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Keywords = photo-thermal radiometry

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18 pages, 7290 KiB  
Review
Photothermal Infrared Radiometry and Thermoreflectance—Unique Strategy for Thermal Transport Characterization of Nanolayers
by Ankur Chatterjee, Mohanachandran Nair Sindhu Swapna, Ameneh Mikaeeli, Misha Khalid, Dorota Korte, Andreas D. Wieck and Michal Pawlak
Nanomaterials 2024, 14(21), 1711; https://doi.org/10.3390/nano14211711 - 27 Oct 2024
Viewed by 904
Abstract
Thermal transport properties for the isotropic and anisotropic characterization of nanolayers have been a significant gap in the research over the last decade. Multiple studies have been close to determining the thermal conductivity, diffusivity, and boundary resistance between the layers. The methods detailed [...] Read more.
Thermal transport properties for the isotropic and anisotropic characterization of nanolayers have been a significant gap in the research over the last decade. Multiple studies have been close to determining the thermal conductivity, diffusivity, and boundary resistance between the layers. The methods detailed in this work involve non-contact frequency domain pump-probe thermoreflectance (FDTR) and photothermal radiometry (PTR) methods for the ultraprecise determination of in-plane and cross-plane thermal transport properties. The motivation of one of the works is the advantage of the use of amplitude (TR signal) as one of the input parameters along with the phase for the determination of thermal parameters. In this article, we present a unique strategy for measuring the thermal transport parameters of thin films, including cross-plane thermal diffusivity, in-plane thermal conductivity, and thermal boundary resistance as a comprehensively reviewed article. The results obtained for organic and inorganic thin films are presented. Precise ranges for the thermal conductivity can be across confidence intervals for material measurements between 0.5 and 60 W/m-K for multiple nanolayers. The presented strategy is based on frequency-resolved methods, which, in contrast to time-resolved methods, make it possible to measure volumetric-specific heat. It is worth adding that the presented strategy allows for accurate (the signal in both methods depends on cross-plane thermal conductivity and thermal boundary resistance) and precise measurement. Full article
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<p>The PTR experimental set-up used in the study.</p>
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<p>Typical FDTR experimental set-up.</p>
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<p>PTR signal dependence vs. temperature for GaAs wafer cover by 50 nm titanium layer at 1 kHz. Fitting confirms <span class="html-italic">T</span><sup>3</sup> law.</p>
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<p>Sensitivity coefficients. (<b>a</b>) Cross-plane thermal conductivity vs. cross-plane diffusivity for different anisotropy coefficients; (<b>b</b>) volumetric heat capacity vs. cross-plane thermal conductivity. For sensitivity analysis for PEDOT:PSS and parameters depicted in Table 5.</p>
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<p>Sensitivity coefficient. (<b>a</b>) Cross-plane thermal conductivity vs. in-plane thermal conductivity for optimized anisotropy coefficients; (<b>b</b>) volumetric heat capacity vs. in-plane thermal conductivity.</p>
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<p>(<b>a</b>) Measured TR phase of 2 μm AlGaAs thin layer covered by 100 nm of gold between 100 Hz and 500 kHz [<a href="#B24-nanomaterials-14-01711" class="html-bibr">24</a>]. (<b>b</b>) PTR results of these samples (covered by 50 nm Ti not Au transducer), sample C is AlGaAs with 2 μm thickness and sample D has 512 nm thickness [<a href="#B27-nanomaterials-14-01711" class="html-bibr">27</a>].</p>
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<p>Variation of (<b>a</b>) amplitude ratios and (<b>b</b>) phase difference with frequency for Al<sub>0.33</sub>Ga<sub>0.67</sub>As alloys with and without C doping [<a href="#B25-nanomaterials-14-01711" class="html-bibr">25</a>].</p>
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<p><span class="html-italic">β<sub>IR</sub></span> of C-doped Al<sub>0.33</sub>Ga<sub>0.67</sub>As thin film (point) as a function of inverse <span class="html-italic">λ</span>. Solid line is <span class="html-italic">β<sub>IR</sub></span> calculated using Equation (10) [<a href="#B25-nanomaterials-14-01711" class="html-bibr">25</a>].</p>
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<p>Variation of amplitude ratios and phase difference with frequency for AlAS/GaAs superlattice samples: sample D (10× 26 nm AlAs and 26 nm GaAs) and sample E (100× 2.6 nm AlAs and 2.6 nm GaAs) [<a href="#B27-nanomaterials-14-01711" class="html-bibr">27</a>].</p>
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<p>Variation of amplitude ratios and phase difference with frequency for a sample at three different temperatures: −50 °C, 0 °C, and 50 °C [<a href="#B24-nanomaterials-14-01711" class="html-bibr">24</a>].</p>
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<p>Molecular structures of push–pull type (B1, B3, B4, and B5) and azobenzene-type (B2) polymers [<a href="#B29-nanomaterials-14-01711" class="html-bibr">29</a>].</p>
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<p>(<b>a</b>) Variation of normalized amplitude and (<b>b</b>) normalized phase with modulation frequency for the B2–B5 azo polymer thin films in their trans state [<a href="#B29-nanomaterials-14-01711" class="html-bibr">29</a>].</p>
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<p>FDTR measurement of a AlAs/GaAs superlattice sample at different temperatures [<a href="#B24-nanomaterials-14-01711" class="html-bibr">24</a>].</p>
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<p>Variation of in-plane and cross-plane thermal conductivity of the AlAs/GaAs superlattice with temperature [<a href="#B24-nanomaterials-14-01711" class="html-bibr">24</a>].</p>
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<p>(<b>a</b>) Normalized amplitude (R); (<b>b</b>) phase (deg.) vs. modulation frequency (kHz) [<a href="#B25-nanomaterials-14-01711" class="html-bibr">25</a>].</p>
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<p>(<b>a</b>) Normalized amplitude (mV) and (<b>b</b>) phase (degrees) as a function of pump modulation frequency variation.</p>
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<p>(<b>a</b>) Sensitivity analysis of thermal properties of PEDOT:PSS (w.r.t.) scanning modulation frequency (Hz) for parameters described in <a href="#nanomaterials-14-01711-t005" class="html-table">Table 5</a>; (<b>b</b>) Reduced chi-square statistic vs. anisotropic coefficient (η).</p>
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<p>(<b>a</b>) Normalized amplitude (R); (<b>b</b>) phase (degree.) vs. variation in pump modulation frequency for PEDOT:PSS.</p>
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17 pages, 8111 KiB  
Article
Photothermal Radiometry Data Analysis by Using Machine Learning
by Perry Xiao and Daqing Chen
Sensors 2024, 24(10), 3015; https://doi.org/10.3390/s24103015 - 9 May 2024
Cited by 1 | Viewed by 1035
Abstract
Photothermal techniques are infrared remote sensing techniques that have been used for biomedical applications, as well as industrial non-destructive testing (NDT). Machine learning is a branch of artificial intelligence, which includes a set of algorithms for learning from past data and analyzing new [...] Read more.
Photothermal techniques are infrared remote sensing techniques that have been used for biomedical applications, as well as industrial non-destructive testing (NDT). Machine learning is a branch of artificial intelligence, which includes a set of algorithms for learning from past data and analyzing new data, without being explicitly programmed to do so. In this paper, we first review the latest development of machine learning and its applications in photothermal techniques. Next, we present our latest work on machine learning for data analysis in opto-thermal transient emission radiometry (OTTER), which is a type of photothermal technique that has been extensively used in skin hydration, skin hydration depth profiles, skin pigments, as well as topically applied substances and skin penetration measurements. We have investigated different algorithms, such as random forest regression, gradient boosting regression, support vector machine (SVM) regression, and partial least squares regression, as well as deep learning neural network regression. We first introduce the theoretical background, then illustrate its applications with experimental results. Full article
(This article belongs to the Special Issue Photonics for Advanced Spectroscopy and Sensing)
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<p>The schematic diagram of OTTER measurements [<a href="#B33-sensors-24-03015" class="html-bibr">33</a>].</p>
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<p>The typical OTTER measurement signals (<b>left</b>) and the corresponding hydration depth profiles (<b>right</b>), analyzed by using enhanced segmented least squares (SLS) fitting algorithm, of skin sites at arm low, arm high, face, finger back, finger front, and forehead.</p>
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<p>The simplified linear skin hydration distribution by fitting the skin hydration profiles in <a href="#sensors-24-03015-f002" class="html-fig">Figure 2</a> with Equation (3). The smooth curves are original profiles, the curves with squared markers are fitted straight line profiles.</p>
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<p>The OTTER skin measurement signals (<b>a</b>) and corresponding skin hydration levels in percentages (<b>b</b>). The different colors represent different measurements.</p>
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<p>The regression results of different machine learning algorithms models. (<b>A</b>) Lasso, (<b>B</b>) elastic net, (<b>C</b>) decision tree, (<b>D</b>) support vector machine, (<b>E</b>) gradient boosting, (<b>F</b>) linear regression, (<b>G</b>) random forest, (<b>H</b>) k-nearest neighbours, (<b>I</b>) extreme gradient boosting, (<b>J</b>) partial least squares (PLS) regression, (<b>K</b>) voting regression, (<b>L</b>) deep learning.</p>
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<p>The regression results of different machine learning algorithms models. (<b>A</b>) Lasso, (<b>B</b>) elastic net, (<b>C</b>) decision tree, (<b>D</b>) support vector machine, (<b>E</b>) gradient boosting, (<b>F</b>) linear regression, (<b>G</b>) random forest, (<b>H</b>) k-nearest neighbours, (<b>I</b>) extreme gradient boosting, (<b>J</b>) partial least squares (PLS) regression, (<b>K</b>) voting regression, (<b>L</b>) deep learning.</p>
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<p>The regression results of different machine learning algorithms models. (<b>A</b>) Lasso, (<b>B</b>) elastic net, (<b>C</b>) decision tree, (<b>D</b>) support vector machine, (<b>E</b>) gradient boosting, (<b>F</b>) linear regression, (<b>G</b>) random forest, (<b>H</b>) k-nearest neighbours, (<b>I</b>) extreme gradient boosting, (<b>J</b>) partial least squares (PLS) regression, (<b>K</b>) voting regression, (<b>L</b>) deep learning.</p>
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<p>The deep learning model architecture.</p>
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<p>The OTTER skin measurement signals (<b>a</b>) and corresponding skin hydration [%] linear distribution depth profiles (<b>b</b>). The different colors represent different measurements.</p>
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<p>The regression results of different machine learning algorithms models, (<b>A</b>) random forest, (<b>B</b>) RidgeCV, (<b>C</b>) partial least squares (PLS) regression, (<b>D</b>) k-nearest neighbours, (<b>E</b>) linear regression, (<b>F</b>) deep learning neural networks.</p>
Full article ">Figure 8 Cont.
<p>The regression results of different machine learning algorithms models, (<b>A</b>) random forest, (<b>B</b>) RidgeCV, (<b>C</b>) partial least squares (PLS) regression, (<b>D</b>) k-nearest neighbours, (<b>E</b>) linear regression, (<b>F</b>) deep learning neural networks.</p>
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<p>The 20 OTTER signals of four different volunteers on the volar forearm (<b>A</b>) and the corresponding 3D presentation (<b>B</b>). The different colors represent different measurements.</p>
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<p>The LDA plot of the first two components of the 20 OTTER signals of four different volunteers on the volar forearm.</p>
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<p>The PCA plot of the first two components of the 20 OTTER signals of four different volunteers on the volar forearm.</p>
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<p>The most important features according to SHAP values.</p>
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20 pages, 5005 KiB  
Review
Recent Progress in Modulated Photothermal Radiometry
by Javier Corona and Nirmala Kandadai
Sensors 2023, 23(10), 4935; https://doi.org/10.3390/s23104935 - 20 May 2023
Cited by 3 | Viewed by 2277
Abstract
In this review, the emerging work using a technique known as modulated photothermal radiometry (MPTR) is evaluated. As MPTR has matured, the previous discussions on theory and modeling have become increasingly limited in their applicability to the current state of the art. After [...] Read more.
In this review, the emerging work using a technique known as modulated photothermal radiometry (MPTR) is evaluated. As MPTR has matured, the previous discussions on theory and modeling have become increasingly limited in their applicability to the current state of the art. After a brief history of the technique, the currently used thermodynamic theory is explained, highlighting the commonly applied simplifications. The validity of the simplifications is explored via modeling. Various experimental designs are compared, and the differences are explored. New applications, as well as emerging analysis techniques, are presented to emphasize the trajectory of MPTR. Full article
(This article belongs to the Section Optical Sensors)
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<p>Heat wave penetration in a 2-layer sample [<a href="#B33-sensors-23-04935" class="html-bibr">33</a>]. Reproduced with permission.</p>
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<p>Schematic of the 3-layer model showing layer dimensions for Pyromark, chromium, and silicon dioxide.</p>
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<p>Plots of the 2<span class="html-italic">D</span> model under the following conditions: (<b>a</b>) spatial temperature response for select frequency and laser radius values; (<b>b</b>) comparison of 1<span class="html-italic">D</span> and 2<span class="html-italic">D</span> models with varying laser radius sizes against the inverse square frequency and penetration depth; (<b>c</b>) 2<span class="html-italic">D</span> model for 4 mm beam radius with varying <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mi>r</mi> </msub> <mo>/</mo> <msub> <mi>k</mi> <mi>z</mi> </msub> </mrow> </semantics></math> ratio for each layer.</p>
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<p>Transient simulation results from [<a href="#B39-sensors-23-04935" class="html-bibr">39</a>] show: (<b>a</b>) spatial temperature distribution at the sample surface for aluminum (<b>i</b>) and copper (<b>ii</b>) samples; (<b>b</b>) transient surface temperature for aluminum (<b>i</b>) and copper (<b>ii</b>) samples, respectively; (<b>c</b>) amplitudes of the temperature variation against frequency, comparing analytical (full triangle) and numerical FEM (empty triangle) solutions for aluminum (upwards-facing triangle) and copper (downwards-facing triangle). Reproduced under CC BY-NC-ND 4.0.</p>
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<p>Various examples of MPTR experimental setups: (<b>a</b>) setup using direct sinusoidal modulation of a 445 nm CW diode laser (<b>i</b>) and internally heated sample holder (<b>ii</b>) [<a href="#B33-sensors-23-04935" class="html-bibr">33</a>]; (<b>b</b>) AOM externally modulated 532 nm CW laser producing a square profile with an externally heated sample [<a href="#B40-sensors-23-04935" class="html-bibr">40</a>]; and (<b>c</b>) optomechanical chopper modulation with lens optics [<a href="#B13-sensors-23-04935" class="html-bibr">13</a>]. Reproduced with permission.</p>
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<p>MPTR measurements on 2-layer samples [<a href="#B33-sensors-23-04935" class="html-bibr">33</a>]: (<b>a</b>) temperature amplitude response showing distinct linear regions for coatings on SS304; (<b>b</b>) extracted thermal conductivity for coatings; (<b>c</b>) linearized response in the bulk region with slope equivalent to effusivity; (<b>d</b>) linear region slope change according to sample temperature on Pyroceran 9606. Reproduced with permission.</p>
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<p>(<b>a</b>) Thermal resistance of film compared to film thickness; (<b>b</b>) thermal resistance versus film thickness at various temperatures showing phase dependence on thermal properties of GeTe films [<a href="#B40-sensors-23-04935" class="html-bibr">40</a>]. Reproduced with permission.</p>
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<p>(<b>a</b>) Graphic showing embedded thermal resistance from delamination where 1 represents the area before and 2 the area after the delamination; (<b>b</b>) temperature amplitude (<b>left</b>) and phase (<b>right</b>) measurements showing distinct signals for various delamination widths where the arrow highlights the start of the delamination [<a href="#B75-sensors-23-04935" class="html-bibr">75</a>]. Reproduced with permission.</p>
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<p>(<b>a</b>) Graphic showing embedded thermal resistance from delamination where 1 represents the area before and 2 the area after the delamination; (<b>b</b>) temperature amplitude (<b>left</b>) and phase (<b>right</b>) measurements showing distinct signals for various delamination widths where the arrow highlights the start of the delamination [<a href="#B75-sensors-23-04935" class="html-bibr">75</a>]. Reproduced with permission.</p>
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14 pages, 3203 KiB  
Article
Pulsed Photothermal Radiometric Depth Profiling of Bruises by 532 nm and 1064 nm Lasers
by Ana Marin, Rok Hren and Matija Milanič
Sensors 2023, 23(4), 2196; https://doi.org/10.3390/s23042196 - 15 Feb 2023
Cited by 2 | Viewed by 2754
Abstract
Optical techniques are often inadequate in estimating bruise age since they are not sensitive to the depth of chromophores at the location of the bruise. To address this shortcoming, we used pulsed photothermal radiometry (PPTR) for depth profiling of bruises with two wavelengths, [...] Read more.
Optical techniques are often inadequate in estimating bruise age since they are not sensitive to the depth of chromophores at the location of the bruise. To address this shortcoming, we used pulsed photothermal radiometry (PPTR) for depth profiling of bruises with two wavelengths, 532 nm (KTP laser) and 1064 nm (Nd:YAG laser). Six volunteers with eight bruises of exactly known and documented times of injury were enrolled in the study. A homogeneous part of the bruise was irradiated first with a 5 ms pulse at 532 nm and then with a 5 ms pulse at 1064 nm. The resulting transient surface temperature change was collected with a fast IR camera. The initial temperature–depth profiles were reconstructed by solving the ill-posed inverse problem using a custom reconstruction algorithm. The PPTR signals and reconstructed initial temperature profiles showed that the 532 nm wavelength probed the shallow skin layers revealing moderate changes during bruise development, while the 1064 nm wavelength provided additional information for severe bruises, in which swelling was present. Our two-wavelength approach has the potential for an improved estimation of the bruise age, especially if combined with modeling of bruise dynamics. Full article
(This article belongs to the Section Physical Sensors)
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<p>Chronological evolution of bruise #1. The arrow indicates the circular area of measurement. The typical bruise color changes described in [<a href="#B37-sensors-23-02196" class="html-bibr">37</a>] can be ascertained, from the bluish fresh bruise, with more pronounced redness a day later, to a brown coloration as bilirubin became prominent after the fourth measurement at 133 h and persisted as a yellow hue until the eventual disappearance of the bruise after 349 h (two weeks).</p>
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<p>Time evolution of (<b>a</b>) KTP (532 nm) and (<b>b</b>) Nd:YAG (1064 nm) signals. The control signal for healthy skin is shown with a dashed line, and the signals for bruised skin at different times after the injury are shown in different colors; the gray arrow indicates the initial temperature increase due to shallow absorbers, and the black arrow indicates the temperature increase due to the blood pool forming deeper in the dermis.</p>
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<p>Inversely reconstructed initial temperature–depth profiles of bruise #1 for laser wavelengths (<b>a</b>) 532 nm and (<b>b</b>) 1064 nm. The profile for healthy skin is shown with a dashed line, and profiles for bruised skin at different times after the injury are shown in different colors; the black arrow indicates a prominent temperature peak, which was not present in the healthy skin.</p>
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<p>Chronological evolution of bruise #2. The arrow indicates the circular area of measurement.</p>
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<p>Inversely reconstructed initial temperature–depth profiles for bruise #2 for laser wavelengths (<b>a</b>) 532 nm and (<b>b</b>) 1064 nm. The profile for healthy skin is shown with a dashed line, and profiles for bruised skin at different times after the injury are shown in different colors.</p>
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<p>Chronological evolution of bruise #3. The circle indicates the area of measurement.</p>
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<p>Inversely reconstructed initial temperature–depth profiles for bruise #3 for laser wavelengths (<b>a</b>) 532 nm and (<b>b</b>) 1064 nm.</p>
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<p>The ratio <span class="html-italic">ε</span> obtained for bruises #1 through #8 for (<b>a</b>) 532 nm and (<b>b</b>) 1064 nm lasers. The error bars represent standard deviations of <span class="html-italic">ε</span>.</p>
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7 pages, 1118 KiB  
Proceeding Paper
Arduino-Based Sensing Platform for Rapid, Low-Cost, and High-Sensitivity Detection and Quantification of Analytes in Fluidic Samples
by Derek Hayden, Sergio Anacleto, Daphne-Eleni Archonta, Nour Khalil, Antonia Pennella, Shadan Qureshi, Alexandre Séguin and Nima Tabatabaei
Eng. Proc. 2022, 27(1), 69; https://doi.org/10.3390/ecsa-9-13277 - 1 Nov 2022
Viewed by 1736
Abstract
Lateral flow assays (LFAs; aka. rapid tests) are inexpensive paper-based devices for rapid and specific detection of analyte of interest (e.g., COVID virus) in fluidic samples. Areas of application of LFAs cover a broad spectrum, spanning from agriculture to food/water safety to point-of-care [...] Read more.
Lateral flow assays (LFAs; aka. rapid tests) are inexpensive paper-based devices for rapid and specific detection of analyte of interest (e.g., COVID virus) in fluidic samples. Areas of application of LFAs cover a broad spectrum, spanning from agriculture to food/water safety to point-of-care medical testing and, most recently, to detection of COVID-19 infection. While these low-cost and rapid tests are specific to the target analyte, their sensitivity and limit of detection are far inferior to their laboratory-based counterparts. In addition, rapid tests normally cannot quantify the concentration of target analyte and only provide qualitative/binary detection. We have developed a low-cost, end-user sensing platform that significantly improves the sensitivity of rapid tests. The developed platform is based on Arduino and utilizes low-cost far infrared, single-element detectors to offer sensitive and semi-quantitative results from commercially available rapid tests. The sensing paradigm integrated to the low-cost device is based on radiometric detection of photothermal responses of rapid tests in the frequency domain when exposed to modulated laser excitation. As a proof of principle, we studied commercially available rapid tests for detection of THC (the principal psychoactive constituent of cannabis) in oral fluid with different concentrations of control positive solutions and, subsequently, interpret them with the developed sensor. Results suggest that the developed end-user sensor is not only able to improve the detection limit of the rapid test by approximately an order of magnitude from 25 ng/mL to 5 ng/mL, but also offers the ability to obtain semi-quantitative insight into concentration of THC in the fluidic samples. Full article
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<p>A competitive-style LFA where the presence or absence of target analyte either blocks or allows binding of antibodies to the antigens in the test line.</p>
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<p>(<bold>a</bold>) Rendering of the device, (<bold>b</bold>) electronic system overview featuring the laser and motor control systems and sensor sampling scheme.</p>
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<p>(<bold>a</bold>) Interpolated and resamples time series of control and test line responses, (<bold>b</bold>) control and test line responses transformed to the frequency domain.</p>
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<p>Results of 10 repetitions per LFA (N = 20 per concentration): (<bold>a</bold>) control line versus normalized test line response, (<bold>b</bold>) normalized responses fit to a quadratic curve, *, **, ***, **** indicate the number of standard deviations between pairs of means.</p>
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17 pages, 2866 KiB  
Article
Erythrocyte-Derived Nanoparticles with Folate Functionalization for Near Infrared Pulsed Laser-Mediated Photo-Chemotherapy of Tumors
by Jenny T. Mac, Raviraj Vankayala, Chi-Hua Lee and Bahman Anvari
Int. J. Mol. Sci. 2022, 23(18), 10295; https://doi.org/10.3390/ijms231810295 - 7 Sep 2022
Cited by 8 | Viewed by 2341
Abstract
Despite its common side effects and varying degrees of therapeutic success, chemotherapy remains the gold standard method for treatment of cancer. Towards developing a new therapeutic approach, we have engineered nanoparticles derived from erythrocytes that contain indocyanine green as a photo-activated agent that [...] Read more.
Despite its common side effects and varying degrees of therapeutic success, chemotherapy remains the gold standard method for treatment of cancer. Towards developing a new therapeutic approach, we have engineered nanoparticles derived from erythrocytes that contain indocyanine green as a photo-activated agent that enables near infrared photothermal heating, and doxorubicin hydrochloride (DOX) as a chemotherapeutic drug. We hypothesize that milliseconds pulsed laser irradiation results in rapid heating and photo-triggered release of DOX, providing a dual photo-chemo therapeutic mechanism for tumor destruction. Additionally, the surface of the nanoparticles is functionalized with folate to target the folate receptor-α on tumor cells to further enhance the therapeutic efficacy. Using non-contract infrared radiometry and absorption spectroscopy, we have characterized the photothermal response and photostability of the nanoparticles to pulsed laser irradiation. Our in vitro studies show that these nanoparticles can mediate photo-chemo killing of SKOV3 ovarian cancer cells when activated by pulsed laser irradiation. We further demonstrate that this dual photo-chemo therapeutic approach is effective in reducing the volume of tumor implants in mice and elicits an apoptotic response. This treatment modality presents a promising approach in destruction of small tumor nodules. Full article
(This article belongs to the Special Issue Development of Responsive Nanoparticles for Cancer Therapy 2.0)
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<p>(<b>A</b>) Zeta potentials and (<b>B</b>) hydrodynamic diameter distributions, as measured by DLS (symbols) and the lognormal fits to the measurements (traces) for the various sets of erythrocyte-derived nanoparticles. Particles were suspended in isotonic PBS during measurements. In panel (<b>A</b>), each bar represents the mean value of three independent measurements, and error bars are the SD values from the mean. Asterisk (*) indicates a statistically significant difference between the mean values for the indicated pairs at <span class="html-italic">p</span> &lt; 0.05 (two-tailed <span class="html-italic">t</span>-test).</p>
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<p>Optical spectra. (<b>A</b>) Absorption and (<b>B</b>) fluorescence emission spectra of free ICG (17.3 µM), DOX (2.6 µM), and ICG (17.6 µM) + DOX (2.6 µM) in isotonic PBS. (<b>C</b>) Absorption and (<b>D</b>) fluorescence of NETs, IDNETs, F-NETs, and F-IDNETs. In panel (<b>A</b>), the right ordinate corresponds to absorbance values of DOX and the left ordinate corresponds to absorbance values of ICG and ICG + DOX. In panels (<b>B</b>,<b>D</b>), the respective left and right ordinates correspond to the fluorescence emission intensities associated with DOX and ICG. Fluorescence emission spectra were obtained in response to 470 and 720 nm for DOX and ICG, respectively.</p>
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<p>Photothermal response of nanoparticles and non-encapsulated materials and DOX release quantification. Representative radiometrically-measured changes in temperature rise of various agents in isotonic PBS in response to 808 nm pulsed (0.5 s) laser irradiation at radiant exposures of (<b>A</b>) <span class="html-italic">D</span><sub>o</sub> = 50 J/cm<sup>2</sup> and (<b>B</b>) <span class="html-italic">D</span><sub>o</sub> = 90 J/cm<sup>2</sup> are shown. Legends in panel (<b>A</b>) also correspond to traces shown in panel (<b>B</b>). (<b>C</b>) Average values of the maximum temperature rise. (<b>D</b>) Percentage of ICG absorbance value retained following irradiation. (<b>E</b>) Mean percentage of DOX released from IDNETs and F-IDNETs in the dark (without laser irradiation) in response to acidic pH 5.5 and in response to 0.5 s laser irradiation at <span class="html-italic">D</span><sub>o</sub> = 25 J/cm<sup>2</sup> in nearly neutral pH 7.6. Error bars in panels (<b>C</b>,<b>E</b>) represent SDs from the mean.</p>
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<p>Effects of pulsed laser irradiation on non-encapsulated (free) and encapsulated ICG absorption. Absorption spectra of (<b>A</b>) free ICG, (<b>B</b>) free ICG + free DOX, (<b>C</b>) NETs, (<b>D</b>) F-NETs, (<b>E</b>) IDNETs, and (<b>F</b>) F-IDNETs in the dark (without laser irradiation) and immediately after pulsed (0.5 s) 808 nm laser irradiation at radiant exposures of <span class="html-italic">D</span><sub>o</sub> 50 and 90 J/cm<sup>2</sup>. All agents were in isotonic PBS during laser irradiation and spectral recordings.</p>
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<p>In vitro results. (<b>A</b>) Falsely colored fluorescence images of SKOV3 ovarian cancer cells after 4 h of incubation with various agents. Blue: DAPI, Green: DOX, and Red: ICG. Scale bars correspond to 50 μm. Panels (<b>B</b>,<b>C</b>) present illustrative radiometrically-measured changes in the temperature of SKOV 3 cells suspensions in response to 808 nm pulsed (0.5 s) laser irradiation at radiant exposures of (<b>B</b>) <span class="html-italic">D</span><sub>o</sub> = 50 J/cm<sup>2</sup> and (<b>C</b>) <span class="html-italic">D</span><sub>o</sub> = 90 J/cm<sup>2</sup> following incubation with various agents. (<b>D</b>) Average values of the maximum temperature rise of SKOV3 cells. (<b>E</b>) Average fraction of cells remaining viable following laser irradiation, with respect to (wrt) control (non-irradiated cells). Error bars in panels (<b>D</b>,<b>E</b>) represent standard deviation values from the mean. Single asterisks (*) indicate statistically significant differences between the indicated pairs (<span class="html-italic">p</span> &lt; 0.05) (one-tailed <span class="html-italic">t</span>-test).</p>
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<p>In vivo results. (<b>A</b>) Representative radiometrically-measured changes in skin surface temperature of intact mice with subcutaneous tumors in response to 808 nm pulsed (0.5 s) laser irradiation at radiant exposure of <span class="html-italic">D</span><sub>o</sub> = 90 J/cm<sup>2</sup> performed at 24 h after i.v. administration of various agents. (<b>B</b>) Mean values of changes in tumor volumes for various treatments relative to initial tumor volume (V<sub>0</sub>) at day zero (just prior to administration of agents). (+) and (−) signs indicate with and without laser irradiation, respectively. Error bars are SDs from the mean (n = 6 animals per treatment). (<b>C</b>) Fluorescence images of sectioned tumors obtained by immunohistochemical staining using FITC-labeled caspase-3 antibody obtained within 24 h after laser irradiation in a subset of animals (scale bars = 50 µm). (<b>D</b>) Spatially averaged fluorescence emission intensity associated with caspase-3 corresponding to the images shown in panel (<b>C</b>) after background subtraction. Single asterisks (*) indicate a statistically significant difference (<span class="html-italic">p</span> &lt; 0.05) between the indicated pairs in panels (<b>B</b>,<b>D</b>) (one-tailed <span class="html-italic">t</span>-test). Error bars in panels (<b>B</b>,<b>D</b>) represent SDs from the mean.</p>
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<p>Average body weights of mice following various treatments. Measurements were obtained every day during week 1 and every other day during week 2. (+) and (−) signs indicate with and without laser irradiation, respectively. Error bars represent SDs from the mean. n = 6 mice per each treatment.</p>
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<p>Radiometric temperature measurements. (<b>A</b>) Experimental setup for calibration and temperature measurements. Further details are provided in the methods sections. (<b>B</b>) Illustrative calibration curve relating radiometric surface temperature to the output voltage of the detector. Red trace represents the mathematical fit to experimental measurements.</p>
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<p>Molecular structures of (<b>A</b>) DOX and (<b>B</b>) ICG.</p>
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18 pages, 4753 KiB  
Article
Investigations of the Thermal Parameters of Hybrid Sol–Gel Coatings Using Nondestructive Photothermal Techniques
by Łukasz Chrobak, Dorota Korte, Hanna Budasheva, Mirosław Maliński, Peter Rodič, Ingrid Milošev and Sylwia Janta-Lipińska
Energies 2022, 15(11), 4122; https://doi.org/10.3390/en15114122 - 3 Jun 2022
Cited by 7 | Viewed by 1498
Abstract
This article presents the results of comparative investigations of thermal parameters of hybrid sol–gel coatings (named TMZ) prepared from tetraethyl orthosilicate and organically modified 3-methacryloxypropyltrimethoxysilane. The coatings were prepared with the addition of zirconium(IV) tetrapropoxide chelated with methacrylic acid. Two series of samples [...] Read more.
This article presents the results of comparative investigations of thermal parameters of hybrid sol–gel coatings (named TMZ) prepared from tetraethyl orthosilicate and organically modified 3-methacryloxypropyltrimethoxysilane. The coatings were prepared with the addition of zirconium(IV) tetrapropoxide chelated with methacrylic acid. Two series of samples were investigated: the first series, TMZ-I, TMZ-II and TMZ-III, with different amounts of zirconium, and the second series, TMZ-I/Ce, TMZ-II/Ce and TMZ-III/Ce, with the addition of cerium nitrate. The influence of the amount of zirconium and cerium on the thermal parameters of the sol–gel coatings was next analyzed. Two non-destructive and photothermal techniques were used for this purpose: photothermal radiometry (PTR) and beam deflection spectroscopy (BDS). The thermal diffusivity and conductivity of the coatings were extracted from the frequency experiments and are presented and discussed. The two-layer model was applied to interpret the photothermal spectra. The results obtained using these two techniques are compared and discussed. Full article
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<p>Schematic block diagram of the experimental setup used for the PTR frequency characteristics measurements.</p>
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<p>Scheme of the experimental setup used to perform the BDS measurements.</p>
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<p>Simplified schematic of the investigated sample.</p>
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<p>Theoretical PTR amplitude and phase characteristics calculated for different values of thermal parameters. Solid green line (<span class="html-italic">α<sub>c</sub></span>, <span class="html-italic">λ<sub>c</sub></span>), solid red line (2 × <span class="html-italic">α<sub>c</sub></span>, 2 × <span class="html-italic">λ<sub>c</sub></span>), solid black line (4 × <span class="html-italic">α<sub>c</sub></span>, 4 × <span class="html-italic">λ<sub>c</sub></span>), solid blue line (<span class="html-italic">α<sub>c</sub></span>/2, <span class="html-italic">λ<sub>c</sub></span>/2) and solid pink line (<span class="html-italic">α<sub>c</sub></span>/4, <span class="html-italic">λ<sub>c</sub></span>/4).</p>
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<p>Schematic of the sample geometry for BDS investigation.</p>
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<p>Theoretical BDS amplitude and phase dependence on the values of thermal conductivity of coating deposited on bulk support for PB height over the sample of 60 μm and its radius in the waist of 40 μm and for different modulation frequencies of the excitation beam (60, 600, 2000 rad/s).</p>
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<p>Theoretical BDS amplitude and phase dependence on the values of thermal diffusivity of coating deposited on bulk support for PB height over the sample of 60 μm and its radius in the waist of 40 μm and for different modulation frequencies of the excitation beam (60, 600, 2000 rad/s).</p>
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<p>The SEM images (5000×) of the TMZ coatings.</p>
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<p>The SEM images (250,000×) of the TMZ coatings.</p>
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<p>Theoretical and experimental PTR amplitude and phase characteristics obtained for the TMZ-I sample.</p>
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<p>Theoretical and experimental PTR amplitude and phase characteristics obtained for the TMZ-I + Ce(NO<sub>3</sub>)<sub>3</sub> sample.</p>
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<p>Theoretical and experimental PTR amplitude and phase characteristics obtained for the TMZ-II sample.</p>
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<p>Theoretical and experimental PTR amplitude and phase characteristics obtained for the TMZ-II + Ce(NO<sub>3</sub>)<sub>3</sub> sample.</p>
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<p>Theoretical and experimental PTR amplitude and phase characteristics obtained for the TMZ-III sample.</p>
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<p>Theoretical and experimental PTR amplitude and phase characteristics obtained for the TMZ-III + Ce(NO<sub>3</sub>)<sub>3</sub> sample.</p>
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<p>Amplitude and phase frequency dependence of TO for TMZ coatings.</p>
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18 pages, 862 KiB  
Protocol
Protocol for a Case Control Study to Evaluate Oral Health as a Biomarker of Child Exposure to Adverse Psychosocial Experiences
by Anna Durbin, Bennett T. Amaechi, Stephen Abrams, Andreas Mandelis, Sara Werb, Benjamin Roebuck, Janet Durbin, Ri Wang, Maryam Daneshvarfard, Konesh Sivagurunathan and Laurent Bozec
Int. J. Environ. Res. Public Health 2022, 19(6), 3403; https://doi.org/10.3390/ijerph19063403 - 14 Mar 2022
Cited by 1 | Viewed by 4061
Abstract
Background: The early identification of children who have experienced adversity is critical for the timely delivery of interventions to improve coping and reduce negative consequences. Self-report is the usual practice for identifying children with exposure to adversity. However, physiological characteristics that signal the [...] Read more.
Background: The early identification of children who have experienced adversity is critical for the timely delivery of interventions to improve coping and reduce negative consequences. Self-report is the usual practice for identifying children with exposure to adversity. However, physiological characteristics that signal the presence of disease or other exposures may provide a more objective identification strategy. This protocol describes a case–control study that assesses whether exposure to adversity is more common in children with tooth enamel anomalies compared to children without such anomalies. Methods: For 150 mother–child pairs from a pediatric dental clinic in Toronto, Canada, maternal interviews will assess the child’s adverse and resilience-building experiences. Per child, one (exfoliated or extracted) tooth will be assessed for suspected enamel anomalies. If anomalies are present, the child is a case, and if absent, the child is a control. Tooth assessment modalities will include usual practice for dental exams (visual assessment) and modalities with greater sensitivity to identify anomalies. Conclusion: If structural changes in children’s teeth are associated with exposure to adversity, routine dental exams could provide an opportunity to screen children for experiences of adversity. Affected children could be referred for follow-up. Full article
(This article belongs to the Special Issue Oral Health and Connections to Mental and Physical Health)
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<p>Study processes.</p>
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23 pages, 2081 KiB  
Review
Emerging Technologies for Dentin Caries Detection—A Systematic Review and Meta-Analysis
by Christa Serban, Diana Lungeanu, Sergiu-David Bota, Claudia C. Cotca, Meda Lavinia Negrutiu, Virgil-Florin Duma, Cosmin Sinescu and Emanuela Lidia Craciunescu
J. Clin. Med. 2022, 11(3), 674; https://doi.org/10.3390/jcm11030674 - 28 Jan 2022
Cited by 10 | Viewed by 4353
Abstract
This systematic review and meta-analysis aimed at assessing the diagnostic accuracy of emerging technologies, such as laser fluorescence (LF), transillumination, light-emitting diode devices, optical coherence tomography (OCT), alternating current impedance spectroscopy, fluorescence cameras (FC), photo-thermal radiometry, and modulated luminescence technology. In vivo and [...] Read more.
This systematic review and meta-analysis aimed at assessing the diagnostic accuracy of emerging technologies, such as laser fluorescence (LF), transillumination, light-emitting diode devices, optical coherence tomography (OCT), alternating current impedance spectroscopy, fluorescence cameras (FC), photo-thermal radiometry, and modulated luminescence technology. In vivo and in vitro results of such non-ionizing, non-invasive, and non-destructive methods’ effectiveness in non-cavitated dentin caries detection are sometimes ambiguous. Following the PRISMA guidelines, 34 relevant research articles published between 2011–2021 were selected. The risk of bias was assessed with a tool tailored for caries diagnostic studies, and subsequent quantitative uni- and bi-variate meta-analysis was carried out in separate sub-groups according to the investigated surface (occlusal/proximal) and study setting (in vivo/in vitro). In spite of the high heterogeneity across the review groups, in vitro studies on LF and FC proved a good diagnostic ability for the occlusal surface, with area under the curve (AUC) of 0.803 (11 studies) and 0.845 (five studies), respectively. OCT studies reported an outstanding performance with an overall AUC = 0.945 (four studies). Promising technologies, such as OCT or FC VistaProof, still need well-designed and well-powered studies to accrue experimental and clinical data for conclusive medical evidence, especially for the proximal surface. Registration: INPLASY202210097. Full article
(This article belongs to the Special Issue Prevention and Management of Dental Caries and Erosive Tooth Wear)
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<p>Flow diagram of the study selection process.</p>
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<p>Risk of bias assessment for the studies include in meta-analysis: (<b>a</b>) in vivo studies; (<b>b</b>) in vitro studies.</p>
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<p>Risk of bias assessment for the studies include in meta-analysis: (<b>a</b>) in vivo studies; (<b>b</b>) in vitro studies.</p>
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<p>Univariate summary plots for the three groups of index tests included in the comprehensive bivariate meta-analysis: LF DD pen occlusal, in vitro; FC VistaProof using optimal cut-off, in vitro; OCT overall. (<b>a</b>) Log DOR forest plots and random effects aggregate estimates. (<b>b</b>) Log DOR funnel plots to illustrate the publication bias as patterns of individual studies clustering around the mean effect.</p>
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<p>Bivariate summary ROC (sROC) curves for the three groups of index tests included in the comprehensive bivariate meta-analysis: LF DD pen occlusal, in vitro; FC VistaProof using optimal cut-off, in vitro; OCT overall. They also integrate the individual studies’ data in a summary bi-dimensional estimate and show its 95% prediction region.</p>
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14 pages, 3225 KiB  
Article
Experimental Validation of Formula for Calculation Thermal Diffusivity in Superlattices Performed Using a Combination of Two Frequency-Domain Methods: Photothermal Infrared Radiometry and Thermoreflectance
by Michał Pawlak, Timo Kruck, Nikolai Spitzer, Dariusz Dziczek, Arne Ludwig and Andreas D. Wieck
Appl. Sci. 2021, 11(13), 6125; https://doi.org/10.3390/app11136125 - 30 Jun 2021
Cited by 11 | Viewed by 2517
Abstract
In this paper, we validate two theoretical formula used to characterize thermal transport of superlattices at different temperatures. These formulas are used to measure cross-plane thermal conductivity and thermal boundary resistance, when it is not possible to obtain heat capacity or thermal diffusivity [...] Read more.
In this paper, we validate two theoretical formula used to characterize thermal transport of superlattices at different temperatures. These formulas are used to measure cross-plane thermal conductivity and thermal boundary resistance, when it is not possible to obtain heat capacity or thermal diffusivity and in-plane thermal conductivity. We find that the most common formula for calculating thermal diffusivity and heat capacity (and density) can be used in a temperature range of −50 °C to 50 °C. This confirms that the heat capacity in the very thin silicon membranes is the same as in bulk silicon, as was preliminary investigated using an elastic continuum model. Based on the obtained thermal parameters, we can fully characterize the sample using a new procedure for characterization of the in-plane and cross-plane thermal transport properties of thin-layer and superlattice semiconductor samples. Full article
(This article belongs to the Special Issue Recent Advances in Application of Coatings and Films)
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<p>Frequency-domain photothermal infrared radiometry set-up with temperature cell. The beam size was 1.8 mm.</p>
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<p>Temperature-dependent of the PTR and TR amplitudes at 1 kHz for a GaAs wafer covered by 50 layer of Ti and 2 µm AlGaAs thin layer with 100 nm of gold layer, respectively. The data are not normalized. The signals were depicted for 1 kHz. Note that PTR amplitude is very stable with error bars within experimental points.</p>
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<p>FD-TR setup.</p>
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<p>SEM image of superlattice sample (samples #3 and #4).</p>
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<p>Cross-plane (<b>left</b>) and in-plane (<b>right</b>) geometry in frequency-domain methods.</p>
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<p>Phase relative sensitivities of model described by Equation (1) to the parameters (RAlGaAs/GaAs = 1.0 × 10<sup>−9</sup> m<sup>2</sup> 2 K/W, kAlGaAs = 12 W/mK, αAlGaAs=6 × 10<sup>−6</sup> m<sup>2</sup> /s, L = 500 nm) and with other parameter values taken from <a href="#applsci-11-06125-t002" class="html-table">Table 2</a>.</p>
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<p>Measured TR phase of 2 µm AlGaAs thin layer covered by 100 nm of gold layer between 100 Hz and 500 kHz. The best fits are obtained using Equation (1).</p>
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<p>PTR experimental results (points) for sample#3 in three different temperatures: −50 °C, 0 °C and 50 °C. Additionally, the best fittings are presented as solid lines using Equation (8).</p>
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<p>TR measurement of sample #4 for different temperatures. Experimental points are circles, while fits are lines. The red (50 °C), black (−50 °C) and blue (0 °C). The best fits are obtained using Equation (1).</p>
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<p>Relation between measured and calculated values of thermal diffusivity as function of temperature.</p>
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<p>Relation between in-plane and cross-plane thermal conductivity measured for AlAs/GaAs superlattice sample as function of temperature.</p>
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19 pages, 3931 KiB  
Article
Noninvasive Monitoring of Dynamical Processes in Bruised Human Skin Using Diffuse Reflectance Spectroscopy and Pulsed Photothermal Radiometry
by Ana Marin, Nina Verdel, Matija Milanič and Boris Majaron
Sensors 2021, 21(1), 302; https://doi.org/10.3390/s21010302 - 5 Jan 2021
Cited by 9 | Viewed by 4830
Abstract
We have augmented a recently introduced method for noninvasive analysis of skin structure and composition and applied it to monitoring of dynamical processes in traumatic bruises. The approach combines diffuse reflectance spectroscopy in visible spectral range and pulsed photothermal radiometry. Data from both [...] Read more.
We have augmented a recently introduced method for noninvasive analysis of skin structure and composition and applied it to monitoring of dynamical processes in traumatic bruises. The approach combines diffuse reflectance spectroscopy in visible spectral range and pulsed photothermal radiometry. Data from both techniques are analyzed simultaneously using a numerical model of light and heat transport in a four-layer model of human skin. Compared to the earlier presented approach, the newly introduced elements include two additional chromophores (β-carotene and bilirubin), individually adjusted thickness of the papillary dermal layer, and analysis of the bruised site using baseline values assessed from intact skin in its vicinity. Analyses of traumatic bruises in three volunteers over a period of 16 days clearly indicate a gradual, yet substantial increase of the dermal blood content and reduction of its oxygenation level in the first days after injury. This is followed by the emergence of bilirubin and relaxation of all model parameters towards the values characteristic for healthy skin approximately two weeks after the injury. The assessed parameter values and time dependences are consistent with existing literature. Thus, the presented methodology offers a viable approach for objective characterization of the bruise healing process. Full article
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Graphical abstract
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<p>Photographs of the bruise in subject <span class="html-italic">a</span> at different times after the injury (see the labels), displaying the characteristic sequence of color changes. Dashed circles indicate the test sites on the bruised area and intact skin in its vicinity (black and white arrow, respectively).</p>
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<p>(<b>a</b>) Diffuse reflectance spectroscopy (DRS) spectrum and (<b>b</b>) pulsed photothermal radiometry (PPTR) signal as obtained from the intact skin site near the bruise in subject <span class="html-italic">a</span> (solid orange curves) and the best-fitting model predictions (dashed lines). Vertical gray arrows in (<b>a</b>) indicate the wavelengths included in the Inverse Monte Carlo (IMC) analysis of the DRS data.</p>
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<p>(<b>a</b>) DRS spectra and (<b>b</b>) PPTR signals obtained from the intact (gray and pink lines) vs. bruised skin site in subject <span class="html-italic">a</span> at different times after the injury (see the legends).</p>
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<p>Time evolutions of the bruise model parameters as assessed from analyses of the data obtained from subject <span class="html-italic">a</span> at different times after injury: (<b>a</b>) Volume fractions of blood in the papillary and reticular dermis, and concentration of bilirubin when confined to the dermal layers; and (<b>b</b>) oxygenation levels of blood in both dermal layers. (The curves have no theoretical basis and serve only as a guide to the eye).</p>
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<p>(<b>a</b>) DRS and (<b>b</b>) PPTR signals acquired form the bruise in subject <span class="html-italic">a</span> at 119 h after injury (orange lines). Dashed lines present the best-fitting model predictions when bilirubin is allowed only in the dermis (light blue), and in both epidermis and dermis (dark blue).</p>
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<p>Relative mismatch (δ, Equation (3)) between the measured and model-predicted diffuse reflectance values in the IMC analyses of the bruises in all three subjects at different times after the injury. Open symbols mark the results of the model with bilirubin only in the dermis, full symbols correspond to the bilirubin present in both epidermis and dermis.</p>
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<p>(<b>a</b>) DRS spectra and (<b>b</b>) PPTR signals obtained from a bruised site in subject <span class="html-italic">a</span> at four different time points (see the legend), and the corresponding model predictions (dashed lines).</p>
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<p>Temporal evolution of the bruise model parameters with bilirubin present in both epidermis and dermis as assessed from measurements in subjects <span class="html-italic">a</span> panels (<b>a</b>,<b>b</b>), <span class="html-italic">b</span> (<b>c</b>,<b>d</b>), and <span class="html-italic">c</span> (<b>e</b>,<b>f</b>). The curves serve only as a guide to the eye. The error bars represent standard deviations from 6 IMC runs.</p>
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<p>Temporal evolution of the bruise model parameters with bilirubin present in both epidermis and dermis as assessed from measurements in subjects <span class="html-italic">a</span> panels (<b>a</b>,<b>b</b>), <span class="html-italic">b</span> (<b>c</b>,<b>d</b>), and <span class="html-italic">c</span> (<b>e</b>,<b>f</b>). The curves serve only as a guide to the eye. The error bars represent standard deviations from 6 IMC runs.</p>
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<p>Comparison of the assessed time evolution of the dermal scattering coefficient (<span class="html-italic">a</span><sub>der</sub>) in three included subjects.</p>
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13 pages, 890 KiB  
Article
Detection of Caries Around Resin-Modified Glass Ionomer and Compomer Restorations Using Four Different Modalities In Vitro
by Tamara Abrams, Stephen Abrams, Koneswaran Sivagurunathan, Veronika Moravan, Warren Hellen, Gary Elman, Bennett Amaechi and Andreas Mandelis
Dent. J. 2018, 6(3), 47; https://doi.org/10.3390/dj6030047 - 16 Sep 2018
Cited by 6 | Viewed by 9774
Abstract
The aim of this study was to evaluate the ability of visual examination (International Caries Detection and Assessment System—ICDAS II), light-emitting diodes (LED) fluorescence (SPECTRA), laser fluorescence (DIAGNODent, DD), photothermal radiometry and modulated luminescence (PTR-LUM, The Canary System, CS) to detect natural decay [...] Read more.
The aim of this study was to evaluate the ability of visual examination (International Caries Detection and Assessment System—ICDAS II), light-emitting diodes (LED) fluorescence (SPECTRA), laser fluorescence (DIAGNODent, DD), photothermal radiometry and modulated luminescence (PTR-LUM, The Canary System, CS) to detect natural decay beneath resin-modified glass ionomer (RMGIC) and compomer restorations in vitro. Twenty-seven extracted human molars and premolars, consisting of 2 control teeth, 10 visually healthy/sound and 15 teeth with natural cavitated lesions, were selected. For the carious teeth, caries was removed leaving some carious tissue on one wall of the preparation. For the sound teeth, 3 mm deep cavity preparations were made. All cavities were restored with RMGIC or compomer restorative materials. Sixty-eight sites (4 sites on sound unrestored teeth, 21 sound sites and 43 carious sites with restorations) were selected. CS and DD triplicate measurements were done at 2, 1.5, 0.5, and 0 mm away from the margin of the restoration (MOR). SPECTRA images were taken, and two dentists provided ICDAS II scoring for the restored surfaces. The SPECTRA data and images were inconclusive due to signal interference from the restorations. Visual examinations of the restored tooth surfaces were able to identify 5 of the 15 teeth with caries. In these situations, the teeth were ranked as having ICDAS II 1 or 2 rankings, but they could not identify the location of the caries or depth of the lesion. CS and DD were able to differentiate between sound and carious tissue at the MOR, but larger variation in measurement, and poorer accuracy, was observed for DD. It was concluded that the CS has the potential to detect secondary caries around RMGIC and compomer restorations more accurately than the other modalities used in this study. Full article
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<p>Caries system detection scales for devices used in this study.</p>
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<p>Examination of caries free margin of a Dyract eXtra restoration.</p>
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<p>Detection of caries beneath Compoglass F restoration margin.</p>
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18 pages, 23274 KiB  
Review
Matched-Filter Thermography
by Nima Tabatabaei
Appl. Sci. 2018, 8(4), 581; https://doi.org/10.3390/app8040581 - 8 Apr 2018
Cited by 23 | Viewed by 9322
Abstract
Conventional infrared thermography techniques, including pulsed and lock-in thermography, have shown great potential for non-destructive evaluation of broad spectrum of materials, spanning from metals to polymers to biological tissues. However, performance of these techniques is often limited due to the diffuse nature of [...] Read more.
Conventional infrared thermography techniques, including pulsed and lock-in thermography, have shown great potential for non-destructive evaluation of broad spectrum of materials, spanning from metals to polymers to biological tissues. However, performance of these techniques is often limited due to the diffuse nature of thermal wave fields, resulting in an inherent compromise between inspection depth and depth resolution. Recently, matched-filter thermography has been introduced as a means for overcoming this classic limitation to enable depth-resolved subsurface thermal imaging and improving axial/depth resolution. This paper reviews the basic principles and experimental results of matched-filter thermography: first, mathematical and signal processing concepts related to matched-fileting and pulse compression are discussed. Next, theoretical modeling of thermal-wave responses to matched-filter thermography using two categories of pulse compression techniques (linear frequency modulation and binary phase coding) are reviewed. Key experimental results from literature demonstrating the maintenance of axial resolution while inspecting deep into opaque and turbid media are also presented and discussed. Finally, the concept of thermal coherence tomography for deconvolution of thermal responses of axially superposed sources and creation of depth-selective images in a diffusion-wave field is reviewed. Full article
(This article belongs to the Special Issue Recent Advances and Applications of Infrared Thermography)
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<p>Simulated filter, noisy response, and matched-filter output for: (<b>a</b>–<b>c</b>) single-frequency response of a single reflector; (<b>d</b>–<b>f</b>) single-frequency response of two reflectors; (<b>g</b>–<b>i</b>) Linear frequency modulation (LFM) response of single reflector; LFM response of two reflectors.</p>
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<p>Schematic models of (<b>a</b>) an opaque sample with blind holes of various depths and (<b>b</b>) a turbid sample with absorber at various depths [<a href="#B21-applsci-08-00581" class="html-bibr">21</a>].</p>
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<p>Simulated (<b>a</b>) spectral responses and (<b>b</b>) Thermal-wave radar (TWR) subtractive surface temperature evolution over blind holes of different thicknesses; (<b>c</b>) simulated TWR outputs for 100 µm thick and semi-infinite steel samples; (<b>d</b>) variation of TWR peak delay time with opaque sample thickness in normal and subtraction modes; (<b>e</b>) effect of TWR excitation waveform parameters on detectability of defects; (<b>f</b>) variation of TWR peak delay time with absorber depth in turbid medium [<a href="#B21-applsci-08-00581" class="html-bibr">21</a>].</p>
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<p>Schematic presentation of TWR experimental setups using (<b>a</b>) a single detector [<a href="#B23-applsci-08-00581" class="html-bibr">23</a>] and (<b>b</b>) an infrared camera [<a href="#B23-applsci-08-00581" class="html-bibr">23</a>], (<b>c</b>) signal processing block diagram of TWR [<a href="#B23-applsci-08-00581" class="html-bibr">23</a>].</p>
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<p>Experimental results obtained using (<b>a</b>,<b>b</b>) single-frequency photothermal radiometry and (<b>c</b>,<b>d</b>) TWR in an opaque sample with blind holes. Remaining thickness of holes are depicted in the insert; (<b>e</b>) experimental TWR responses from glass slides of various thicknesses painted on the black on the front or back surface [<a href="#B21-applsci-08-00581" class="html-bibr">21</a>].</p>
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<p>TWR imaging of transparent sample with iso-depth absorbers of different absorption coefficients using (<b>a</b>) Cross-correlation (CC) amplitude (unit: a.u.); (<b>b</b>) CC peak delay time (unit: millisecond); (<b>c</b>) CC phase (unit: degree) and their mean horizontal profiles; (<b>d</b>–<b>f</b>), respectively. Chirp parameters: 0.01–1 Hz in 6 s.</p>
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<p>Schematic procedure of Binary Phase Coding (BPC) signal construction in time (<b>top</b>) and frequency (<b>bottom</b>) domains for a 7-bit code and 5 Hz carrier [<a href="#B22-applsci-08-00581" class="html-bibr">22</a>].</p>
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<p>Theoretical CC (<b>a</b>) phase; (<b>b</b>) peak delay time (left and bottom axis) and amplitude (right and top axis) curves as a function of the subsurface absorber depth using properties of dental enamel: <span class="html-italic">μ<sub>a</sub></span> = 100 [m<sup>−1</sup>], <span class="html-italic">μ<sub>s</sub></span> = 6000 [m<sup>−1</sup>], <span class="html-italic">g</span> = 0.96, <span class="html-italic">r</span> = 0.65, <span class="html-italic">k</span> = 0.9 [Wm<sup>−1</sup>k<sup>−1</sup>], <span class="html-italic">α</span> = 5 × 10<sup>−7</sup> [m<sup>2</sup>s<sup>−1</sup>]. The numbers accompanying the curves determine the carrier frequency according to the inset of part (<b>a</b>) [<a href="#B22-applsci-08-00581" class="html-bibr">22</a>].</p>
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<p>(<b>a</b>) cross section of step wedge light-absorbing inclusion in a turbid medium; (<b>b</b>) conventional lock-in thermography (LIT) phase (unit: degree); (<b>c</b>) BPC peak delay time (unit: millisecond); and (<b>d</b>) BPC phase images (unit: degree) of the step wedge sample using thermal coherence tomography (TCT) (16-bit code). The curve in each image shows the mean horizontal profile of the corresponding contrast parameter; (<b>e</b>) pictures of the interrogated surface and cross section of a goat bone; TCT phase images of goat bone at (<b>f</b>) 10 Hz and (<b>g</b>) 1 Hz carrier frequencies [<a href="#B22-applsci-08-00581" class="html-bibr">22</a>].</p>
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<p>Simulated (<b>a</b>) TWR (0.1 Hz–4.9 Hz, 6.4 s) and (<b>b</b>) TCT (2.5 Hz, 16-bit coding) responses of turbid sample containing absorbers at several depths. Comparison of simulated phase responses using LIT, TWR, and TCT for absorbers in turbid medium. Turbid medium properties: <span class="html-italic">μ<sub>a</sub></span> = 100 [m<sup>−1</sup>], <span class="html-italic">μ<sub>s</sub></span> = 6000 [m<sup>−1</sup>], <span class="html-italic">g</span> = 0.96, <span class="html-italic">r</span> = 0.65, <span class="html-italic">k</span> = 0.9 [Wm<sup>−1</sup>k<sup>−1</sup>], <span class="html-italic">α</span> = 5 × 10<sup>−7</sup> [m<sup>2</sup>s<sup>−1</sup>].</p>
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<p>(<b>a</b>) teeth matrix with hidden inter-proximal early caries. The rectangle shows the imaged area; (<b>b</b>) conventional LIT and (<b>c</b>) BPC phase images; TCT images obtained at (<b>d</b>) 2.7ms; (<b>e</b>) 29.7 ms; and (<b>f</b>) 45.9 ms delay times. The white color depicts the pixels coherent to the delayed matched filter. The arrow indicates the hidden interproximal caries [<a href="#B22-applsci-08-00581" class="html-bibr">22</a>].</p>
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Article
Photothermal Radiometry for Skin Research
by Perry Xiao
Cosmetics 2016, 3(1), 10; https://doi.org/10.3390/cosmetics3010010 - 29 Feb 2016
Cited by 16 | Viewed by 6600
Abstract
Photothermal radiometry is an infrared remote sensing technique that has been used for skin and skin appendages research, in the areas of skin hydration, hydration gradient, skin hydration depth profiling, skin thickness measurements, skin pigmentation measurements, effect of topically applied substances, transdermal drug [...] Read more.
Photothermal radiometry is an infrared remote sensing technique that has been used for skin and skin appendages research, in the areas of skin hydration, hydration gradient, skin hydration depth profiling, skin thickness measurements, skin pigmentation measurements, effect of topically applied substances, transdermal drug delivery, moisture content of bio-materials, membrane permeation, and nail and hair measurements. Compared with other technologies, photothermal radiometry has the advantages of non-contact, non-destructive, quick to make a measurement (a few seconds), and being spectroscopic in nature. It is also colour blind, and can work on any arbitrary sample surfaces. It has a unique depth profiling capability on a sample surface (typically the top 20 µm), which makes it particularly suitable for skin measurements. In this paper, we present a review of the photothermal radiometry work carried out in our research group. We will first introduce the theoretical background, then illustrate its applications with experimental results. Full article
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Figure 1

Figure 1
<p>The schematic diagram for OTTER hydration measurements (<b>a</b>) and skin pigments measurements (<b>b</b>).</p>
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<p>The OTTER signal and its average radiation depth, left <span class="html-italic">Y</span> axis is the normalized signal intensity, right <span class="html-italic">Y</span> axis is the mean detection depth, and <span class="html-italic">X</span> axis is normalized time [<a href="#B10-cosmetics-03-00010" class="html-bibr">10</a>].</p>
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<p>The OTTER signals of different types of skin (<b>a</b>); different position on forearm (<b>b</b>); hot water effect (<b>c</b>); and bruised skin (<b>d</b>) [<a href="#B10-cosmetics-03-00010" class="html-bibr">10</a>].</p>
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<p>The typical OTTER signals of different skin sites, including hair and nail (<b>a</b>), and the corresponding water concentration depth profiles using SLS fitting technique (<b>b</b>).</p>
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<p>The 3D water concentration depth profiles along the volar forearm.</p>
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<p>The OTTER signals of DMSO penetrating through skin (<b>a</b>) and the corresponding DMSO concentration depth profiles using SLS fitting technique after different tape stripping (<b>b</b>).</p>
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<p>The OTTER signals of <span class="html-italic">in vivo</span> human fingernail (<b>a</b>) and corresponding water concentration depth profiles using SLS fitting technique before and after a 15-min soaking (<b>b</b>).</p>
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<p>Solvents penetration through <span class="html-italic">in vivo</span> human fingernail, (<b>a</b>) decanol, (<b>b</b>) glyceral, and (<b>c</b>) butyl acetate. For each solvent, the left plot shows the solvent concentration depth profiles and the right plot shows the corresponding solvent concentration in nail at 5 min, 8 min and 10 min after the time of solvent application. Reproduced with permission from [<a href="#B24-cosmetics-03-00010" class="html-bibr">24</a>], published by Elsevier B.V., 2011.</p>
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<p>Solvents penetration through <span class="html-italic">in vivo</span> human fingernail, (<b>a</b>) decanol, (<b>b</b>) glyceral, and (<b>c</b>) butyl acetate. For each solvent, the left plot shows the solvent concentration depth profiles and the right plot shows the corresponding solvent concentration in nail at 5 min, 8 min and 10 min after the time of solvent application. Reproduced with permission from [<a href="#B24-cosmetics-03-00010" class="html-bibr">24</a>], published by Elsevier B.V., 2011.</p>
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<p>OTTER signals of <span class="html-italic">ex vivo</span> human hair (<b>a</b>), and corresponding water concentration depth profiles in hair before and after soaking (<b>b</b>).</p>
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