Name: resolving water, proteins, and lipids from in vivo confocal raman spectra of stratum corneum through a chemometric approach
Uploaded: 2019-09-26T15:00:00
Description: Watch this Scientific Journal Video about Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach at JoVE.com

The authors greatly acknowledge the financial support from the corporate function analytical and personal cleansing care department. We want to express our gratitude to analytical associate directors Ms. Jasmine Wang and Dr. Robb Gardner for their guidance and support and Ms. Li Yang for her help on data collection.

This study was approved by the institutional review committee of Beijing Children&#8217;s Hospital in compliance with the ethical guidelines of the 1975 Declaration of Helsinki. It was conducted according to ICH guidelines for Good Clinical Practice. The study took place from May to July 2015.
1. Collection of in vivo confocal Raman spectra from human subjects with atopic dermatitis
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	<li>Include subjects in compliance with the following criteria.
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		<li>Include subjects between the...

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M., Matts, P. J., Kaczvinsky, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Changes in Stratum Corneum Thickness, Water Gradients and Hydration by Moisturizers. , (2012).</li><li>Pudney, P. D., M&#233;lot, M., Caspers, P. J., Van, D. P. A., Puppels, G. 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M., Matts, P. J., Hadgraft, J., Lane, M. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Influence+of+niacinamide+containing+formulations+on+the+molecular+and+biophysical+properties+of+the+stratum+corneum.">Influence of niacinamide containing formulations on the molecular and biophysical properties of the stratum corneum.</a> International Journal of Pharmaceutics. 441 (1-2), 192-201 (2013).</li><li>Boireau-Adamezyk, E., Baillet-Guffroy, A., Stamatas, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Age-dependent+changes+in+stratum+corneum+barrier+function.">Age-dependent changes in stratum corneum barrier function.</a> Skin Research and Technology. 20 (4), 409-415 (2014).</li><li>Pezzotti, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Raman+spectroscopy+of+human+skin:+looking+for+a+quantitative+algorithm+to+reliably+estimate+human+age.">Raman spectroscopy of human skin: looking for a quantitative algorithm to reliably estimate human age.</a> Journal of Biomedical Optics. 20 (6), 065008 (2015).</li><li>Mlitz, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impact+of+filaggrin+mutations+on+Raman+spectra+and+biophysical+properties+of+the+stratum+corneum+in+mild+to+moderate+atopic+dermatitis.">Impact of filaggrin mutations on Raman spectra and biophysical properties of the stratum corneum in mild to moderate atopic dermatitis.</a> Journal of the European Academy of Dermatology and Venereology. 26 (8), 983-990 (2012).</li><li>Janssens, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Lipid+to+protein+ratio+plays+an+important+role+in+the+skin+barrier+function+in+patients+with+atopic+eczema.">Lipid to protein ratio plays an important role in the skin barrier function in patients with atopic eczema.</a> British Journal of Dermatology. 170 (6), 1248-1255 (2014).</li><li>Faiman, R., Larsson, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assignment+of+the+C+H+stretching+vibrational+frequencies+in+the+Raman+spectra+of+lipids.">Assignment of the C H stretching vibrational frequencies in the Raman spectra of lipids.</a> Journal of Raman Spectroscopy. 4 (4), 387-394 (1976).</li><li>Edwards, H. 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N., de Sterke, J., Hauser, M., von Stetten, O., van der Pol, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Lipid+uptake+and+skin+occlusion+following+topical+application+of+oils+on+adult+and+infant+skin.">Lipid uptake and skin occlusion following topical application of oils on adult and infant skin.</a> Journal of Dermatological Science. 50 (2), 135-142 (2008).</li><li>Choe, C., Lademann, J., Darvin, M. 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J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> In vivo Skin Characterization by Confocal Raman Microspectroscopy. , (2003).</li><li>Jaumot, J., de Juan, A., Tauler, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MCR-ALS+GUI+2.0:+New+features+and+applications.">MCR-ALS GUI 2.0: New features and applications.</a> Chemometrics and Intelligent Laboratory Systems. 140, 1-12 (2015).</li><li>Choe, C., Choe, S., Schleusener, J., Lademann, J., Darvin, M. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modified+normalization+method+in+in+vivo+stratum+corneum+analysis+using+confocal+Raman+microscopy+to+compensate+nonhomogeneous+distribution+of+keratin.">Modified normalization method in in vivo stratum corneum analysis using confocal Raman microscopy to compensate nonhomogeneous distribution of keratin.</a> Journal of Raman Spectroscopy. , (2019).</li><li>Wise, B. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chemometrics+tutorial+for+PLS_Toolbox+and+Solo.">Chemometrics tutorial for PLS_Toolbox and Solo.</a> Eigenvector Research, Inc. 3905, 102-159 (2006).</li></ol>

During the data collection, as described in section 2 and 3 of the protocol, each depth profile was collected in an area with contact between the instrument window and the skin by finding the darker areas from the microscopic images highlighted in the red circles in Figure 2C. Once these areas were located, it was critical to start the depth profile above the skin surface to accurately determine the location of the skin surface for the data analysis procedure. The location o...

In clinical studies, in vivo confocal Raman spectroscopy has shown its unique ability for determining stratum corneum thickness and water content1,2,3,4, and tracking the penetration of active materials topically applied to the skin5,6. As a noninvasive approach, confocal Raman spectroscopy detects molecular signals based on vibrational modes. Thus, labeling is not needed7. In vivo confocal Raman spectroscopy provides chemical information with depth resolution based on the confocal nature of the technique. This depth-dependent information is very useful in studying the effects of skin care products4,8, aging9,10, seasonal changes3, as well as skin barrier function diseases, such as atopic dermatitis11,12. There is a lot of information in the high frequency region of confocal Raman spectroscopy (2,500&#8211;4,000 cm-1), where water produces distinct peaks in the region between 3,250&#8211;3,550 cm-1. However, the Raman peaks of proteins and lipids, which are centered between approximately 2,800&#8211;3,000 cm-1, overlap each other because the signals are mainly produced from methylene (-CH2-) and methyl (-CH3) groups13. This overlapped information presents a technical challenge when obtaining relative amounts of individual molecular species. Peak fitting14,15 and selective peak position12,16 approaches have been used to resolve this challenge. However, it is difficult for these single peak-based methods to extract pure component information because multiple Raman peaks from the same component change simultaneously17. In our recent publication18, an MCR approach was proposed to elucidate the pure component information. Using this approach, three components (water, proteins, and lipids) were extracted from a large in vivo confocal Raman spectroscopic dataset.
The execution of large clinical studies can be demanding on individuals collecting in vivo spectroscopic data. In some cases, spectral acquisition can require operating equipment for many hours in a day and the study can extend up to weeks or months. Under these conditions, spectroscopic data may be generated by equipment operators that lack the technical expertise to identify, exclude, and correct for all sources of spectroscopic artifacts. The resulting data set may contain a small fraction of spectroscopic outliers that need to be identified and excluded from the data prior to analysis. This paper illustrates in detail a chemometric analysis process to &#34;clean up&#34;&#160;a clinical Raman dataset before analyzing the data with MCR. To successfully remove the outliers, the types of outliers and the potential cause for the generation of the outlier spectra need to be identified. Then, a specific approach can be developed to remove the targeted outliers. This requires prior knowledge of the dataset, including a detailed understanding about the data generation process and the study design. In this dataset, the majority of outliers are low signal-to-noise spectra and originate primarily from 1) spectra collected above the skin surface (6,208 out of 30,862), and 2) strong contribution to the spectrum from fluorescent room light (67 out of 30,862). Spectra collected above the skin surface produce a weak Raman response, as the laser focal point approaches the skin surface and is mostly in the instrument window below the skin. Spectra with a strong contribution from fluorescent room light are generated due to either instrument operator error or subject movement, which produces a condition where the confocal Raman collection window is not fully covered by the subject&#8217;s body site. Although these types of spectral artifacts could be identified and remediated during spectral acquisition by a spectroscopic expert at the time of data acquisition, the trained instrument operators used in this study were instructed to collect all data unless a catastrophic failure was observed. The task of identifying and excluding outliers is incorporated into the data analysis protocol. The protocol presented is developed to resolve this challenge. To address the low signal-to-noise spectra above the skin surface, the location of the skin surface needs to be determined first to allow removal of spectra collected above the skin surface. The location of the skin surface is defined as the depth where the Raman laser focal point is half in the skin and half out of the skin as illustrated in Supplemental Figure 1. After removing low signal-to-noise spectra, a principal component analysis (PCA) is implemented to extract the factor dominated by fluorescent room light peaks. These outliers are removed based on the score value of the corresponding factor.
This protocol provides detailed information for how six principal components are determined in the MCR process. This is done through a PCA analysis followed by spectral shape comparison between the loadings for models generated with a different number of principal components. The experimental process for data collection of reference materials as well as the human subjects is also explained in detail.&#160;

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	<tbody>
		<tr height="33">
			<td height="33" style="height:34px;width:319px;">Bovine Serum Albumin</td>
			<td style="width:360px;">Sigma-Aldrich</td>
			<td style="width:136px;"></td>
			<td style="width:240px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:319px;">Cholesterol</td>
			<td style="width:360px;">Sigma-Aldrich</td>
			<td style="width:136px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:319px;">Cholesterol 3-sulfate sodium</td>
			<td style="width:360px;">Sigma-Aldrich</td>
			<td style="width:136px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:319px;">D-Erythro-Dihydrosphingosine</td>
			<td style="width:360px;">Sigma-Aldrich</td>
			<td style="width:136px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:319px;">DI water</td>
			<td style="width:360px;"></td>
			<td style="width:136px;"></td>
			<td>Purified with Milipore(18.2M&#937;)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:319px;">Gen2-SCA skin analyzer</td>
			<td style="width:360px;">River Diagnostics, Rotterdam, The Netherlands</td>
			<td style="width:136px;">Gen2</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:319px;">Matlab 2018b</td>
			<td style="width:360px;">Mathwork</td>
			<td style="width:136px;">2018b</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:319px;">N-behenoyl-D-erythro-sphingosine</td>
			<td style="width:360px;">Avanti Polar Lipids, Inc.</td>
			<td style="width:136px;"></td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:319px;">N-Lignoceroyl-D-erythro-sphinganine(ceramide)</td>
			<td style="width:360px;">Avanti Polar Lipids, Inc.</td>
			<td style="width:136px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:319px;">Oleic Acid</td>
			<td style="width:360px;">Sigma-Aldrich</td>
			<td style="width:136px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:319px;">Palmitic Acid</td>
			<td style="width:360px;">Sigma-Aldrich</td>
			<td style="width:136px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:319px;">Palmitoleic Acid</td>
			<td style="width:360px;">Sigma-Aldrich</td>
			<td style="width:136px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:319px;">PLS_Toolbox version 8.2</td>
			<td style="width:360px;">Eigenvector Research Inc.</td>
			<td align="right" style="width:136px;">8.2</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:319px;">RiverICon</td>
			<td style="width:360px;">River Diagnostics, Rotterdam, The Netherlands</td>
			<td style="width:136px;">version 3.2</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:319px;">Squalene</td>
			<td style="width:360px;">Sigma-Aldrich</td>
			<td style="width:136px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:319px;">Stearic Acid</td>
			<td style="width:360px;">Sigma-Aldrich</td>
			<td style="width:136px;"></td>
			<td></td>
		</tr>
	</tbody>
</table>

resolving water, proteins, and lipids from in vivo confocal raman spectra of stratum corneum through a chemometric approach

Development of this in vivo confocal Raman spectroscopic method enables the direct measurement of water, proteins, and lipids with depth resolution in human subjects. This information is very important for skin-related diseases and characterizing skin care product performance. This protocol illustrates a method for confocal Raman spectra collection and the subsequent analysis of the spectral dataset leveraging chemometrics. The goal of this method is to establish a standard protocol for data collection and provide general guidance for data analysis. Preprocessing (e.g., removal of outlier spectra) is a critical step when processing large datasets from clinical studies. As an example, we provide guidance based on prior knowledge of a dataset to identify the types of outliers and develop specific strategies to remove them. A principal component analysis is performed, and the loading spectra are compared with spectra from reference materials to select the number of components used in the final multivariate curve resolution (MCR) analysis. This approach is successful for extracting meaningful information from a large spectral dataset.

In this clinical study, in vivo confocal Raman spectra were collected from 28 subjects from 4&#8211;18 years old. A total of 30,862 Raman spectra were collected with the data collection protocol mentioned above. This large spectral dataset contains 20% spectral outliers as shown in Figure 4A. The low signal-to-noise outlier spectra were removed after determining the skin surface, followed by the PCA to identify the spectra with room light features. The third factor in this PCA model is i...

Watch this Scientific Journal Video about Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach at JoVE.com

Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach

Here, we present a protocol for collection of confocal Raman spectra from human subjects in clinical studies combined with chemometric approaches for spectral outlier removal and the subsequent extraction of key features.

Development of this in vivo confocal Raman spectroscopic method enables the direct measurement of water, proteins, and lipids with depth resolution in ...

Direct measurement of water, proteins, and lipids with depth resolution in human subjects is very important for skin-related diseases, for the characterization of skincare product performance. This method, along with the subsequent analysis, leverages chemometrics to extract chemical information. The main advantage of this technique is that it allows for the clinical Raman data set to be collected by trained instrument operators that lack technical expertise to identify, exclude, and remediate all sources of spectroscopic artifacts.The resulting data set can then be processed to identify outliers that need to be excluded from the date prior to analysis. During the data analysis, a key challenge is the removal of the outliers, and identification of the number of the key components in the data set. The approach show in this video leverages of prior knowledge of the clinical data set and chemometrics approach to successfully extract the water, protein and lipid with depth resolution.Demonstrating the procedure will be Li Yang, technician from our C&T lab. To begin, have the subject place a marked lesion body site or control site in close contact with the imaging window of the in vivo confocal Raman instrument. Be sure that they cover the whole window to avoid the impact of room light on imaging.Then, open the software and move the focus until a spectrum similar to the one shown here, is seen. Afterwards, move the focus 10 microns away from skin surface. Collect data for 26 steps with a two-micron step size in the frequency region shown here, using an exposure time of one second.Measure eight replicates for each area, lasting up to 15 minutes in total. First, use the command window and MATLAB to change the file extension of the collected data from ric to mat. Then load the mat file to the MATLAB software platform, as shown here.Correct the data set's baseline using the automatic weighted least squares method, by going to the PLS_Workspace window and right-clicking the imported data set, scrolling to Analyze, selecting Other Tools and clicking on Preprocessing. In the window that pops up, click on Show. Then, scroll down the Available Methods toolbar to the Automatic Weighted Leased Squares Baseline filtering, and select Add.Next, click on Ok to set the options and to apply preprocessing to the data. Save this as Spectra_baseline. Next, return to the command window and replace the data using the baseline corrected result.Now, go to the Text Editor and run the program as shown here. This will sum up the values between 2910 and 2965 inverse centimeters to obtain the intensity values under each Raman spectrum from the 26 consecutive steps measurement, and store them in an Excel file. In MATLAB, go to the Workspace and set the path for the data in Depth_save, as shown.Next, use the process described here to interpolate the instrument offset value from 26 to 260 using the linspace function in MATLAB. This process will interpolate the intensity value from 26 to 260 using the spline method, leveraging the newly generated 260 position values. Additionally, it will use the 260 position and intensity values as x and y inputs for the polyfit function respectively, setting the degree value to 20.Then, it will use the output coefficients and the 260 extended position values as the input for polyval, to obtain the final 260 intensity values. Next, it will calculate the mean intensity and find the point in the curve which is closest to the mean intensity. It will also change the depth value according to the skin surface, in the known two-micron step size.Now, run the program. Load the Raman spectra data set after removal of the outer skin spectra into the PLS_Toolbox software, under the MATLAB platform and right-click the data set to choose Analyze and then select PCA. Next, click on Choose Preprocessing.Select Normalize as the preprocessing approach. Then, choose none for the cross-validation. Then, build the model using the three components for the PCA decomposition analysis.Now, remove the cover on the in vivo Ramen instrument collection window, and collect the room light spectra in the high frequency region using the same parameters used for the reference material's data collection. Identify the room light effect factor through comparison with the room light background. Now, review the scores and remove the spectra with the significantly higher corresponding score value than normal.This means removing score values of more than 99.8%of the whole data set, which is 0.16 in this study. Save the resulting calibration X-block data. Finally, go to the PLS_Workspace Browser and edit the new file.Select the Row Labels and go to Hard Delete Excluded to permanently delete the excluded data before re-saving the file. Begin by correcting the Raman spectra baseline using the same just shown. Next, perform the PCA analysis on the preprocessed data set.Plot the eigenvalues in logarithmic scale along with the number of components by clicking on the Choose Component button, and select log(eigenvalues)as the y value. To perform multivariate curve resolution analysis, first use the data selection button to load the data set into the MCR_main software. Manually choose the number of components and set the component number to between three and eight.Then, under the Initial Estimation tab, click on the Pure button. Next, select Concentration and click the Do button. Once the screen refreshes, click the Ok button, and then on Continue to move to the next page.Now, click Continue, and under Implementation, apply fnnls. Then, select six from the drop-down menu for the number of species with non-negativity profiles, and click Continue. On the next page, choose the same parameters and click Continue.To determine the location at the skin surface, the area under the protein's Raman peak was integrated to obtain the depth profile of the protein signal. The skin surface was defined as the location where the intensity value from the interpolated depth profile was closest to the mean intensity. The exact location of the skin surface does not need to coincide with an experimental data point.A total of 30, 862 Raman spectra were collected with the data collection protocol described in this video. This large spectral data set contains 20%spectral outliers. Proper identification and removal of the outlier spectra is important to achieve a proper data set.Here, one can see the contribution from room lights, superimposed on a reference spectra of Roomlight. Principle component analysis was performed on the preprocessed confocal Raman data set, and the eigenvalue, along with the number of factors used, are plotted here. A significant decrease in the eigenvalue was observed for factor nine.This observation suggests investigating models with the number of principle components varying between three and eight factors for inclusion in the multivariate curve resolution model. When attempting this procedure, it is critical to start a depth profile above the skin surface, to accurately determine the location of the skin surface. This methodology enables the investigation into the impact of skincare products on key skin components, including water, proteins and lipids.

Research

JoVE Journal

Chemistry

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry (UPLC-HRMS)

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS

Here we present a workflow to analyze the metabolic profiles for biological samples of interest including; cells, serum, or tissue. The sample is first ...

A Simple and Rapid Protocol for Measuring Neutral Lipids in Algal Cells Using Fluorescence

Algae are considered excellent candidates for renewable fuel sources due to their natural lipid storage capabilities. Robust monitoring of algal ...

An Inverse Analysis Approach to the Characterization of Chemical Transport in Paints

The ability to directly characterize chemical transport and interactions that occur within a material (i.e., subsurface dynamics) is a vital component in ...

Water in Oil Emulsions: A New System for Assembling Water-soluble Chlorophyll-binding Proteins with Hydrophobic Pigments

Chlorophylls (Chls) and bacteriochlorophylls (BChls) are the primary cofactors that carry out photosynthetic light harvesting and electron transport. ...

In Situ Characterization of Hydrated Proteins in Water by SALVI and ToF-SIMS

In Situ Characterization of Hydrated Proteins in Water by SALVI and ToF-SIMS

This work demonstrates in situ characterization of protein biomolecules in the aqueous solution using the System for Analysis at the Liquid Vacuum ...

Preparation of Biopolymer Aerogels Using Green Solvents

Although the first reports on aerogels made by Kistler1 in the 1930s dealt with aerogels from both inorganic oxides (silica and others) and biopolymers ...

Laboratory Production of Biofuels and Biochemicals from a Rapeseed Oil through Catalytic Cracking Conversion

The work is based on a reported study which investigates the processability of canola oil (bio-feed) in the presence of bitumen-derived heavy gas oil ...

A Novel Technique for Raman Analysis of Highly Radioactive Samples Using Any Standard Micro-Raman Spectrometer

A novel approach for the Raman measurement of nuclear materials is reported in this paper. It consists of the enclosure of the radioactive sample in a ...

Observation and Analysis of Blinking Surface-enhanced Raman Scattering

From a single molecule at a silver nanoaggregate junction, blinking surface-enhanced Raman scattering (SERS) is observed. Here, a protocol is presented on ...

Vibrational Spectra of a N719-Chromophore/Titania Interface from Empirical-Potential Molecular-Dynamics Simulation, Solvated by a Room Temperature Ionic Liquid