Differential Analysis in Polysulfide Silane Coupling Agents by High Mass Accuracy MSn and Multivariate Statistical Technique

Significance of the Topic

Silane coupling agents are critical organosilicone additives that bond hydrophilic silica fillers to hydrophobic rubber matrices, greatly improving tire strength, wear resistance and rolling efficiency. Accurate characterization of these polysulfide silanes is essential for quality control, performance optimization and sustainable tire development.

Study Objectives and Overview

This work focuses on four commercial batches of Bis(triethoxysilylpropyl) polysulfide (TESPP) produced by different manufacturers. Although all samples share the same nominal structure, the goal was to differentiate them based on impurity profiles and subtle structural variations using high mass accuracy MSn and multivariate statistical analysis.

Methodology and Instrumentation

• Sample Preparation: Individual TESPP solutions were prepared by 1:10 000 dilution in methanol and combined into a pooled quality control (QC) sample to assess analytical reproducibility.
• Liquid Chromatography: Shim-pack XR-ODS column (2.0 mm × 75 mm, 2.2 µm) on a Prominence UFLC system, gradient elution from 70% to 100% acetonitrile in water with 5 mmol ammonium formate, flow rate 0.45 mL/min, column temperature 40 °C.
• Mass Spectrometry: LCMS-IT-TOF operated in positive electrospray mode (m/z 100–1000), probe voltage 4.5 kV, CDL and block heater at 200 °C. MSn data were acquired for structural elucidation and processed with Formula Predictor software.
• Data Analysis: Multivariate statistical processing with SIMCA-P to generate principal component analysis (PCA) score and loading plots, highlighting sample similarities and unique features.

Main Results and Discussion

PCA score plots revealed that samples A and B co-cluster, indicating nearly identical compositions. In contrast, samples C and D displayed distinct separation due to unique impurity peaks. High-accuracy MSn assignment identified characteristic mono-, tri- and tetrasulfide by-products: for example, Bis(triethoxysilylpropyl) trisulfide variants with methyl substitution were detected only in samples C and D. Structural assignments were confirmed by accurate mass measurements (<1 ppm error) and neutral loss patterns across MS2 and MS3 spectra. These results demonstrate the ability to profile vendor-specific impurity distributions in supposedly identical TESPP materials.

Benefits and Practical Applications

• Quality Assurance: Enables rapid vendor screening and batch verification based on impurity fingerprints.
• Performance Control: Links specific impurity profiles to rubber compound dispersion and mechanical properties.
• Regulatory Compliance: Supports traceability and standardization of coupling agent formulations in tire production.

Future Trends and Opportunities

Advances in ultrahigh resolution mass spectrometry and data-driven algorithms will further refine structural characterization of silane coupling agents. Integration of real-time MS monitoring into production lines could enable immediate feedback control. Machine learning approaches applied to large multivariate datasets may predict performance outcomes based on coupling agent profiles, accelerating formulation development.

Conclusion

The combined use of high mass accuracy MSn and multivariate statistical techniques offers a powerful strategy to distinguish identically structured polysulfide silane coupling agents from different manufacturers. This approach effectively uncovers vendor-specific impurity patterns, supporting improved material quality control and tire performance optimization.

Reference

No external literature references were provided in the original text.