Research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-20-2-0154. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.

1. Data import
<ol>
	<li>To import data, press the Import Data button. Select the type of spectrum being imported-either excitation or emission.
	<ol>
		<li>Once a spectrum type is chosen, ensure that the MATLAB file explorer will appear. From this window, select the desired file and press Open. Supported file types include .TXT, .CSV, .XLS, and .XLSX. 
		NOTE: Some data processing is performed automatically before plotting the imported data. This includes: detection of x-axis units (wavenumber or wavelength) and conversion to wavenumber, if appropriate; intensity normalization of the highest peak to 1; calculation of the energy quantity, if appropriate; and estimation of the quantum spacing. These values are calculated based on detected data peaks with energy quantity assigned to the wavenumber value of the highest energy peak and quantum spacing based on the averaged distance between vicinal peaks, which necessitates the detection of at least two peaks.</li>
	</ol>
	</li>
	<li>To load any of the pre-packaged sample&#160;spectra, press the button corresponding to the desired spectrum, found under Info | Sample Spectra. Nine sample spectra come pre-packaged with the application.</li>
	<li>To load and plot more than one spectrum at a time, activate the check box Allow multiple data spectra on axes under Settings | General | Figure Settings.</li>
	<li>To select a different loaded spectrum than is currently active, press the Select Spectrum to Fit button and then choose the desired spectrum from the list shown on the newly visible Select Spectrum panel.</li>
</ol>
2. Data processing
NOTE: The user may wish to perform data processing prior to the fitting process. Available processes include:
<ol>
	<li>Selection of a peak to act as a basis for normalization: To select a peak to act as the basis for intensity normalization, press the Select peak for normalization button found under Settings | General. Follow the instructions shown on screen. The default peak for intensity normalization is the highest intensity peak found during import.</li>
	<li>Conversion between x-axis units: To convert the x-axis&#39;s units between wavenumber (cm-1) and wavelength (nm), toggle the slider found under Settings | X-Axis to the desired mode (either Wavenumber or Wavelength). Intensity as well as x-axis units will be adjusted for all loaded spectra, using the equation given above.</li>
	<li>Constraining of x-axis range: To manually constrain the x-axis range, select Manually adjust X-axis and fit limits under Settings | X-Axis. Then, use the revealed controls to specify the x-axis range. By default, the application will automatically expand and contract the x-axis range to fit all loaded data points.</li>
	<li>Alternative E0 calculation methods: To select an alternative calculation method for the energy quantity, select the desired method found under Settings | Fit. The default method is Full FCLSA fit. To change to another method, select the corresponding radial button and follow onscreen instructions19.</li>
</ol>
3. Manual fitting
NOTE: Based on the amount of structure visible in the spectrum, it may be highly advantageous to initialize the fitting parameters with appropriate estimates prior to optimization. This initialization can decrease the time required for optimization and helps to ensure that the values returned by optimization are realistic for the spectrum.
<ol>
	<li>Plot the fit function with its current parameter values by pressing the Plot Fit Function button.</li>
	<li>Using a combination of the coarse and fine adjustment buttons, sliders, and edit fields, adjust parameter values to increase the goodness of fit for loaded data. By default, the coefficient of determination (R2) is displayed in the upper-left-hand corner of the graph. Use this as a quantitative measure of the goodness of fit to guide the choice of parameter values. 
	NOTE: Because the energy quantity (E0) and quantum spacing (&#295;&#969;)&#160;are calculated by the application upon data import, it is recommended that these values be held constant or minimally varied when fitting by-hand.</li>
	<li>By default, this application utilizes&#160;the single mode Franck-Condon Lineshape Analysis formula as it is most relevant for room temperature spectra.&#160;If desired, such as when fitting 77 K spectra, toggle between the single and double mode in Settings | Fit. 
	NOTE: Overparameterization becomes a larger issue when fitting in double mode as opposed to single mode due to the increased number of free-floating parameter values. Broad, structureless emission spectra pose the largest issue to the fitting algorithms and may result in cross-correlation between FCLSA parameters, particularly between &#916;v&#189;&#160;and S. When fitting spectra, it is imperative that the obtained FCLSA parameters are verified to be physically realistic using literature precedence as a guide.</li>
</ol>
4. Optimization
<ol>
	<li>After satisfactory initial parameters are found, further optimization can be performed. To do this, press the blue Optimize Fit button. Optimization will run and replot the fit function with newly optimized parameter values.</li>
	<li>Two optimization options are provided: the least-squares and simplex methods. To switch between these two methods, toggle to the desired method in Settings | Optimization.</li>
	<li>If desired, customize the optimization method using the settings found under Settings | Optimization. 
	NOTE: In order to provide the user with control over the optimization routines, the following customization options are possible for both optimization methods:
	<ol>
		<li>Fix a parameter&#8217;s values: To fix a parameter&#8217;s value during optimization, press the check box in the edit field corresponding to the desired parameter.</li>
		<li>Custom bounding of a parameter during optimization: To reveal the custom bounding options, activate the Allow custom parameter bounding during optimization check box in Settings | Optimization. To specify custom bounds for a parameter&#8217;s value during optimization, use the controls revealed by pressing the Custom bounds button under the edit field corresponding to the desired parameter.</li>
		<li>Custom end triggers for optimization: To adjust the maximum number of iterations, termination tolerance on model value, or termination tolerance on coefficient values, activate the corresponding check box in Settings | Optimization and enter the desired value into the corresponding edit field. 
		NOTE: The following customizations are available only for the Least-Squares optimization:</li>
		<li>Goodness-of-fit statistics: To display goodness-of-fit statistics (degree-of-freedom adjusted coefficient of determination, sum of squares due to error, degrees of freedom in the error, and root mean square error) after optimization is complete, activate the check box found under Settings | Optimization.</li>
		<li>Robust fit options: To activate robust fit options, select the desired menu from the dropdown list under Settings | Optimization. By default, this option is off. If desired, activate Least Absolute Residual or Bisquare Weights fitting, which give lower weight to outlier data points.</li>
		<li>Threshold data weighting: To preferentially weigh data points above a threshold intensity, choose an intensity to act as a threshold and a weight multiplier to apply to all points above that threshold. By default, this option is set to on, and threshold and weight multiplier are set to 0.1 and 1.2, respectively. These options are available in Settings | Data Weighting.</li>
		<li>Extrema data weighting: To preferentially weight data points surrounding local extrema (peaks and valleys), select the number of data points surrounding each extremum to apply the preferential weights to, as well as what the weight multiplier should be for those points. These points are more feature rich as they are directly associated with the fitted parameters. Look for extrema data weighting settings under Settings | Data Weighting which is set to on as a default setting, and the number of points and weight multiplier are set to 5 and 5, respectively. To visually identify which data points are used in the preferential weighting, select Fill salient data points.</li>
	</ol>
	</li>
</ol>
5. Chromaticity and free energy calculations
<ol>
	<li>Ensure that the optimized fit of the data and the associated parameter values are satisfactory before proceeding to additional calculations. To perform these calculations, press the Calculate button, located at the bottom of the Calculations pane. 
	NOTE: The first value returned, labeled &#916;GES (cm-1), is the free energy of the excited state calculated using the equation shown below. The default unit for this value is inverse centimeters (cm-1), but units of electronvolt (eV) and joule (J) are also available. The calculation for free energy of the excited state is given by the equation 
	<img alt="Equation 6" src="/files/ftp_upload/62425/62425eq06.jpg" />
	<ol>
		<li>To change the unit, select the desired option from the dropdown list box found under Settings | Calculations. This value is determined based on the energy quantity (E0), full width at half-max (&#916;v&#189;), Boltzmann constant (kB), and environmental temperature of the experiment (T). The value for experimental temperature is assumed to be 298 K but can be specified as 77 K or any other temperature20.
		<ol>
			<li>To change the experimental temperature, choose the desired option under Settings | Calculations | Experimental Temperature. 
			NOTE: The second value returned is the CIE chromaticity coordinate, calculated from the currently selected spectrum. When plotted on a chromaticity diagram, this value shows the predicted color of the active data spectrum.</li>
		</ol>
		</li>
		<li>To display the chromaticity diagram with coordinate plotted, press the pop-out button (represented by a square with an arrow pointing towards the upper-right-hand corner) next to the Chromaticity Coordinate text box.</li>
		<li>To check for the predicted color of the sample, use the third calculation, shown as a colored rectangle. This estimation is based off the same calculation that yielded the chromaticity coordinate. By default, CIE Standard Illuminant D65 is used to make this prediction. To change the illuminant, select the desired option from the dropdown menu labeled White Point in Settings | Calculations.</li>
	</ol>
	</li>
	<li>To calculate CIE chromaticity coordinates and color values for multiple loaded spectra simultaneously, activate the corresponding checkbox found under Settings | Calculations. 
	NOTE: This setting is on by default. Once a second spectrum is plotted, the icon on the pop-out button next to the Chromaticity Coordinate label will change from the square with an arrow pointing to the upper-right-hand corner to three dots (&#183; &#183; &#183;).
	<ol>
		<li>Press the &#183; &#183; &#183; button to reveal a panel labeled Select Spectra. Select the desired spectra from this panel and choose to Export Values as a table and/or Display Diagram to reveal the chromaticity diagram with all coordinates plotted and labeled.</li>
	</ol>
	</li>
</ol>
6. Data export
<ol>
	<li>Once again, ensure that the fit of loaded data is satisfactory, and that all desired calculations have been performed. To export both loaded and calculated data, press the Export Data button. There are six data export options: Figure, Parameter Values, Spectrum Data Points, Fit Data Points, Color Values, and Chromaticity Diagram.
	<ol>
		<li>To export the displayed plot as a figure pre-formatted for publication or presentation, select Figure. This formatting can be disabled under Settings | General | Figure Settings. Supported file types include .EPS (vector-graphics file), .JPG, .PNG, and .PDF.</li>
		<li>To export all parameter values&#8212;with or without calculated values&#8212;as a table, select Parameter values. Inclusion of calculated values is toggleable under Settings | Calculations and supported file types are .CSV, .TXT, .DAT, .XLS, and .XLSX.</li>
		<li>To export data of the currently selected spectrum as a series of x-y data points, select Spectrum data points. x-values will use either wavenumber (cm-1) or wavelength (nm) units, depending on how the graph is defined via the settings. Supported file types are the same as above in 6.1.2.</li>
		<li>To export the fit as a series of x-y data points, again dependent on the current mode of the axes, select Fit data points. Supported file types are the same as above in 6.1.2.</li>
		<li>To export chromaticity and CIE coordinates as well as the predicted color as an RGB value, if enabled, select Color Values. Supported file types are the same as above in 6.1.2.</li>
		<li>To export the chromaticity diagram with the chromaticity coordinates plotted on it associated with the loaded spectrum, select Chromaticity Diagram. Supported file types are .EPS, .JPG, .PNG, and .PDF.</li>
	</ol>
	</li>
</ol>

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P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dual+catalysis+strategies+in+photochemical+synthesis.">Dual catalysis strategies in photochemical synthesis.</a> Chemical Reviews. 116 (17), 10035-10074 (2016).</li><li>Shon, J. -. H., Teets, T. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Photocatalysis+with+transition+metal+based+photosensitizers.">Photocatalysis with transition metal based photosensitizers.</a> Comments on Inorganic Chemistry. 40 (2), 53-85 (2020).</li><li>Yersin, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Highly Efficient OLEDs with Phosphorescent Materials. , (2008).</li><li>Longhi, E., Cola, L. D., Zysman-Colman, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Iridium(III) Complexes for OLED Application in Iridium(III) in Optoelectronic and Photonics Applications. , 205-274 (2017).</li><li>Thejokalyani, N., Dhoble, S. 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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=Photophysical+properties+of+polypyridyl+carbonyl+complexes+of+rhenium(I).">Photophysical properties of polypyridyl carbonyl complexes of rhenium(I).</a> Journal of the Chemical Society, Dalton Transactions. , 849-858 (1991).</li><li>Johansson, P. G., Zhang, Y., Meyer, G. J., Galoppini, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Homoleptic+"star"+Ru(II)+polypyridyl+complexes:+Shielded+chromophores+to+study+charge-transfer+at+the+sensitizer-TiO2+interface.">Homoleptic "star" Ru(II) polypyridyl complexes: Shielded chromophores to study charge-transfer at the sensitizer-TiO2 interface.</a> Inorganic Chemistry. 52 (14), 7947-7957 (2013).</li><li>Farnum, B. H., Jou, J. J., Meyer, G. 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Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular-engineered+[Ir(Fppy)2(Mepic)]+towards+efficient+blue-emission.">Molecular-engineered [Ir(Fppy)2(Mepic)] towards efficient blue-emission.</a> New Journal of Chemistry. 39 (8), 6367-6376 (2015).</li><li>McClure, L. J., Ford, P. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ligand+macrocycle+effects+on+the+photophysical+properties+of+rhodium(III)+complexes:+a+detailed+investigation+of+cis-+and+trans-dicyano+(1,4,8,11-tetraazacyclotetradecane)+rhodium(III)+and+related+species.">Ligand macrocycle effects on the photophysical properties of rhodium(III) complexes: a detailed investigation of cis- and trans-dicyano (1,4,8,11-tetraazacyclotetradecane) rhodium(III) and related species.</a> The Journal of Physical Chemistry. 96 (16), 6640-6650 (1992).</li><li>Motley, T. C., Troian-Gautier, L., Brennaman, M. K., Meyer, G. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Excited-state+decay+pathways+of+tris(bidentate)+cyclometalated+ruthenium(II)+compounds.">Excited-state decay pathways of tris(bidentate) cyclometalated ruthenium(II) compounds.</a> Inorganic Chemistry. 56 (21), 13579-13592 (2017).</li><li>Dossing, A., Ryu, C. K., Kudo, S., Ford, P. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Competitive+bimolecular+electron-+and+energy-transfer+quenching+of+the+excited+state(s)+of+the+tetranuclear+copper(I)+cluster+Cu4I4py4.+Evidence+for+large+reorganization+energies+in+an+excited-state+electron+transfer.">Competitive bimolecular electron- and energy-transfer quenching of the excited state(s) of the tetranuclear copper(I) cluster Cu4I4py4. Evidence for large reorganization energies in an excited-state electron transfer.</a> Journal of the American Chemical Society. 115 (12), 5132-5137 (1993).</li><li>Ashford, D. L., 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=Controlling+ground+and+excited+state+properties+through+ligand+changes+in+ruthenium+polypyridyl+complexes.">Controlling ground and excited state properties through ligand changes in ruthenium polypyridyl complexes.</a> Inorganic Chemistry. 53 (11), 5637-5646 (2014).</li><li>Least-Squares (Model Fitting) Algorithms. MathWorks Available from: <a target="_blank" href="https://www.mathworks.com/help/optim/ug/least-squares-model-fitting-algorithms.html">https://www.mathworks.com/help/optim/ug/least-squares-model-fitting-algorithms.html</a> (2020)</li><li>Mor&#233;, J. J., Watson, G. 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The authors have nothing to disclose.

This application provides an easy and rapid analysis of emission spectra through two main methods commonly used in the photophysical community. The first is Franck-Condon Lineshape Analysis (FCLSA), which gives insight into the energetics and vibronic coupling associated with decay of excited state molecules back to their ground states. This is achieved by optimizing parameter values to maximize the goodness of fit of a spectrum using one of two possible FCLSA modelling equations. The second method of analysis provides insight into the observed color of the light emitted from the molecule. By combining tristimulus color curves with provided intensity data, the CIE coordinate can be calculated. This determination allows for the highly accurate color prediction of both absorption and emission spectra.
Experimental photoluminescence spectra are commonly measured using a photomultiplier tube (PMT) or charge coupled device (CCD) as a detector and plotted as emission intensity versus wavelength (nm). Many photophysical characterizations, including FCLSA and calculation of the free energy of the excited state, are performed in wavenumber space, as demonstrated by the use of <img alt="Equation 7" src="/files/ftp_upload/62425/62425eq07.jpg" /> (cm-1) in the corresponding equations above. In addition to the x-axis conversion, the emission intensity as measured versus wavelength, denoted as I(&#955;) must be converted to <img alt="Equation 5" src="/files/ftp_upload/62425/62425eq05.jpg" />. This application automatically identifies the original x-axis units of imported spectral data as either wavelength (nm) or wavenumber (cm-1). By default, the application then converts the spectral data, normalizes the spectrum to unity at the maximum intensity peak, and plots the spectrum as &#34;Normalized <img alt="Equation 5" src="/files/ftp_upload/62425/62425eq05.jpg" /> vs. wavenumber (cm-1)&#34; to indicate that the correct intensity conversion was applied. Although it is recommended that all fitting be performed using wavenumber units, the application can also plot the spectrum as &#34;Normalized I(&#955;) vs. wavelength (nm)&#34; by following the instructions in section 2 above.
There are two optimization algorithms available for use in the application. The default option is damped least-squares, which utilizes the Levenberg-Marquardt algorithm21. Combining a version of gradient descent and the Gauss-Newton algorithm, this algorithm finds local, not necessarily global, minima. While this is a significant limitation, the algorithm offers advantages in its customizability-this method can take into account preferential weighting of data points, perform robust fitting, and display advanced goodness-of-fit statistics22. The alternative method of optimization is derivative-free, powered by the Nelder-Mead simplex algorithm23. This algorithm uses a heuristic method to return a global minimum of the given cost function (in this case, a sum of squared differences between predicted and observed intensities). The simplex method has been used for FCLSA before, though the code implementing it was never published24.
Both the least-squares and simplex optimization methods work best for structured spectra that exhibit narrow, well defined, and symmetric peaks. As spectra become less structured, meaning that they lose symmetry and the peaks broaden, these methods lead to less robust fits where parameters can become highly correlated. Typically, spectra recorded at low temperatures or in rigid media are more structured compared to those obtained near room temperature or in fluid solution12,25,26. The robust fit options included with the least-squares method can help to alleviate this issue. This problem can be significantly diminished if one or more of the parameters are fixed to a constant value during optimization. For instance, IR spectroscopy experiments can be used to determine relevant quantum spacing (&#295;&#969;) values. Alternatively, relevant literature values can be used to set custom bounds for the parameters.
In some instances, the FCLSA fit, and parameters obtained from the optimization routines do not adequately represent the data even when robust fit options or fixed parameters are employed. This is a failure of the fitting algorithms and may be associated with the multiple FCLSA fitting parameters (potential overparameterization) or spectral shape of the data (featureless spectra). In these cases, further improvement of the fits may be obtained using a &#34;by-hand fit&#34; of the data with manipulation of the FCLSA parameters. The adequacy of such fits can be assessed visually and quantified by comparing goodness-of-fit statistics that are automatically included in the plot.
A general routine to follow for an accurate by-hand fit consists of the following five steps: First, determine an initial estimate for E0&#160;manually or automatically using one of the three methods provided. By default, the parameter&#39;s value is assigned to the wavenumber associated with the highest intensity peak detected upon data import. Alternatively, the user can define E0 as the wavenumber at which the emission spectrum intersects its corresponding excitation spectrum. The final method to determine E0 uses the so-called X% Rule, where X = 1 or 10. In this method, E0 is assigned to a wavenumber X% of the full width at half-maximum (FWHM) intensity of the most prominent data peak assuming a Gaussian band shape. The second step in the by-hand fitting protocol is to calculate &#295;&#969; based on quantum spacing observed in the structure of the emission spectrum. If possible, refer to the IR spectrum of the molecule and try to correlate the photoluminescence-based value to a strong band in the IR spectrum. Third, determine S based on the relative intensities of spectral peaks. Fourth, determine a rough &#916;v&#189;&#160;based on bandwidth. Fifth, iteratively readjust S and&#160;&#916;v&#189;&#160;as necessary.
The difficulty with performing FCLSA using broad, relatively featureless spectra was demonstrated through the fitting procedure for 9,10-diphenylanthracene in fluid solution at 292 K compared to that performed for the more structured spectrum obtained in frozen glass at 77 K. When fitting the room temperature spectrum, optimization returned an initial coefficient of determination of 0.9971 that was improved to 0.9994 through by-hand tuning of the parameters and visual inspection of the results. In contrast, by-hand fitting of the low temperature variant was unnecessary due to the fine structure of the spectrum that resulted in a coefficient of determination equal to 0.9991 after simplex optimization.
In many instances, both optimization routines (least-squares and simplex) return very similar results. This is indicative of them finding a global minimum for the FCLSA parameters. In general, the least-squares method tends to be better suited to data that is noisy, is not well structured, or contains many near-zero data points at the spectrum&#8217;s tails. Conversely, the simplex method tends to return better fits than the least-squares method for data that is well structured and possesses few outlier points. In these cases, the simplex method typically requires little by-hand pre-optimization of parameter values and no adjustment after optimization. For those cases in which the data&#8217;s noise or overall lack of structure prevents a high-quality fit using either of the provided optimization methods, it is recommended that the by-hand fitting method (see above) be employed with no subsequent optimization.
This application offers several advantages over previous implementations of Franck-Condon Lineshape Analysis. The first and most important advantage is that it is free, publicly accessible, and fully transparent. This is accomplished by posting the code to GitHub, providing access to anyone with a computer and internet connection (https://github.com/USArmyResearchLab/ARL_Spectral_Fitting). Not only can anyone access this application, but they can also view the underlying code. This provides an opportunity for community-sourced feedback and development. An additional advantage lies in the ease-of-use of this application. No background knowledge of computer science or command line interaction is required. Rather, this software employs a simple graphical user interface (GUI) that enables researchers of all backgrounds to perform the spectral analyses described above. Furthermore, this application provides the user multiple options for control over the optimization methods and can be used to determine the free energy of the excited state. Finally, the software calculates and reports several useful color values including chromaticity coordinates, CIE coordinates, RGB, and hexadecimal color codes. All of these analyses can be accomplished in seconds, requiring only that the user press a button.

Photoluminescence measurements, comprising both fluorescence and phosphorescence spectra, are widely used throughout various academic fields and industrial applications1. Photocatalysts are increasingly used in organic synthesis for the production of complex and valuable target molecules2,3,4. In order to determine the energetics of photocatalysts, the excited state energy is routinely estimated using emission spectra. The development of novel lighting materials, such as organic light emitting diode (OLED) luminophores, necessitates that the observed color output be characterized and reported5,6. Commission international de l&#39;&#233;clairage (CIE) color coordinates are routinely used for this purpose7.
The purpose of the ARL Spectral Fitting application is to provide a quick and easy method to augment spectral data through meaningful analysis that is widely accessible both in terms of ease-of-use and availability (https://github.com/USArmyResearchLab/ARL_Spectral_Fitting). This software performs several routine spectral processing functions automatically for the user, including data normalization and conversion between wavelength,&#160;&#955;, and wavenumber, <img alt="Equation 1" src="/files/ftp_upload/62425/62425eq01.jpg" />, units with appropriate intensity scaling as shown in the equation below1. The software is capable of handling a variety of input and output file formats. Several advanced analyses are easily performed using the software such as the calculation of CIE and chromaticity coordinates, color prediction, determination of the excited state free energy (&#916;GES) in various units, and FCLSA for the determination of the FCLSA parameters8.
<img alt="Equation 2" src="/files/ftp_upload/62425/62425eq02.jpg" />
A graphical user interface (GUI)-based application was pursued because it allows any researcher to perform this analysis and requires no background knowledge of computer science. This application was written in MATLAB, using its App Designer tool. Outside of ARL Spectral Fitting, finding a publicly-accessible implementation of an application designed to perform Franck-Condon Lineshape Analysis is practically impossible. This is because research groups do not publicly release their implementations, preferring instead to keep them proprietary.
Franck-Condon Lineshape Analysis (FCLSA) is often used in the photophysical characterization of novel compounds because of the rich information it conveys about the molecule9,10,11,12,13,14. Each of the four parameters (six if in double mode) gives information about the excited state of the molecule. The energy quantity, or 0-0 energy gap, (E0) is the difference in zeroth energy levels of the ground and excited states of the molecule. The full width at half maximum (&#916;v&#189;)&#160;informs about the widths of individual vibronic lines. The electron-vibrational coupling constant, or Huang-Rhys factor, (S) is a dimensionless calculation based on the equilibrium displacement between ground and excited states of the molecule15. Finally, the quantum spacing parameter (&#295;&#969;) is the distance between vibrational modes that govern the nonradiative decay of a molecule.
The equations for single and double mode FCLSA are as follows:
<img alt="Equation 3" src="/files/ftp_upload/62425/62425eq03.jpg" /> 
<img alt="Equation 4" src="/files/ftp_upload/62425/62425eq04.jpg" />
where the parameters are as previously defined. In the double mode equation, S and &#295;&#969; are separated into medium (M) and low (L) energy terms. <img alt="Equation 5" src="/files/ftp_upload/62425/62425eq05.jpg" /> is the intensity at the wavenumber v10,16,17,18. In both equations, the summation is performed over N quantum levels with a default value of N = 5, as is commonly used in the literature11, but any integer can be specified in the ARL Spectral Fitting Software under Settings | Fit.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>ARL Spectral Fitting</td>
			<td>Army Research Laboratory</td>
			<td>v1.0</td>
			<td>https://github.com/USArmyResearchLab/ARL_Spectral_Fitting/releases/tag/v1.0</td>
		</tr>
		<tr>
			<td>MATLAB</td>
			<td>MathWorks</td>
			<td>R2020b</td>
			<td>https://www.mathworks.com/products/matlab.html</td>
		</tr>
	</tbody>
</table>

arl spectral fitting as an application to augment spectral data via franck-condon lineshape analysis and color analysis

The ARL Spectral Fitting application provides a free, publicly accessible, and fully transparent method for performing Franck-Condon Lineshape Analysis (FCLSA) on spectral data, in addition to CIE color coordinate determination and basic spectral processing. While some of the features may be found in commercial software or in programs made by&#160;academic research groups, we believe that ARL Spectral Fitting is the only application that possesses all three of the aforementioned attributes.
This program is intended as a standalone, GUI-based application for use by an average laboratory researcher without requiring any coding knowledge or proprietary software. In addition to the standalone executable hosted on ARL GitHub, the associated MATLAB files are available for use and further development.
FCLSA augments the information found in luminescence spectra, providing meaningful insight into the relationship between the ground and excited states of a photoluminescent species. This insight is achieved by modeling spectra with two versions (modes) of an equation that are characterized by either four or six parameters, depending on which mode is used. Once optimized, the value of each of these parameters can be used to gain insight into the molecule, as well as to perform further analysis (for example, the free energy content of the excited-state molecule). This application provides tools for easy by-hand fitting of imported data, as well as two methods for optimizing this fit-damped least-squares fitting, powered by the Levenberg-Marquardt algorithm, and derivative-free fitting utilizing the Nelder-Mead simplex algorithm. Furthermore, estimations of sample color can be performed and reported in CIE and RGB coordinates.

Using the fitting routine described above, Franck-Condon Lineshape Analysis was performed on two spectra that come pre-packaged with the application: the room temperature (292 K) and low temperature (77 K) emission spectra for 9,10-diphenylanthracene dissolved in toluene. Measurements were obtained using a spectrofluorometer with fluid solutions in 1 cm cuvettes and a standard cuvette holder for room temperature measurements. The low temperature measurements were obtained by immersing NMR tubes into liquid nitrogen in a dewar to generate frozen glass samples. All spectra were corrected for the detector response. A single mode fit was sufficient for the room temperature spectrum, while double mode was used to model the low temperature spectrum. Color analysis was performed on both spectra and found to yield similar estimates.
To fit the room temperature spectrum, by-hand adjustment was used after least-squares optimization with default customizations. The final parameter values obtained were as follows: E0 = 24380 cm-1, &#916;v&#189;&#160;= 1200 cm-1, S = 1.25, &#295;&#969; = 1280 cm-1. The resulting coefficient of determination calculated was 0.99947 as shown in Figure 1. Calculation of free energy of the excited state using these parameter values yielded a value of 25,000 cm-1.
Simplex optimization was used to fit the low temperature spectrum. By-hand adjustment was not necessary after optimization. The final parameter values obtained were as follows: E0 = 24764 cm-1, &#916;v&#189; = 746 cm-1, S1 = 1.13, &#295;&#969;1 = 1382 cm-1, S2 = 0.31, &#295;&#969;2 = 651 cm-1. The resulting coefficient of determination calculated was 0.9991 as shown in Figure 2. Calculation of free energy of the excited state using these parameter values yielded a value of 25,700 cm-1.
Color analysis of the low temperature spectrum yielded the following results: chromaticity coordinate = [0.15819, 0.03349], CIE coordinate = [0.19571, 0.041432, 1], and predicted RGB value = [67, 0, 233]. The values obtained for the room temperature spectrum were very similar to that of the low temperature spectrum with unperceivable color differences.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/62425/62425fig01.jpg" /> 
Figure 1: Single mode fit of 9,10-diphenylanthracene (292 K): This figure shows the room temperature emission spectrum of 9,10-diphenylanthracene and its FCLSA fit function, achieved through least-squares optimization followed by by-hand adjustment of parameter values. This is an example of a loosely structured spectrum. <a href="https://www.jove.com/files/ftp_upload/62425/62425fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/62425/62425fig02.jpg" /> 
Figure 2: Double mode fit of 9,10-diphenylanthracene (77 K): This figure shows the low temperature emission spectrum of 9,10-diphenylanthracene and its FCLSA fit function, achieved through a simplex optimization. This is an example of a highly structured spectrum. <a href="https://www.jove.com/files/ftp_upload/62425/62425fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis at JoVE.com

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

This protocol introduces Franck-Condon Lineshape Analyses (FCLSA) of emission spectra and serves as a tutorial for the use of ARL Spectral Fitting software. The open-source software provides an easy and intuitive way to perform advanced analysis of emission spectra including excited state energy calculations, CIE color coordinate determination, and FCLSA.

The ARL Spectral Fitting application provides a free, publicly accessible, and fully transparent method for performing Franck-Condon Lineshape Analysis ...

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2.3K Views.  DEVCOM Army Research Laboratory (ARL). This protocol introduces Franck-Condon Lineshape Analyses (FCLSA) of emission spectra and serves as a tutorial for the use of ARL Spectral Fitting software. The open-source software provides an easy and intuitive way to perform advanced analysis of emission spectra including excited state energy calculations, CIE color coordinate determination, and FCLSA.

Video: ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis

Proton Transfer Reaction (PTR), combined with a Time-of-Flight (ToF) Mass Spectrometer (MS) is an analytical approach based on chemical ionization that belongs to the Direct-Injection Mass Spectrometric (DIMS) technologies. These techniques allow the rapid determination of volatile organic compounds (VOCs), assuring high sensitivity and accuracy. In general, PTR-MS requires neither sample preparation nor sample destruction, allowing real time and non-invasive analysis of samples. PTR-MS are exploited in many fields, from environmental and atmospheric chemistry to medical and biological sciences. More recently, we developed a methodology based on coupling PTR-ToF-MS with an automated sampler and tailored data analysis tools, to increase the degree of automation and, consequently, to enhance the potential of the technique. This approach allowed us to monitor bioprocesses (e.g. enzymatic oxidation, alcoholic fermentation), to screen large sample sets (e.g. different origins, entire germoplasms) and to analyze several experimental modes (e.g. different concentrations of a given ingredient, different intensities of a specific technological parameter) in terms of VOC content. Here, we report the experimental protocols exemplifying different possible applications of our methodology: i.e. the detection of VOCs released during lactic acid fermentation of yogurt (on-line bioprocess monitoring), the monitoring of VOCs associated with different apple cultivars (large-scale screening), and the in vivo study of retronasal VOC release during coffee drinking (nosespace analysis).

Proton Transfer Reaction Time of Flight Mass Spectrometry allows high-sensitivity, rapid and non-invasive analysis of volatile organic compounds. To demonstrate its potential, we give three examples: lactic acid fermentation of yogurt (on-line bioprocess monitoring), different apple genotypes (large-scale screening), and retronasal space after drinking coffee (nosespace analysis).

Proton Transfer Reaction (PTR), combined with a Time-of-Flight (ToF) Mass Spectrometer (MS) is an analytical approach based on chemical ionization that ...

Using Capillary Electrophoresis to Quantify Organic Acids from Plant Tissue: A Test Case Examining Coffea arabica Seeds

Carboxylic acids are organic acids containing one or more terminal carboxyl (COOH) functional groups. Short chain carboxylic acids (SCCAs; carboxylic acids containing three to six carbons), such as malate and citrate, are critical to the proper functioning of many biological systems, where they function in cellular respiration and can serve as indicators of cell health. In foods, organic acid content can have significant impact on taste, with increased SCCA levels resulting in a sour or &#34;acid&#34; taste. Because of this, methods for the rapid analysis of organic acid levels are of particular interest to the food and beverage industries. Unfortunately, however, most methods used for SCCA quantification are dependent on time-consuming protocols requiring the derivatization of samples with hazardous reagents, followed by costly chromatographic and/or mass spectrometric analyses. This method details an alternate method for the detection and quantification of organic acids from plant material and food samples using free zonal capillary electrophoresis (CZE), sometimes simply referred to as capillary electrophoresis (CE). CZE provides a cost-effective method for measuring SCCAs with a low limit of detection (0.005 mg/ml). This article details the extraction and quantification of SCCAs from plant samples. While the method provided focuses on measurement of SCCAs from coffee beans, the method provided can be applied to multiple plant-based food materials.

Using Capillary Electrophoresis to Quantify Organic Acids from Plant Tissue: A Test Case Examining Coffea arabica Seeds

This article presents a method for the detection and quantification of organic acids from plant material using free zonal capillary electrophoresis. An example of the potential application of this method, determining the effects of a secondary fermentation on organic acid levels in coffee seeds, is provided.

Carboxylic acids are organic acids containing one or more terminal carboxyl (COOH) functional groups. Short chain carboxylic acids (SCCAs; carboxylic ...

Electrochemical Impedance Spectroscopy as a Tool for Electrochemical Rate Constant Estimation

Electrochemical impedance spectroscopy (EIS) was used for advanced characterization of organic electroactive compounds along with cyclic voltammetry (CV). In the case of fast reversible electrochemical processes, current is predominantly affected by the rate of diffusion, which is the slowest and limiting stage. EIS is a powerful technique that allows separate analysis of stages of charge transfer that have different AC frequency response. The capability of the method was used to extract the value of charge transfer resistance, which characterizes the rate of charge exchange on the electrode-solution interface. The application of this technique is broad, from biochemistry up to organic electronics. In this work, we are presenting the method of analysis of organic compounds for optoelectronic applications.

Electrochemical impedance spectroscopy (EIS) of species that undergo reversible oxidation or reduction in solution was used for determination of rate constants of oxidation or reduction.

Electrochemical impedance spectroscopy (EIS) was used for advanced characterization of organic electroactive compounds along with cyclic voltammetry (CV). ...

Raman and IR Spectroelectrochemical Methods as Tools to Analyze Conjugated Organic Compounds

In the presented work, two spectroelectrochemical techniques are discussed as tools for the analysis of the structural changes occurring in the molecule on the vibrational level of energy. Raman and IR spectroelectrochemistry can be used for advanced characterization of the structural changes in the organic electroactive compounds. Here, the step-by-step analysis by means of Raman and IR spectroelectrochemistry is shown. Raman and IR spectroelectrochemical techniques provide complementary information about structural changes occurring during an electrochemical process, i.e. allows for the investigation of redox processes and their products. The examples of IR and Raman spectroelectrochemical analysis are presented, in which the products of the redox reactions, both in solution and solid state, are identified.

A protocol of step-by-step Raman and IR spectroelectrochemical analysis is presented.

In the presented work, two spectroelectrochemical techniques are discussed as tools for the analysis of the structural changes occurring in the molecule ...

Application of Voltage in Dynamic Light Scattering Particle Size Analysis

Dynamic light scattering (DLS) is a common method for characterizing the size distribution of polymers, proteins, and other nano- and microparticles. Modern instrumentation permits measurement of particle size as a function of time and/or temperature, but currently there is no simple method for performing DLS particle size distribution measurements in the presence of applied voltage. The ability to perform such measurements would be useful in the development of electroactive, stimuli-responsive polymers for applications such as sensing, soft robotics, and energy storage. Here, a technique using applied voltage coupled with DLS and a temperature ramp to observe changes in aggregation and particle size in thermoresponsive polymers with and without electroactive monomers is presented. The changes in aggregation behavior observed in these experiments were only possible through the combined application of voltage and temperature control. To obtain these results, a potentiostat was connected to a modified cuvette in order to apply voltage to a solution. Changes in polymer particle size were monitored using DLS in the presence of constant voltage. Simultaneously, current data were produced, which could be compared with particle size data, to understand the relationship between current and particle behavior. The polymer poly(N-isopropylacrylamide) (pNIPAM) served as a test polymer for this technique, as pNIPAM's response to temperature is well-studied. Changes in the lower-critical solution temperature (LCST) aggregation behavior of pNIPAM and poly(N-isopropylacrylamide)-block-poly(ferrocenylmethyl methacrylate), an electrochemically active block-copolymer, in the presence of applied voltage are observed. Understanding the mechanisms behind such changes will be important when trying to achieve reversible polymer structures in the presence of applied voltage.

Here, a protocol to apply voltage to solution during dynamic light scattering particle size measurements with the intent to explore the effect of voltage and temperature changes on polymer aggregation is presented.

Dynamic light scattering (DLS) is a common method for characterizing the size distribution of polymers, proteins, and other nano- and microparticles. ...

Synthesis of Aptamer-PEI-g-PEG Modified Gold Nanoparticles Loaded with Doxorubicin for Targeted Drug Delivery

Due to drug resistance and toxicity in healthy cells, use of doxorubicin (DOX) has been limited in clinical cancer therapy. This protocol describes the designing of poly(ethylenimine) grafted with polyethylene glycol (PEI-g-PEG) copolymer functionalized gold nanoparticles (AuNPs) with loaded aptamer (AS1411) and DOX through amide reactions. AS1411 is specifically bonded with targeted nucleolin receptors on cancer cells so that DOX targets cancer cells instead of healthy cells. First, PEG is carboxylated, then grafted to branched PEI to obtain a PEI-g-PEG copolymer, which is confirmed by 1H NMR analysis. Next, PEI-g-PEG copolymer coated gold nanoparticles (PEI-g-PEG@AuNPs) are synthesized, and DOX and AS1411 are covalently bonded to AuNPs gradually via amide reactions. The diameter of the prepared AS1411-g-DOX-g-PEI-g-PEG@AuNPs is ~39.9 nm, with a zeta potential of -29.3 mV, indicating that the nanoparticles are stable in water and cell medium. Cell cytotoxicity assays show that the newly designed DOX loaded AuNPs are able to kill cancer cells (A549). This synthesis demonstrates the delicate arrangement of PEI-g-PEG copolymers, aptamers, and DOX on AuNPs that are achieved by sequential amide reactions. Such aptamer-PEI-g-PEG functionalized AuNPs provide a promising platform for targeted drug delivery in cancer therapy.

In this protocol, doxorubicin-loaded AS1411-g-PEI-g-PEG modified gold nanoparticles are synthesized via three-step amide reactions. Then, doxorubicin is loaded and delivered to target cancer cells for cancer therapy.

Due to drug resistance and toxicity in healthy cells, use of doxorubicin (DOX) has been limited in clinical cancer therapy. This protocol describes the ...

Preparation of Nanoparticles for ToF-SIMS and XPS Analysis

Nanoparticles have gained increasing attention in recent years due to their potential and application in different fields including medicine, cosmetics, chemistry, and their potential to enable advanced materials. To effectively understand and regulate the physico-chemical properties and potential adverse effects of nanoparticles, validated measurement procedures for the various properties of nanoparticles need to be developed. While procedures for measuring nanoparticle size and size distribution are already established, standardized methods for analysis of their surface chemistry are not yet in place, although the influence of the surface chemistry on nanoparticle properties is undisputed. In particular, storage and preparation of nanoparticles for surface analysis strongly influences the analytical results from various methods, and in order to obtain consistent results, sample preparation must be both optimized and standardized. In this contribution, we present, in detail, some standard procedures for preparing nanoparticles for surface analytics. In principle, nanoparticles can be deposited on a suitable substrate from suspension or as a powder. Silicon (Si) wafers are commonly used as substrate, however, their cleaning is critical to the process. For sample preparation from suspension, we will discuss drop-casting and spin-coating, where not only the cleanliness of the substrate and purity of the suspension but also its concentration play important roles for the success of the preparation methodology. For nanoparticles with sensitive ligand shells or coatings, deposition as powders is more suitable, although this method requires particular care in fixing the sample.

A number of different procedures for preparing nanoparticles for surface analysis are presented (drop casting, spin coating, deposition from powders, and cryofixation). We discuss the challenges, opportunities, and possible applications of each method, particularly regarding the changes in the surface properties caused by the different preparation methods.

Nanoparticles have gained increasing attention in recent years due to their potential and application in different fields including medicine, cosmetics, ...

Capillary Electrophoresis-based Hydrogen/Deuterium Exchange for Conformational Characterization of Proteins with Top-down Mass Spectrometry

Resolving conformational heterogeneity of multiple protein states that coexist in solution remains one of the main obstacles in the characterization of protein therapeutics and the determination of the conformational transition pathways critical for biological functions, ranging from molecular recognition to enzymatic catalysis. Hydrogen/deuterium exchange (HDX) reaction coupled with top-down mass spectrometric (MS) analysis provides a means to characterize protein higher-order structures and dynamics in a conformer-specific manner. The conformational resolving power of this technique is highly dependent on the efficiencies of separating protein states at the intact protein level and minimizing the residual non-deuterated protic content during the HDX reactions. 
Here we describe a capillary electrophoresis (CE)-based variant of the HDX MS approach that aims to improve the conformational resolution. In this approach, proteins undergo HDX reactions while migrating through a deuterated background electrolyte solution (BGE) during the capillary electrophoretic separation. Different protein states or proteoforms that coexist in solution can be efficiently separated based on their differing charge-to-size ratios. The difference in electrophoretic mobility between proteins and protic solvent molecules minimizes the residual non-deuterated solvent, resulting in a nearly complete deuterating environment during the HDX process. The flow-through microvial CE-MS interface allows efficient electrospray ionization of the eluted protein species following a rapid mixing with the quenching and denaturing modifier solution at the outlet of the sprayer. The online top-down MS analysis measures the global deuteration level of the eluted intact protein species, and subsequently, the deuteration of their gas-phase fragments. This paper demonstrates this approach in differential HDX for systems, including the natural protein variants coexisting in milk.

Presented here is a protocol for a capillary electrophoresis-based hydrogen/deuterium exchange (HDX) approach coupled with top-down mass spectrometry. This approach characterizes the difference in higher-order structures between different protein species, including proteins in different states and different proteoforms, by conducting concurrent differential HDX and electrophoretic separation.

Resolving conformational heterogeneity of multiple protein states that coexist in solution remains one of the main obstacles in the characterization of ...

Decoding Transient Absorption - Processing, Fitting, and Interpreting Data

An Introduction to Processing, Fitting, and Interpreting Transient Absorption Data

Transient absorption (TA) spectroscopy is a powerful time-resolved spectroscopic method used to track the evolution of excited-state processes through changes in the system&#39;s absorption spectrum. Early implementations of TA were confined to specialized laboratories, but the evolution of commercial turn-key systems has made the technique increasingly available to research groups across the world. Modern TA systems are capable of producing large datasets with high energetic and temporal resolution that are rich in photophysical information. However, processing, fitting, and interpreting TA spectra can be challenging due to the large number of excited-state features and instrumental artifacts. Many factors must be carefully considered when collecting, processing, and fitting TA data in order to reduce uncertainty over which model or set of fitting parameters best describes the data. The goal of data preparation and fitting is to reduce as many of these extraneous factors while preserving the data for analysis. In this method, beginners are provided with a protocol for processing and preparing TA data as well as a brief introduction to selected fitting procedures and models, specifically single wavelength fitting and global lifetime analysis. Commentary on a number of commonly encountered data preparation challenges and methods of addressing them is provided, followed by a discussion of the challenges and limitations of these simple fitting methods.

Author Spotlight: Exploring Light-Driven Chemical Reactions and Energy-Harnessing Devices in Photochemical Research

This protocol is a beginner's entryway into processing, fitting, and interpreting transient absorption spectra. The focus of this protocol is the preparation of datasets, and fitting using both single wavelength kinetics and global lifetime analysis. Challenges associated with transient absorption data and its fitting are discussed.

Transient absorption (TA) spectroscopy is a powerful time-resolved spectroscopic method used to track the evolution of excited-state processes through ...