Name: o-cresol concentration online measurement based on near infrared spectroscopy via partial least square regression
Uploaded: 2019-11-08T12:30:00
Description: Watch this Scientific Journal Video about O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression at JoVE.com

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 ...

<ol>
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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.

<table border="0" cellpadding="0" cellspacing="0" style="width:536px;" width="535">
	<tbody>
		<tr height="20">
			<td height="20" style="height:20px;width:319px;">MPA II Multi Purpose FT-NIR Analyzer</td>
			<td style="width:67px;">Bruker</td>
			<td style="width:88px;">1</td>
			<td style="width:63px;"></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Fiber Optic Probes(Liquid phase)</td>
			<td>Bruker</td>
			<td>1</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Liquid chromatography analyzer&#160;</td>
			<td>/</td>
			<td>1</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Laboratory Equipment and Supplies(e.g. test tube, etc.)</td>
			<td>/</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">MATLAB</td>
			<td>MathWork</td>
			<td>1</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">OPUS</td>
			<td>Bruker</td>
			<td>1</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Principal computer</td>
			<td>DELL</td>
			<td>1</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">The Unscrambler</td>
			<td>CAMO</td>
			<td>1</td>
			<td></td>
		</tr>
	</tbody>
</table>

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 ...

Watch this Scientific Journal Video about O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression at JoVE.com

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 ...

The significance of this experimental manuscript lies in the applications of near-infrared spectroscopy and the data mining algorithms to the online monitoring of actual process industry. The biggest advantages of this technology lie in its rapidity. The nondestructive property of its near-infrared detection technology and the practice of partial least squares, or PLS, algorithm.To begin, connect the spectrometer as described in the text protocol. To set up measurement parameters, use the OPUS software. Navigate to the Measure menu, and select the Advanced Measurement command.On the dialog that opens, define the measurement parameters on the different tabs. To store the experiment file, click the Advanced tab. Define the resolution as four inverse centimeters.Define the number of scans as 16 scans in the sample background scan time entry fields. Define the path to automatically store the measuring data from 4, 000 inverse centimeters to 12, 500 inverse centimeters. Determine the data type for the result spectrum as absorbance, and save the experiment file.Now click the Optic tab. Click the aperture setting dropdown list, and select the same value used to acquire a sample spectrum. Then click the Basic tab.Click the Background Single Channel button, and place the sample into the optical path of the spectrometer to measure the sample spectrum. Define the sample description and sample form in the particular entry field. This information is stored together with the spectrum.Now click the Sample Single Channel button to start the online measurement. Save the NIR spectrum of each scan as an OPUS file. Use the software OPUS to read the original spectral set.On the File menu, click the Load File command. On the dialog that opens, select the particular spectrum file. Click the Open button.The spectrum is displayed in the spectrum window. With the spectral pre-processing function in, obtain spectral data set pre-processed with the first-order derivative. First open the Unscrambler, which is a multivariate data analysis and experimental design software.Then select the Import command under File. Import the OPUS file as an original NIR spectral dataset. Select the Transform command under Modify.Then select the Savitzky-Golay derivatives under Derivatives. Define the samples and variables as all samples and all variables in Scope. Also define the number of smoothing points as 13 and the derivative as first derivative in Parameters.Click OK to start the derivative. Perform vector normalization on the sample spectra to normalize the value of the absorbance. Select the normalization command under Modify.Define the samples and variables as all samples and all variables in Scope. Select vector normalization in the type. Click OK to perform vector normalization.To select the appropriate number of principal components, open MATLAB and import the MAT file containing the preprocessed near-infrared spectral data by dragging the MAT file to the work space. 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.Work in MATLAB to extract 15 principal components according to the optimization objective and the OLSR model between the extracted principal components and the predicted values of the O-cresol concentration. Determine the R-squared values and the trend with increasing number of principal components. Select 10 as the appropriate number of principal components with the R-squared value of 0.9917.To validate the goodness of fit and accuracy of the PLSR model, repeat the modeling process with 10 principal components. 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 mean square prediction error of cross validation or MSPECV. Plotted here are the residuals, which refer to the difference between the O-cresol content reference value and the PLSR model estimate.The plotted data shows that PLSR for the measurement of the O-cresol content based on the NIR spectral data has high accuracy. The cross validation mean square error is a measure of the degree of difference between the reference and the predicted O-cresol content. The smaller the value, the better the accuracy of the predictive model describing O-cresol content.Mean square prediction error of cross validation for the O-cresol concentration measurement based on the PLSR decreases as the number of principal components increases. The error reaches an acceptable minimum at 10 principal components. This proves that the PLSR results in high stability for the measurement of O-cresol concentration using NIRS.While attempting this procedure, the most important step is to accurately obtain the reference values of compositions because this is the basis for all the preprocessing and the modeling performed in the later stage. In addition to PLS, some current popular machine learning algorithms such as deep learning and decision tree can be used in this procedure, too. Combined with NIRS detection technology, we believe that the proposed data mining is a meaningful template for application in the process of industrial automation to intelligent transformation in modern industry.

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

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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 ...

In Situ Surface Temperature Measurement in a Conveyor Belt Furnace via Inline Infrared Thermography

Measuring the surface temperature of objects that are processed in conveyor belt furnaces is an important tool in process control and quality assurance. Currently, the surface temperature of objects processed in conveyor belt furnaces is typically measured via thermocouples. However, infrared (IR) thermography presents multiple advantages compared to thermocouple measurements, as it is a contactless, real-time, and spatially resolved method. Here, as a representative proof-of-concept example, an inline thermography system is successfully installed into an IR lamp powered solar firing furnace, which is used for the contact firing process of industrial Si solar cells. This protocol describes how to install an IR camera into a conveyor belt furnace, conduct a customer correction of a factory calibrated IR camera, and perform the evaluation of spatial surface temperature distribution on a target object.

This protocol describes how to install an infrared camera into a conveyor belt furnace, conduct a customer correction of a factory calibrated IR camera, and evaluate the spatial surface temperature distribution of an object of interest. The example objects are industrial silicon solar cells.

Measuring the surface temperature of objects that are processed in conveyor belt furnaces is an important tool in process control and quality assurance. ...

Study of Protein Dynamics via Neutron Spin Echo Spectroscopy

Most human body proteins' activity and functionality are related to configurational changes of entire subdomains within the protein crystal structure. The crystal structures build the basis for any calculation that describes the structure or dynamics of a protein, most of the time with strong geometrical restrictions. However, these restrictions from the crystal structure are not present in the solution. The structure of the proteins in the solution may differ from the crystal due to rearrangements of loops or subdomains on the pico to nanosecond time scale (i.e., the internal protein dynamics time regime). The present work describes how slow motions on timescales of several tens of nanoseconds can be accessed using neutron scattering. In particular, the dynamical characterization of two major human proteins, an intrinsically disordered protein that lacks a well-defined secondary structure and a classical antibody protein, is addressed by neutron spin echo spectroscopy (NSE) combined with a wide range of laboratory characterization methods. Further insights into protein domain dynamics were achieved using mathematical modeling to describe the experimental neutron data and determine the crossover between combined diffusive and internal protein motions. The extraction of the internal dynamic contribution to the intermediate scattering function obtained from NSE, including the timescale of the various movements, allows further vision into the mechanical properties of single proteins and the softness of proteins in their nearly natural environment in the crowded protein solution.

Study of Protein Dynamics via Neutron Spin Echo Spectroscopy

The present protocol describes methods for investigating the structure and dynamics of two model proteins that have an important role in human health. The technique combines bench-top biophysical characterization with neutron spin echo spectroscopy to access the dynamics at time and length scales relevant for protein interdomain motions.

Most human body proteins' activity and functionality are related to configurational changes of entire subdomains within the protein crystal structure. The ...