This work was supported by the National Natural Science Foundation of China (Grant Nos. 61722306 and 61473137) and National First-class Discipline Program of Light Industry Technology and Engineering (LITE2018-025).

1. NIR spectrum data acquisition with Fourier transform (FT)-NIR process spectrometer
<ol>
	<li>Install the liquid phase optical fiber probe of the near-infrared spectrometer at the outlet of the polyphenyl ether product. And open the OPUS software on the upper computer connected to the instrument and start to configure the measurement.</li>
	<li>Connecting to spectrometer
	<ol>
		<li>On the Measure menu, select the Optic Setup and Service command, or click the icon from the toolbar.</li>
		<li>On the dialog that opens, click the Optical Bench tab.</li>
		<li>Check whether the spectrometer settings are ok. If yes, close the dialog. If no, continue with step 4.</li>
		<li>From the Configuration drop-down list, select the particular spectrometer type.</li>
		<li>Enter the spectrometer&#8217;s IP address into the Optical Bench URL entry field.</li>
		<li>Click the Connect button.</li>
	</ol>
	</li>
	<li>Setting up measurement parameters
	<ol>
		<li>On the Measure menu, select the Measurement command, or click the icon from the toolbar.</li>
		<li>On the dialog that opens, define the measurement parameters on the different tabs. 
		NOTE: Details on the individual measurement parameters are described in the OPUS Reference Manual.</li>
		<li>Click the Accept &#38; Exit button.</li>
	</ol>
	</li>
	<li>Storing experiment file
	<ol>
		<li>On the Measure menu, select the Advanced Measurement command. Then, click the Advanced tab.</li>
		<li>On the dialog that opens, define the resolution as 4 cm-1.</li>
		<li>Define the number of scans as 16 scans in the Sample/Background Scan Time entry fields.</li>
		<li>Define the path to automatically store the measuring data from 4,000 cm-1-12,500 cm-1.</li>
		<li>Determine the data type for the result spectrum as Absorbance.</li>
		<li>Click the Save button.</li>
		<li>On the dialog that opens, define a name for the experiment file and save this name.</li>
	</ol>
	</li>
	<li>Measuring background spectrum
	<ol>
		<li>On the Measure menu, select the Advanced Measurement command.</li>
		<li>Click the Optic tab.</li>
		<li>On the dialog that opens, click the Aperture setting drop-down list and select the same value used to acquire a sample spectrum.</li>
		<li>Click the Basic tab.</li>
		<li>On the dialog that opens, click the Background Single Channel button.</li>
	</ol>
	</li>
	<li>Measuring sample spectrum
	<ol>
		<li>Place the sample into the optical path of the spectrometer. The way in which this is done depends on the spectrometer configuration.</li>
		<li>On the Measure menu, select the Advanced Measurement command.</li>
		<li>Click the Basic tab.</li>
		<li>On the dialog that opens, define the sample description and sample form in the particular entry field. This information is stored together with the spectrum.</li>
		<li>Click the Sample Single Channel button to start online measurement. And save the NIR spectrum of each scan as OPUS file.</li>
	</ol>
	</li>
	<li>Collect the polyphenylene samples every 6 h and test the o-cresol concentration with liquid chromatography in the laboratory of industry to obtain a chemical reference value. 
	NOTE: Laboratory staff of industry field take each polyphenyl ether sample from the outlet of the liquid phase polyphenyl ether. The o-cresol content in each sample was measured three times by liquid chromatography. Then, the mean value of the results of the three times analysis was taken as the reference value of the o-cresol content to reduce the accidental error.</li>
	<li>Obtain 600 chemical reference values of o-cresol concentration in the laboratory. The calibration range of o-cresol concentration is from 42.1063 mg/1 g polyphenyl ether product to 51.6763 mg/1 g polyphenyl ether product.</li>
	<li>Combine the NIR spectra at the given test times with the chemical reference values of the o-cresol concentration.</li>
	<li>Use the software OPUS to read the original spectral set as shown in Figure 1.
	<ol>
		<li>On the File menu, click the Load File command.</li>
		<li>On the dialog that opens, select the particular spectrum file.</li>
		<li>Click the Open button. The spectrum is displayed in the spectrum window.</li>
	</ol>
	</li>
</ol>
2. NIR spectroscopy data pre-processing
<ol>
	<li>With the spectral preprocessing function in, obtain spectral dataset preprocessed with first-order derivative.
	<ol>
		<li>Open The Unscrambler which is a multivariate data analysis and experimental design software, select the Import command under File. Import the OPUS file as original NIR spectral dataset.</li>
		<li>Select Transform command under Modify. And select the Savitzky Golay Derivatives under Derivatives.</li>
		<li>Define the Samples and Variables as All Samples and All Variables in Scope. And define the number of Smoothing points as 13 and the Derivative as 1st derivative in Parameters.</li>
		<li>Click OK to start the derivative. 
		CAUTION: The increase of smoothness can reduce the sharp fluctuations of the curve, reduce the noise effect but also weaken the characteristics of the curve and make the curve distorted. Therefore, the appropriate smoothness selected according to the observation of the actual fluctuation intensity of the curve and the effect after processing.</li>
	</ol>
	</li>
	<li>Perform vector normalization on the sample spectra to normalize the value of the absorbance.
	<ol>
		<li>Select the Normalization command under Modify.</li>
		<li>Define the Samples and Variables as All Samples and All Variables in Scope.</li>
		<li>Select Vector normalization in the Type.</li>
		<li>Click OK to perform vector normalization.</li>
	</ol>
	</li>
</ol>
3. Establishment of PLSR model
<ol>
	<li>Creation of the NIR spectral data set 
	<ol>
		<li>Open Uncrambler.exe, select Export under File with the Matlab files to export the preprocessed spectral data set into .mat File and to obtain the spectral data set X automatically with 2203 variables.</li>
		<li>Obtain a complete NIR spectral dataset X (a matrix of 600 rows and 2203 columns) and the corresponding chemical reference values Y (a vector of 600 rows) in the form of .mat file for subsequent analysis and modeling.</li>
	</ol>
	</li>
	<li>Selection of the appropriate number of principal components
	<ol>
		<li>Open Matlab and import the .mat file containing the preprocessed near-infrared spectral data into the workspace by dragging the .mat file to the workspace. 
		NOTE: The .mat file stores the near-infrared spectral data X as an independent variable and the o-cresol content of the product as a dependent variable in the form of two matrices.</li>
		<li>Open the programmed .m file in the Editor. Click Open under the Editor option, select the compiled .m file in the file storage directory, and then click Confirm.</li>
		<li>Extract 15 principal components according to the optimization objective of Equation 1 and the OLSR model between the extracted principal components and the predicted values of the o-cresol concentration with the program containing the command plsregress() in Matlab. 
		[XL, YL, XS, YS, BETA, PCTVAR, MSE] = plsregress(X,Y,ncomp,&#8217;CV&#8217;,k); 
		Consult the MATLAB help document to get the usage details and the return value. 
		NOTE: <img alt="Equation 3" src="/files/ftp_upload/59077/59077eq3.jpg" /> Equation 1<img alt="Equation 4" src="/files/ftp_upload/59077/59077eq4.jpg" /> 
		<img alt="Equation 5" src="/files/ftp_upload/59077/59077eq5.jpg" /> , and <img alt="Equation 6" src="/files/ftp_upload/59077/59077eq6.jpg" /> is the ith principal components of the NIR spectral data; 
		<img alt="Equation 7" src="/files/ftp_upload/59077/59077eq7.jpg" /> is the projection of the ith principal components of the NIR spectral data; 
		<img alt="Equation 8" src="/files/ftp_upload/59077/59077eq8.jpg" /> is the Pearson correlation coefficient for the ith principal components and the o-cresol concentration.</li>
		<li>Obtain the <img alt="Equation 1" src="/files/ftp_upload/59077/59077eq1.jpg" /> value of the NIR spectral data and the predicted values for the different principal components using Equation 2. 
		NOTE: <img alt="Equation 9" src="/files/ftp_upload/59077/59077eq9.jpg" /> Equation 2 
		<img alt="Equation 10" src="/files/ftp_upload/59077/59077eq10.jpg" /> is the sum of squares due to error and is defined as <img alt="Equation 11" src="/files/ftp_upload/59077/59077eq11.jpg" /> ; 
		<img alt="Equation 12" src="/files/ftp_upload/59077/59077eq12.jpg" /> is the total sum of squares and is defined as <img alt="Equation 13" src="/files/ftp_upload/59077/59077eq13.jpg" />; 
		<img alt="Equation 14" src="/files/ftp_upload/59077/59077eq14.jpg" /> is the reference value of the o-cresol concentration of test dataset; 
		<img alt="Equation 15" src="/files/ftp_upload/59077/59077eq15.jpg" /> is the predicted value of the o-cresol concentration of test dataset; 
		<img alt="Equation 16" src="/files/ftp_upload/59077/59077eq16.jpg" /> is the mean value of reference value of the o-cresol concentration of test dataset; 
		<img alt="Equation 17" src="/files/ftp_upload/59077/59077eq17.jpg" /> is the number of samples of test dataset.</li>
		<li>Determine the <img alt="Equation 1" src="/files/ftp_upload/59077/59077eq1.jpg" /> values and the trend with increasing number of principal components as shown in Figure 2. Select 10 as the appropriate number of principal components with the <img alt="Equation 1" src="/files/ftp_upload/59077/59077eq1.jpg" /> value of 0.9917. 
		NOTE:<img alt="Equation 18" src="/files/ftp_upload/59077/59077eq18.jpg" /> value is the proportion of the variance in the dependent variable that is predictable by the independent variables. The higher the <img alt="Equation 1" src="/files/ftp_upload/59077/59077eq1.jpg" /> value is, the higher the goodness-of-fit is and vice versa.</li>
	</ol>
	</li>
	<li>Validation of the goodness-of-fit and accuracy of the PLSR model with 10 principal components by using the command plsregress().
	<ol>
		<li>Repeat the modeling process with 10 principal components as steps 3.2.1-3.2.5 with 10 principal components.</li>
		<li>Evaluate the model based on a 10-fold cross-validation using the plots of the percent variance explained in the NIR spectral data, the residuals, and the MSPECV.</li>
		<li>Plot the percent variance explained in NIR spectral data, the residuals, and the MSPECV as Figures 3, 4, and 5.</li>
		<li>Tabulate the evaluation indicators of <img alt="Equation 1" src="/files/ftp_upload/59077/59077eq1.jpg" />,<img alt="Equation 2" src="/files/ftp_upload/59077/59077eq2.jpg" />, and MSPE of 10-fold cross validation for the PLSR model for a quantitative analysis as shown in Table 1. 
		NOTE: The equations of <img alt="Equation 2" src="/files/ftp_upload/59077/59077eq2.jpg" /> and MSPE are shown as Equation 3 and Equation 4. 
		<img alt="Equation 19" src="/files/ftp_upload/59077/59077eq19.jpg" /> Equation 3 
		<img alt="Equation 20" src="/files/ftp_upload/59077/59077eq20.jpg" /> Equation 4 
		<img alt="Equation 21" src="/files/ftp_upload/59077/59077eq21.jpg" /> is the covariance of reference value and predicted value of o-cresol concentration; <img alt="Equation 22" src="/files/ftp_upload/59077/59077eq22.jpg" /> is the standard deviation of reference value of o-cresol concentration; 
		<img alt="Equation 23" src="/files/ftp_upload/59077/59077eq23.jpg" /> is the standard deviation of predicted value of o-cresol concentration.</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

This protocol describes the process of performing the PLSR on the measurement of the o-cresol concentration remaining in the liquid product of polyphenylene ether with NIRS.
The two critical steps in this process are the pre-processing of the original NIR spectral data and the variables selection of the high-dimensional NIR spectral data.
Generally, the non-systematic background interference leads to the non-systematic scattering deviation or baseline drift of NIR s...

Near infrared (NIR) spectroscopy (NIRS) has gained wide acceptance as a fast, efficient, non-destructive, and non-polluting modern analytical technology; the method has been used during the past several years for product quality detection and analysis and chemical component measurement in industrial processes. The most essential specialty of the method is its ability to record spectra for solid and liquid samples without any pre-processing, making NIRS especially suitable for the direct and rapid detection and analysis of natural and synthetic products1,2. Unlike traditional sensors that measure process variables (e.g., temperature, pressure, liquid level, etc.) at a macroscopic scale and inevitably suffer the external noise and background interference, NIRS detects the structural information of the chemical composition at microscopic and molecular scales. Thus, essential information can be measured more accurately and effectively than with other methods3,4.
Polyphenyl ether, as one of the engineering plastics, are widely used due to its heat resistance, flame retardant, insulation, electrical properties, dimensional stability, impact resistance, creep resistance, mechanical strength and other properties5. More importantly, it is non-toxic and harmless compared to other engineering plastics. At present, 2,6-xylenol is one of the basic raw materials for the synthesis of polyphenylene ether, and it is usually prepared by catalyzed alkylation of phenol with methanol method6. There are two main products of this preparation method, o-cresol and 2,6-xylenol. After a series of separation and extraction steps, 2,6 xylenol is used to produce polyphenylene ether. However, trace amounts of o-cresol remain in 2,6-xylenol. O-cresol does not participate in the synthesis of polyphenylene ether and will remain in the polyphenylene ether product, resulting in a decrease in product quality or even the substandard. At present, most companies still analyze the compositions of complex organic mixtures such as liquid phase polyphenyl ether products containing impurities (e.g., o-cresol) by physical or chemical separation analysis such as chromatography7,8. The separation principle of chromatography is the use of the mixture of compositions in the fixed phase and the flow phase in the dissolution, analysis, adsorption, desorption or other affinity of the minor differences in the performance. When the two phases move relative to each other, the compositions are separated by the above actions repeatedly in the two phases. Depending on the object, it usually takes a few minutes to a few tens of minutes to complete a complex material separation operation. It can be seen that the measurement efficiency is low.
Nowadays, the measurement of product quality and the advanced control technology based on this analysis for the modern fine process chemical materials industry is the key direction to further improve product quality. In the process industry of polyphenyl ether production, real-time measurement of o-cresol content in polyphenylene ether product is of great development significance. Chromatographic analysis clearly cannot meet the requirements of advanced control technology for real-time measurement of substances and signal feedback. Therefore, we propose the partial least squares regression (PLSR) method to establish a linear model between the NIRS data and the o-cresol concentration, which realize the online measurement of o-cresol content in the liquid polyphenylene ether product of outlet.
The pre-processing for NIRS plays the most important role prior to multivariate statistical modeling. NIRS wavenumbers in the NIR spectrum and the particle sizes of biological samples are comparable, so it is known for unexpected scatter effects that has influence on the recorded sample spectra. By performing appropriate pre-processing methods, these effects are easy to be eliminated largely9. The most commonly used pre-processing techniques in NIRS are categorized as scatter correction and spectral derivative methods. First group of methods includes multiplicative scatter correction, detrending, standard normal variate transformations, and normalization. The spectral derivation methods include the use of the first and second derivatives.
Prior to developing a quantitative regression model, it is important to remove the unsystematic scatter variations from the NIRS data because they have a significant influence on the accuracy of the predictive model, its complexity and parsimony. The selection of a suitable pre-processing method should always depend on the subsequent modeling step. Here, if the NIR spectral dataset does not follow the Lambert-Beer law, then other factors tend to compensate for the non-ideal behavior of the prediction for predicted components. The disadvantage of the existence of such needless factors leads to the increase of model complexity, even most likely, a reduction in the robustness. Thus, the application of spectral derivatives and a conventional normalization to the spectral data is an essential part of the method.
After spectral preprocessing, the NIRS data with a high signal-to-noise ratio and low background interference are obtained. Modern NIRS analysis provides the rapid acquisition of large amounts of absorbance over an appropriate spectral range. The chemical composition of the sample is then predicted by extracting the relevant variables using the information contained in the spectral curve. Generally, NIRS is combined with multivariate analysis techniques for qualitative or quantitative analyses10. A multivariate linear regression (MLR) analysis is commonly used for developing and mining the mathematical relationship between the data and the components in industrial processes and has been widely used in NIRS analysis.
However, there are two fundamental problems when implementing an MLR for preprocessed NIRS data. One problem is the variable redundancy. The high dimensionality of the NIRS data often renders the prediction of a dependent variable unreliable because variables are included that have no correlation with the components. These redundant variables reduce the information efficiency of the spectral data and affect the accuracy of the model. In order to eliminate the variable redundancy, it is essential to develop and maximize the correlation between the NIRS data and the predicted components.
Another problem is the issue of multicollinearity in the NIRS data. One of the important assumptions of multiple linear regression models is that there is no linear relationship between any of the explanatory variables of the regression model. If this linear relationship exists, it is proved that there is multicollinearity in the linear regression model and the assumption is violated. In multiple linear regressions, such as an ordinary least squares regression (OLSR), multiple correlations between the variables affect the parameter estimation, increase the model error, and affect the stability of the model. To eliminate the multilinear correlation between the NIR spectral data, we use variable selection methods that maximize the inherent variability of the samples.
Here, we propose to use the PLSR, which is a generalization of multiple linear regression that has been widely used in the field of NIRS11,12. The PLSR integrates the basic functions of the MLR, canonical correlation analysis (CCA), and principal component analysis (PCA) and combines the forecasting analysis with a non-model data connotation analysis. The PLSR can be divided into two parts. The first part selects the components of the characteristic variables and the predicted components by partial least squares analysis (PLS). PLS maximizes the inherent variability of principal components by making the covariance of the principal components and predicted components as large as possible when extracting the principal components. Next, the OLSR model of o-cresol concentration is established for the principal components selected. PLSR is suitable for the analysis of noisy data with numerous independent variables that are strongly collinear and highly correlated and for the simultaneous modeling of several response variables. Also, PLSR extracts the effective information of the sample spectra, overcomes the problem of multicollinearity, and has the advantages of strong stability and high prediction accuracy13,14.
The following protocol describes the process of using the PLSR model for measuring the o-cresol concentration using NIR spectral data. The reliability and accuracy of the model are evaluated quantitatively by using the determination coefficient (<img alt="Equation 1" src="/files/ftp_upload/59077/59077eq1.jpg" />), the prediction correlation coefficient (<img alt="Equation 2" src="/files/ftp_upload/59077/59077eq2.jpg" />) and the mean square prediction error of cross-validation (MSPECV). Moreover, to intuitively show the advantages of the PLSR, the evaluation indicators are visualized in several plots for a qualitative analysis. Finally, evaluation indicators of an experiment are presented in table format to quantitatively illustrate the reliability and precision of the PLSR model.

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o-cresol concentration online measurement based on near infrared spectroscopy via partial least square regression

Unlike macroscopic process variables, near-infrared spectroscopy provides process information at the molecular level and can significantly improve the prediction of the components in industrial processes. The ability to record spectra for solid and liquid samples without any pretreatment is advantageous and the method is widely used. However, the disadvantages of analyzing high-dimensional near-infrared spectral data include information redundancy and multicollinearity of the spectral data. Thus, we propose to use partial least squares regression method, which has traditionally been used to reduce the data dimensionality and eliminate the collinearity between the original features. We implement the method for predicting the o-cresol concentration during the production of polyphenylene ether. The proposed approach offers the following advantages over component regression prediction methods: 1) partial least squares regression solves the multicollinearity problem of the independent variables and effectively avoids overfitting, which occurs in a regression analysis due to the high correlation between the independent variables; 2) the use of the near-infrared spectra results in high accuracy because it is a non-destructive and non-polluting method to obtain information at microscopic and molecular scales.

The predicted value of o-cresol Impurity in polyphenyl ether products is obtained by PLSR-based near-infrared spectroscopy. Figure 2 and Figure 3 respectively show the reliability of the method in the feature selection stage from the curve of the decision coefficient and the error interpretation percentage increasing with the number of principal components.
Specifically, please note that in the ...

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O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

The protocol describes a method of predicting o-cresol concentration during the production of polyphenylene ether using near-infrared spectroscopy and partial least squares regression. To describe the process more clearly and completely, an example of predicting the o-cresol concentration during the production of polyphenylene is used to clarify the steps.

Unlike macroscopic process variables, near-infrared spectroscopy provides process information at the molecular level and can significantly improve the ...

o-cresol-concentration-online-measurement-based-on-near-infrared

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6.5K Views.  Jiangnan University. The protocol describes a method of predicting o-cresol concentration during the production of polyphenylene ether using near-infrared spectroscopy and partial least squares regression. To describe the process more clearly and completely, an example of predicting the o-cresol concentration during the production of polyphenylene is used to clarify the steps.

Video: O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

High-speed Particle Image Velocimetry Near Surfaces

Multi-dimensional and transient flows play a key role in many areas of science, engineering, and health sciences but are often not well understood. The complex nature of these flows may be studied using particle image velocimetry (PIV), a laser-based imaging technique for optically accessible flows. Though many forms of PIV exist that extend the technique beyond the original planar two-component velocity measurement capabilities, the basic PIV system consists of a light source (laser), a camera, tracer particles, and analysis algorithms. The imaging and recording parameters, the light source, and the algorithms are adjusted to optimize the recording for the flow of interest and obtain valid velocity data. 
Common PIV investigations measure two-component velocities in a plane at a few frames per second. However, recent developments in instrumentation have facilitated high-frame rate (&gt; 1 kHz) measurements capable of resolving transient flows with high temporal resolution. Therefore, high-frame rate measurements have enabled investigations on the evolution of the structure and dynamics of highly transient flows. These investigations play a critical role in understanding the fundamental physics of complex flows.
A detailed description for performing high-resolution, high-speed planar PIV to study a transient flow near the surface of a flat plate is presented here. Details for adjusting the parameter constraints such as image and recording properties, the laser sheet properties, and processing algorithms to adapt PIV for any flow of interest are included.

A procedure for studying transient flows near boundaries using high-resolution, high-speed particle image velocimetry (PIV) is described here. PIV is a non-intrusive measurement technique applicable to any optically accessible flow by optimizing several parameter constraints such as the image and recording properties, the laser sheet properties, and analysis algorithms.

Multi-dimensional and transient flows play a key role in many areas of science, engineering, and health sciences but are often not well understood. The ...

Proton Transfer and Protein Conformation Dynamics in Photosensitive Proteins by Time-resolved Step-scan Fourier-transform Infrared Spectroscopy

Monitoring the dynamics of protonation and protein backbone conformation changes during the function of a protein is an essential step towards understanding its mechanism. Protonation and conformational changes affect the vibration pattern of amino acid side chains and of the peptide bond, respectively, both of which can be probed by infrared (IR) difference spectroscopy. For proteins whose function can be repetitively and reproducibly triggered by light, it is possible to obtain infrared difference spectra with (sub)microsecond resolution over a broad spectral range using the step-scan Fourier transform infrared technique. With ~102-103 repetitions of the photoreaction, the minimum number to complete a scan at reasonable spectral resolution and bandwidth, the noise level in the absorption difference spectra can be as low as ~10-4, sufficient to follow the kinetics of protonation changes from a single amino acid. Lower noise levels can be accomplished by more data averaging and/or mathematical processing. The amount of protein required for optimal results is between 5-100 &#181;g, depending on the sampling technique used. Regarding additional requirements, the protein needs to be first concentrated in a low ionic strength buffer and then dried to form a film. The protein film is hydrated prior to the experiment, either with little droplets of water or under controlled atmospheric humidity. The attained hydration level (g of water / g of protein) is gauged from an IR absorption spectrum. To showcase the technique, we studied the photocycle of the light-driven proton-pump bacteriorhodopsin in its native purple membrane environment, and of the light-gated ion channel channelrhodopsin-2 solubilized in detergent.

Key steps of protein function, in particular backbone conformational changes and proton transfer reactions, often take place in the microsecond to millisecond time scale. These dynamical processes can be studied by time-resolved step-scan Fourier-transform infrared spectroscopy, in particular for proteins whose function is triggered by light.

Monitoring the dynamics of protonation and protein backbone conformation changes during the function of a protein is an essential step towards ...

Characterizing Far-infrared Laser Emissions and the Measurement of Their Frequencies

The generation and subsequent measurement of far-infrared radiation has found numerous applications in high-resolution spectroscopy, radio astronomy, and Terahertz imaging. For about 45 years, the generation of coherent, far-infrared radiation has been accomplished using the optically pumped molecular laser. Once far-infrared laser radiation is detected, the frequencies of these laser emissions are measured using a three-laser heterodyne technique. With this technique, the unknown frequency from the optically pumped molecular laser is mixed with the difference frequency between two stabilized, infrared reference frequencies. These reference frequencies are generated by independent carbon dioxide lasers, each stabilized using the fluorescence signal from an external, low pressure reference cell. The resulting beat between the known and unknown laser frequencies is monitored by a metal-insulator-metal point contact diode detector whose output is observed on a spectrum analyzer. The beat frequency between these laser emissions is subsequently measured and combined with the known reference frequencies to extrapolate the unknown far-infrared laser frequency. The resulting one-sigma fractional uncertainty for laser frequencies measured with this technique is &#177; 5 parts in 107. Accurately determining the frequency of far-infrared laser emissions is critical as they are often used as a reference for other measurements, as in the high-resolution spectroscopic investigations of free radicals using laser magnetic resonance. As part of this investigation, difluoromethane, CH2F2, was used as the far-infrared laser medium. In all, eight far-infrared laser frequencies were measured for the first time with frequencies ranging from 0.359 to 1.273 THz. Three of these laser emissions were discovered during this investigation and are reported with their optimal operating pressure, polarization with respect to the CO2 pump laser, and strength.

We describe the generation of far-infrared radiation using an optically pumped molecular laser along with the measurement of their frequencies with heterodyne techniques. The experimental system and techniques are demonstrated using difluoromethane (CH2F2) as the laser medium whose results include three new laser emissions and eight measured laser frequencies.

The generation and subsequent measurement of far-infrared radiation has found numerous applications in high-resolution spectroscopy, radio astronomy, and ...

Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation

Shape Memory Alloys (SMA) using elastocaloric cooling processes have the potential to be an environmentally friendly alternative to the conventional vapor compression based cooling process. Nickel-Titanium (Ni-Ti) based alloy systems, especially, show large elastocaloric effects. Furthermore, exhibit large latent heats which is a necessary material property for the development of an efficient solid-state based cooling process. A scientific test rig has been designed to investigate these processes and the elastocaloric effects in SMAs. The realized test rig enables independent control of an SMA&#39;s mechanical loading and unloading cycles, as well as conductive heat transfer between SMA cooling elements and a heat source/sink. The test rig is equipped with a comprehensive monitoring system capable of synchronized measurements of mechanical and thermal parameters. In addition to determining the process-dependent mechanical work, the system also enables measurement of thermal caloric aspects of the elastocaloric cooling effect through use of a high-performance infrared camera. This combination is of particular interest, because it allows illustrations of localization and rate effects &#8212; both important for efficient heat transfer from the medium to be cooled.
The work presented describes an experimental method to identify elastocaloric material properties in different materials and sample geometries. Furthermore, the test rig is used to investigate different cooling process variations. The introduced analysis methods enable a differentiated consideration of material, process and related boundary condition influences on the process efficiency. The comparison of the experimental data with the simulation results (of a thermomechanically coupled finite element model) allows for better understanding of the underlying physics of the elastocaloric effect. In addition, the experimental results, as well as the findings based on the simulation results, are used to improve the material properties.

Experimental methods for investigation of solid state cooling processes and characterization of elastocaloric material properties of Shape Memory Alloys (SMA) are presented. A custom-built test rig has been designed for controlling and comprehensive monitoring of elastocaloric cooling processes. Furthermore, it provides a validation platform for thermomechanically coupled modeling approaches.

Shape Memory Alloys (SMA) using elastocaloric cooling processes have the potential to be an environmentally friendly alternative to the conventional vapor ...

The Evolution of Silica Nanoparticle-polyester Coatings on Surfaces Exposed to Sunlight

Corrosion of metallic surfaces is prevalent in the environment and is of great concern in many areas, including the military, transport, aviation, building and food industries, amongst others. Polyester and coatings containing both polyester and silica nanoparticles (SiO2NPs) have been widely used to protect steel substrata from corrosion. In this study, we utilized X-ray photoelectron spectroscopy, attenuated total reflection infrared micro-spectroscopy, water contact angle measurements, optical profiling and atomic force microscopy to provide an insight into how exposure to sunlight can cause changes in the micro- and nanoscale integrity of the coatings. No significant change in surface micro-topography was detected using optical profilometry, however, statistically significant nanoscale changes to the surface were detected using atomic force microscopy. Analysis of the X-ray photoelectron spectroscopy and attenuated total reflection infrared micro-spectroscopy data revealed that degradation of the ester groups had occurred through exposure to ultraviolet light to form COO&#903;, -H2C&#903;, -O&#903;, -CO&#903; radicals. During the degradation process, CO and CO2 were also produced.

Two types of surfaces, polyester-coated steel and polyester coated with a layer of silica nanoparticles, were studied. Both surfaces were exposed to sunlight, which was found to cause substantial changes in the chemistry and nanoscale topography of the surface.

Corrosion of metallic surfaces is prevalent in the environment and is of great concern in many areas, including the military, transport, aviation, ...

Optical Trap Loading of Dielectric Microparticles In Air

We demonstrate a method to trap a selected dielectric microparticle in air using radiation pressure from a single-beam gradient optical trap. Randomly scattered dielectric microparticles adhered to a glass substrate are momentarily detached using ultrasonic vibrations generated by a piezoelectric transducer (PZT). Then, the optical beam focused on a selected particle lifts it up to the optical trap while the vibrationally excited microparticles fall back to the substrate. A particle may be trapped at the nominal focus of the trapping beam or at a position above the focus (referred to here as the levitation position) where gravity provides the restoring force. After the measurement, the trapped particle can be placed at a desired position on the substrate in a controlled manner. 
In this protocol, an experimental procedure for selective optical trap loading in air is outlined. First, the experimental setup is briefly introduced. Second, the design and fabrication of a PZT holder and a sample enclosure are illustrated in detail. The optical trap loading of a selected microparticle is then demonstrated with step-by-step instructions including sample preparation, launching into the trap, and use of electrostatic force to excite particle motion in the trap and measure charge. Finally, we present recorded particle trajectories of Brownian and ballistic motions of a trapped microparticle in air. These trajectories can be used to measure stiffness or to verify optical alignment through time domain and frequency domain analysis. Selective trap loading enables optical tweezers to track a particle and its changes over repeated trap loadings in a reversible manner, thereby enabling studies of particle-surface interaction.

A protocol for launching and stably trapping selected dielectric microparticles in air is presented.

We demonstrate a method to trap a selected dielectric microparticle in air using radiation pressure from a single-beam gradient optical trap. Randomly ...

Generation of Size-controlled Poly (ethylene Glycol) Diacrylate Droplets via Semi-3-Dimensional Flow Focusing Microfluidic Devices

Uniform and size-controllable poly (ethylene glycol) diacrylate (PEGDA) droplets could be produced via the flow focusing process in a microfluidic device. This paper proposes a semi-three-dimensional (semi-3D) flow-focusing microfluidic chip for droplet formation. The polydimethylsiloxane (PDMS) chip was fabricated using the multi-layer soft lithography method. Hexadecane containing surfactant was used as the continuous phase, and PEGDA with the ultraviolet (UV) photo-initiator was the dispersed phase. Surfactants allowed the local surface tension to drop and formed a more cusped tip which promoted breaking into tiny micro-droplets. As the pressure of dispersed phase was constant, the size of droplets became smaller with increasing continuous phase pressure before dispersed phase flow was broken off. As a result, droplets with size variation from 1 &#181;m to 80 &#181;m in diameter could be selectively achieved by changing the pressure ratio in two inlet channels, and the average coefficient of variation was estimated to be below 7%. Furthermore, droplets could turn into micro-beads by UV exposure for photo-polymerization. Conjugating biomolecules on such micro-beads surface have many potential applications in the fields of biology and chemistry.

Generation of Size-controlled Poly ethylene Glycol Diacrylate Droplets via Semi-3-Dimensional Flow Focusing Microfluidic Devices

Here, we present a protocol to illustrate the fabrication processes and verifying experiments of a semi-three-dimensional (semi-3D) flow-focusing microfluidic chip for droplet formation.

Uniform and size-controllable poly (ethylene glycol) diacrylate (PEGDA) droplets could be produced via the flow focusing process in a microfluidic device. ...

A Fabrication and Measurement Method for a Flexible Ferroelectric Element Based on Van Der Waals Heteroepitaxy

Flexible non-volatile memories have received much attention as they are applicable for portable smart electronic device in the future, relying on high-density data storage and low-power consumption capabilities. However, the high-quality oxide based nonvolatile memory on flexible substrates is often constrained by the material characteristics and the inevitable high-temperature fabrication process. In this paper, a protocol is proposed to directly grow an epitaxial yet flexible lead zirconium titanate memory element on muscovite mica. The versatile deposition technique and measurement method enable the fabrication of flexible yet single-crystalline non-volatile memory elements necessary for the next generation of smart devices.

In this paper, we present a protocol to directly grow an epitaxial yet flexible lead zirconium titanate memory element on muscovite mica.

Flexible non-volatile memories have received much attention as they are applicable for portable smart electronic device in the future, relying on ...

Near-Infrared Temperature Measurement Technique for Water Surrounding an Induction-heated Small Magnetic Sphere

A technique to measure the temperature of water and non-turbid aqueous media surrounding an induction-heated small magnetic sphere is presented. This technique utilizes wavelengths of 1150 and 1412 nm, at which the absorption coefficient of water is dependent on temperature. Water or a non-turbid aqueous gel containing a 2.0-mm- or 0.5-mm-diameter magnetic sphere is irradiated with 1150 nm or 1412 nm incident light, as selected using a narrow bandpass filter; additionally, two-dimensional absorbance images, which are the transverse projections of the absorption coefficient, are acquired via a near-infrared camera. When the three-dimensional distributions of temperature can be assumed to be spherically symmetric, they are estimated by applying inverse Abel transforms to the absorbance profiles. The temperatures were observed to consistently change according to time and the induction heating power.

A technique utilizing wavelengths of 1150 and 1412 nm to measure the temperature of water surrounding an induction-heated small magnetic sphere is presented.

A technique to measure the temperature of water and non-turbid aqueous media surrounding an induction-heated small magnetic sphere is presented. This ...

Method Development for Contactless Resonant Cavity Dielectric Spectroscopic Studies of Cellulosic Paper

The current analytical techniques for characterizing printing and graphic arts substrates are largely ex situ and destructive. This limits the amount of data that can be obtained from an individual sample and renders it difficult to produce statistically relevant data for unique and rare materials. Resonant cavity dielectric spectroscopy is a non-destructive, contactless technique which can simultaneously interrogate both sides of a sheeted material and provide measurements which are suitable for statistical interpretations. This offers analysts the ability to quickly discriminate between sheeted materials based on composition and storage history. In this methodology article, we demonstrate how contactless resonant cavity dielectric spectroscopy may be used to differentiate between paper analytes of varying fiber species compositions, to determine the relative age of the paper, and to detect and quantify the amount of post-consumer waste (PCW) recycled fiber content in manufactured office paper.

A protocol for the non-destructive analysis of the fiber content and relative age of paper.

The current analytical techniques for characterizing printing and graphic arts substrates are largely ex situ and destructive. This limits the amount of ...