezer-based strategies, including dielectric and plasmonic configurations, show promise due to their [...] Read more.
Micro and nano-scale manipulation of living matter is crucial in biomedical applications for diagnostics and pharmaceuticals, facilitating disease study, drug assessment, and biomarker identification. Despite advancements, trapping biological nanoparticles remains challenging. Nanotweezer-based strategies, including dielectric and plasmonic configurations, show promise due to their efficiency and stability, minimizing damage without direct contact. Our study uniquely proposes an inverted hybrid dielectric–plasmonic nanobowtie designed to overcome the primary limitations of existing dielectric–plasmonic systems, such as high costs and manufacturing complexity. This novel configuration offers significant advantages for the stable and long-term trapping of biological objects, including strong energy confinement with reduced thermal effects. The metal’s efficient light reflection capability results in a significant increase in energy field confinement (EC) within the trapping site, achieving an enhancement of over 90% compared to the value obtained with the dielectric nanobowtie. Numerical simulations confirm the successful trapping of 100 nm viruses, demonstrating a trapping stability greater than 10 and a stiffness of 2.203 fN/nm. This configuration ensures optical forces of approximately 2.96 fN with an input power density of 10 mW/μm2 while preserving the temperature, chemical–biological properties, and shape of the biological sample. Full article
(This article belongs to the Special Issue Nanotechnology-Based Optical Sensors for Biomedical Applications)
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Figure 1
<p>Schematic workflow diagram of the research article.</p>
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<p>Schematic of the investigated nanobowtie in SOI platform with a metal layer.</p>
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<p>(<b>a</b>) Electric field intensity distribution at the trapping site in the middle plane of the nanostructure at z = 200 nm + t<sub>Ag</sub> for the bowtie with t<sub>Ag</sub> = 50 nm by considering P<sub>in</sub> = 10 mW/μm<sup>2</sup>. (<b>b</b>) Zoom-in of (<b>a</b>).</p>
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<p>(<b>a</b>) ΔEC (%) vs. metal layer thickness (nm) with gold and (<b>b</b>) silver layer.</p>
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<p>Steady-state temperature rise distributions ∆T(K) = T − T<sub>0</sub>, T<sub>0</sub> = 293.15 K for the silver layer configuration at z = 200 nm + t<sub>Ag</sub> for P<sub>in</sub> = 10 mW/μm<sup>2</sup>.</p>
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<p>(<b>a</b>) Temperature increase ∆T in the trapping area as a function of the input optical power for nanocavity with gold layer; (<b>b</b>) silver layer.</p>
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<p>Schematic and lateral view of the nanocavity (z–y directions) with a trapped particle (d = 100 nm).</p>
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<p>(<b>a</b>) Representation of the intensity of electric field along the trapping site of the nanocavity. (<b>b</b>) Optical force trend vs. displacement of nanoparticle in configuration with gold layer (<b>c</b>) and silver layer.</p>
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<p>(<b>a</b>) Stability trapping vs. ∆T for nanobowtie with gold layer; (<b>b</b>) for nanobowtie with silver layer.</p>
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<p>(<b>a</b>) Stability trapping by varying the input power P<sub>in</sub> for nanobowtie with a gold layer; (<b>b</b>) for nanobowtie with a silver layer.</p>
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<p>Trapping stability as a function of the particle diameter and the thickness of two dielectric layers for nanobowtie with a silver layer with P<sub>in</sub> = 10 mW/μm<sup>2</sup>.</p>
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15 pages, 5702 KiB  
Article
A Multimode Detection Platform for Biothiols Using BODIPY Dye-Conjugated Gold Nanoparticles
by Panangattukara Prabhakaran Praveen Kumar
Colorants 2024, 3(3), 214-228; https://doi.org/10.3390/colorants3030015 (registering DOI) - 12 Aug 2024
Viewed by 149
Abstract
This study explored the synthesis and application of BODIPY-functionalized gold nanoparticles (AuNPs) for the sensitive detection of biothiols via an indicator displacement assay coupled with surface-enhanced Raman scattering (SERS) techniques, alongside their efficacy for in vitro cancer cell imaging. Moreover, the assay allowed [...] Read more.
This study explored the synthesis and application of BODIPY-functionalized gold nanoparticles (AuNPs) for the sensitive detection of biothiols via an indicator displacement assay coupled with surface-enhanced Raman scattering (SERS) techniques, alongside their efficacy for in vitro cancer cell imaging. Moreover, the assay allowed for the visible colorimetric detection of biothiols under normal and ultraviolet light conditions. The BODIPY (boron-dipyrromethene) fluorophores were strategically conjugated to the surface of gold nanoparticles, forming a robust nanohybrid that leverages the plasmonic properties of AuNPs for enhanced spectroscopic sensitivity. The detection mechanism exploited the displacement of the BODIPY indicator upon interaction with biothiols, triggering a measurable change in fluorescence and SERS signals. This dual-mode sensing approach provides high selectivity and sensitivity for biothiol detection, with detection limits reaching nanomolar concentrations using fluorescence and femtomolar concentration for cysteine using SERS. Furthermore, the BODIPY-AuNP complexes demonstrated excellent biocompatibility and photostability, facilitating their use in the fluorescence imaging of biothiol presence within cellular environments and highlighting their potential for diagnostic and therapeutic applications in biomedical research. Full article
(This article belongs to the Special Issue Feature Papers in Colorant Chemistry)
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Figure 1
<p>Characterization of <b>NC1</b>. (<b>A</b>) UV–visible and (<b>B</b>) emission spectra for DBDP in acetone (50 μM), L-tryptophan AuNPs (50 μg/mL), and nanocomposite <b>NC1</b> (50 μg/mL) in acetate buffer. λ<sub>exc</sub> = 490 nm. (<b>C</b>,<b>D</b>) represent transmission electron microscopy and dynamic light scattering experiments for <b>NC1</b>.</p>
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<p>The interaction of biothiols with <b>NC1</b>. (<b>A</b>–<b>C</b>) represent the changes in extinction spectra for <b>NC1</b> after the interaction with various amounts of L-cysteine, L-homocysteine, and L-glutathione, respectively. (<b>D</b>–<b>F</b>) represent the changes in the emission spectra of <b>NC1</b> with the addition of L-cysteine, L-homocysteine, and L-glutathione, respectively, where [<b>NC1</b> = 50 μg/mL]. Studies were performed in acetate buffer pH = 9. Emission spectra were collected by exciting the samples at 490 nm (λexc = 490 nm).</p>
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<p>(<b>A</b>) Photographs for the visual color changes in <b>NC1</b> under ambient and UV light in acetate buffer (pH 9.0) with various L-amino acids and S<sup>2−</sup> ions at 25 °C, where (i) <b>NC1</b> (ii) <b>NC1</b> + L-Gly (100 μM), (iii) <b>NC1</b> + L-Val (100 μM), (iv) <b>NC1</b> + L-leu (100 μM), (v) <b>NC1</b> + L-Lys (100 μM), (vi) <b>NC1</b> + L-Cyst (100 μM), (vii) <b>NC1</b> + L-Cys (3 μM), (viii) <b>NC1</b> + L-Hcy (3 μM), (ix) <b>NC1</b> + L-GSH (3 μM), (x) <b>NC1</b> + L-His (100 μM), (xi) <b>NC1</b> + L-Asp (100 μM), (xii) <b>NC1</b> + L-Glu (100 μM), (xiii) <b>NC1</b> + L-Trp (100 μM), (xiv) <b>NC1</b> + L-Thr (100 μM), (xv) <b>NC1</b> + L-Ala (100 μM), (xvi) <b>NC1</b> + L-Met (100 μM), (xvii) <b>NC1</b> + L-Pro (100 μM), (<b>NC1</b> + H<sub>2</sub>S (100 μM). (<b>B</b>,<b>C</b>) represent the extinction and emission spectra for DBDP NPs (50 μg/mL) and <b>NC1</b> (50 μg/mL) + 3 μM L-Cys, respectively. (<b>D</b>) Transmission electron microscopy images for DBDP NPs.</p>
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<p>(<b>A</b>) UV–visible and (<b>B</b>) emission spectra for <b>NC1</b> (50 μg/mL) with L-Cys (3 μM) alone, in different pH (9, 7.4) conditions, and with various interfering biological molecules such as FBS, HSA, and BSA. (<b>C</b>,<b>D</b>) represent the visual colorimetric response for (i) <b>NC1</b> (50 μg/mL) alone (ii) <b>NC1</b> + L-Cys in pH = 9, (iii) <b>NC1</b> + L-Cys in pH = 7.4, (iv) <b>NC1</b> + L-Cys in FBS, (v) <b>NC1</b> + L-Cys in HAS, and (vi) <b>NC1</b> + L-Cys in BSA. λ<sub>exc</sub> = 490 nm.</p>
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<p>(<b>A</b>–<b>C</b>) SERS spectra of <b>NC1</b> after interaction with various concentrations of L-Cys, L-Hcy, and L-GSH, respectively, where thiols were added from (10<sup>−6</sup> M to 10<sup>−17</sup> M) to <b>NC1</b> (50 μg/mL) (<b>D</b>–<b>F</b>) Linear relationship between SERS peak intensities at 674 cm<sup>−1</sup> and biothiol concentrations (L-Cys, L-Hcy, and L-GSH), respectively. Excitation: 633 nm laser (6.8 mW); exposure time: 10 s.</p>
Full article ">Figure 6
<p>Surface-enhanced Raman scattering spectra for the dried samples of <b>NC1</b> with biothiols. (<b>A</b>) SERS spectra for dried samples of <b>NC1</b> with various concentrations of L-Cys. (<b>B</b>) SERS Raman mapping for <b>NC1</b> with various concentrations of L-Cys. (<b>C</b>,<b>D</b>) represent the SERS spectra and Raman mapping images for <b>NC1</b> with various concentrations of L-Hcy, respectively. (<b>E</b>,<b>F</b>) SERS and Raman mapping images for the dried samples from <b>NC1</b> with various concentrations of L-GSH.</p>
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<p>Bright-field and fluorescence microscopy images of L929 (normal) cell lines and MDA-MB 231 (cancer) cell lines following a 24 h incubation with <b>NC1</b> at a concentration of 12.5 and 50 μg/mL.</p>
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<p>Developed paper strips from <b>NC1</b> for the detection of biothiols. (<b>A</b>) shows the color changes from <b>NC1</b> paper strips by the addition of various amounts of L-Cys under day and UV light. (<b>B</b>) The color changes in the <b>NC1</b> paper strips by the addition of L-Hcy under day and UV light conditions. (<b>C</b>) The observed color changes in the <b>NC1</b> paper strips by the addition of L-GSH under day and UV light conditions, respectively.</p>
Full article ">Scheme 1
<p>Working principle for prepared nanocomposite from DBDP dye and AuNPs for detection of biothiols using IDA principle.</p>
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12 pages, 1929 KiB  
Article
Targeting N-Acetylglucosaminidase in Staphylococcus aureus with Iminosugar Inhibitors
by Janja Sluga, Tihomir Tomašič, Marko Anderluh, Martina Hrast Rambaher, Gregor Bajc, Alen Sevšek, Nathaniel I. Martin, Roland J. Pieters, Marjana Novič and Katja Venko
Antibiotics 2024, 13(8), 751; https://doi.org/10.3390/antibiotics13080751 - 10 Aug 2024
Viewed by 444
Abstract
Bacteria are capable of remarkable adaptations to their environment, including undesirable bacterial resistance to antibacterial agents. One of the most serious cases is an infection caused by multidrug-resistant Staphylococcus aureus, which has unfortunately also spread outside hospitals. Therefore, the development of new [...] Read more.
Bacteria are capable of remarkable adaptations to their environment, including undesirable bacterial resistance to antibacterial agents. One of the most serious cases is an infection caused by multidrug-resistant Staphylococcus aureus, which has unfortunately also spread outside hospitals. Therefore, the development of new effective antibacterial agents is extremely important to solve the increasing problem of bacterial resistance. The bacteriolytic enzyme autolysin E (AtlE) is a promising new drug target as it plays a key role in the degradation of peptidoglycan in the bacterial cell wall. Consequently, disruption of function can have an immense impact on bacterial growth and survival. An in silico and in vitro evaluation of iminosugar derivatives as potent inhibitors of S. aureus (AtlE) was performed. Three promising hit compounds (1, 3 and 8) were identified as AtlE binders in the micromolar range as measured by surface plasmon resonance. The most potent compound among the SPR response curve hits was 1, with a KD of 19 μM. The KD value for compound 8 was 88 μM, while compound 3 had a KD value of 410 μM. Full article
(This article belongs to the Special Issue Recent Advances in Antimicrobial Drug Discovery, 2nd Edition)
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Figure 1
<p>(<b>a</b>) 3D binding model of the NAG-NAM central unit on the AtlE surface (PDB ID: 4PI7); (<b>b</b>) 2D modeled interactions of the NAG-NAM central unit with AtlE (red residue represents the hydrogen bond acceptor, green residue represents the hydrogen bond donor).</p>
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<p>(<b>a</b>) 3D binding model of compounds <b>1</b>, <b>3</b> and <b>8</b> on the AtlE surface (PDB ID: 4PI7); (<b>b</b>) 2D modeled interactions of compounds <b>1</b>, <b>3</b> and <b>8</b> with AtlE (red residues represent the hydrogen bond acceptors, green residues represent the hydrogen bond donor, and yellow residues represent the hydrophobic interactions).</p>
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<p>Representative SPR sensorgram (response curves) for compound 1-deoxynojirimycin at two different concentrations.</p>
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<p>(<b>a</b>) Representative SPR sensorgrams (response curves) and (<b>b</b>) representative saturation curves with evaluated <span class="html-italic">K</span><sub>D</sub> for compounds <b>1</b>, <b>3</b> and <b>8</b> at different concentrations. SPR analysis of compound <b>1</b>, <b>3</b> and <b>8</b> interactions with the immobilized AtlE. Compounds were injected across immobilized AtlE in serial dilutions for 60 s at a rate of 30 mL/min, and the dissociation was followed for 50 s. Sensorgrams are shown along with the apparent equilibrium dissociation constant (<span class="html-italic">K<sub>D</sub></span>) determined from the response curves as a function of the compound concentration injected across AtlE. <span class="html-italic">K<sub>D</sub></span> values are the mean ± standard deviation of three titrations. The data were fitted to the steady-state affinity binding model.</p>
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13 pages, 1635 KiB  
Article
Optical Biosensor Based on Porous Silicon and Tamm Plasmon Polariton for Detection of CagA Antigen of Helicobacter pylori
by Guoguang Rong, Alexey Kavokin and Mohamad Sawan
Sensors 2024, 24(16), 5153; https://doi.org/10.3390/s24165153 - 9 Aug 2024
Viewed by 197
Abstract
Helicobacter pylori (H. pylori) is a common pathogen with a high prevalence of infection in human populations. The diagnosis of H. pylori infection is critical for its treatment, eradication, and prognosis. Biosensors have been demonstrated to be powerful for the rapid [...] Read more.
Helicobacter pylori (H. pylori) is a common pathogen with a high prevalence of infection in human populations. The diagnosis of H. pylori infection is critical for its treatment, eradication, and prognosis. Biosensors have been demonstrated to be powerful for the rapid onsite detection of pathogens, particularly for point-of-care test (POCT) scenarios. In this work, we propose a novel optical biosensor, based on nanomaterial porous silicon (PSi) and photonic surface state Tamm Plasmon Polariton (TPP), for the detection of cytotoxin-associated antigen A (CagA) of H. pylori bacterium. We fabricated the PSi TPP biosensor, analyzed its optical characteristics, and demonstrated through experiments, with the sensing of the CagA antigen, that the TPP biosensor has a sensitivity of 100 pm/(ng/mL), a limit of detection of 0.05 ng/mL, and specificity in terms of positive-to-negative ratio that is greater than six. From these performance factors, it can be concluded that the TPP biosensor can serve as an effective tool for the diagnosis of H. pylori infection, either in analytical labs or in POCT applications. Full article
(This article belongs to the Special Issue Optical Biosensors and Applications)
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<p>The PSi TPP biosensor structure and its optical measurement configuration. The Au NPs embedded in the first LP PSi layer means that Au NP can infiltrate into the nanopores of porous silicon. The picture on the bottom left shows the setup of the 12 × 8 biosensor array and its sequential measurement by an in-house developed equipment mainly consisting of a two-dimensional moving stage to carry and position the sensor array and a fiber spectrometer to take the optical measurement.</p>
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<p>(<b>a</b>) Cross-sectional scanning electron microscopy image of the TPP biosensor structure. The periodic layered structure of the porous silicon DBR is clearly visible. The gold thin film is the bright layer on top of the porous silicon DBR with the thickness not to scale due to a focusing issue; (<b>b</b>) atomic force microscopy image of a Au thin film surface morphology, showing the nanoporous structure of Au due to conformal deposition onto porous silicon; (<b>c</b>) Color map of electrical field strength distribution profile of TPP biosensor simulated by COMSOL V5.5. The field peak resides in the first (or top) PSi layer close to the thin metal film. Light is incident from the left side of the TPP device in air with a power of 1 W/m; (<b>d</b>) An example of the reflection spectrum of the TPP device is that of where the resonant state manifests as the reflection minimum at a wavelength of around 730 nm; (<b>e</b>) Example of the redshift of the TPP resonance wavelength upon the specific biomolecular binding of 3 ng/mL CagA antigen with the CagA antibody immobilized on the biosensor surface beforehand. The spectrum of the TPP biosensor both before (solid curve) and after (dashed curve) binding is shown. The redshift of the resonance minimum, indicated as arrows, is around 360 pm. (<b>f</b>) Example TPP resonance spectra upon exposure to PBS buffer with CagA antibody immobilized on the biosensor surface beforehand. The spectrum of the TPP biosensor both before (solid curve) and after (dashed curve) PBS is shown. The shift of resonance minimum, indicated as arrows, is 0 pm.</p>
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<p>Response characterization of CagA antigen detection for detecting varying concentrations of CagA antigen in the PBS buffer, with both PSi TPP (black circles) and PSi DBR (grey circles) biosensors. Error bars on the experimental data points (solid circle) show a standard error from five experiments, with each experiment using a different biosensor. The linear fittings (dashed curves) are performed to match the data points. The linear equations go through the origin and the quality of the fitting is also given in the figure.</p>
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<p>Specificity test and competitivity test of the PSi TPP biosensor with the objective of CagA antigen detection. CagA has a concentration of 1 ng/mL and all other nonspecific species concentrations of 5 ng/mL. Error bars on the experimental data bars (solid rectangle) show the standard error from five experiments, with each experiment using a different biosensor.</p>
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18 pages, 8359 KiB  
Article
Analyses of an Ultra-Wideband Absorber from UV-B to Middle-IR Utilizing a Square Nanopillar and a Square Hollow Embedded in a Square Cavity of the Top Layer of Multilayer Metamaterials
by Chia-Te Liao, Pei-Xiu Ke, Chia-Min Ho, Cheng-Fu Yang and Tung-Lung Wu
Photonics 2024, 11(8), 742; https://doi.org/10.3390/photonics11080742 - 8 Aug 2024
Viewed by 340
Abstract
In this study, an ultra-wideband absorber spanning from UV-B to middle-IR was designed and analyzed using a novel structure. The multilayer metamaterial, arranged from bottom to top, consisted of an Al metal layer, a lower SiO2 layer, a graphite layer, another SiO [...] Read more.
In this study, an ultra-wideband absorber spanning from UV-B to middle-IR was designed and analyzed using a novel structure. The multilayer metamaterial, arranged from bottom to top, consisted of an Al metal layer, a lower SiO2 layer, a graphite layer, another SiO2 layer, a thin Ti layer, and a top SiO2 layer. The top layer of SiO2 had a 200 nm square cavity etched out, and then a square Ti nanopillar and a square Ti hollow outside a Ti nanopillar were embedded. This specific arrangement was chosen to maximize the absorption properties across a broad spectrum. The absorption spectrum of the designed absorber was thoroughly analyzed using the commercial finite element analysis software COMSOL Multiphysics® (version 6.0). This analysis confirmed that the combination of these various components achieved perfect absorption and an ultra-wideband response. The synergistic interaction between the layers and the nanopillars structure contributed significantly to the absorber’s efficiency, making it a promising candidate for applications requiring broad-spectrum absorption. The comprehensive analyses of the parameters for different structures demonstrated that the effects of guided-mode resonance, coupling resonance, optical impedance matching, and propagating surface plasmon resonance existed in the investigated structure. The optimal model, determined through analyses using COMSOL Multiphysics®, showed that the broadband absorption in the range of 270 to 3600 nm, spanning from UV-B to middle-IR, exceeded 90.0%. The average absorption rate within this range was 0.967, with the highest reaching a near-perfect absorptivity of 99.9%. We also compared three absorption spectra in this study: the t1–t6 flat structure, the t1–t5 flat structure with t6 featuring a square cavity, and the structure proposed in this study. This demonstrates that a square nanopillar and a square hollow embedded in a square cavity can enhance the absorptive properties of the absorber. Full article
(This article belongs to the Special Issue Emerging Trends in Metamaterials and Metasurfaces Research)
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<p>The matrix-like structure of the investigated absorber.</p>
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<p>(<b>a</b>) Structure, (<b>b</b>) side view with parameters, and (<b>c</b>) top view with parameters.</p>
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<p>Effect of the thickness of the t2 SiO<sub>2</sub> layer on the (<b>a</b>) absorption spectrum and (<b>b</b>) the absorptivity of the absorber with the investigated structure.</p>
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<p>Effect of the thickness of the t3 graphite layer on the (<b>a</b>) absorption spectrum and (<b>b</b>) the absorptivity of the absorber with the investigated structure.</p>
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<p>Effect of the thickness of the t4 SiO<sub>2</sub> layer on the (<b>a</b>) absorption spectrum and (<b>b</b>) the absorptivity of the absorber with the investigated structure.</p>
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<p>Effect of the height of the t8 square nanopillar on the (<b>a</b>) absorption spectrum and (<b>b</b>) the absorptivity of the absorber with the investigated structure.</p>
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<p>Comparison of the absorptivity spectra for the originally designed parameters and the modified parameters.</p>
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<p>The investigated absorber (<b>a</b>) without a square nanopillar and a square hollow and (<b>b</b>) without a square nanopillar and a square hollow and without a square cavity in the top layer of multilayer metamaterials.</p>
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<p>The absorption spectra for different absorbers, including the investigated absorber, the absorber with the six continuous planes, and the investigated absorber without a square nanopillar and a square hollow.</p>
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<p>Optical impendence for different absorbers, including the investigated absorber, the absorber with the six continuous planes, and the investigated absorber without a square nanopillar and a square hollow.</p>
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<p>Intensity distributions of (<b>a</b>) the electric field and (<b>b</b>) the magnetic field, with different normal incident wavelengths.</p>
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<p>Intensity distributions of (<b>a</b>) the electric field and (<b>b</b>) the magnetic field, with different normal incident wavelengths.</p>
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<p>Absorptivity distribution of (<b>a</b>) TE-polarized light and (<b>b</b>) TM-polarized light with normal direction and various oblique incidence angles.</p>
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<p>Absorption spectrum of the investigated absorber with the excavating square matrix cavities in the top SiO<sub>2</sub> layer and placing a metal Ti square nanopillar and a metal Ti square hollow inside each cavity.</p>
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16 pages, 3882 KiB  
Article
Rational Design and Optimization of Plasmonic Nanohole Arrays for Sensing Applications
by Daniela Lospinoso, Adriano Colombelli, Roberto Rella and Maria Grazia Manera
Chemosensors 2024, 12(8), 157; https://doi.org/10.3390/chemosensors12080157 - 8 Aug 2024
Viewed by 343
Abstract
The design and optimization of plasmonic nanohole arrays (NHAs) as transducers for efficient bioanalytical sensing is a rapidly growing field of research. In this work, we present a rational method for tailoring the optical and functional properties of Au NHAs realized on planar [...] Read more.
The design and optimization of plasmonic nanohole arrays (NHAs) as transducers for efficient bioanalytical sensing is a rapidly growing field of research. In this work, we present a rational method for tailoring the optical and functional properties of Au NHAs realized on planar transparent substrates. Experimental and numerical results demonstrate how the far- and near-field properties of the NHAs can be controlled and optimized for specific sensing applications, proving a valuable insight into the distribution of electric fields generated on the nanostructured metal surface and the depth of penetration into the surrounding media. Metal thickness is found to play a crucial role in determining the sensing volume, while the diameter of the nanoholes affects the localization of the electromagnetic field and the extent of the decay field. The remarkable surface and bulk refractive index sensitivities observed a rival performance of more complex geometric designs reported in the recent literature, showcasing their outstanding potential for chemo-biosensing applications. Full article
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<p>Geometrical domains used for the simulation of a hexagonal array of nanoholes in thin metal film (<b>a</b>). An example of mesh element distribution and local refinement for the simulation of a thin polyelectrolyte layer (<b>b</b>) and a schematic illustration of the simulated unit cell (<b>c</b>).</p>
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<p>AFM atomic force microscopy height images of the three samples with diameters of (<b>a</b>) (320 ± 20) nm, (<b>b</b>) (230 ± 20) nm, (<b>c</b>) (240 ± 20) nm. In the bottom line, topographic image profiles show the corresponding height of the gold film: 100 nm (<b>a</b>,<b>b</b>) and 30 nm (<b>c</b>), respectively. Scale bars: 1 µm.</p>
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<p>Experimental (solid line) and calculated (dot line) transmittance spectra of the of nanoholes array samples in water: principal resonances are highlighted for LD100 samples in (<b>a</b>) for SD100 samples in (<b>b</b>) and for SD30samples in (<b>c</b>). The acronym LD stand for Larger Diameters while SD for Smaller Diameters. The different investigated thicknesses (100 and 30 nm) are reported at the end of samples names.</p>
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<p>Transmittance spectra of the three sample families immersed in liquids with increasing refractive index (solid lines). Dotted lines represent transmittance spectra in air. For thinner samples SD30, λ<sub>B</sub> and λ<sub>G</sub> spectral features present a maximum and a minimum, indicated by * and **, respectively. The magnification of the resonances exhibiting the most intense shifts are shown in the insets.</p>
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<p>Transmittance spectra of the typical three sample families covered with a growing number of polyelectrolyte layers: one spectrum for every three layers is reported. On the right, vertical cross sections of the local field’s distribution of the samples with an overlay of ten layers at the wavelengths of the highlighted resonances; corresponding horizontal cross sections at the metal/polyelectrolyte interface are shown in the inset.</p>
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<p>Examples of calibration curves for each of the three sample families: the wavelength variation of the resonances indicated in the legend is reported as the thickness of the deposited PEM varies.</p>
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<p>Summary histograms of the estimated RIS (y axis, left side) and surface sensitivity values (<span class="html-italic">y</span> axis, right side) at resonance wavelengths (<span class="html-italic">x</span> axis) for the three sample families examined.</p>
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12 pages, 2312 KiB  
Article
Aqueous Synthesis of Au10Pt1 Nanorods Decorated with MnO2 Nanosheets for the Enhanced Electrocatalytic Oxidation of Methanol
by Ting Li, Yidan Liu, Yibin Huang, Zhong Yu and Lei Huang
Molecules 2024, 29(16), 3753; https://doi.org/10.3390/molecules29163753 - 7 Aug 2024
Viewed by 328
Abstract
Developing novel catalysts with high activity and high stability for the methanol oxidation reaction (MOR) is of great importance for the ever-broader applications of methanol fuel cells. Herein, we present a facile technique for synthesizing Au10Pt1@MnO2 catalysts using [...] Read more.
Developing novel catalysts with high activity and high stability for the methanol oxidation reaction (MOR) is of great importance for the ever-broader applications of methanol fuel cells. Herein, we present a facile technique for synthesizing Au10Pt1@MnO2 catalysts using a wet chemical method and investigate their catalytic performance for the MOR. Notably, the Au10Pt1@MnO2-M composite demonstrated a significantly high peak mass activity of 15.52 A mg(Pt)−1, which is 35.3, 57.5, and 21.9 times greater than those of the Pt/C (0.44 A mg(Pt)−1), Pd/C (0.27 A mg(Pt)−1), and Au10Pt1 (0.71 A mg(Pt)−1) catalysts, respectively. Comparative analysis with commercial Pt/C and Pd/C catalysts, as well as Au10Pt1 HSNRs, revealed that the Au10Pt1@MnO2-M composite exhibited the lowest initial potential, the highest peak current density, and superior CO anti-poisoning capability. The results demonstrate that the introduction of MnO2 nanosheets, with excellent oxidation capability, not only significantly increases the reactive sites, but also promotes the reaction kinetics of the catalyst. Furthermore, the high surface area of the MnO2 nanosheets facilitates charge transfer and induces modifications in the electronic structure of the composite. This research provides a straightforward and effective strategy for the design of efficient electrocatalytic nanostructures for MOR applications. Full article
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<p>(<b>a</b>) UV–VIS absorption spectra of the samples; (<b>b</b>) TEM image of Au NPs, (<b>c</b>) Au<sub>10</sub>Pt<sub>1</sub> HSNRs, and (<b>d</b>) Au<sub>10</sub>Pt<sub>1</sub>@MnO<sub>2</sub>-M; local amplification of HRTEM images of (<b>e</b>) Au<sub>10</sub>Pt<sub>1</sub> HSNRs and (<b>f</b>) MnO<sub>2</sub> nanosheets; (<b>g</b>) selected area electron diffraction (SAED) image.</p>
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<p>XPS spectra of Au<sub>10</sub>Pt<sub>1</sub>@MnO<sub>2</sub>-M: (<b>a</b>) survey; (<b>b</b>) Au; (<b>c</b>) Pt; (<b>d</b>) Mn.</p>
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<p>TEM images of different KMnO<sub>4</sub> additions: (<b>a</b>) Au<sub>10</sub>Pt<sub>1</sub>@MnO<sub>2</sub>-L; (<b>b</b>) Au<sub>10</sub>Pt<sub>1</sub>@MnO<sub>2</sub>-M; (<b>c</b>) Au<sub>10</sub>Pt<sub>1</sub>@MnO<sub>2</sub>-H.</p>
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<p>TEM images of Au<sub>10</sub>Pt<sub>1</sub>@MnO<sub>2</sub>-M at different reaction temperatures: (<b>a</b>) 35 °C; (<b>b</b>) 45 °C; (<b>c</b>) 60 °C.</p>
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<p>The performance of different catalysts for MOR in 1 M KOH and 1 M CH<sub>3</sub>OH solutions: (<b>a</b>) CV curve of specific activity; (<b>b</b>) CV curve per mass of noble metals; (<b>c</b>) mass activity at the highest current density; (<b>d</b>) chronoamperometry measurement curves.</p>
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32 pages, 8768 KiB  
Review
Sensing with Molecularly Imprinted Membranes on Two-Dimensional Solid-Supported Substrates
by Lishuang Wang, Nan Li, Xiaoyan Zhang, Ivan Bobrinetskiy, Ivana Gadjanski and Wangyang Fu
Sensors 2024, 24(16), 5119; https://doi.org/10.3390/s24165119 - 7 Aug 2024
Viewed by 415
Abstract
Molecularly imprinted membranes (MIMs) have been a focal research interest since 1990, representing a breakthrough in the integration of target molecules into membrane structures for cutting-edge sensing applications. This paper traces the developmental history of MIMs, elucidating the diverse methodologies employed in their [...] Read more.
Molecularly imprinted membranes (MIMs) have been a focal research interest since 1990, representing a breakthrough in the integration of target molecules into membrane structures for cutting-edge sensing applications. This paper traces the developmental history of MIMs, elucidating the diverse methodologies employed in their preparation and characterization on two-dimensional solid-supported substrates. We then explore the principles and diverse applications of MIMs, particularly in the context of emerging technologies encompassing electrochemistry, surface-enhanced Raman scattering (SERS), surface plasmon resonance (SPR), and the quartz crystal microbalance (QCM). Furthermore, we shed light on the unique features of ion-sensitive field-effect transistor (ISFET) biosensors that rely on MIMs, with the notable advancements and challenges of point-of-care biochemical sensors highlighted. By providing a comprehensive overview of the latest innovations and future trajectories, this paper aims to inspire further exploration and progress in the field of MIM-driven sensing technologies. Full article
(This article belongs to the Special Issue Biosensors for Point-of-Care Diagnostics)
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<p>Overview of the principle and classification for molecularly imprinted membranes (MIMs) based on 2D solid-supported substrates for point-of-care testing via integration with electrochemical sensing, FET, QCM, SPR, and SERS technologies.</p>
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<p>The conceptual overview of the main synthetic methods covered in this review, which includes the merits of each method.</p>
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<p>The synthetic process of MIMs by in situ polymerization. (<b>A</b>) The MIM was synthesized using photo-radical polymerization on a thin solution layer composed of liquid pre-polymerization solution, which was sandwiched between a derivatized and a quartz cover glass slide [<a href="#B73-sensors-24-05119" class="html-bibr">73</a>]. (<b>B</b>) In situ synthesis of MIM in a microreactor [<a href="#B58-sensors-24-05119" class="html-bibr">58</a>].</p>
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<p>The technique of phase inversion employed in the fabrication of MIMs. (<b>A</b>) Schematic representation of a hybrid MIM made from cellulose acetate (CA) prepared by phase inversion for detecting salicylic acid (SA) [<a href="#B78-sensors-24-05119" class="html-bibr">78</a>]. (<b>B</b>) Manufacturing MIMs through the phase-inversion technique, facilitated by magnetic field forces [<a href="#B62-sensors-24-05119" class="html-bibr">62</a>].</p>
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<p>Preparation of MIMs by sol−gel polymerization. (<b>A</b>) The schematic illustrates the fabrication of an MIP sensor and the naloxone assay using the sol−gel method [<a href="#B81-sensors-24-05119" class="html-bibr">81</a>]. (<b>B</b>) Preparation process for preparing boron−affinity sol−gel MIM [<a href="#B82-sensors-24-05119" class="html-bibr">82</a>].</p>
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<p>The electrochemical polymerization method for the synthesis of MIMs. (<b>A</b>) The fabrication process of Fe<sub>3</sub>O<sub>4</sub>@MIP/rGO/GCE by electropolymerization [<a href="#B84-sensors-24-05119" class="html-bibr">84</a>]. (<b>B</b>) Preparation mechanism of electropolymerized chiral molecularly imprinted polymer sensor and schematic diagram of levamisole detection [<a href="#B85-sensors-24-05119" class="html-bibr">85</a>].</p>
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<p>Practical implementations within electrochemical MIM systems. (<b>A</b>) Schematic illustration of the detecting process via microsensor and the DPV curves of 3−NT [<a href="#B96-sensors-24-05119" class="html-bibr">96</a>]. (<b>B</b>) The procedure for electrochemical detection of ncovNP from nasopharyngeal swab samples and the sensor’s selective response towards diverse proteins [<a href="#B104-sensors-24-05119" class="html-bibr">104</a>].</p>
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<p>Application of SERS with MIMs in real-world scenarios. (<b>A</b>) The schematic depicts the Raman scattering mechanism in a multifunctional capillary SERS sensor and illustrates the sensor’s response to various analytes [<a href="#B129-sensors-24-05119" class="html-bibr">129</a>]. (<b>B</b>) Construction of the boron−affinity molecularly imprinted sensor and the logarithmic concentration curve of CEA detection based on the average intensity observed at 2024 cm<sup>−1</sup> [<a href="#B133-sensors-24-05119" class="html-bibr">133</a>].</p>
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<p>Utilization of SPR in MIMs for real-world applications. (<b>A</b>) Fabrication of a printed plasma sensor for detecting tumor necrosis factor-α (TNF-α) and response diagrams for three different analytes [<a href="#B150-sensors-24-05119" class="html-bibr">150</a>]. (<b>B</b>) Schematic depiction illustrating the procedure of assembling troponin I (TnI) peptide-imprinted sensor and the response of imprinted and non-imprinted sensors at a concentration of 1 mg/L troponin I-C complex (TnIC) [<a href="#B151-sensors-24-05119" class="html-bibr">151</a>].</p>
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<p>Application of QCM coupled with MIMs for practical use in scientific research. (<b>A</b>) Preparation of MIM sensor array for simultaneous assessment of lipoproteins and selectivity curves [<a href="#B163-sensors-24-05119" class="html-bibr">163</a>]. (<b>B</b>) Schematic illustration of the combination of hepatitis B core antigen (HBcAg) and recognition cavities, along with the corresponding frequency responses of the sensor to HBcAg and human serum albumin (HSA) [<a href="#B165-sensors-24-05119" class="html-bibr">165</a>].</p>
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<p>Practical FET applications in the field of MIMs. (<b>A</b>) Principle of preparation and detection of MIP-based extended-gate FET (EG−FET) [<a href="#B187-sensors-24-05119" class="html-bibr">187</a>]. (<b>B</b>) Preparation process of the molecular-imprinted ISFET and generation principle of imprinted cavies on the ISFET gate [<a href="#B188-sensors-24-05119" class="html-bibr">188</a>].</p>
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<p>Utilization of nanoISFET sensors in PoC applications. Development of nanoscale ISFET arrays with point-of-care capability for ultra-sensitive detection of cytokines in cell cultures [<a href="#B190-sensors-24-05119" class="html-bibr">190</a>].</p>
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12 pages, 3899 KiB  
Article
Hybrid Polystyrene–Plasmonic Systems as High Binding Density Biosensing Platforms
by Charles M. Darr, Juiena Hasan, Cherian Joseph Mathai, Keshab Gangopadhyay, Shubhra Gangopadhyay and Sangho Bok
Int. J. Mol. Sci. 2024, 25(16), 8603; https://doi.org/10.3390/ijms25168603 - 7 Aug 2024
Viewed by 311
Abstract
Sensitive, accurate, and early detection of biomarkers is essential for prompt response to medical decisions for saving lives. Some infectious diseases are deadly even in small quantities and require early detection for patients and public health. The scarcity of these biomarkers necessitates signal [...] Read more.
Sensitive, accurate, and early detection of biomarkers is essential for prompt response to medical decisions for saving lives. Some infectious diseases are deadly even in small quantities and require early detection for patients and public health. The scarcity of these biomarkers necessitates signal amplification before diagnosis. Recently, we demonstrated single-molecule-level detection of tuberculosis biomarker, lipoarabinomannan, from patient urine using silver plasmonic gratings with thin plasma-activated alumina. While powerful, biomarker binding density was limited by the surface density of plasma-activated carbonyl groups, that degraded quickly, resulting in immediate use requirement after plasma activation. Therefore, development of stable high density binding surfaces such as high binding polystyrene is essential to improving shelf-life, reducing binding protocol complexity, and expanding to a wider range of applications. However, any layers topping the plasmonic grating must be ultra-thin (<10 nm) for the plasmonic enhancement of adjacent signals. Furthermore, fabricating thin polystyrene layers over alumina is nontrivial because of poor adhesion between polystyrene and alumina. Herein, we present the development of a stable, ultra-thin polystyrene layer on the gratings, which demonstrated 63.8 times brighter fluorescence compared to commercial polystyrene wellplates. Spike protein was examined for COVID-19 demonstrating the single-molecule counting capability of the hybrid polystyrene-plasmonic gratings. Full article
(This article belongs to the Special Issue Recent Advances on Bioreceptors and Nanomaterial-Based Biosensors)
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<p>The structure of plasmonic gratings consists of Ag, Al<sub>2</sub>O<sub>3</sub>, and polystyrene on top of PMSSQ.</p>
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<p>The relationship between the film thickness and the concentration (Inset: the relationship in low concentration).</p>
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<p>Optical profilometry after tape test of thin polystyrene films over (<b>a</b>) unsilanized alumina, (<b>b</b>) TMCS, (<b>c</b>) P Silane, (<b>d</b>) N Silane, and (<b>e</b>) G Silane.</p>
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<p>Atomic force microscopy of polystyrene over alumina-coated gratings (<b>a</b>–<b>d</b>) and flat silver (<b>e</b>–<b>h</b>): (<b>a</b>,<b>e</b>) as-prepared ALD alumina; (<b>b</b>,<b>f</b>) 5 nm polystyrene; (<b>c</b>,<b>g</b>) 7 nm polystyrene; and (<b>d</b>,<b>h</b>) 9 nm polystyrene. (Scale bar = 1 µm).</p>
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<p>FTIR spectra of (<b>a</b>) commercial Nunc plates and PS resin and (<b>b</b>) 5 nm and 50 nm PS films.</p>
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<p>Fluorescence intensity of PS coated grating, grating without PS, and commercial plates.</p>
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<p>Schematic of the rectangular wells of the 24-well adapter showing the maximum incidence angles, α = 19.6° and β = 24.1°.</p>
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<p>(<b>a</b>) A fluorescence image of 100 fg/mL of S-protein with 6 μm × 6 μm grids, and (<b>b</b>) single molecule counting from plasmonic grating with various concentrations of spike protein between 1 fg/mL and 10 pg/mL.</p>
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9 pages, 7038 KiB  
Article
The Rapid Detection of Paclitaxel-Induced Changes in Cervical Cancer Cells Using an Ultrasensitive Biosensor
by Liwen Zhang, Gan Chen, Yating Hao and Yan Peng
Photonics 2024, 11(8), 735; https://doi.org/10.3390/photonics11080735 - 7 Aug 2024
Viewed by 322
Abstract
Background: Paclitaxel is a widely used cancer treatment drug that has a significant inhibitory effect on cervical cancer cells (HeLa cells). This study aims to investigate the effects of paclitaxel on HeLa cells and evaluate the application of terahertz (THz) spectroscopy and surface [...] Read more.
Background: Paclitaxel is a widely used cancer treatment drug that has a significant inhibitory effect on cervical cancer cells (HeLa cells). This study aims to investigate the effects of paclitaxel on HeLa cells and evaluate the application of terahertz (THz) spectroscopy and surface plasmon resonance (SPR) biosensors in this process. Methods: We utilized an SPR biosensor in conjunction with THz spectroscopy to measure the terahertz absorbance spectra of HeLa cells exposed to various concentrations of paclitaxel. The minimum number of cells used for detection was 15.25 × 105. At the same time, cell proliferation levels were assessed through proliferation assays and compared with the terahertz spectroscopy data. Results: The experimental results indicated that with the increasing concentration of paclitaxel, the terahertz absorbance spectra of HeLa cells exhibited a blue shift, and cell proliferation was significantly inhibited. The results of the proliferation assays were consistent with the terahertz spectroscopy data, validating the effectiveness of this method. Conclusion: This study demonstrates that the combination of THz spectroscopy and SPR biosensors is a promising technology that can provide a simple, rapid, and low-cost method for studying chemistry–biology relationships, especially in the field of drug evaluation. Full article
(This article belongs to the Special Issue New Trends in Terahertz Photonics)
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<p>Structural formula of paclitaxel. (<b>A</b>) Schematic of cellular incorporation of paclitaxel (<b>B</b>). Terahertz workflow diagram (<b>C</b>).</p>
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<p>Terahertz time-domain spectrometer TAS7400SP (<b>A</b>). SEM image of the device (<b>B</b>). Diagram of the internal workings of the THz-TDS (TAS7400SP) (<b>C</b>). Absorption diagram of the RTA (<b>D</b>).</p>
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<p>Cell viability test (<b>A</b>,<b>B</b>). Effect of drug levels on the survival of HeLa cells (<b>D</b>,<b>E</b>) and corresponding frequency shift as a function (<b>C</b>), with a bar graph of the corresponding frequency shifts (<b>F</b>) for various paclitaxel concentrations in HeLa cells.</p>
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<p>Spectra of absorbance (<b>A</b>). Magnified portion of absorbance spectra (<b>B</b>).</p>
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<p>The morphological changes in Hela cells following drug exposure were observed using light microscopy. The (<b>A</b>–<b>G</b>) graphs illustrate the alterations in cell shape observed following treatment with paclitaxel at concentrations between 0 and 100 nM, with (<b>H</b>,<b>I</b>) graphs providing further detail on the effects of 40 and 100 nM concentrations, respectively.</p>
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13 pages, 1653 KiB  
Article
Surface Plasmon Resonance Immunosensor for Direct Detection of Antibodies against SARS-CoV-2 Nucleocapsid Protein
by Viktorija Lisyte, Asta Kausaite-Minkstimiene, Benediktas Brasiunas, Anton Popov and Almira Ramanaviciene
Int. J. Mol. Sci. 2024, 25(16), 8574; https://doi.org/10.3390/ijms25168574 - 6 Aug 2024
Viewed by 391
Abstract
The strong immunogenicity of the SARS-CoV-2 nucleocapsid protein is widely recognized, and the detection of specific antibodies is critical for COVID-19 diagnostics in patients. This research proposed direct, label-free, and sensitive detection of antibodies against the SARS-CoV-2 nucleocapsid protein (anti-SCoV2-rN). Recombinant SARS-CoV-2 nucleocapsid [...] Read more.
The strong immunogenicity of the SARS-CoV-2 nucleocapsid protein is widely recognized, and the detection of specific antibodies is critical for COVID-19 diagnostics in patients. This research proposed direct, label-free, and sensitive detection of antibodies against the SARS-CoV-2 nucleocapsid protein (anti-SCoV2-rN). Recombinant SARS-CoV-2 nucleocapsid protein (SCoV2-rN) was immobilized by carbodiimide chemistry on an SPR sensor chip coated with a self-assembled monolayer of 11-mercaptoundecanoic acid. When immobilized under optimal conditions, a SCoV2-rN surface mass concentration of 3.61 ± 0.52 ng/mm2 was achieved, maximizing the effectiveness of the immunosensor for the anti-SCoV2-rN determination. The calculated KD value of 6.49 × 10−8 ± 5.3 × 10−9 M confirmed the good affinity of the used monoclonal anti-SCoV2-rN antibodies. The linear range of the developed immunosensor was from 0.5 to 50 nM of anti-SCoV2-rN, where the limit of detection and the limit of quantification values were 0.057 and 0.19 nM, respectively. The immunosensor exhibited good reproducibility and specificity. In addition, the developed immunosensor is suitable for multiple anti-SCoV2-rN antibody detections. Full article
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Graphical abstract
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<p>Simplified schematic illustration of SPR immunosensor fabrication and direct anti-SCoV2-rN detection. (1) Formation of MUA SAM on the surface of the SPR sensor chip. (2) Covalent immobilization of SCoV2-rN using carbodiimide conjugation chemistry. (3) Interaction of immobilized SCoV2-rN with specific antibodies—anti-SCoV2-rN.</p>
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<p>Dependence of Au/SCoV2-rN/anti-SCoV2-rN complex dissociation and surface regeneration efficiency on the (<b>A</b>) nature of the regeneration solution and (<b>B</b>) regeneration duration using 10 mM NaOH and 0.5% SDS solution. Conditions: 500 nM SCoV2-rN; 10 nM anti-SCoV2-rN. Error bars represent the standard deviation of three measurements (n = 3).</p>
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<p>Effect of the pH value of acetate buffer used for SCoV2-rN dilution on the magnitude of the response of SCoV2-rN and anti-SCoV2-rN immune complex formation. Conditions: 500 nM SCoV2-rN; immobilization duration—1200 s; 15 nM anti-SCoV2-rN; 50 mM acetate buffers with different pH. Error bars represent the standard deviation of three measurements (n = 3).</p>
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<p>Effect of initial SCoV2-rN concentration on the magnitude of the response to SCoV2-rN immobilization and interaction with anti-SCoV2-rN. Conditions: SCoV2-rN was diluted in 50 mM acetate buffer, pH 5.3; 15 nM anti-SCoV2-rN. Error bars represent the standard deviation of three measurements (n = 3).</p>
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<p>SPR kinetic study of immune complex formation between immobilized SCoV2-rN protein and anti-SCoV2-rN present in the buffer. Conditions: 500 nM SCoV2-rN; interaction time—1500 s; 1, 3, 5, 10, and 30 nM anti-SCoV2-rN.</p>
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<p>(<b>A</b>) SPR sensograms recorded during the analysis of solutions with different anti-SCoV2-rN concentrations using a direct immunoassay format and (<b>B</b>) a calibration curve. (<b>C</b>) Dependence of SPR angle shift on anti-SCoV2-rN concentration. Conditions: 500 nM SCoV2-rN; interaction time—600 s; 0.3, 0.5, 1, 3, 5, 10, 15, 20, 30, 40, and 50 nM of anti-SCoV2-rN. Error bars represent the standard deviation of three measurements (n = 3).</p>
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<p>Non-specific interaction study. The SPR signal registered during (<b>1</b>) non-specific binding of anti-COMP antibodies on the SCoV2-rN-modified SPR sensor chip surface, and (<b>2</b>) formation of the immune complex of immobilized SCoV2-rN and anti-SCoV2-rN antibodies present in the sample. The study was performed by injecting 50 nM of anti-COMP or anti-SCoV2-rN in 10 mM PBS solution, pH 7.4.</p>
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14 pages, 30120 KiB  
Article
Nanosensors Based on Bimetallic Plasmonic Layer and Black Phosphorus: Application to Urine Glucose Detection
by Fatima Houari, Mohamed El Barghouti, Abdellah Mir and Abdellatif Akjouj
Sensors 2024, 24(15), 5058; https://doi.org/10.3390/s24155058 - 5 Aug 2024
Viewed by 306
Abstract
This paper presents a new biosensor design based on the Kretschmann configuration, for the detection of analytes at different refractive indices. Our studied design consists of a TiO2/SiO2 bi-layer sandwiched between a BK7 prism and a bimetallic layer of Ag/Au [...] Read more.
This paper presents a new biosensor design based on the Kretschmann configuration, for the detection of analytes at different refractive indices. Our studied design consists of a TiO2/SiO2 bi-layer sandwiched between a BK7 prism and a bimetallic layer of Ag/Au plasmonic materials, covered by a layer of black phosphorus placed below the analyte-containing detection medium. The different layers of our structure and analyte detection were optimized using the angular interrogation method. High performance was achieved, with a sensitivity of 240 deg/RIU and a quality factor of 34.7 RIU−1. This biosensor can detect analytes with a wide refractive index range between 1.330 and 1.347, such as glucose detection in urine samples using a refractive index variation of 103. This capability offers a wide range of applications for biomedical and biochemical detection and selectivity. Full article
(This article belongs to the Section Physical Sensors)
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<p>Schematic design of the proposed bimetallic SPR biosensor.</p>
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<p>(<b>a</b>) Variation of the reflectance with respect to the incidence angle for the refractive index n<sub><span class="html-italic">s</span></sub> = 1.330 (black line) and n<sub><span class="html-italic">s</span></sub> = 1.335 (red line), (<b>b</b>) reflected light intensity versus thicknesses of Ag (<math display="inline"><semantics> <msub> <mi mathvariant="normal">d</mi> <mrow> <mi>A</mi> <mi>g</mi> </mrow> </msub> </semantics></math>), (<b>c</b>) evolution of the resonance angle, and (<b>d</b>) variation of sensitivity with thickness of Ag layer. For SPR nanostructure with 5 nm of Au.</p>
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<p>Reflectance intensity as a function of the angle of incidence of the proposed SPR biosensor, BK7/Ag-Au/SM: (<b>a</b>) with the TiO<sub>2</sub> layer and (<b>b</b>) with the TiO<sub>2</sub>/SiO<sub>2</sub> layer, for two sensing mediums n<sub><span class="html-italic">s</span></sub> = 1.330 and 1.335.</p>
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<p>Variation of electric and magnetic field intensity of proposed SPR sensor BK7/TiO<sub>2</sub>/ SiO<sub>2</sub>/Ag/Au, (<b>a</b>) in accordance with the distance normal to the interface and (<b>b</b>) as a function of the incidence angle at a wavelength of 633 nm, for the RI of detection medium 1.330.</p>
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<p>(<b>a</b>,<b>b</b>) Reflectance intensity curves for different BP monolayers [(<b>a</b>) for n<sub><span class="html-italic">s</span></sub> = 1.330, (<b>b</b>) for n<sub><span class="html-italic">s</span></sub> = 1.335]; (<b>c</b>) evolution of the sensitivity, DA, and QF; (<b>d</b>) sensitivity enhancement as a function of the BP monolayer number for proposed SPR biosensors: BK7/TiO<sub>2</sub>/SiO<sub>2</sub>/Ag-Au/BP/SM.</p>
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<p>(<b>a</b>) The 1D curve of electric field intensity as a function of the distance prism–sensing interface; (<b>b</b>) 2D and 3D plots of the distribution of the electric field norm across the optimized SPR design; (<b>c</b>,<b>d</b>) 2D and 3D plots of SPP modes in the x- and y-component, respectively, of the electric field.</p>
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<p>(<b>a</b>) Glucose level biodetection in the urine samples using concentration variation; (<b>b</b>) resonance angle shift of SPR curves with varying RI of the different glucose concentration level.</p>
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18 pages, 11424 KiB  
Article
High-Sensitivity Refractive Index Sensor with Dual-Channel Based on Surface Plasmon Resonance Photonic Crystal Fiber
by Fengmin Wang, Yong Wei and Yanhong Han
Sensors 2024, 24(15), 5050; https://doi.org/10.3390/s24155050 - 4 Aug 2024
Viewed by 517
Abstract
In order to achieve a high-precision synchronous detection of two different refractive index (RI) analytes, a D-type surface plasmon resonance (SPR) photonic crystal fiber (PCF) RI sensor based on two channels is designed in this paper. The sensor uses a D-shaped planar region [...] Read more.
In order to achieve a high-precision synchronous detection of two different refractive index (RI) analytes, a D-type surface plasmon resonance (SPR) photonic crystal fiber (PCF) RI sensor based on two channels is designed in this paper. The sensor uses a D-shaped planar region of the PCF and a large circular air hole below the core as the sensing channels. Surface plasmon resonance is induced by applying a coating of gold film on the surface. The full-vector finite-element method (FEM) is used to optimize the structural parameters of the optical fiber, and the sensing characteristics are studied, including wavelength sensitivity, RI resolution, full width at half maximum (FWHM), figure of merit (FOM), and signal-to-noise ratio (SNR). The results show that the channel 1 (Ch 1) can achieve RI detection of 1.36–1.39 in the wavelength range of 1500–2600 nm, and the channel 2 (Ch 2) can achieve RI detection of 1.46–1.57 in the wavelength range of 2100–3000 nm. The two sensing channels can detect independently or simultaneously measure two analytes with different RIs. The maximum wavelength sensitivity of the sensor can reach 30,000 nm/RIU in Channel 1 and 9900 nm/RIU in Channel 2. The RI resolutions of the two channels are 3.54 × 10−6 RIU and 10.88 × 10−6 RIU, respectively. Therefore, the sensor realizes dual-channel high- and low-RI synchronous detection in the ultra-long wavelength band from near-infrared to mid-infrared and achieves an ultra-wide RI detection range and ultra-high wavelength sensitivity. The sensor has a wide application prospect in the fields of chemical detection, biomedical sensing, and water environment monitoring. Full article
(This article belongs to the Collection Optical Fiber Sensors)
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<p>The cross-sections of the proposed SPR-PCF dual-channel refractive index sensor.</p>
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<p>Schematic for the fiber-fabrication process. The white circle represents the air holes. The gold layer is marked in red.</p>
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<p>Schematic of the proposed PCF-SPR RI sensor setup.</p>
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<p>Flow chart of double-sample detection. The white and green circles represent air holes and cured adhesives, respectively. The gold layer is marked in red. Analyte 1 and Analyte 2 are labeled blue and yellow, respectively.</p>
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<p>The confinement loss and dispersion relationship of the fundamental mode and SPP mode in the two sensing channels at <span class="html-italic">n</span><sub>1</sub> = 1.38 and <span class="html-italic">n</span><sub>2</sub> = 1.51. Insets are electric field distributions of various wavelengths. The color of the insets represents the strength of electric field, and its unit is v/m.</p>
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<p>Resonance peak versus RI of the two sensing channels for (<b>a</b>) <span class="html-italic">n</span><sub>1</sub> = 1.38, <span class="html-italic">n</span><sub>2</sub> = 1.50, (<b>b</b>) <span class="html-italic">n</span><sub>1</sub> = 1.38, <span class="html-italic">n</span><sub>2</sub> = 1.51, and (<b>c</b>) <span class="html-italic">n</span><sub>1</sub> = 1.37, <span class="html-italic">n</span><sub>2</sub> = 1.51.</p>
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<p>Confinement loss spectra for different air-hole diameters of (<b>a</b>) Channel 1 and (<b>b</b>) Channel 2. The other structural parameters are <span class="html-italic">n</span><sub>1</sub> = 1.39, <span class="html-italic">n</span><sub>2</sub> = 1.49, <span class="html-italic">d</span><sub>2</sub> = 2400 nm, <span class="html-italic">c</span> = 1000 nm, <span class="html-italic">Ʌ</span> = 2000 nm, <span class="html-italic">h</span> = 2400 nm, <span class="html-italic">t</span><sub>1</sub> = 50 nm, and <span class="html-italic">t</span><sub>2</sub> = 60 nm.</p>
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<p>Confinement loss spectra for different hole spacings of (<b>a</b>) Channel 1 and (<b>b</b>) Channel 2. The other structural parameters are <span class="html-italic">n</span><sub>1</sub> = 1.39, <span class="html-italic">n</span><sub>2</sub> = 1.49, <span class="html-italic">d</span><sub>1</sub> = 1080 nm, <span class="html-italic">d</span><sub>2</sub> = 2400 nm, <span class="html-italic">c</span> = 1000 nm, <span class="html-italic">h</span> = 2400 nm, <span class="html-italic">t</span><sub>1</sub> = 50 nm, and <span class="html-italic">t</span><sub>2</sub> = 60 nm.</p>
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<p>Confinement loss spectra for various Channel 2 diameters of (<b>a</b>) Channel 1 and (<b>b</b>) Channel 2. The other structural parameters are <span class="html-italic">n</span><sub>1</sub> = 1.39, <span class="html-italic">n</span><sub>2</sub> = 1.49, <span class="html-italic">d</span><sub>1</sub> = 1080 nm, <span class="html-italic">Ʌ</span> = 2000 nm, <span class="html-italic">c</span> = 1000 nm, <span class="html-italic">h</span> = 2400 nm, <span class="html-italic">t</span><sub>1</sub> = 50 nm, and <span class="html-italic">t</span><sub>2</sub> = 60 nm.</p>
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<p>Confinement loss spectra for different polishing depths of (<b>a</b>) Channel 1 and (<b>b</b>) Channel 2. The other structural parameters are <span class="html-italic">n</span><sub>1</sub> = 1.39, <span class="html-italic">n</span><sub>2</sub> = 1.49, <span class="html-italic">d</span><sub>1</sub> = 1080 nm, <span class="html-italic">d</span><sub>2</sub> = 2400 nm, <span class="html-italic">Ʌ</span> = 2000 nm, <span class="html-italic">c</span> = 1000 nm, <span class="html-italic">t</span><sub>1</sub> = 50 nm, and <span class="html-italic">t</span><sub>2</sub> = 60 nm.</p>
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<p>Confinement loss spectra for different gold-layer thickness <span class="html-italic">t</span><sub>1</sub> of (<b>a</b>) Channel 1 and (<b>b</b>) Channel 2. The other structural parameters are <span class="html-italic">n</span><sub>1</sub> = 1.39, <span class="html-italic">n</span><sub>2</sub> = 1.49, <span class="html-italic">d</span><sub>1</sub> = 1080 nm, <span class="html-italic">d</span><sub>2</sub> = 2400 nm, <span class="html-italic">Ʌ</span> = 2000 nm, <span class="html-italic">c</span> = 1000 nm, <span class="html-italic">h</span> = 2400 nm, and <span class="html-italic">t</span><sub>2</sub> = 60 nm.</p>
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<p>Confinement loss spectra for different gold-layer thickness <span class="html-italic">t</span><sub>2</sub> of (<b>a</b>) Channel 1 and (<b>b</b>) Channel 2. The other structural parameters are <span class="html-italic">n</span><sub>1</sub> = 1.39, <span class="html-italic">n</span><sub>2</sub> = 1.49, <span class="html-italic">d</span><sub>1</sub> = 1080 nm, <span class="html-italic">d</span><sub>2</sub> = 2400 nm, <span class="html-italic">Ʌ</span> = 2000 nm, <span class="html-italic">c</span> = 1000 nm, <span class="html-italic">h</span> = 2400 nm, and <span class="html-italic">t</span><sub>1</sub> = 50 nm.</p>
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<p>Effect of fabrication deviation on (<b>a</b>) Channel 1 and (<b>b</b>) Channel 2.</p>
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<p>Confinement losses for different analytes in the RI ranges of (<b>a</b>) 1.36–1.39 for Channel 1 and (<b>b</b>) 1.46–1.57 for Channel 2.</p>
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<p>Variation of resonance wavelength with analyte RI as the RI is changed from (<b>a</b>) 1.36 to 1.39 in Channel 1 and (<b>b</b>) 1.46 to 1.57 in Channel 2.</p>
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<p>Relationship between <span class="html-italic">FOM</span>, <span class="html-italic">FWHM</span>, and RI of analytes in (<b>a</b>) Channel 1 and (<b>b</b>) Channel 2.</p>
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<p>Relationship between <span class="html-italic">SNR</span> and analyte RI in (<b>a</b>) Channel 1 and (<b>b</b>) Channel 2.</p>
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20 pages, 6107 KiB  
Article
The Tunable Parameters of Graphene-Based Biosensors
by Talia Tene, Jiří Svozilík, Dennys Colcha, Yesenia Cevallos, Paola Gabriela Vinueza-Naranjo, Cristian Vacacela Gomez and Stefano Bellucci
Sensors 2024, 24(15), 5049; https://doi.org/10.3390/s24155049 - 4 Aug 2024
Viewed by 317
Abstract
Graphene-based surface plasmon resonance (SPR) biosensors have emerged as a promising technology for the highly sensitive and accurate detection of biomolecules. This study presents a comprehensive theoretical analysis of graphene-based SPR biosensors, focusing on configurations with single and bimetallic metallic layers. In this [...] Read more.
Graphene-based surface plasmon resonance (SPR) biosensors have emerged as a promising technology for the highly sensitive and accurate detection of biomolecules. This study presents a comprehensive theoretical analysis of graphene-based SPR biosensors, focusing on configurations with single and bimetallic metallic layers. In this study, we investigated the impact of various metallic substrates, including gold and silver, and the number of graphene layers on key performance metrics: sensitivity of detection, detection accuracy, and quality factor. Our findings reveal that configurations with graphene first supported on gold exhibit superior performance, with sensitivity of detection enhancements up to 30% for ten graphene layers. In contrast, silver-supported configurations, while demonstrating high sensitivity, face challenges in maintaining detection accuracy. Additionally, reducing the thickness of metallic layers by 30% optimizes light coupling and enhances sensor performance. These insights highlight the significant potential of graphene-based SPR biosensors in achieving high sensitivity of detection and reliability, paving the way for their application in diverse biosensing technologies. Our findings pretend to motivate future research focusing on optimizing metallic layer thickness, improving the stability of silver-supported configurations, and experimentally validating the theoretical findings to further advance the development of high-performance SPR biosensors. Full article
(This article belongs to the Special Issue Electrochemical Sensors and Biosensors Based on Graphene)
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<p>Biosensor illustration. Proposed biosensors by using different metallic substrate configurations: (<b>A</b>) gold or silver, (<b>B</b>) silver/gold, and (<b>C</b>) gold/silver.</p>
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<p>Reflectance (%) before adsorption. SRP curves as a function of the angle of incidence (°), increasing the number of graphene layers from L0 (no graphene layer) to L9 (nine graphene layers). (<b>A</b>) prism/gold/graphene/sensing medium, (<b>B</b>) prism/silver/gold/graphene/sensing medium, (<b>C</b>) prism/silver/graphene/sensing medium, and (<b>D</b>) prism/gold/silver/graphene/sensing medium. (<b>B</b>,<b>D</b>) correspond to the calculations by reducing the thickness of each metallic substrate by 30%.</p>
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<p>Reflectance (%) after adsorption. SRP curves as a function of the angle of incidence (°), increasing the number of graphene layers from L0 (no graphene layer) to L9 (nine graphene layers). (<b>A</b>) prism/gold/graphene/sensing medium, (<b>B</b>) prism/silver/gold/graphene/sensing medium, (<b>C</b>) prism/silver/graphene/sensing medium, and (<b>D</b>) prism/gold/silver/graphene/sensing medium. (<b>B</b>,<b>D</b>) correspond to the calculations by reducing the thickness of each metallic substrate by 30%. (<b>F</b>) and (<b>G</b>) SPR resonance curves before/after adsorption for the conventional sensor (L0 and L0 + Ads) and the monolayer graphene sensor (L1 and L1 + Ads), assuming a refractive index change Δ<span class="html-italic">n</span> = 0.005, for the P/Au/G/M and P/Ag/G/M sensors, respectively.</p>
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<p>ATR minimum (%). Reflectance intensity as a function of the number of graphene layers, considering different metallic substrate configurations. (<b>A</b>) graphene first supported on gold after adsorption and (<b>B</b>) graphene first supported on silver after adsorption.</p>
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<p>ATR angle (°). Angle position as a function of the number of graphene layers, considering different metallic substrate configurations. (<b>A</b>) graphene first supported on gold before adsorption, (<b>B</b>) graphene first supported on gold after adsorption, (<b>C</b>) graphene first supported on silver before adsorption, and (<b>D</b>) graphene first supported on silver after adsorption.</p>
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<p>Normalized Reflectance after adsorption. SRP curves as a function of the angle of incidence (°), considering one graphene layer (L1). (<b>A</b>) Graphene first supported on gold and (<b>B</b>) Graphene first supported on silver.</p>
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<p>Normalized Reflectance after adsorption. SRP curves as a function of the angle of incidence (°), considering ten graphene layers (L10). (<b>A</b>) Graphene first supported on gold and (<b>B</b>) Graphene first supported on silver.</p>
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<p>Sensitivity enhancement (%). Sensitivity variation with reference to the conventional biosensor as a function of the number of graphene layers, considering different metallic substrate configurations.</p>
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<p>Full width at half maximum (FWHM) analysis (°). FWHM as a function of the number of graphene layers, considering different metallic substrate configurations. (<b>A</b>,<b>B</b>) graphene first supported on gold before/after adsorption, (<b>C</b>,<b>D</b>) graphene first supported on silver before/after adsorption. Note that it only included systems with a 30% reduction in each metallic substrate.</p>
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<p>Sensitivity to refractive index change. Sensitivity (°/<span class="html-italic">RIU</span>) as a function of the number of graphene layers, considering different metallic substrate configurations. Note that it only included systems with a 30% reduction in each metallic substrate.</p>
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<p>Detection Accuracy (DA). DA (dimensionless) as a function of the number of graphene layers, considering different metallic substrate configurations. Note that it only included systems with a 30% reduction in each metallic substrate.</p>
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<p>Quality Factor (QF). QF (<span class="html-italic">RIU</span><sup>−1</sup>) as a function of the number of graphene layers, considering different metallic substrate configurations. Note that it only included systems with a 30% reduction in each metallic substrate.</p>
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