Differential Analysis in vulcanizing accelerators for rubber products by High mass Accuracy MSn and Multivariate Statistical Technique

Importance of the Topic

Vulcanization accelerators are crucial for effective cross-linking of rubber polymers with sulfur, determining the durability and performance of tire materials. Characterizing subtle differences among structurally similar accelerators from multiple suppliers is vital for quality control and product optimization.

Study Objectives and Overview

This study aims to apply high mass accuracy tandem mass spectrometry (MSn) combined with multivariate statistical analysis to differentiate sulfenamide-based vulcanizing accelerators produced by various manufacturers. By profiling chemical composition and identifying characteristic components, the method evaluates sample-specific analogues and impurities.

Methodology

Sample preparation involved dissolving five N-(tert-butyl)-2-benzothiazole sulfenamide (NS) and five N-cyclohexyl-2-benzothiazole sulfenamide (CZ) samples at 100 mg/L in tetrahydrofuran and acetonitrile. Quality control mixtures were used to monitor system stability. Data processing included:

• Automatic peak detection and matrix construction with Profiling Solution.
• Principal component analysis (PCA) in SIMCA-P+ to reveal sample clustering and characteristic peaks.
• Structural analogue search using MetID Solution and formula prediction via Formula Predictor.

Instrumentation

• LCMS-IT-TOF (Shimadzu) with ESI(+) ionization, 4.5 kV, CDL 200 °C, nebulizing gas 1.5 L/min, scan m/z 100–1000.
• Shim-pack XR-ODS column (2.0 mm × 75 mm, 2.2 μm), 40 °C, 0.45 mL/min, gradient from water (5 mM ammonium acetate) to acetonitrile, injection volume 1 μL.
• Data analysis software: Profiling Solution, SIMCA-P+, MetID Solution, Formula Predictor.

Key Results and Discussion

Principal component analysis successfully separated NS and CZ sample groups, with loading plots highlighting unique mass features per sample. Extracted ion chromatograms identified characteristic peaks. MSn experiments elucidated structures of analogues, for instance identifying a CH2-added phenyl derivative in one NS sample. Additional by-products and impurities were mapped across manufacturers.

Benefits and Practical Applications

• Robust differentiation of accelerators supports supplier evaluation and selection.
• Quality control protocols can detect trace analogues and impurities.
• Insights into synthesis by-product profiles enable process optimization.

Future Trends and Potential Applications

• Integration of MSn data with machine-learning algorithms for predictive compositional profiling.
• Real-time online monitoring of accelerator production and formulation quality.
• Extension of the approach to other polymer additives and complex mixtures.

Conclusion

The combination of high mass accuracy MSn and multivariate statistics provides an effective strategy for differential analysis of structurally similar vulcanizing accelerators, enabling detailed profiling of analogues and impurities for enhanced quality control in tire manufacturing.

References

• Goda T, Nakajima H, Yamaki S, Nishine T, Furuta M, Hamada N. Differential Analysis in vulcanizing accelerators for rubber products by High mass Accuracy MSn and Multivariate Statistical Technique. ASMS 2012.