Using Deconvolution Analysis to Detect Vulcanizing Accelerator-Related Compounds in Tire Rubber

Significance of the topic

The analysis of vulcanizing accelerator-related compounds in tire rubber is critical for materials development, quality control, and environmental assessment. Vulcanizing accelerators and their transformation products influence tire performance, aging, and potential toxicological impact. Rapid, reliable identification of trace accelerator residues and additive breakdown products supports formulation optimization, failure analysis, and regulatory compliance for automotive and environmental monitoring applications.

Objectives and overview of the study

This application note demonstrates how deconvolution software (LabSolutions Insight Explore GCMS) combined with pyrolysis-GC-MS (Py-GC-MS) and a detector-splitting flame photometric detector selective for sulfur (FPD(S)) can detect and characterize accelerator-related and other additive-derived compounds in tire rubber. The work aims to show improved detection of low-abundance components that are obscured by coelution in conventional total ion chromatograms (TIC) and to illustrate how complementary library resources (NIST 2023 and Shimadzu Polymer Additives Library Ver. 2) support identification.

Methods and analytical workflow

• Sample preparation: Approximately 0.5 mg of tire rubber was placed in a pyrolysis sample cup and analyzed by thermal desorption (TD)-GC/MS (Py-GC-MS), minimizing sample pretreatment.
• Instrument configuration: The GC-MS system employed a detector splitting arrangement to enable simultaneous MS and FPD(S) detection, allowing selective, high-sensitivity monitoring of sulfur-containing species while collecting full-scan MS data.
• Data processing: GC-MS data were processed with LabSolutions Insight Explore GCMS deconvolution analysis to resolve coeluting signals and extract component spectra and chromatograms.
• Compound identification: Deconvoluted spectra were searched against NIST (2023) and Shimadzu’s Polymer Additives Library Ver. 2, which includes additive classification metadata to support assignment as vulcanizing accelerators or other additive classes.

Used instrumentation

• Pyrolysis-GC-MS with thermal desorption (Py-GC-MS)
• Gas chromatograph–mass spectrometer: GCMS-QP2020 NX / GCMS-QP2050 (system family cited)
• Detector splitting system enabling simultaneous MS and FPD(S) (flame photometric detector selective for sulfur)
• Data analysis software: LabSolutions Insight Explore GCMS (deconvolution feature)
• Mass spectral libraries: NIST MS Library (2023) and Polymer Additives Library Ver. 2 (Shimadzu)

Main results and discussion

• Deconvolution enabled automatic detection and spectral extraction for trace peaks that were not clearly resolvable in the TIC. Peaks designated No. 4 and No. 5 were highlighted as examples where deconvolution recovered component spectra and allowed library searches to propose candidate identities.
• FPD(S) data provided orthogonal information about sulfur content, improving confidence in assignments for sulfur-containing compounds when MS library matches alone were ambiguous. For instance, a top NIST match without sulfur was deprioritized when the FPD(S) trace indicated the actual component contained sulfur, leading to selection of N-phenyl-2-benzothiazole as a more plausible identity.
• Nine additive-related compounds were reported. Compounds 1–6 were sulfur-containing benzothiazole derivatives (e.g., benzothiazole, 2-(methylmercapto)benzothiazole, 2-phenylbenzothiazole, N-phenyl-2-benzothiazolamine, 2,2'-bibenzothiazole) and were classified as vulcanizing accelerator-related based on library classification and FPD(S) response, indicating a benzothiazole-based accelerator was used in the tire formulation.
• Compounds 7–9 were traced to non-accelerator additives: compound 7 matched to the anti-degradant 6PPD (N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine), and compounds 8 and 9 were phenolic antioxidant derivatives (alpha-methylbenzyl phenol isomers and tris(1-phenylethyl)phenol derivatives).
• The combined approach (deconvolution + FPD(S) + targeted additive library) provided higher sensitivity and qualitative accuracy than relying on TIC peaks and MS library matches alone, particularly for trace components and where isobaric or coeluting species complicate interpretation.

Practical benefits and applications

• Improved detection of trace accelerator residues and pyrolysis products supports tire R&D, failure analysis, quality control, and forensic investigations of tire aging or formulation.
• FPD(S) coupling adds selective sulfur detection, which is crucial for unequivocal identification of sulfur-containing vulcanization chemistries that would otherwise be misassigned by MS library matches alone.
• Deconvolution reduces analyst time by automating component extraction in complex chromatograms and generates cleaner spectra for library searching, increasing throughput for screening studies.
• Library classification (Polymer Additives Library Ver. 2) streamlines interpretation by linking identified spectra to additive classes (accelerators, anti-degradants, antioxidants), aiding decision making in formulation and regulatory contexts.

Future trends and potential uses

• Expansion and curation of specialized additive libraries, including more pyrolysis-derived transformation products, will increase identification accuracy for complex polymer matrices.
• Integration of high-resolution mass spectrometry (HRMS) or tandem MS could provide structural confirmation for deconvoluted components and help resolve isomeric candidates.
• Machine-learning assisted deconvolution and automated annotation workflows will likely accelerate data interpretation and reduce false positives in highly complex samples.
• Quantitative extensions of pyrolysis-deconvolution workflows, using internal standards and calibration strategies, could enable residue-level monitoring for regulatory or environmental exposure assessments (e.g., 6PPD derivatives in runoff).
• Broader application to microplastics, environmental matrices, and post-consumer rubber recycling streams to monitor additive fate and transformation products.

Conclusions

Deconvolution analysis implemented in LabSolutions Insight Explore GCMS, combined with simultaneous FPD(S) detection and dedicated additive libraries, enhances detection and characterization of vulcanizing accelerator-related compounds in tire rubber. This combined approach resolves trace components obscured by coelution, improves confidence for sulfur-containing identifications, and leverages additive classification metadata to support practical interpretation. The workflow is applicable to formulation analysis, quality assurance, and environmental screening where trace additive residues and pyrolysis products are relevant.

References

• Kudo Y., Miyamoto A., Aoyama Y., Kitano R., Using Deconvolution Analysis to Detect Vulcanizing Accelerator-Related Compounds in Tire Rubber, Shimadzu Application News, First Edition Apr. 2026.
• Shimadzu Corporation, Polymer Additives Library Ver. 2, Shimadzu technical database, 2026.
• NIST Mass Spectrometry Library, 2023 edition.
• Shimadzu Application News No. 01-00901, Analysis of Base Material and Additives in Tire Rubber — Pyrolysis-GC-MS/FPD Detector Splitting.