Method for the in situ Measurement of pH and Alteration Extent for

Subscriber access provided by University of South Dakota

Article

A method for the in-situ measurement of pH and alteration extent for aluminoborosilicate glasses using Raman spectroscopy Benjamin Parruzot, Joseph V Ryan, Amanda M. Lines, Samuel A. Bryan, James Joseph Neeway, Sayandev Chatterjee, Craig D Lukins, and Amanda J. Casella Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b00960 • Publication Date (Web): 10 Sep 2018 Downloaded from http://pubs.acs.org on September 10, 2018

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 28 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry

A method for the in-situ measurement of pH and alteration extent for aluminoborosilicate glasses using Raman spectroscopy Benjamin Parruzot1,*, Joseph V. Ryan1,*, Amanda M. Lines1, Samuel A. Bryan1,*, James J. Neeway1, Sayandev Chatterjee1, Craig D. Lukins1, Amanda J. Casella1

1

Pacific Northwest National Laboratory, Energy and Environment Directorate, Richland, WA 99352 USA

*Corresponding authors: Benjamin Parruzot: Phone: +1 (509) 375-6678; fax: +1 (509) 372-5997; Email address: [email protected] Joseph Ryan: Phone: +1 (509) 372-4809; Email address: [email protected] Samuel Bryan: Phone: +1 (509) 375-5648; Email address: [email protected]

ACS Paragon Plus Environment

–1/28–

Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 2 of 28

Abstract Characterization of long-term processes occurring during alteration of aluminoborosilicate glasses is relevant for natural as well as man-made materials. Static dissolution tests are a common setup for such studies, but the obtained results and related errors are impacted by the frequency and protocol of samplings performed to determine release via solution analysis, e.g. ICP-OES. A non-invasive method was developed to continuously monitor glass alteration based on in-situ Raman spectrometry of the solution contained in the alteration vessel. The alteration of a benchmark glass, the environment assessment (EA) glass, for 7 days at 90 °C showed the pH and boron concentration results obtained from solution monitoring and ICP-OES quantification were similar to the pH and boron results obtained from chemometric modelling of the Raman spectra and within error of previously published results in similar conditions. The errors on altered amounts of glass based on B release were similar for both in-situ Raman and ICP-OES. The new Raman method provides a more detailed picture of real time monitoring of an alteration experiment, with intervals between monitoring times as short as dozens of seconds. The in-situ Raman method also helps to reduce perturbation to experiments caused by the physical sampling of aliquots (including temperature excursions, re-equilibration with atmosphere, volume variation, and potential chemical contamination) by limiting their number and frequency.

Keywords: Raman spectroscopy; In-situ monitoring; Glass corrosion; Chemometric Analysis ACS Paragon Plus Environment

–2/28–

Page 3 of 28 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Silicate glasses are altered upon contact with water: chemical elements are released into solution while new solid phases are formed. Determining the processes involved in this alteration as well as the rates at which they occur is of relevance e.g. for geology1, geo-engineering2, compliance with health regulations3, archaeology4,5, and nuclear waste disposal6,7.

Aluminoborosilicate glasses have been of particular importance in nuclear waste vitrification and have been internationally studied for the past several decades. Three different stages of alteration are generally identified for silicate glasses6: a first phase during which glass dissolution occurs at a high rate (stage I) transitioning to a second phase expected to prevail during long-term alteration during which the glass is altered at a slow “residual” rate (stage II)8,9. In specific conditions, generally at high pH (> 10) and/or high temperature (> 90 °C), a resumption of alteration during which the rate increases again has been observed for certain glass compositions10 (stage III).

Glass alteration rates are generally determined through quantification of elemental concentrations measured in solution aliquots that are sampled at discrete intervals (or by having one or several separate static experiments for each time interval) by traditional solution analysis techniques such as inductively coupled plasma-optical emission spectrometer (ICP-OES). This method allows a precise determination of the mass balance of each element present in the solution and allows to calculate relative elemental partitioning between glass, solution, and secondary products. This allows detailed insight about the processes occurring as the glass alters. During aluminoborosilicate glass alteration, boron is not retained in alteration products and is thus fully released into solution as the glass is altered. This makes boron a good choice for an “alteration tracer”, an element representative of the quantity of altered glass at any given time in the experiment. Such a tracer defines the overall alteration progress and is used to calculate the rate of glass alteration.

However, taking a solution aliquot may cause some perturbations to the system. By opening the alteration vessel for sampling, evaporation of the solution will cause an artificial, albeit slight, increase in concentration of the elements – particularly if the alteration temperature is high, such as in static tests at 90 °C. As the vial is open, the


–3/28–


Page 4 of 28

exposure of the solution to atmospheric gases such as CO2 can also cause the dissolution of these gases and trigger precipitation of specific artificial secondary phases and/or the reduction in solution pH caused by the presence of carbonic acid. Solution aliquots can only be sampled at discrete time points and a limited number of times to avoid excessive changes to experimental conditions (e.g. change of geometric-surface-area-of-glass to volume-ofsolution ratio (SA/Vgeometric) by reduction of the overall solution volume, artificial dilution of the solution if the volume of the aliquots is replenished by an equal volume of water. As an indication, the maximal tolerance of SA/Vgeometric variation due to evaporation prescribed for Product Consistency Tests (PCT) is 5 %11).

In parallel to solution aliquots, pH may be measured in the alteration vessel as another important parameter recorded during alteration experiments. When doing so, a glass electrode is typically used to measure pH directly in the vessel or in the solution aliquots. Glass electrodes typically contain a Cl–containing solution and a counter ion (K+, Ag+…) that are released through the electrode’s membrane during pH measurement and contaminate the solution.

Discrete pH measurement and aliquot sampling for tests at elevated temperatures also generally requires the removal of the alteration vessel from its oven, and by doing so, risks a drop in temperature. This affects the accuracy of pH measurements and impacts the integrity of the experiment itself by risking the precipitation of minerals from dissolved species. Although this risk is generally mitigated by minimizing the time that the vessel is outside of the oven, reducing the need for sampling ongoing experiments is the most direct risk reduction strategy. Thus, solution samples are often taken less frequently with increasing experimental duration, sometimes several months to a year apart for long-term experiments12–14. Because of this, any sudden change in the experiment progress or solution conditions may be hard to detect and precisely quantify. A good example of such a change in conditions is Stage III behavior when a sudden (and presently unpredictable) increase in the alteration rate – sometimes years after the experiment was started – may lead to complete alteration between two sampling dates. Obviously, this makes the exact time of resumption and the accelerated rate difficult to quantify beyond simply bounding the timing and rate between the two sampling times. ACS Paragon Plus Environment

–4/28–

Page 5 of 28 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


The in-situ monitoring technique proposed here is based on Raman spectroscopy and allows the frequent determination of the extent of glass alteration through the quantification of boron concentration in solution. Since the alteration vessel remains unopened in the oven, this addresses most of the issues identified above: evaporation of the solution, external contamination, and temperature variation, which are all eliminated or significantly reduced. The speciation of boron in solution also varies with pH as B(OH)3 predominates at pH at 20 °C < 9.27 (thereafter the notation pH20 °C will be used) whereas B(OH)4– predominates at pH20 °C > 9.27

15

. This allows for

determination of the pH from the relative intensities of the two Raman bands corresponding to each species at ≈ 877 cm–1 and ≈ 745 cm–1 for B(OH)3 and B(OH)4– respectively. This technique was established by George and Brow for borate glass dissolution at room temperature using a µ-Raman spectrometer

16

. In the present work, we

apply the in-situ Raman monitoring technique to borosilicate glasses (Environmental Assesment “EA” glass) using a specifically designed Raman spectrometer and alteration vessels to allow monitoring in enclosed systems at 90 °C. Boron concentration and solution pH were determined through chemometric modeling from the Raman data and compared to data measured by ICP-OES. Chemometric modelling is a form of multivariate analysis used to quantify species under complex solutions conditions such as those observed in the present case where the number of parameters evolving simultaneously (pH and all elements released in solution from the glass).


–5/28–


Page 6 of 28

Experimental section In-situ Raman Monitoring setup Raman measurements were performed with an InPhotonics RS2000 Raman spectrometer containing a thermoelectrically cooled-charged coupled device (CCD) detector operating at – 55 °C; a 670 nm, 150 mW, visible diode laser as the excitation source; and a focused fiber-optic InPhotonics Raman probe operated in a 180°-back reflection mode. The Raman probe is enclosed within a stainless steel sleeve with a quartz optical window allowing direct solution measurement. The laser beam focal point was 5 mm beyond the end of the laser probe quartz window and into the interrogated solution. The measured laser intensity at the sample was typically 50 mW. MoleCue acquisition software (InPhotonics) was used to process all Raman data.

For measurement of solution standards, the Raman probe and sleeve were immersed within the boric acid/borate solution contained in a thermostated titration cup. For each of the solutions used to develop the chemometric model, 10 acquisitions were taken and averaged per sample with an integration time of 20 seconds for each acquisition.

Chemometric model development Data acquisition In total, 105 individual boric acid/borate solutions were prepared and analyzed using Raman spectroscopy to incorporate variations in pH and borate concentrations expected within the EA glass dissolution experiments and to produce a training set for predictive model construction.

Boric acid/borate solutions were prepared to contain 50 to 5000 ppm of boron (4.62 to 462 mmol·L–1, H3BO3 Alfa Aesar, 99.9% purity), with pH90 °C varying from 3.7 to 12.1 (solution ID 1 through 101, Table S1). A standardized solution of sodium hydroxide was added to adjust the pH90 °C using a standardized pH electrode.


–6/28–

Page 7 of 28 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Four sets of ten spectra from the initial and final solutions from DIRaman (experiments in deionized 18.2 MΩ·cm-1 water, DI) and SBRaman glass dissolution experiments (experiments in a solution spiked in boron, SB; see details about these experiments below) were also included within the training set data (solution ID 102 through 105, table S1).

Raw spectral files were collected into a matrix database within a MATLAB environment (version 8.5.0, Mathworks Inc., Natick, MA, USA).

Selection of regions of interest The selection of one or more spectral ranges containing chemical information, as well as the exclusion of spectral ranges that do not contain analyte spectral information, are widely used strategies to improve partial least squares (PLS) regression models17,18. To construct the pH and total boron models, the spectral regions associated with the boric acid (B(OH)3, ν1 peak at 877 cm–1) and borate bands (B(OH)4–, ν1 peak at 745 cm–1) in the Raman fingerprint region (500 to 2000 cm–1) and the water band region (3000 to 3600 cm–1) were independently considered for inclusion within the model. These regions are shown on the TOC figure and on figure S1, together with further details provided in the Supporting Materials.

Data analysis Multivariate analysis of the spectra was performed using commercial software (PLS Toolbox version 8.2.1, Eigenvector Research Inc., Wenatchee, WA, USA). The PLS regression method used in this study was applied to the data matrices to develop a quantitative predictive model for the concentration of each component by correlating the spectral data with concentration data within the database. The PLS method has been used extensively in the field of chemistry18,19 including modeling of spectroscopic data20–23. PLS analysis was performed using the SIMPLS algorithm24 within the PLS Toolbox. The Locally Weighted Regression (LWR) method was used within the PLS Toolbox. The LWR models work by choosing a subset of the calibration data (the "local" calibration samples) to create a "local" model for a given new sample25. The local calibration samples are identified as the ACS Paragon Plus Environment

–7/28–


Page 8 of 28

samples closest to a new sample in the score space of a PCA model. This provides a powerful option to explore data sets that can display non-linear behavior. The quality of the models to predict on new data were assessed by evaluating the regression coefficient (R2) of the fit of prediction and the root mean square error of calibration (RMSEC).

Before chemometric analysis, several pretreatment and transform steps were performed on the spectral data. A first derivative of the spectral data was utilized to reduce baseline offset effects. The derivatives of all the Raman spectra were computed by the Savitsky-Golay method26, using second-order smoothing through a 15-point moving average. This step was followed by mean centering of the samples18. Mean centering subtracts the mean absorbance value from each sample, placing the centroid of the data set at the origin and removing an overall bias from the data set. For systems where a zero point of the measurement scale is arbitrary (e.g. temperature measured in degrees Celsius) or a non-zero intercept is expected (such as pH), mean centering is recommended. In the final analysis, a pretreatment including a first derivative followed by mean centering was used.

Glass alteration experiments Experimental conditions Alteration experiments were performed with Savannah River National Laboratory (SRNL) Environmental Assessment (EA) glass whose synthesis, characterization and composition are detailed in Jantzen et al. 27.

Alteration experiments were performed in static conditions at 90 °C and with a SA/Vgeometric ratio of about 2000 m– 1

. 304 stainless steel reactors specifically designed to accommodate the Raman probe for in-situ measurement were

used (as shown on the Table of contents figure). A control experiment in absence of Raman probe was run in parallel in the same conditions. A 75 to 150 µm (100 to 200 mesh) powder size fraction was prepared by crushing in tungsten carbide jars and sieving. Before use, the powder was washed several times to eliminate fines following the ASTM C1285-14 procedure11. A known mass of solution was first added in the reactor: either ultrapure ASTM type I water (resistivity > 18.0 MΩ·cm) or a 250 ppm boron spiked solution prepared from H3BO3. The 250 ppm ACS Paragon Plus Environment

–8/28–

Page 9 of 28 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


boron solution was used to have an initial concentration of boron in solution that would be quantifiable by in-situ Raman monitoring. After collecting Raman spectra of the solution itself for several hours to obtain its initial pH and boron concentration, the glass powder was added into the reactor and started to release boron in solution from glass alteration. All experimental preparation details are reported in Table 1A. Each Raman spectrum for the dissolution experiments was acquired for 20 seconds.

Solution analysis method At the end of the experiment, the solution was collected for analysis by ICP-OES and the final pH was measured at room temperature using a glass electrode (Accumet accu·pHast® pH combination electrode). The pH was recalculated at 90 °C using the Geochemist’s Workbench software and the concentration of major in elements solution as obtained from ICP-OES analyzes (Al, B, Ca, K, Li, Mg, Na, Si). The measured and recalculated pH values at room temperatures and at 90 °C respectively are reported in Table 1B and Table 1C. The initial pH of the ultrapure water used for experiments DIBlank, DIControl and DIRaman was not measured. The ultrapure water initially used was estimated to be at equilibrium with an atmospheric CO2 fugacity of 3.8 × 10–4.15

Major cations were analyzed quantitatively using a PerkinElmer Optima 8300 dual view inductively coupled plasma-optical emission spectrometer (ICP-OES) and an Elemental Scientific SC4 DX FAST auto-sampler interface. The instrument was calibrated using standards made by the High-Purity Standards Corporation (Charleston, SC, USA) and verified using standards made by Inorganic Ventures. Blanks were also analyzed throughout analysis to ensure background signals and potential carryover effects were not a factor. All calibration verification were required to be within ± 10% of the target concentrations. A 1 ppm Lu, Sc, Te and/or Y solution was added as an on line internal standard to all samples, standards and blanks to demonstrate the stability of the instrument and sample introduction system. All samples and standards were diluted with 2% HNO3 (Fisher Scientific, Optima trace metal grade) and ASTM type I water (resistivity > 18.0 MΩ·cm). When the analyte could not be quantified, the Estimated Quantification Limit (EQL) was reported in Table 1D and corrected for each


–9/28–


Page 10 of 28

sample dilution when applicable. Relative uncertainties on the measured ICP concentrations were considered to be ± 10%.

Calculation of glass alteration parameters From the final solution composition, the normalized mass loss of glass with respect to element i (NL(i), g·m–2) can be calculated for the elements present in the glass using equation (1):

ܰ‫ܮ‬ሺ݅ሻ =

ሾ݅ሿ௙௜௡௔௟ − ሾ݅ሿ௜௡௜௧௜௔௟ ߬௜ × ܵ‫ܣ‬ൗܸ ௚௘௢௠௘௧௥௜௖

(1)

where [i] final (g·m–3) represents the concentration of i released in solution at the end of the experiment, τi is the mass fraction of element i in the glass (see Table 1E, unitless), and SA/Vgeometric is the glass-geometric-surface-areato-solution-volume ratio (m–1). In the case of the 250 ppm spiked boron solution, the initial concentration [i] initial (g·m–3) was subtracted from the final concentration to only account for the amount of boron released during glass alteration. Error on the normalized mass loss was estimated by propagation of errors: the relative error on ICP concentrations was 10%, and the error from the variability of SA/Vgeometric between the different tests was accounted using 2 standard deviations (2σ) of the average SA/Vgeometric for all 4 tests containing glass powder.

Error for the normalized mass losses calculated from the Raman data was treated in a similar way. As mentioned in the data analysis section, the RMSEC and R2 of model calculations are a strong indicator of model performance. These provide details on how well the model measures its own calibration set. SA/Vgeometric error was identical as above. The errors on the concentrations were two-fold: 1/ the dispersion of the values used to quantify [i] final and [i] initial, defined as 2σ of the average; and 2/ the uncertainty from the model that can be expected to exhibit an error equal to the root mean square error of calibration (RMSEC).


–10/28–

Page 11 of 28 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Results and Discussion Calibration of the in-situ Raman system The chemometric model was constructed from the analysis of solutions 1 through 101. As a result, the pH and total boron concentration predicted by the model can be compared to the values measured in solution. An ideal model would result in a Y = X function. The results of the models for predicting total boron concentration and pH based on Raman spectroscopic data are shown in Figure 1A and Figure 1B respectively, with a 95% confidence interval. The linear fit for the predicted vs. measured boron concentration is Y = 0.995 × X + 7.00 (R2 = 0.998), and the fit for the predicted vs measured pH is Y = 0.979 × X + 0.194 (R2 = 0.985). These equations, close to Y = X, demonstrate the ability of the model to predict concentration and pH accurately for [B] total with the note that the models are limited to the boron and pH ranges utilized in the training set, respectively 2.4 to 5000 ppm boron and pH90 °C from 3.7 to 12.1.

The parameters of the PLS models used for predicting solution composition include the number of samples used in the LWR model, the root mean square error of calibration (RMSEC), and the R2 calculated by the model performance. The R2 value can provide a detail on how well the measured results conform to the known values while the RMSEC will provide an anticipated error in measurement (calculated value ± RMSEC). For pH, 50 samples were used in the LWR model giving a RMSEC of 0.247 pH units (R2 = 0.984). For B concentration, 100 samples were used in the LWR giving a RMSEC of 68.5 ppm (R2 = 0.998).

Alteration of EA glass in pure water Measured parameters are reported in Table 1 for experiments DIBlank, DIControl and DIRaman: initial and final pH (Table 1B and C), elemental concentrations measured by ICP-OES (Table 1D) and NL values (Table 1E). Concentration from DIBlank – containing solution only – is used as the background concentration [i] initial for the experiments containing glass. The altered solids retrieved from experiment DIControl were analyzed by powder x-ray diffraction: no crystalline alteration products were observed. ACS Paragon Plus Environment

–11/28–


Page 12 of 28

As usually observed during silicate glass alteration, the pH90 °C increases from slightly acidic, due to carbonation of the water used to start the experiment, to about 9.5 over the course of the 7-day experiment. The measured pH value at room temperature after 7 days of alteration of about 11.9 ± 0.1 is within the uncertainty of the value of 11.85 ± 0.2 previously reported for EA glass in the same experimental conditions27.

The NL values for all elements showing significant release in solution (B, Li, Na, and Si) are within uncertainty of each other for experiments DIControl and DIRaman, thus showing no effect on glass alteration of the addition of the Raman monitoring setup. The absolute NL values for these 4 elements from experiment DIControl are all within uncertainty, although all higher than the ones from previous measurements on EA glass reported by Jantzen et al.27 (recalculated from reported normalized concentrations using a SA/Vgeometric of 1955 m–1, with a 2σ error: NL(B) = 8.54 ± 1.25 g·m–2; NL(Li) = 4.89 ± 0.75 g·m–2; NL(Na) = 6.83 ± 0.92 g·m–2; and NL(Si) = 2.01 ± 0.38 g·m–2). The same observation can be made for experiment DIRaman although in this case NL(B) and NL(Na) are larger but not within uncertainty of the previous measurements on EA glass reported by Jantzen et al.27. This could however be explained by the SA/Vgeometric of DIRaman being the highest of all 4 experiments, thus increasing the surface area of glass initially exposed to solution and the initial release of these low to non-retained elements.

The 650 cm–1 to 900 cm–1 range of the Raman spectra recorded as a function of time for experiment DIRaman are shown in Figure 2A (Figure 2B shows the same data, from an “overhead” view). At initial times prior to EA glass addition (0 to 23 h) there is no signature in this region observed. At the point of EA glass addition (23 h), the borate band at 745 cm–1 (symmetric stretching) associated with the dissolution of the glass is apparent and increases with time for the remainder of the experiment. A band identified as silicate at 770 cm–1 (νsym(Si-O)) appears along with the borate band28,29.

The model results for boron concentration and pH values for DIRaman are shown in Figure 2C and D respectively, together with ICP-OES and pH electrode measured values. For the boron measurement (Figure 2C), the boron value predicted by the model is approximately zero for the first 23 h of the experiment. At the time of EA glass


–12/28–

Page 13 of 28 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


addition (at 23 h), the increase in boron concentration is immediate and continues throughout the experiment. The boron values determined by ICP during the experiment are shown in Figure 2C and match the values predicted by the model.

The initial and final concentrations in the DIRaman experiment were also determined from the modelled boron concentration presented on Figure 2C. The initial concentration was determined from the data acquired before adding the glass into the alteration vessel: it was determined to be 11.9 ± 87.3 ppm (0 to 22.3 h, 39 points, 2σ = 18.8 ppm). The final concentration was determined using all data points within 7.00 ± 0.14 days11: it was determined to be 815.5 ± 82.8 ppm (188.3 to 191.3 h, 6 points, 2σ = 13.6 ppm). These errors include both the dispersion of the averaged values (2σ) and the modelling error (RMSEC equal to 68.5 ppm).

The measured pH (Figure 2D) follows the trend of the amount of Boron released: from a value predicted by the chemometric model of approximately 5.8 from 0 to 23 h, then an immediate increase at the time of EA glass addition that continues throughout the experiment. This increase is fast during the first 80 hours after glass addition, then slows down for the remainder of the monitoring period. The pH value determined by pH electrode measurement at the initial and final stages of this experiment is also shown in Figure 2D and matches the values predicted by the model.

Alteration of EA glass in borate buffer solution Measured parameters are reported in Table 1B through E for experiments SBBlank, SBControl and SBRaman identically as detailed above for DIBlank, DIControl and DIRaman. Here as well, the concentration from the blank experiment containing no glass, SBBlank, was used as [i] initial for experiments SBControl and SBRaman.

Although behavior for experiments SBControl and SBRaman is similar to observations made previously for experiments DIControl and DIRaman with increasing pH and elemental concentrations in solution as the glass is altered, a major difference arises from the buffering effect of the 250 ppm boron solution initially added into the vessel. This solution prevents the pH from raising as much as in the pure water experiments (pH90 °C = 9.1 versus ACS Paragon Plus Environment

–13/28–


Page 14 of 28

9.5 respectively). With the pH remaining lower, the total amount of glass altered is reduced and so are all the normalized mass losses for B, Si, Li and Na (e.g. NL[B, SBControl] ≈ 0.2 × NL[B, DIControl]). The difference between NL(B) for experiments SBControl and SBRaman is small but significant, unlike other low-retention elements such as Na and Li for which NL is identical within uncertainty. For NL(B), this difference could be explained by the initial presence of boron in solution reducing the accuracy of the quantification of released boron during glass alteration. Thus, with this statement and considering Si, Li and Na releases, the absence of influence of the presence of the Raman apparatus on the glass alteration is confirmed, as already observed in the case of alteration in pure water (experiments DIControl and DIRaman).

The Raman spectra recorded as a function of time for SBRaman are shown in Figure 2E (Figure 2F shows the same data from an “overhead” view) for the 650 cm–1 to 900 cm–1 range. Prior to EA glass addition (0 to 70 h) there is a signature observed for B(OH)3 (870 cm–1) consistent with the 250 ppm boron solution at pH90 °C = 5.1 added to the system at the start of the experiment. After EA glass addition (70 h), two events are simultaneously observed: the B(OH)3 band at 870 cm–1 immediately starts to diminish, and the B(OH)4– band at 745 cm–1 associated with the emergence of borate into solution appears and increases with time for the remainder of the experiment. As in the DIRaman, a band identified as silicate at 770 cm–1 also appears along with the borate band. In this experiment, due to the use of a spiked boron solution as the initial solution, the baseline signal is more complex due to the presence of this initial species. Despite this, spectral preprocessing was able to remove the effect and chemometric models were able to accurately measure both boron and pH.

The model results for boron concentration and pH values for SBRaman are shown in Figure 2G and H respectively, together with ICP-OES and pH electrode measured values. The Raman-derived boron value is approximately 275 ppm from 0 to 70 h, and then increases immediately after glass addition at 70 h and throughout the experiment. Similarly to experiment DIRaman, the Raman-derived boron values match the values measured by ICP-OES. The initial Raman-derived concentration was determined to be 290.0 ± 90.6 ppm (0 to 68.8 h, 120 points, 2σ = 22.1 ppm). The final concentration was determined to be 468.7 ± 73.7 ppm (234.3 to 237.2 h, 6 points, 2σ = 5.2 ppm). ACS Paragon Plus Environment

–14/28–

Page 15 of 28 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


These errors include both the dispersion of the averaged values (2σ) and the modelling error (RMSEC, equal to 68.5 ppm).

The Raman-derived pH values follows the same trend, starting at approximately 5 then increasing from glass addition throughout the experiment. This increase occurs at a fast rate during the first 24 hours after glass addition, then slows down throughout the remainder of the duration. These value matches the pH values measured in solution.

Comparison of the in-situ Raman technique to traditional techniques Use of pure water vs. use of a borate buffer solution. Boron is an alteration tracer and thus shows no retention in any of the alteration products. In consequence, addition of exogenous boron in the solution should not interact with any solid product and thus should not impact the glass alteration processes30. Addition of boron in solution did help in quantifying the initial amount of boron via in-situ Raman monitoring with lower signal-to-noise ratios than the initial quantification of boron in pure water. However, the pH increase observed in pure water was limited in the borate solution by buffering effect. As the pH was lower, alteration rates were lower when the borate buffer was used and thus make a more detailed comparison of the pure water experiments to the borate buffer experiments difficult. The amount of buffering impact could be due to the ratio of boron added compared to the release from the glass, so that a system with more glass might be less affected. It may also be possible to adjust the initial concentration of boron in the solution and its initial pH so that the glass dissolution could be less impacted by the solution.

pH, [B], and NL quantification. First, it is important to note that the range of pH and boron concentration covered by the training set used to build the chemometric model also sets the limits of the application of this model. All pH and boron concentrations measured during the alteration experiments (Figure 2) matched the range of the solutions used in the training set. The pH obtained from chemometric modelling of the Raman data were equal to the pH measured in the alteration vessels (Figure 2D and H). The modelling error for the pH values is about ± 0.25 pH ACS Paragon Plus Environment

–15/28–


Page 16 of 28

units (RMSEC), larger than the values of ± 0.1 to ± 0.2 units generally considered for a measurement with a pH probe at the experimental temperature, see e.g. Parruzot et al.1 or Mercado-Depierre et al.31 and references therein. The boron concentrations obtained from Raman and ICP-OES techniques were also observed to match. The 2σ uncertainty from the averaging of boron concentrations from the chemometric modelling is low (< 25 ppm) even if a large number of data points is used (> 10): models are therefore outputting data with reasonable precision, though an error of 68.5 ppm is still understood from RMSEC values.

These concentrations were used to calculate the normalized mass losses (NL) of EA glass for all experiments after 7 days of alteration compared on Figure 3. For tests altered in similar conditions (started in pure water or started in a 250 ppm boron solution) the NL(B) value obtained by ICP-OES analysis of the solution (green bars) matched the NL(B) value obtained by chemometric modelling of the in-situ Raman data (red bar). The overall uncertainty is similar but slightly larger for the Raman-derived values compared to the ICP-derived values, this is due to the total uncertainties on the Raman-derived boron initial and final concentrations being generally larger.

Time resolution. As shown in Figure 2, the time resolution obtained by in-situ monitoring of the solution during alteration can be as high as a few dozen points per hour, showing a thorough picture of the evolution of the solution throughout the experiment’s duration. In both DIRaman and SBRaman experiments, the ICP-OES only provide a total amount of glass altered at the end of the 7 day experiment. Data from the in-situ Raman monitoring shows, for example, that 75% of the total alteration after seven days has already occurred 2 to 3 days after adding the glass in solution (Figure 2C and G) and that the pH value approaches its final value 3 to 4 days after the experiment was started (Figure 2D and H). Thus the Raman method allows for a much more detailed temporal evolution of glass alteration compared to traditional collection of solution aliquots.

Reduced impact on the alteration experiment. The experiment was not perturbed in order to obtain the Raman information: no solution samples were taken, nor was a pH electrode used (thus avoiding K contamination in the experiment solution from the KCl contained in the glass electrode), nor was the vessel taken out of the oven


–16/28–

Page 17 of 28 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


causing a variations in experimental temperature. As shown by the identical Si, B, Li, and Na normalized mass losses for experiment pairs DIControl and DIRaman, and SBControl and SBRaman respectively (Figure 3, Table 1), the addition of the in-situ monitoring equipment does not affect the alteration behavior of the glass. Note that the variation within experiment pairs is slightly higher for Mg, Fe and Al, but those elements are almost entirely retained in the alteration products and their concentration in solution is low, making their quantification more prone to error.

The results show that in-situ Raman monitoring can be successfully used to monitor glass alteration, particularly for long-term tests where the number of physical samplings is often limited. This is of particular interest if the altered glass exhibits a resumption in the alteration rate: in-situ monitoring would then provide information about the exact trigger time and show the alteration rate evolution during alteration resumption. Solution sampling would only be necessary in cases where a detailed mass balance for all glass elements is desired. In the specific case of nuclear waste glass qualification32 it can also be noted that the release of boron from the benchmark EA glass was successfully quantified by in-situ Raman monitoring in this study. Thus, this monitoring technique could also be used as a faster and easier-to-implement alternative method to PCT-A testing providing direct quantification of boron release, without the necessity to determine post-alteration ICP-OES elemental concentrations.


–17/28–


Page 18 of 28

Conclusion A new technique was developed to monitor the extent and rate of glass alteration based on boron release with insitu using Raman spectroscopy. PCT-A experiments using EA glass were performed using specially-designed alteration vessels capable of holding the Raman equipment. The Raman equipment was shown not to have any measurable effect on the glass alteration experiment. The pH and boron concentration data were obtained by chemometric modeling of three regions of interest of the Raman spectra: two boron bands (borate and boric acid bands, 745 and 877 cm–1) and the water region (3000 to 3600 cm–1). These results were found to match the values measured using a pH electrode and ICP-OES.

By allowing constant quantification of the pH and boron concentration in solution, the alteration extent can be quantified in near real-time. This reduces the frequency at which solution aliquots are necessary and thus avoids perturbation of the experiment (e.g. evaporation, SA/V ratio variation, introduction of contaminants, temperature variation, etc.) and could permit improved observation and quantification of sudden changes occurring during the experiment, especially when occurring at long time periods where the current method of solution sampling is limited in frequency and number of samples. One notable example could be the monitoring of the onset and evolution of alteration resumption. Many other solution species are active in Raman spectroscopy and could be interrogated using a chemometric model to provide more information relevant to glass alteration processes: one example is the silicate band that has been observed during alteration of silicate glasses. Other species relevant to other materials could also be analyzed (e.g. phosphate, sulfate…).

More generally, this technique is particularly interesting when the sample to be studied is not easily accessible. For example when a measurement may need to be taken in a remote location where the sampling process may disturb the measurement (redox, pressure, temperature) or where sampling may not be practical or possible (e.g. remote operating vehicles, radioactive work in hot cells, work under controlled atmosphere in gloveboxes).


–18/28–

Page 19 of 28 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Acknowledgements This work was jointly funded by the US Department of Energy Office of Nuclear Energy (Materials Recovery and Waste Form Development) and the Office of Environmental Management (Tank Waste Management, EM-21). Pacific Northwest National Laboratory is a multi-program national laboratory operated for the U.S. Department of Energy by Battelle Memorial Institute under Contract DE-AC05-76RL01830.

The authors would also like to thank Ian Leavy, Steven Baum, Michelle Snyder and Ben Williams (PNNL/EED) for their help with ICP-OES analyzes as well as Jarrod Crum (PNNL/EED) for his help with the XRD analysis.


–19/28–


Page 20 of 28

Supporting Information The supporting information provides more details regarding the calibration of the Raman signal acquired during the experiments presented in this article. The solutions used in the calibration set are presented in details together with a figure illustrating the signal variation with concentration at a fixed pH.


–20/28–

Page 21 of 28 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


References (1)

Parruzot, B.; Jollivet, P.; Rébiscoul, D.; Gin, S. Long-Term Alteration of Basaltic Glass: Mechanisms and Rates. Geochim. Cosmochim. Acta 2015, 154, 28–48.

(2)

Galeczka, I.; Wolff-Boenisch, D.; Oelkers, E. H.; Gislason, S. R. An Experimental Study of Basaltic Glass– H2O–CO2 Interaction at 22 and 50 °C: Implications for Subsurface Storage of CO2. Geochim. Cosmochim. Acta 2014, 126, 123–145.

(3)

Angeli, F.; Jollivet, P.; Charpentier, T.; Fournier, M.; Gin, S. Structure and Chemical Durability of Lead Crystal Glass. Environ. Sci. Technol. 2016, 50 (21), 11549–11558.

(4)

Michelin, A.; Leroy, E.; Neff, D.; Dynes, J. J.; Dillmann, P.; Gin, S. Archeological Slag from Glinet: An Example of Silicate Glass Altered in an Anoxic Iron-Rich Environment. Chem. Geol. 2015, 413, 28–43.

(5)

Weaver, J. L.; McCloy, J. S.; Ryan, J. V.; Kruger, A. A. Ensuring Longevity: Ancient Glasses Help Predict Durability of Vitrified Nuclear Waste. Am. Ceram. Soc. Bull. 2016, 95 (4), 18–23.

(6)

Vienna, J. D.; Ryan, J. V.; Gin, S.; Inagaki, Y. Current Understanding and Remaining Challenges in Modeling Long-Term Degradation of Borosilicate Nuclear Waste Glasses. Int. J. Appl. Glass Sci. 2013, 4 (4), 283–294.

(7)

Frugier, P.; Gin, S.; Minet, Y.; Chave, T.; Bonin, B.; Godon, N.; Lartigue, J.-E.; Jollivet, P.; Ayral, A.; De Windt, L.; Santarini, G. SON68 Nuclear Glass Dissolution Kinetics: Current State of Knowledge and Basis of the New GRAAL Model. J. Nucl. Mater. 2008, 380 (1–3), 8–21.

(8)

Gin, S.; Neill, L.; Fournier, M.; Frugier, P.; Ducasse, T.; Tribet, M.; Abdelouas, A.; Parruzot, B.; Neeway, J.; Wall, N. The Controversial Role of Inter-Diffusion in Glass Alteration. Chem. Geol. 2016, 440, 115–123.

(9)

Geisler, T.; Nagel, T.; Kilburn, M. R.; Janssen, A.; Icenhower, J. P.; Fonseca, R. O. C.; Grange, M.; Nemchin, A. A. The Mechanism of Borosilicate Glass Corrosion Revisited. Geochim. Cosmochim. Acta 2015, 158, 112–129.

(10) Fournier, M.; Gin, S.; Frugier, P. Resumption of Nuclear Glass Alteration: State of the Art. J. Nucl. Mater. 2014, 448 (1–3), 348–363. (11) ASTM International. Standard Test Methods for Determining Chemical Durability of Nuclear, Hazardous, and Mixed Waste Glasses and Multiphase Glass Ceramics: The Product Consistency Test (PCT); C1285-14; ASTM International: West Conshohocken, USA, 2014; p 27. (12) Ribet, S.; Muller, I. S.; Pegg, I. L.; Gin, S.; Frugier, P. Compositional Effects on the Long-Term Durability of Nuclear Waste Glasses: A Statistical Approach. In Scientific Basis for Nuclear Waste Management XXVIII; Hanchar, J. M., StroesGascoyne, S., Browning, L., Eds.; 2004; Vol. 824, pp 309–314.


–21/28–


Page 22 of 28

(13) Gin, S.; Beaudoux, X.; Angéli, F.; Jégou, C.; Godon, N. Effect of Composition on the Short-Term and LongTerm Dissolution Rates of Ten Borosilicate Glasses of Increasing Complexity from 3 to 30 Oxides. J. NonCryst. Solids 2012, 358 (18–19), 2559–2570. (14) Jantzen, C. M.; Trivelpiece, C. L.; Crawford, C. L.; Pareizs, J. M.; Pickett, J. B. Accelerated Leach Testing of GLASS (ALTGLASS): I. Informatics Approach to High Level Waste Glass Gel Formation and Aging. Int. J. Appl. Glass Sci. 2017, 8 (1), 69–83. (15) CRC Handbook of Chemistry and Physics, 90th Edition, 90th edition.; Lide, D. R., Ed.; Boca Raton, 2009. (16) George, J. L.; Brow, R. K. In-Situ Characterization of Borate Glass Dissolution Kinetics by µ-Raman Spectroscopy. J. Non-Cryst. Solids 2015, 426, 116–124. (17) Beebe, K. R.; Pell, R. J.; Seasholtz, M. B. Chemometrics: A Practical Guide; Wiley: New York, NY, USA, 1998. (18) Sharaf, M. A.; Illman, D. L.; Kowalski, B. R. Chemometrics; Wiley: New-York, NY, USA, 1986. (19) Martens, H.; Naes, T. Multivariate Calibration. I. Concepts and Distinctions. TrAC Trends Anal. Chem. 1984, 3 (8), 204–210. (20) Levitskaia, T. G.; Bryan, S. A.; Creim, J. A.; Curry, T. L.; Luders, T.; Thrall, K. D.; Peterson, J. M. Optical Spectroscopy and Multivariate Analysis for Biodosimetry and Monitoring of Radiation Injury to the Skin. Drug Dev. Res. 2012, 73 (5), 252–273. (21) Casella, A. J.; Levitskaia, T. G.; Peterson, J. M.; Bryan, S. A. Water O–H Stretching Raman Signature for Strong Acid Monitoring via Multivariate Analysis. Anal. Chem. 2013, 85 (8), 4120–4128. (22) Casella, A. J.; Ahlers, L. R. H.; Campbell, E. L.; Levitskaia, T. G.; Peterson, J. M.; Smith, F. N.; Bryan, S. A. Development of Online Spectroscopic PH Monitoring for Nuclear Fuel Reprocessing Plants: Weak Acid Schemes. Anal. Chem. 2015, 87 (10), 5139–5147. (23) Schroll, C. A.; Lines, A. M.; Heineman, W. R.; Bryan, S. A. Absorption Spectroscopy for the Quantitative Prediction of Lanthanide Concentrations in the 3LiCl–2CsCl Eutectic at 723 K. Anal Methods 2016, 8 (43), 7731–7738. (24) De Jong, S.; Wise, B. M.; Ricker, N. L. Canonical Partial Least Squares and Continuum Power Regression. J. Chemom. 2001, 15 (2), 85–100. (25) Lines, A. M.; Adami, S. R.; Sinkov, S. I.; Lumetta, G. J.; Bryan, S. A. Multivariate Analysis for Quantification of Plutonium(IV) in Nitric Acid Based on Absorption Spectra. Anal. Chem. 2017, 89 (17), 9354–9359. (26) Savitzky, A.; Golay, M. J. E. Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Anal. Chem. 1964, 36 (8), 1627–1639.


–22/28–

Page 23 of 28 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(27) Jantzen, C. M.; Bibler, N. E.; Beam, D. C.; Crawford, C. L.; Pickett, M. A. Characterization of the Defense Waste Processing Facility (DWPF) Environmental Assessment (EA) Glass Standard Reference Material.; WSRC-TR-92-346, rev. 1; Westinghouse Savannah River Co.: Aiken, SC, USA, 1992. (28) Gout, R.; Pokrovski, G. S.; Schott, J.; Zwick, A. Raman Spectroscopic Study of Aluminum Silicate Complexes at 20 C in Basic Solutions. J. Solut. Chem. 2000, 29 (12), 1173–1186. (29) McIntosh, G. J.; Swedlund, P. J.; Söhnel, T. Experimental and Theoretical Investigations into the CounterIntuitive Shift in the Antisymmetric ν(Si–O) Vibrational Modes upon Deuteration of Solvated Silicic Acid (H 4 SiO 4 ). Phys Chem Chem Phys 2011, 13 (6), 2314–2322. (30) Scheetz, B. E.; Freeborn, W. P.; Smith, D. K.; Anderson, C.; Zolensky, M.; White, W. B. The Role of Boron in Monitoring the Leaching of Borosilicate Glass Waste Forms. MRS Proc. 1984, 44, 129–134. (31) Mercado-Depierre, S.; Fournier, M.; Gin, S.; Angeli, F. Influence of Zeolite Precipitation on Borosilicate Glass Alteration under Hyperalkaline Conditions. J. Nucl. Mater. 2017, 491, 67–82. (32) US DOE - Office of Environmental Management. Waste Acceptance Product Specifications (WAPS) for Vitrified High-Level Waste Forms; DOE/EM-0093; US DOE - Office of Environmental Management: Washington, DC, USA, 2012; p 43 pages.


–23/28–


Page 24 of 28

Table 1: Experimental parameters and results for all experiments performed in this study. A: Experiment preparation and conditions; B and C: Measured and recalculated pH values at 90°C; D: ICP-OES elemental concentrations; E: Normalized mass losses calculated for major elements in the experiments containing glass.

Experiment Solution type Solution Mass (g) Raman monitoring? Glass Mass (g) Glass addition time SA/Vgeometric (m–1) Total duration Initial pH20 °C Final pH20 °C Initial pH90 °C Final pH90 °C Al B Fe Li Na Si

Al B Fe Li Na Si

Element mass fraction in glass 0.0196 0.0351 0.0629 0.0198 0.1246 0.2278

DIBlank

DIControl

DIRaman SBBlank SBControl A – Experimental Parameters Ultrapure Water Ultrapure Water Ultrapure Water 250 ppm B spiked 250 ppm B spiked 53.9323 54.8966 53.9289 50.6227 50.7341 No No Yes No No – 5.4122 5.5157 – 5.0909 – At start 23 h – At start – 1902 1973 – 1936 169 h 169 h 192 h 168 h 168 h B – Measured pH at room temperature (no unit) – – – 6.2 6.2 5.7 11.9 11.9 6.3 9.7 C – Recalculated pH at 90 °C (no unit) 5.8 5.8 5.8 5.1 5.1 7.1 9.5 9.6 6.2 9.1 D – ICP-OES measured concentration et the end of experiment (ppm) < 0.17 3.10 1.28 < 0.17 2.17 2.38 711 805 278 425 < 0.10 7.10 0.42 < 0.10 8.21 0.56 206 229 0.28 74.1 1.09 2000 2220 0.77 497 < 0.55 1020 1110 < 0.55 147

SBRaman 250 ppm B spiked 47.3264 Yes 4.7528 70 h 1937 238 h 6.2 9.8 5.1 9.1 3.00 476 13.6 86.1 573 178

E – Normalized mass loss (g·m–2) – – – – – –

0.083 ± 0.009 11 ± 1 0.059 ± 0.006 5.4 ± 0.6 8.4 ± 0.9 2.4 ± 0.2

0.033 ± 0.003 12 ± 1 0.0034 ± 0.0004 5.8 ± 0.6 9.0 ± 0.9 2.5 ± 0.3


– – – – – –

0.057 ± 0.006 2.2 ± 0.3 0.067 ± 0.007 1.9 ± 0.2 2.1 ± 0.2 0.33 ± 0.03

0.079 ± 0.008 2.9 ± 0.4 0.11 ± 0.01 2.2 ± 0.2 2.4 ± 0.2 0.40 ± 0.04

–24/28–

Page 25 of 28 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 1: Results of the models for predicting total boron concentration (A) and pH (B) based on Raman spectroscopic data shown (in part) in the TOC figure and SI materials. The data are presented as predicted vs known (measured) values of boron and pH with 95% confidence interval.


–25/28–


Page 26 of 28

Figure 2: In-situ Raman monitoring results for experiments DIRaman (A-D) and SBRaman (E-H). Raman spectra recorded as a function of time for DIRaman – 3D view (A) and “overhead” view (B); and for SBRaman 3D view (E) and “overhead” view (F). Model results for the prediction of boron concentration for DIRaman (C) and for SBRaman (G). Model results for the prediction of pH values for DIRaman (D) and for SBRaman (H).


–26/28–

Page 27 of 28 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 3: Comparison of the final NL(B) for all experiments (with the initial solution being either 18.2 MΩ·cm water [DI] or a solution spiked with boron [SB], see details in text) measured either from ICP-OES concentrations (green) or from in-Situ Raman determination (red). For comparison, NL(B) from Jantzen et al.27 is reported in blue, with 2σ uncertainty.


–27/28–


Page 28 of 28

For TOC only.


–28/28–

Method for the in situ Measurement of pH and Alteration Extent for

Recommend Documents