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Formulation and optimization of radiation-curable nonisocyanate polyurethane wood coatings by mixture experimental design

Journal of Coatings Technology and Research volume 18, pages 695–715 (2021)Cite this article

Abstract

Radiation-curable urethane acrylates have been extensively used and successfully implemented in industrial wood coatings thanks to their capability to provide a balance of mechanical and chemical properties. However, isocyanates, one of the main building blocks in the conventional urethane acrylates, pose toxicity and health hazards both in the manufacturing and application, and therefore, are targeted for restricted use and limited occupational exposure in the impending environmental regulations. In this regard, this study presents the development of urethane acrylate oligomers using nonisocyanate approaches and their application in industrial wood coatings. Two acrylic-functional nonisocyanate polyurethane oligomers (NIPU-ACs), NIPU-AC-2 with longer urethane chains and higher flexibility and NIPU-AC-1 with shorter urethane chains, were synthesized as main building blocks of radiation-curable wood coatings. Next, a series of 20 wood coating systems were formulated using a D-optimal mixture experimental design methodology to find the optimized quaternary mixtures composed of the two synthetic NIPU-ACs, and trimethylolpropane triacrylate (TMPTA) and dipropylene glycol diacrylate (DPGDA) as reactive diluents. The results of the study revealed that at a constant 5 wt% of TMPTA, formulations rich in NIPU-AC-2 showed higher impact resistance, scratch resistance, and pull-off adhesion, which was attributed to the flexible structure of this oligomer. On the other hand, coatings including higher contents of NIPU-AC-1, which induces higher crosslink density (XLD) to the system, demonstrated higher Tg and pendulum hardness. Evaluation of the optimal coatings not only validated the predictability of models, but also determined that the coatings have promising stain and abrasion resistance, and good thermal stability. The results show that NIPU-ACs developed in this study have excellent potential as an alternative to conventional urethane acrylates in the development of low-VOC and sustainable industrial wood coatings.

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Acknowledgments

The authors would like to thank Robert Roggers, Hamid Mortazavian, and Micheal Czaplicki at L&L Products, MI, USA, for their technical support including conducting the GPC studies. They also extend their thanks to Dr. Gregg Wilmes and Ehsan Zarshenas, at the Chemistry department at Eastern Michigan University, for helping with performing and analyzing the 1HNMR spectroscopy.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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  1. Coatings Research Institute, Eastern Michigan University, Ypsilanti, MI, USA

    Forough Zareanshahraki & Vijay Mannari

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Appendix 1

Appendix 1

“Appendix 1” presents the detailed statistical treatment and ANOVA for the models developed by the Design-Expert software for each of the properties as a function of compositional variables. The figure numbers mentioned here refer to the figures in the manuscript.

A: Effect of coating composition on the solvent resistance of the cured coatings

The data in Fig. 5 are fitted with a quadratic model, with statistical measures for interactions of the variables as summarized in Table 9. The Model F-value of 12.68 implies the model is significant. There is only a 0.01% chance that an F-value this large could occur due to noise. Values of “Prob > F” less than 0.0500 indicate model terms are significant. Values greater than 0.1000 indicate the model terms are not significant. Thus, the interactions between A and B are not significant. The “Lack of Fit F-value” of 0.71 implies the Lack of Fit is not significant relative to the pure error. There is a 66.17% chance that a “Lack of Fit F-value” this large could occur due to noise. Other statistics of interest include: Std. Dev. = 14.49, R2 = 0.9022, Mean = 181.25, Adj R2 = 0.8310, C.V.% = 7.99, Pred. R2 = 0.3975. The final equation in terms of pseudo-components is:

Table 9 ANOVA analysis of solvent resistance of the wood coating compositions
Full size table
$$ \begin{aligned} {\text{MEK}}\;{\text{double}}\;{\text{rubs}} & = \,106.87\, \times \,A\, + \,88.92\, \times \,B - 1007.22\, \times \,C \, \\ & \quad - \;1224.77\, \times \,D\, + \,56.30\, \times \,A\, \times \,B\, + \,2028.34\, \times \,A\, \times \,C\, \\ & \quad + \,2334.63\, \times \,A\, \times \,D\, + \,1999.84\, \times \,B\, \times \,C\, + \,2206.62\, \times \,B\, \times \,D. \\ \end{aligned} $$

The final equation in terms of real components is:

$$ \begin{aligned} {\text{MEK}}\;{\text{double}}\;{\text{rubs}} & = 106.8671\, \times \,{\text{NIPU}} - AC - 1\, + \,88.9247\, \times \,{\text{NIPU}} - AC \\ & \quad - \;2 - 1007.2186\, \times \,{\text{DPGDA}} - 1224.7733\, \times \,{\text{TMPTA}}\, + \,56.2952\, \times \,{\text{NIPU}} - AC \\ & \quad - 1\, \times \,{\text{NIPU}} - AC - 2\, + \,2028.3381\, \times \,{\text{NIPU}} - AC \\ & \quad - 1\, \times \,{\text{DPGDA}}\, + \,2334.6263\, \times \,{\text{NIPU}} - AC - 1\, \times \,{\text{TMPTA}} \\ & \quad + \,1999.8408\, \times \,{\text{NIPU}} - AC - 2\, \times \,{\text{DPGDA}}\, + \,2206.61667\, \times \,{\text{NIPU}} - AC - 2\, \times \,{\text{TMPTA}}. \\ \end{aligned} $$

B: Effect of coating composition on T g of the cured coatings

The data were fitted with a reduced quadratic model, as shown in Table 10. The Model F-value of 24.40 implies that the model is significant. There is only a 0.01% chance that an F-value this large could occur due to noise. In this case, A, C, BC, BD, CD are significant model terms. The “Lack of Fit F-value” of 5.05 implies the Lack of Fit is not significant relative to the pure error. There is a 4.55% chance that a “Lack of Fit F-value” this large could occur due to noise. Other statistics of interest include: Std. Dev. = 4.35, R2 = 0.9185, Mean = 49.10, Adj R2 = 0.8808, C.V.% = 8.86. The final equation in terms of pseudo-components is:

Table 10 ANOVA analysis of Tg of the wood coating compositions
Full size table
$$ \begin{aligned} T_{g} & = \,81.08\, \times \,A\, + \,6.30\, \times \,B - 37.76\, \times \,C - 10.70\, \times \,D\, + \,135.37\, \\ & \quad \times \,B\, \times \,C\, + \,204.24\, \times \,B\, \times \,D - 570.09\, \times \,C\, \times \,D \\ \end{aligned} $$

The final equation in terms of real components is:

$$ \begin{aligned} T_{g} & = \,81.07901\, \times \,{\text{NIPU}} - AC - 1\, + \,6.30461\, \times \,{\text{NIPU}} - AC - 2\, + \,37.75805\, \times \,{\text{DPGDA}} \\ & \quad - \;10.69751\, \times \,{\text{TMPTA}}\, + \,135.36726\, \times \,{\text{NIPU}} - AC - 2\, \times \,{\text{DPGDA}} \\ & \quad + \,204.24282\, \times \,{\text{NIPU}} - AC - 2\, \times \,{\text{TMPTA}} - 570.09013\, \times \,{\text{DPGDA}}\, \times \,{\text{TMPTA}} \\ \end{aligned} $$

C: Effect of coating composition on pendulum hardness of the cured coatings

ANOVA showed that the best model to fit the correlation between the composition and pendulum hardness was a linear mixture model and the statistical measures for interactions of the variables are given in Table 11. The Model F-value of 18.12 implies that the model is significant. There is only a 0.01% chance that an F-value this large could occur due to noise. The “Lack of Fit F-value” of 0.95 implies the Lack of Fit is not significant relative to the pure error. There is a 56.52% chance that a “Lack of Fit F-value” this large could occur due to noise. Other important statistics are: Std. Dev. = 9.70, R2 = 0.7726, Mean = 80.55, Adj R2 = 0.7299, C.V.% = 12.04, Pred. R2 = 0.6294. The final equation in terms of pseudo-components is:

Table 11 ANOVA analysis of pendulum hardness of the wood coating compositions
Full size table
$$ {\text{Pendulum}}\;{\text{hardness}}\, = \,97.97\, \times \,A\, + \,41.94\, \times \,B\, + \,118.03\, \times \,C\, + \,114.84\, \times \,D $$

Final equation in terms of real components is:

$$ \begin{aligned} {\text{Pendulum}}\;{\text{hardness}} & = \,97.97386\, \times \,{\text{NIPU}} - AC - 1\, + \,41.94059\, \times \,{\text{NIPU}} - AC \\ & \quad - 2\, + \,118.02928\, \times \,{\text{DPGDA}}\, + \,114.83504\, \times \,{\text{TMPTA}} \\ \end{aligned} $$

D: Effect of coating composition on Pencil Hardness of the cured coatings

The Model F-value of 8.04 implies the model is significant. There is only a 0.16% chance that an F-value this large could occur due to noise. In this case, A, B, D, AB, AD, BD, CD are significant model terms. The “Lack of Fit F-value” of 1.17 implies the Lack of Fit is not significant relative to the pure error (Table 12). There is a 43.26% chance that a “Lack of Fit F-value” this large could occur due to noise. Other statistics of interest include: Std. Dev. = 0.33, R2 = 0.8786, Mean = 4.45, Adj R2 = 0.7693, C.V.% = 7.41, Pred. R2 = 0.3276. The final equation in terms of pseudo-components is:

Table 12 ANOVA analysis of scratch resistance of the wood coating compositions
Full size table
$$ \begin{aligned} {\text{Pencil}}\;{\text{Hardness}} & = \,4.76\, \times \,A\, + \,4.99\, \times \,B\, + \,16.7\, \times \,C - 29.76\, \times \,D \\ & \quad {-}\;4.54\, \times \,A\, \times \,B\, + \,20.78\, \times \,A\, \times \,C\, + \,45.54\, \times \,A\, \times \,D \\ & \quad {-}\;16.44\, \times \,B\, \times \,C\, + \,50.08\, \times \,B\, \times \,D\, + \,38.48\, \times \,C\, \times \,D. \\ \end{aligned} $$

The final equation in terms of real components is:

$$ \begin{aligned} {\text{Pencil}}\;{\text{hardness}} & = \,4.75947\, \times \,{\text{NIPU}} - AC - 1\, + \,4.99095\, \times \,{\text{NIPU}} - AC - 2\, \\ & \quad + \,16.70394\, \times \,{\text{DPGDA }} - 29.75701\, \times \,{\text{TMPTA}} - 4.54262\, \times \,{\text{NIP}}U - AC \\ & \quad - 1\, \times \,{\text{NIPU}} - AC - 2 - 20.78343\, \times \,{\text{NIPU}} - AC - 1\, \times \,{\text{DPGDA}}\, + \,45.54028 \\ & \quad \times \,{\text{NIPU}} - AC - 1\, \times \,{\text{TMPTA}} - 16.44116\, \times \,{\text{NIPU}} - AC - 2\, \times \,DPGDA\, \\ & \quad + \,50.08208\, \times \,{\text{NIPU}} - AC - 2\, \times \,{\text{TMPTA}}\, + \,38.47557\, \times \,{\text{DPGDA}}\, \times \,{\text{TMPTA}}. \\ \end{aligned} $$

E: Effect of coating composition on Impact Reistance of the cured coatings

The impact resistance data was fitted with a special cubic model, with statistical measures for interactions of the variables as summarized in Table 13. The Model F-value of 245.77 implies the model is significant. There is only a 0.01% chance that an F-value this large could occur due to noise. B, D, AB, AC, AD, BC, BD, CD, ABC, ACD, BCD are significant model terms. The “Lack of Fit F-value” of 0.39 implies the Lack of Fit is not significant relative to the pure error. There is a 55.75% chance that a “Lack of Fit F-value” this large could occur due to noise. Other statistics of interest include: Std. Dev. = 3.00, R-Squared = 0.9981, Mean = 61.00, Adj R2 = 0.9941, C.V.% = 4.92. The final equation in terms of pseudo-components is:

Table 13 ANOVA analysis of impact resistance of the wood coating compositions
Full size table
$$ \begin{aligned} {\text{Impact}}\;{\text{resistance}} & = \,2.05\, \times \,A\, + \,102.40\, \times \,B - 231.33\, \times \,C\, + \,791.23\, \times \,D\, \\ & \quad + \,111.11\, \times \,A\, \times \,B\, + \,561.68\, \times \,A\, \times \,C{-}1018.16\, \times \,A\, \times \,D\, \\ & \quad + \,655.87\, \times \,B\, \times \,C - 955.35\, \times \,B\, \times \,D{-}8446.76\, \times \,C\, \times \,D \\ & \quad {-}2139.08\, \times \,A\, \times \,B\, \times \,C{-}933.84\, \times \,A\, \times \,B\, \times \,D \, \\ & \quad + 11756.99\, \times \,A\, \times \,C\, \times \,D\, + \,11627.50\, \times \,B\, \times \,C\, \times \,D. \\ \end{aligned} $$

The final equation in terms of real components is:

$$ \begin{aligned} {\text{Impact}}\;{\text{resistance}} & = \,2.04707\, \times \,{\text{NIPU}} - AC - 1\, + \,102.39738\, \times \,{\text{NIPU}} - AC - 2 \\ & \quad - 231.32809\, \times \,{\text{DPGDA}}\, + \,791.22854\, \times \,{\text{TMPTA}}\, + \,111.11111\, \times \,{\text{NIPU}} - AC \\ & \quad - 1\, \times \,{\text{NIPU}} - AC - 2\, + \,561.68284\, \times \,{\text{NIPU}} - AC - 1\, \times \,{\text{DPGDA}}{-}1018.15997 \\ & \quad \times \,{\text{NIPU}} - AC - 1\, \times \,{\text{TMPTA}}\, + \,655.86833\, \times \,{\text{NIPU}} - AC - 2\, \times \,{\text{DPGDA}} \\ & \quad - 955.34552\, \times \,{\text{NIPU}} - AC - 2\, \times \,{\text{TMPTA}}{-}8446.76246\, \times \,{\text{DPGDA}}\, \times \,{\text{TMPTA}} \\ & \quad {-}2139.07785\, \times \,{\text{NIPU}} - AC - 1\, \times \,{\text{NIPU}} - AC - 2\, \times \,{\text{DPGDA}} \\ & \quad {-}933.83995\, \times \,{\text{NIPU}} - AC - 1\, \times \,{\text{NIPU}} - AC - 2\, \times \,{\text{TMPTA}} \\ & \quad + 11756.99178\, \times \,{\text{NIPU}} - AC - 1\, \times \,{\text{DPGDA}}\, \times \,{\text{TMPTA}}\, \\ & \quad + \,11,627.50194\, \times \,{\text{NIPU}} - AC - 2\, \times \,{\text{DPGDA}}\, \times \,{\text{TMPTA}}. \\ \end{aligned} $$

F: Effect of coating composition on Pull-Off Adhesion strength of the cured coatings

The pull-off adhesion data were fitted with a special cubic model, with statistical measures for interactions of the variables as summarized in Table 14. The Model F-value of 9.78 implies the model is significant. There is only a 0.07% chance that an F-value this large could occur due to noise. AB, AC, AD, BC, BD are significant model terms. The “Lack of Fit F-value” of 1.74 implies the Lack of Fit is not significant relative to the pure error. There is a 27.91% chance that a “Lack of Fit F-value” this large could occur due to noise. Other statistics of interest include: Std. Dev. = 24.34, R-Squared = 0.8979, Mean = 421.82, Adj R2 = 0.8061, C.V.% = 5.77, and Pred. R2 = 0.6180. The final equation in terms of pseudo-components is:

Table 14 ANOVA analysis of pull-off adhesion strength of the wood coating compositions
Full size table
$$ \begin{aligned} {\text{Pull - off}}\;{\text{adhesion}}\,{\text{strength}} & = \,363.89\, \times \,A\, + \,453.81\, \times \,B - 1454.78\, \times \,C - 2029.40\, \times \,D \\ & \quad - 221.70\, \times \,A\, \times \,B\, + \,2477.15\, \times \,A\, \times \,C{-}3734.88\, \times \,A\, \times \,D\, \\ & \quad + \,2859.48\, \times \,B\, \times \,C\, + \,3145.62\, \times \,B\, \times \,D\, + \,2377.28\, \times \,C\, \times \,D. \\ \end{aligned} $$

The final equation in terms of actual components is:

$$ \begin{aligned} {\text{Pull - off}}\;{\text{adhesion}}\;{\text{strength}} & = \,363.89226\, \times \,{\text{NIPU}} - AC - 1\, + \,453.81355\, \times \,{\text{NIPU}} - AC \\ & \quad - 2 - 1454.77869\, \times \,{\text{DPGDA}} - 2029.39507\, \times \,{\text{TMPTA}} \\ & \quad - 221.70188\, \times \,{\text{NIPU}} - AC - 1\, \times \,{\text{NIPU}} - AC - 2\, + \,2477.15235\, \times \,{\text{NIPU}} \\ & \quad - AC - 1\, \times \,{\text{DPGDA}}\, + \,3734.88009\, \times \,{\text{NIPU}} - AC - 1\, \times \,{\text{TMPTA}} \\ & \quad + \,2859.48292\, \times \,{\text{NIPU}} - AC - 2\, \times \,{\text{DPGDA}}\, + \,3145.6163\, \times \,{\text{NIPU}} - AC \\ & \quad - 2\, \times \,{\text{TMPTA}}\, + \,2377.28139\, \times \,{\text{DPGDA}}\, \times \,{\text{TMPTA}}. \\ \end{aligned} $$

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Zareanshahraki, F., Mannari, V. Formulation and optimization of radiation-curable nonisocyanate polyurethane wood coatings by mixture experimental design. J Coat Technol Res 18, 695–715 (2021). https://doi.org/10.1007/s11998-020-00453-x

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