Monitoring of the Degree of Condensation in Alkoxysiloxane Layers by

Article pubs.acs.org/IECR

Monitoring of the Degree of Condensation in Alkoxysiloxane Layers by NIR Reflection Spectroscopy Gabriele Mirschel, Ulrike Helmstedt, Tom Scherzer,* Ulrich Decker, and Lutz Prager Chemical Department, Leibniz Institute of Surface Modification (IOM), Permoserstrasse 15, D-04318 Leipzig, Germany S Supporting Information *

ABSTRACT: This paper introduces a novel analytical approach for monitoring the degree of condensation of thin siloxane films, which is potentially suitable for in-line process control during the deposition of such layers, e.g., to polymer films. Nearinfrared (NIR) reflection spectroscopy in combination with chemometric methods was used as a process monitoring tool. The state of the formation of the inorganic Si−O−Si network in partially condensed 3-methacryloxypropyltrimethoxysilane batches was analyzed by inverse gated 29Si NMR spectroscopy. Results were expressed in terms of different relative ratios of the Ti species (i.e., structures with different numbers of Si−O−Si units per Si atom). These data were used for calibration of the NIR method, which was applied to thin layers printed on a polymer foil with a thickness of ∼2.2 g m−2. The root-mean-square error of prediction (RMSEP) for the determination of the ratio of the Ti species from the NIR spectra was found to be less than 3%. The error of the reference data from 29Si NMR spectroscopy is 4%, which results in an overall error of 5%. Moreover, the thickness of siloxane layers was determined by this method in a range from 2.5 to 5.5 g m−2 using gravimetry for calibration (prediction error ∼0.3 g m−2).

■

INTRODUCTION Alkoxysilanes are common precursors for the formation of Si− O−Si networks. Generally, the preparation of those networks proceeds in a sol−gel process via hydrolysis and condensation of alkoxysilane-based materials.1 Using silanes functionalized with polymerizable groups such as (meth)acrylate or epoxy groups, hybrid organic−inorganic networks can be synthesized. In addition to silicon, other heteroatoms (Al, Ti, Zn) can be included in the network. In comparison with common organic polymers, the resulting compounds may exhibit exceptional properties like enhanced stiffness, hardness, or abrasion resistance. Surfaces of various materials like polymer films, textiles, wooden, or metallic products can be coated with alkoxysilane-based formulations in order to provide them with a high-grade finish. In recent years, extensive investigation has been carried out on this topic in terms of the development of functional coatings. Hybrid organic−inorganic polymers were found to have unique properties, which can be controlled by the composition of the formulation, the parameters of the synthesis process, and final post-treatment after application.2,3 Due to the resulting broad range of well-defined tailor-made properties, such organic−inorganic layers have found a wide spectrum of applications, e.g. abrasion resistant coatings, gas barrier, or corrosion protective films, adhesion promoters for coatings, dental materials, as well as surface modification of powders, nanoparticles,4 and textile fibers or fabrics.5,6 Furthermore, a promising application is the development of adhesives for gas barrier laminates, which are able to significantly improve inorganic barrier coatings (SiOx, AlOx).7 Besides their preparation in classical sol−gel procedures, hybrid organic−inorganic compounds based on alkoxysilanes can be also synthesized by simultaneous organic−inorganic photoinduced polymerization.8−10 © XXXX American Chemical Society

Anyway, prior to application, the alkoxysilane formulations are commonly hydrolyzed by addition of an acid and water. Silanols are formed, which subsequently undergo cross-linking to some extent by condensation at enhanced temperature (50− 100 °C). After wet application to the surface, the resulting layer has to be cured. In the case of precursors with polymerizable organic groups, the polymerization process of these functionalities, e.g. methacrylic or epoxy groups, can be initiated by UV irradiation or by thermal treatment leading to the formation of the organic subnetwork. The progress of this reaction can be monitored by means of infrared (IR) spectroscopy utilizing the disappearance of the peaks of the CCH2 stretching vibration in methacrylates at 1635 cm−1 or the symmetrical stretching mode of the epoxide ring at 790 cm−1 in cycloaliphatic epoxides.11 Simultaneously, the formation of the inorganic network proceeds. Depending on the temperature of the curing process, further condensation of the remaining silanol groups to Si−O−Si bonds occurs. The statistical distribution of the number of Si−O−Si bonds per Si atom is a measure for the current state of the condensation process. For example, in the case of the trialkoxysilane precursor 3-methacryloxypropyltrimethoxysilane (MEMO), each Si atom can exhibit 0, 1, 2, or 3 Si−O−Si units (species termed as T0, T1, T2, and T3; see Scheme 1). The distribution of the various Ti species is the essential information required for control of the curing process. In general, the progress of the hydrolysis and condensation reactions of alkoxysilanes can be characterized by various analytical techniques such as GC-MS,12,13 ATR-FTIR,14,15 Received: July 30, 2014 Revised: October 7, 2014 Accepted: October 7, 2014

A

dx.doi.org/10.1021/ie503025z | Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

Industrial & Engineering Chemistry Research

Article

reflectance of thin layers having different thicknesses. Moreover, this method was used for in-line monitoring of the thickness of silica layers, which were deposited on polymer films with an intended thickness of ∼100 nm in a pilot-scale roll-to-roll process at web speeds up to 10 m min−1.33 On the basis of these promising results, the intention of the present paper was to develop an efficient NIR-spectroscopic method for monitoring the production process of alkoxysilanebased siloxane layers, which should have the potential for inline surveillance. Parameters that should be controlled were both the thickness of the layers immediately after application and the current state of the inorganic condensation process after curing, especially the quantitative ratio of the various Ti species, which is crucial for the material properties of the resulting thin films.34−40 Analytical data on the latter parameter were obtained by 29Si NMR spectroscopy. They were used as reference values for the calibration of the chemometric models developed for the evaluation of the NIR spectra and the prediction of the contents of the Ti species in unknown samples. This novel approach allows to combine the high precision and the excellent analytical potential of 29Si NMR spectroscopy with the specific requirements of process control.

Scheme 1. Structure of 3-Methacryloxypropyltrimethoxysilan (MEMO, left) and Designation of the Different Degrees of Si−O−Si Network Formation with Corresponding 29Si Chemical Shift Regions (right)a

a

R = −H or −CH3; R′ = −[(CH2)3O(CO)(CH3)CCH2].

■

silicomolybdate analysis,16−21 trapping of intermediates and isolation of products, optical absorption, or extractionquenching techniques.22 However, 29Si NMR spectroscopy is the only technique that is able to directly determine the ratio of the different Ti structures. The analysis is based on the different specific resonance shifts of the various Ti species with respect to liquid tetramethylsilane in the 29Si NMR spectrum (see Scheme 1).23 However, the performance of 29Si NMR spectroscopy for quantitative analytics is limited by the low natural abundance, the small and negative gyromagnetic ratio as well as the long longitudinal relaxation time (T1) of the 29Si nucleus. Therefore, it is a laborious and time-consuming analytical technique, in particular, when the magic-angle spinning (MAS) technique has to be used for the analysis of solid state samples. For direct monitoring of industrial processes, 29Si NMR spectroscopy is thus generally inapplicable. Rather, there is a need for costefficient and fast alternatives that are qualified for process control. Near-infrared (NIR) reflection spectroscopy is widely used for process control for a long time.24,25 Recently, it has been shown, that it is also a powerful tool for in-line monitoring of the application and UV curing of thin acrylic coatings and printed layers with a thickness in the range of a few micrometers only.26−29 In particular, parameters such as the conversion after UV curing, thickness (or coating weight) of the layer, drying state, etc., can be determined in real-time and directly in the coating machine or printing press. Quantitative data are obtained by use of chemometric approaches on the basis of the partial least-squares (PLS) algorithm30 using suitable reference methods. It has been demonstrated that high precision of the data can be achieved during in-line monitoring. In general, NIR spectroscopy is predominantly suited for the characterization of organic materials containing C−H, O−H, or N−H bonds. However, in a preceding paper it was shown that the thickness of inorganic layers can be determined by this method as well. In particular, the thickness of silica layers in the submicrometer range was measured with an estimated error of about 20%.31 The layers had been prepared by photoinduced oxidative conversion of silazane-based precursors.32 Quantitative analysis of the spectra was based on tiny differences in the

EXPERIMENTAL SECTION Synthesis and Characterization of Siloxane Batches. 3-Methacryloxypropyltrimethoxysilane (Dynasylan MEMO) was purchased from Evonik Industries (Hanau, Germany) and used as received. Its NMR data were as follows: 1H NMR (CD 3 CN): δ (ppm): 0.66 (t, 2H, 3 J H−H = 8.4 Hz, CH2CH2CH2Si); 1.73 (dt, 2H, CH2CH2CH2Si); 1.91 (s, 3H, H2CC(CH3)C); 3,53 (s, 9H, Si(OCH3)3); 4,07 (t, 2H, 3JH−H = 6,8 Hz, CH2CH2CH2Si); 5,57 (s, 1H, HHCC(CH3)COOC); 6,06 (s, 1H, HHCC(CH3)COOC); 29Si NMR (CDCN): δ (ppm): 42.4 (s CH2Si(OCH3)3). For each experiment a mixture of the catalyst (HCl or acetic acid as denoted in Table S1 in the Supporting Information) and 3 mol equiv of water was added dropwise to MEMO with continuous stirring at room temperature. The mixture was heated to reflux (72−74 °C) with an oil bath. After reflux, the resulting methanol was removed with a rotary evaporator for 30 min at 40 °C. Reflux times, catalyst amounts, batch sizes, and solvents were varied in order to obtain 22 batches with different levels of condensation (see Table S1 in the Supporting Information). The hydrolyzed and partially condensed alkoxysilanes were characterized by NMR spectroscopy. High-resolution 1H NMR (600.13 MHz) and 29Si NMR spectra (119.23 MHz) were recorded using a Bruker Avance-600 II+ spectrometer (Bruker, Rheinstetten, Germany). Deuterated acetonitrile (CD3CN) was used as solvent. The concentration of the modified silanes was 66% (w/w). All spectra were collected at room temperature with 8 scans for 1H NMR and 512 scans for 29Si NMR, respectively. The spectra were referenced to tetramethylsilane (TMS, δ = 0.0 ppm). Inverse gated experiments were performed for quantitative evaluation by 29Si NMR. The percentages of the 29Si species with different degrees of condensation (T1 to T3) were determined by signal integration. Preparation and Characterization of Layers for the Calibration of Chemometric Models. Calibration samples made from different siloxane batches were prepared by printing them on a 100 μm polyethylene naphthalate film (PEN; Teonex Q 65 FA, purchased from Pütz GmbH + Folien KG, Taunusstein, Germany; stored under controlled ambient B



Article

conditions at 25 °C, 50% RH) using a laboratory-scale printing machine (Printability Tester C1; IGT Reprotest, Amsterdam, The Netherlands). For UV curing, 3 wt % Irgacure 1173 (BASF, Ludwigshafen, Germany) was added as a photoinitiator immediately before application in order to minimize the influence of the hydroxyl group on the condensation process. Layers for calibration of the NIR spectra to the degree of condensation were applied with a coating thickness of (2.20 ± 0.05) g m−2, whereas samples for calibration to the coating weight were prepared with a range from 2.50 to 5.50 g m−2. UV curing via free radical polymerization of the methacrylate groups of the MEMO-based layers was carried out under inert conditions in a pilot-scale curing line equipped with a medium pressure mercury lamp (100 W cm−1; uv-technik Meyer GmbH, Ortenberg, Germany). At a line speed of 10 m min−1 and 100% power of the UV lamp, the radiant exposure was measured to be 900 mJ cm−2. The resulting coating weight was determined by gravimetry using an analytical balance. The error of these measurements was 0.001 g m−2. NIR Spectroscopy. NIR spectra were recorded using a process spectrometer uniSPEC2.2S (LLA Instruments GmbH, Berlin, Germany), which is based on a holographic grating and an InGaAs photodiode array detector with 512 elements, which covers a spectral range from 1100 to 2220 nm at a resolution of 2 nm/pixel. The PSPD probe head contains a tungsten lamp as light source. It is linked to the spectrometer by a fiber optical cable. Immediately after UV curing, the NIR spectra for calibration were taken in transflection mode using a ceramic reflector. A diffuser plate in front of the probe head suppressed interferences, which occur in thin layers of optically high-grade films. The printed film strips were moved through the probe beam in order to get an average spectrum characterizing the whole surface of the sample. For each sample ten average spectra obtained from about 1500 accumulations were recorded. Quantitative analysis of the spectral data was carried out with chemometric approaches using the KustaSpec software package, which is supplied with the spectrometer. All calibrations were based on the PLS algorithm using the test set method for internal validation.30 Whereas the calibration models for the degree of condensation of the siloxane relied on the PLS2 method, which is able to predict more than one parameter (i.e., the concentrations of various siloxane structures T1, T2, T3; see Scheme 1) from spectral data,41 the calibration for the coating weight was based on the PLS1 algorithm. More details about the calibration procedures are given in the Results and Discussion section.

since PRAs are known to influence reaction rates, whereas the latter NMR experiments are difficult to apply in case of various possible polarization transfer paths within more complicated molecules than demonstrated in the literature (trimethylethoxysilane).42 Thus, we decided to resign a full quantitative determination of Ti species and to focus on their relative ratios obtained by spectral acquisition within a reasonable time. The acquisition delay was set to seven seconds, hence 512 scans could be measured within an hour. In order to determine the error caused by the shorter measurement time, samples of one batch (batch 6 in Table S1) were exemplary measured with a delay of either 7 or 340 s. The difference of the portion for each species obtained with the two methods was determined to be 3% for T1, 8% for T2, and 11% for T3. These differences result from the sum of errors made by (1) the reaction progress during a long 29Si NMR measurement with 340 s delay and (2) the error due to incomplete relaxation of the 29Si nuclei. Since in the intended technical process only the stability of the ratio and not its absolute value are of concern, the above-mentioned procedure reveals a practically applicable way to calibrate the NIR methodology with a relative error for measurement/ integration of four percent. For our investigations, 22 different condensation conditions were tested in order to vary the proportion of the Si species. Longer reaction times caused a decrease of the fraction of T1, while the contents of T2 and T3 were increasing. Use of a solvent went in parallel with an increase of higher condensed species, most likely due to increased mobility of the molecules. Hydrolysis with strong acids like hydrochloric acid (HCl) was observed to proceed within a few minutes followed by a condensation process yielding high percentages of T2 species. The use of acetic acid as a catalyst resulted in slower hydrolysis and an increase in T1 species (compared to HCl-catalyzed reactions). Exemplary 1H and 29Si NMR spectra, the experimental details of each batch as well as the corresponding ratios of Ti derived from the NMR data are given in the Supporting Information (Figures S1 and S2, Table S1). Prediction of the Contents of T1−T3. For quantitative analysis of NIR spectra, multivariate chemometric methods are typically applied, which make use of the complete spectrum or at least significant parts of it. Most chemometric calibration models are based on the PLS algorithm. They relate the variation in the spectra to the parameter(s) of interest. If only one parameter has to be determined, a PLS1 model is sufficient. In case of more than one parameter, a PLS2 approach has to be used. In the present investigation, the prediction of the relative contents of T1, T2, and T3 is the objective of the analysis. Consequently, a PLS2 model was applied. Generally, any chemometric model has to cover the complete range of each parameter, which may occur during application of the model. Furthermore, a sufficient number of calibration samples is required in order to get a stable and powerful calibration model. Therefore, 88 printed calibration samples were prepared from the siloxane batches with different relative contents of T1, T2, and T3, i.e. four samples per batch. NIR spectra were recorded immediately after UV curing. Some typical NIR spectra of siloxane layers printed on PEN film and their dependence on the degree of condensation in the layers are shown in Figure S3a in the Supporting Information. The relative contents of T1−T3 obtained from the NMR spectra of the siloxanes served as reference data for the calibration process. Spectral data were divided randomly in

■

RESULTS AND DISCUSSION NMR Investigations of the Partially Condensed Alkoxysilanes. For quantitative analysis of NMR spectra, full relaxation of the nuclei has to be allowed before repetition of the measurement. Using a nonhydrolyzed MEMO-sample, the longitudinal relaxation time T1 of the 29Si signal was determined to be 68 s, resulting in a delay of 340 s (5 × T1) after each scan in order to allow full relaxation. With a minimum of 500 to 1000 scans for acceptable signal-to-noise ratios, this acquisition time is much too long for practical application. Standard methods to accelerate acquisition of quantitative data are addition of paramagnetic relaxation agents (PRAs) or use of polarization transfer by adapted INEPT sequences. Both proved unsuitable for process monitoring, C



Article

equal parts into a calibration and a validation set (i.e., spectra of two samples of each siloxane in each set). Chemometric models were created using the PLS2 approach and the test set method. The calculated models were optimized by optional limitation of the spectral range (in order to exclude the noisy range at low wavelengths, i.e. below 1135 or 1410 nm, which comes from detector noise) as well as by application of various preprocessing methods to the spectral data, e.g. baseline correction, normalization (minimum−maximum method), application of the first and the second derivative (moving local filter method), etc. All pretreatment methods were used either individually or in combination. For each parameter Ti in the PLS2 model, the root-mean-square error of prediction (RMSEP), the standard error of prediction (SEP), the bias, and the coefficient of determination (R2) as a measure of the explained variance in the spectra were calculated.30,41 The resulting values were used for comparison and evaluation of the various models. The model with the lowest RMSEP and the highest R2 was selected for further investigation. The optimum PLS2 model developed in this study was based on 6 factors. It was achieved by normalization of the spectra and application of the first derivative. Additionally, the spectral range was limited to 1135 to 2217 nm. The calibration curves of this model are given in Figure 1. For reasons of clarity, they are plotted separately for T1, T2, and T3 as 2D projections from the multidimensional model. Due to the chemical complexity of the system and strong interdependencies between the various reactions with different rate constants, data points are inevitably unevenly distributed over the range of ratios covered by the calibration. A modified version of this figure is given as Figure S4 in the Supporting Information, which shows lines for ±10% relative deviation, that is lines with slopes 0.9 and 1.1, respectively, and an intercept at zero. This representation helps to assess the distance of the data to the unit slope line. The performance of the created PLS2 calibration model to predict the content of Ti species in thin layers was verified with independent test samples, which were neither included in the calibration nor in the validation set used for the calibration process. Samples were prepared separately and analyzed in a similar manner like the calibration samples. The results of the prediction of the degree of condensation in the layers printed on PEN film using the PLS2 calibration model shown in Figure 1 are summarized in Figure 2. The predicted values of T1−T3 are plotted against the corresponding data determined by 29Si NMR spectroscopy. For each fraction with a certain degree of condensation (T1, T2, T3), a close correlation of the predicted values with the reference data was found. The RMSEP errors were found to be 2.5% for T3 and about 2.9% for T1 and T2, respectively (note: these errors refer to the percentages of the Ti species, i.e. they are not relative errors). Moreover, the error of the reference data from 29Si NMR spectroscopy is 4%, which results in an overall error of 5%. Nevertheless, it was proven that NIR spectroscopy in combination with a PLS2 calibration model can be used for the quantitative determination of the degree of condensation in thin layers with a precision that is regarded to be sufficient for process control. Evidently, condensation reactions proceed during aging of the cured layers until all silanol groups are converted into siloxane structures. The intention of this study was to monitor the status immediately after coating and the time-dependent progress of these reactions quantitatively by a nondestructive analytical method. For this purpose, layers of sample 1 (see

Figure 1. PLS2 calibration model for siloxanes: 2D projections of the calibration data to the content of T1 (a), T2 (b), and T3 (c) species, respectively (an advanced version of this figure is given as Figure S4 in the Supporting Information).

Table S1) were applied to PEN film and stored after UV curing at room temperature for 18 days. After recording NIR reflection spectra, the fractions of T1−T3 were predicted. Data are given in Table 1. The results show a significant decrease of T1 and a marginal reduction of T2 after aging, whereas T3 slightly increased from 42.8 to 46.8%, which is in line with the progress of the condensation reaction. After accelerated further aging at enhanced temperature, which was carried out in two stages, the procedure was repeated. A significant increase of T3 was accompanied by further decline of T1 and T2. These results clearly demonstrate that quantitative data about the conD



Article

Prediction of the Coating Weight. In addition to the prediction of the degree of condensation from the NIR spectra, the coating weight of printed layers should be determined. For this purpose, layers of batch 6 (see Table S1) were applied to PEN films with different coating weights. The chemometric calibration procedure was carried out similar to that described above. However, since the coating weight was the only parameter of interest, the PLS1 algorithm was used for calibration. For building up a stable and efficient PLS1 calibration model, 32 samples with coating weights between 2.5 and 5.5 g m−2 were prepared by printing. NIR spectra were recorded after UV curing. Their dependence on the coating weight of the layers is shown in Figure S3b in the Supporting Information. Again, the spectral data were split in a calibration and a validation set (each of them containing the spectra of 16 samples). Afterward, a number of different calibration models was developed by application of various methods of spectral preprocessing. Similar to the procedures described above, the errors RMSEP and SEP, the bias, and R2 were calculated for each model and used for evaluation. In the present study on the coating weight, simple application of the first derivative to the spectra was found to result in the model with the lowest RMSEP and SEP (both 0.23 g m−2) and the highest R2 (0.98). The curve of this model is given in Figure 3.

Figure 3. PLS1 calibration model for the coating weight of thin siloxane layers on PEN film (an advanced version of this figure is given as Figure S6 in the Supporting Information).

The prediction capability of the developed model was examined with a number of independent samples. The results of the prediction of their coating weights from their NIR spectra are summarized in Figure 4. Data are plotted versus the corresponding coating weights determined by gravimetry. The prediction error was found to be only about 0.3 g m−2. This low RMSEP clearly reveals the high precision of the data that can be achieved from the NIR spectra using chemometric methods in spite of the low thickness of such layers. Consequently, this spectroscopic approach can not only be used for quantitative determination of the degree of condensation, but it is also suited for the prediction of the coating weight of thin siloxane layers on polymer films.

Figure 2. Prediction of the degree of condensation in independent samples of the siloxanes using the PLS2 calibration model shown in Figure 1 (an advanced version of this figure is given as Figure S5 in the Supporting Information).

Table 1. Relative Contents of T1−T3 Directly after UV Curing and after Various Consecutive Aging Steps time [h]

temperature [°C]

T1 [%]

T2 [%]

T3 [%]

0 432 2.5 19.5

22 22 120 120

4.6 2.5 1.2 1.5

52.6 50.7 47.0 41.6

42.8 46.8 51.0 56.3

■

CONCLUSIONS In this work, we proposed a novel analytical approach for monitoring of material properties and other parameters of thin siloxane layers, which is potentially suitable for process control. NIR reflection spectroscopy in combination with chemometric

densation reaction in thin layers of siloxanes can be determined by NIR reflection spectroscopy after previous calibration of the method with NMR data. E



Article

(2) Haas, K.-H.; Wolter, H. Synthesis, Properties and Applications of Inorganic-Organic Copolymers (ORMOCERs). Curr. Opin. Solid State Mater. Sci. 1999, 4, 571. (3) Haas, K.-H.; Amberg-Schwab, S.; Rose, K.; Schottner, G. Functionalized Coatings Based on Inorganic-Organic Polymers (ORMOCERs) and Their Combination with Vapor Deposited Inorganic Thin Films. Surf. Coat. Technol. 1999, 111, 72. (4) Bauer, F.; Ernst, H.; Decker, U.; Findeisen, M.; Gläsel, H. J.; Langguth, H.; Hartmann, E.; Mehnert, R.; Peuker, C. Preparation of Scratch and Abrasion Resistant Polymeric Nanocomposites by Monomer Grafting onto Nanoparticles, 1 - FTIR and Multi-Nuclear NMR Spectroscopy to the Characterization of Methacryl Grafting. Macromol. Chem. Phys. 2000, 201, 2654. (5) Mahltig, B.; Textor, T. Nanosols & Textiles; World Scientific Publishing Co: Singapore, 2008. (6) Bahners, T.; Textor, T.; Opwis, K.; Schollmeyer, E. Recent Approaches to Highly Hydrophobic Textile Surfaces. J. Adhes. Sci. Technol. 2008, 22, 285. (7) Schmidt, M.; Rodler, N.; Miesbauer, O.; Rojahn, M.; Vogel, T.; Dorfler, R.; Kucukpinar, E.; Langowski, H. C. Adhesion and Barrier Performance of Novel Barrier Adhesives Used in Multilayered HighBarrier Laminates. J. Adhes. Sci. Technol. 2012, 26, 2405. (8) Chemtob, A.; Versace, D. L.; Belon, C.; Croutxé-Barghorn, C.; Rigolet, S. Concomitant Organic-Inorganic UV-Curing Catalyzed by Photoacids. Macromolecules 2008, 41, 7390. (9) Belon, C.; Chemtob, A.; Croutxé-Barghorn, C.; Rigolet, S.; Schmitt, M.; Bistac, S.; Le Houerou, V.; Gauthier, C. Nanocomposite Coatings via Simultaneous Organic-Inorganic Photo-Induced Polymerization: Synthesis, Structural Investigation and Mechanical Characterization. Polym. Internat. 2010, 59, 1175. (10) Croutxé-Barghorn, C.; Belon, C.; Chemtob, A. Polymerization of Hybrid Sol-Gel Materials Catalyzed by Photoacids Generation. J. Photopolym. Sci. Technol. 2010, 23, 129. (11) Croutxé-Barghorn, C.; De Brito, M.; Allonas, X.; Belon, C.; Chemtob, A.; Ni, L.; Moreau, N.; De Paz, H.; El Fouhaili, B.; Dietlin, C. Organic and Hybrid Interpenetrated Polymer Networks for Advanced Materials. J. Photopolym. Sci. Technol. 2012, 25, 131. (12) Kazmierski, K.; Chojnowski, J.; McVie, J. The Acid-Catalyzed Condensation of Methyl Substituted Model Oligosiloxanes Bearing Silanol and Ethoxysilane Functions. Eur. Polym. J. 1994, 30, 515. (13) Bilda, S.; Lange, D.; Popowski, E.; Kelling, H. Zum Kondensationsverhalten von Silanolen. IV: Die sauer katalysierte Kondensation von Organodimethylsilanolen in Dioxan/Wasser. Z. Anorg. Allg. Chem. 1987, 550, 186. (14) Tejedor-Tejedor, M. I.; Paredes, L.; Anderson, M. A. Evaluation of ATR-FTIR Spectroscopy as an “in situ” Tool for Following the Hydrolysis and Condensation of Alkoxysilanes under Rich H2O Conditions. Chem. Mater. 1998, 10, 3410. (15) Leyden, D. E.; Atwater, J. B. Hydrolysis and Condensation of Alkoxysilanes Investigated by Internal Reflection FTIR Spectroscopy. J. Adhes. Sci. Technol. 1991, 5, 815. (16) Coradin, T.; Livage, J. Effect of Some Amino Acids and Peptides on Silicic Acid Polymerization. Colloids Surf., B 2001, 21, 329. (17) Coradin, T.; Durupthy, O.; Livage, J. Interactions of AminoContaining Peptides with Sodium Silicate and Colloidal Silica: A Biomimetic Approach of Silicification. Langmuir 2002, 18, 2331. (18) Coradin, T.; Coupe, A.; Livage, J. Interactions of Bovine Serum Albumin and Lysozyme with Sodium Silicate Solutions. Colloids Surf., B 2003, 29, 189. (19) Perry, C. C.; Keeling-Tucker, T. Crystalline Silica Prepared at Room Temperature from Aqueous Solution in the Presence of Intrasilica Bioextracts. Chem. Commun. 1998, 2587. (20) Menzel, H.; Horstmann, S.; Behrens, P.; Barnreuther, P.; Krueger, I.; Jahns, M. Chemical Properties of Polyamines with Relevance to the Biomineralization of Silica. Chem. Commun. 2003, 2994. (21) Mizutani, T.; Nagase, H.; Fujiwara, N.; Ogoshi, H. Silicic Acid Polymerization Catalyzed by Amines and Polyamines. Bull. Chem. Soc. Jpn. 1998, 71, 2017.

Figure 4. Prediction of the coating weights of independent samples of siloxane layers on PEN film (an advanced version of this figure is given as Figure S7 in the Supporting Information).

methods was used as process monitoring tool. In particular, the state of the formation of the inorganic Si−O−Si network (expressed in terms of different relative ratios of the Ti species) in UV-cured alkoxysilane-based layers with a thickness of only about 2.2 g m−2 was predicted after calibration of the method with data obtained from inverse gated 29Si NMR spectroscopy. The prediction error (RMSEP) of the NIR method was found to be less than 3%. Moreover, the error of the reference data from 29Si NMR spectroscopy is 4%, which results in an overall error of 5%. The thickness of siloxane layers was determined in a range from 2.5 to 5.5 g m−2 with a prediction error about 0.3 g m−2. It is well-known that the prediction of physical and chemical parameters from NIR spectra by using chemometric methods is an extremely powerful technique. However, this extraordinary sensitivity is accompanied by a potential influence of other parameters on the measurement process, which are not related to the actual parameter of interest. Possible effects might come for example from batch-to-batch variations of the silane and the polymeric substrate film, the moisture content in the film, or the ambient conditions. Before setting up this method for a real process control application in a technical deposition process, it will be necessary to address these questions.

■

ASSOCIATED CONTENT

S Supporting Information *

Additional material as described in the text. This material is available free of charge via the Internet at http://pubs.acs.org.

■

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS The authors would like to thank Susann Richter for realization of the syntheses and Evelin Bilz and Jana Taube for technical assistance with the printing experiments.

■

REFERENCES

(1) Brinker, C. J.; Scherer, G. Sol-Gel Science: The Physics and Chemistry of Sol-Gel Processing; Academic Press: Boston, 1990. F



Article

(42) Delak, K. M.; Farrar, T. C.; Sahai, N. 29Si NMR Sensitivity Enhancement Methods for the Quantitative Study of Organosilicate Hydrolysis and Condensation. J. Non-Cryst. Solids 2005, 351, 2244.

(22) Osterholz, F. D.; Pohl, E. R. Kinetics of the Hydrolysis and Condensation of Organofunctional Alkoxysilanes: A Review. J. Adhes. Sci. Technol. 1992, 6, 127. (23) Pfleiderer, B.; Albert, K.; Bayer, E. Investigations by 29Si CrossPolarization Magic Angle Spinning NMR-Spectroscopy of Reaction Pathways of Silica-Gel Polyfunctional Modification. J. Chromatogr. 1990, 506, 343. (24) Bakeev, K. A. Process Analytical Technology, 2nd ed.; Blackwell Publishing: Oxford, 2010. (25) Burns, D. A.; Ciurczak, E. W. Handbook of Near-Infrared Analysis, 3rd ed.; CRC Press: New York, 2008. (26) Mirschel, G.; Heymann, K.; Scherzer, T. Simultaneous In-Line Monitoring of the Conversion and the Coating Thickness in UVCured Acrylate Coatings by Near-Infrared Reflection Spectroscopy. Anal. Chem. 2010, 82, 8088. (27) Mirschel, G.; Heymann, K.; Savchuk, O.; Genest, B.; Scherzer, T. In-Line Monitoring of the Thickness of Printed Layers by NIR Spectroscopy at a Printing Press. Appl. Spectrosc. 2012, 66, 765. (28) Mirschel, G.; Savchuk, O.; Scherzer, T.; Genest, B. The Effect of Different Gloss Levels on In-Line Monitoring of the Thickness of Printed Layers by NIR Spectroscopy. Anal. Bioanal. Chem. 2012, 404, 573. (29) Mirschel, G.; Savchuk, O.; Scherzer, T.; Genest, B. Process Control of Printing Processes with In-Line NIR Spectroscopy and Elimination of the Influence of the Substrate on the Prediction of the Coating Weight. Prog. Org. Coat. 2013, 76, 86. (30) Martens, H.; Næs, T. Multivariate Calibration; Wiley: Chichester, 1989. (31) Scherzer, T.; Mirschel, G.; Heymann, K.; Prager, L.; Buchmeiser, M. R. Determination of the Thickness of SilazaneBased SiOx Coatings in the Submicrometer Range by Near-Infrared Reflection Spectroscopy. Appl. Spectrosc. 2009, 63, 239. (32) Prager, L.; Dierdorf, A.; Liebe, H.; Naumov, S.; Stojanović, S.; Heller, R.; Wennrich, L.; Buchmeiser, M. R. Conversion of Perhydropolysilazane into an SiOx Network Triggered by Vacuum Ultraviolet Irradiation: Access to Flexible, Transparent Barrier Coatings. Chem.Eur. J. 2007, 13, 8522. (33) Scherzer, T.; Mirschel, G.; Heymann, K.; Prager, L. In Near Infrared Spectroscopy; Saranwong, S., Kasemsumran, S., Thanapase, W., Williams, P., Eds.; IM Publications: Chichester, 2010; pp 803−807. (34) Fyfe, C. A.; Aroca, P. P. Quantitative Kinetic Analysis by HighResolution 29Si NMR Spectroscopy of the Initial Stages in the Sol-Gel Formation of Silica Gel from Tetraethoxysilane. Chem. Mater. 1995, 7, 1800. (35) Rankin, S. E.; Sefcik, J.; McCormick, A. V. Trimethylethoxysilane Liquid-Phase Hydrolysis Equilibrium and Dimerization Kinetics: Catalyst, Nonideal Mixing, and the Condensation Route. J. Phys. Chem. A 1999, 103, 4233. (36) Kelts, L. W.; Effinger, N. J.; Melpolder, S. M. Sol-Gel Chemistry Studied by 1H and 29Si Nuclear Magnetic Resonance. J. Non-Cryst. Solids 1986, 83, 353. (37) Vanbeek, J. J.; Seykens, D.; Jansen, J. B. H. 29Si NMR Monitoring and Kinetic Modelling of an Acid-Catalyzed TMOS SolGel System with Molar H2O/Si = 8. J. Non-Cryst. Solids 1992, 146, 111. (38) Alam, T. M.; Assink, R. A.; Loy, D. A. Hydrolysis and Esterification in Organically Modified Alkoxysilanes: A 29Si NMR Investigation of Methyltrimethoxysilane. Chem. Mater. 1996, 8, 2366. (39) Brunet, F. Polymerization Reactions in Methyltriethoxysilane Studied through 29Si NMR with Polarization Transfer. J. Non-Cryst. Solids 1998, 231, 58. (40) Kinrade, S. D.; Swaddle, T. W. Silicon-29 NMR Studies of Aqueous Silicate Solutions. 2. Transverse 29Si Relaxation and the Kinetics and Mechanism of Silicate Polymerization. Inorg. Chem. 1988, 27, 4259. (41) Brereton, R. Chemometrics: Data Analysis for the Laboratory and the Chemical Plant; Wiley: Chichester, 2003. G


Monitoring of the Degree of Condensation in Alkoxysiloxane Layers by

Recommend Documents