Discriminating between Microsamples of Similar Resins with a Combination of FTIR and Thermal Analysis Instruments

Significance of the topic

The identification and characterization of polymer microsamples is critical across quality control, failure analysis, environmental microplastics assessment, and contamination tracking. Rapid spectroscopic screening often distinguishes polymer classes, but closely related resins (similar base polymer, low-level additives, or different thermal histories) require complementary thermal analysis to resolve subtle differences. Combining ATR-FTIR with DSC and TG-DTA provides orthogonal information on chemical functionality, crystallinity/thermal history, and inorganic additive content, enabling confident discrimination of microsamples down to sub-milligram quantities.

Objectives and overview of the study

This application study demonstrated how a combined workflow—ATR-FTIR (IRSpirit-TX), differential scanning calorimetry (DSC-60 Plus), and simultaneous thermogravimetry–differential thermal analysis (DTG-60)—can discriminate six polypropylene (PP) microsamples (0.2–0.5 mg each). The goals were to: (1) identify polymer type and surface additives by FTIR-ATR, (2) probe thermal properties and manufacturing/thermal history by DSC, and (3) quantify inorganic residues and identify thermally stable additives by TG-DTA and ATR-FTIR of residues.

Used instrumentation

• IRSpirit-TX FTIR spectrophotometer with QATR-S diamond ATR accessory (FTIR conditions: 4000–400 cm⁻¹, 4 cm⁻¹ resolution, 20 scans, SqrTriangle apodization).
• DSC-60 Plus differential scanning calorimeter (aluminum pan, 20 °C/min, 0 → 200 °C, N₂ purge 50 mL/min).
• DTG-60 simultaneous DTA–TG (aluminum pan, 20 °C/min, room temperature → 500 °C, air purge 100 mL/min).

Methodology

• Sample selection: six PP particles (labelled 1–6), ~0.2–0.5 mg, ~1–2 mm² each.
• Step 1 — ATR-FTIR: direct surface measurement to detect polymer backbone signals, additive fingerprints (e.g., talc), copolymer markers (CH₂ rocking at ~720 cm⁻¹), and oxidation markers (C=O stretch at ~1730 cm⁻¹).
• Step 2 — DSC: thermal scans to compare melting behavior (peak shape, shoulders), melting temperature, and heat of fusion to infer crystallinity and thermal history.
• Step 3 — TG-DTA: destructive mass-loss analysis to measure total weight loss to 500 °C and estimate inorganic residue levels; residues collected and analyzed by ATR-FTIR to identify inorganic fillers (talc, glass fiber).

Main results and discussion

• FTIR findings:
  • Sample 1 exhibited extra peaks (~1000, 680, 520 cm⁻¹) matching talc, indicating talc as an additive.
  • Samples 3–6 showed a CH₂ rocking vibration at ~720 cm⁻¹ consistent with ethylene comonomer incorporation (copolymers); sample 2 lacked this peak and was inferred to be a PP homopolymer.
  • Samples 1, 3, and 4 displayed a C=O stretching band near 1730 cm⁻¹, indicating oxidative degradation.
• DSC observations:
  • Sample 6 showed a shoulder on the melting peak (multimodal melting), suggesting a distinct thermal history or different crystalline populations; a repeat DSC run removed the shoulder, indicating the shoulder was due to previous processing-induced thermal history (annealing/heat treatment).
  • Sample 4 exhibited a clearly reduced heat of fusion relative to the other samples, consistent with lower overall crystalline fraction—potentially due to a high filler/additive content interfering with crystallization.
• TG-DTA and residue analysis:
  • TG showed near 100% weight loss for sample 6 (no significant inorganic residue), while samples 3–5 left measurable residue, with sample 4 leaving the largest residue fraction—consistent with higher inorganic additive content.
  • ATR-FTIR analysis of residues identified glass fiber in the residue from sample 3, and both glass fiber and talc in residues from samples 4 and 5. These fillers were not always detected at the original sample surface by ATR-FTIR because fillers may be embedded and not surface-exposed or present at trace levels.
• Integrated interpretation led to distinct characterizations of all six samples (summarized below):
  1. Contains talc and shows signs of oxidative degradation.
  2. Likely a PP homopolymer (no 720 cm⁻¹ CH₂ rocking).
  3. Contains glass fiber; probable copolymer; signs of oxidation.
  4. Contains glass fiber and talc (highest filler fraction); oxidatively degraded; lower heat of fusion.
  5. Contains trace talc and glass fiber; probable copolymer.
  6. Heat-treated (thermal history evident); probable copolymer; no inorganic residue.

Benefits and practical applications of the method

• Combining ATR-FTIR, DSC, and TG-DTA enables sensitive discrimination among visually similar resin microsamples by providing complementary chemical, thermal, and compositional information.
• ATR-FTIR rapidly identifies polymer class, copolymer content (e.g., ethylene comonomer indicators), trace organic degradation products, and abundant surface fillers.
• DSC reveals crystallinity variations and thermal history effects (processing-induced annealing, multiple crystal populations) that are not visible spectroscopically.
• TG-DTA quantifies inorganic filler fraction and thermal stability; residue analysis by ATR-FTIR permits identification of inorganic additives (talc, glass fiber) not exposed on the surface.
• Applicable workflows: contamination/root-cause analysis, microplastic characterization, forensic polymer identification, and QC of recycled/residual plastics streams where sample mass is limited.

Future trends and potential uses

• Increasing integration and automation: hyphenated systems (FTIR with microscale thermal probes) and automated workflows for sequential nondestructive spectroscopy followed by targeted thermal analysis will speed throughput and reduce sample handling.
• Microspectroscopy and micro-thermal analysis to map heterogeneity within single particles (spatially resolved FTIR, micro-DSC/TGA).
• Advanced data analytics: chemometrics and machine learning applied to combined spectral and thermal datasets to classify polymer types, additive packages, and degradation states with higher confidence from small datasets.
• Standardization efforts for micro-sample protocols (sample handling, pan choice, heating rates) to improve inter-laboratory comparability for microplastics and trace contamination studies.
• Improved detector sensitivity and ATR accessories enabling lower sample masses and better detection of buried fillers or thin surface layers.

Conclusion

A sequential analytical workflow using ATR-FTIR, DSC, and TG-DTA successfully discriminated six polypropylene microsamples with differing additive content, oxidative degradation states, copolymer vs. homopolymer identity, and thermal histories. ATR-FTIR provided fast surface chemical identification; DSC highlighted thermal-history-induced differences in melting behavior and crystallinity; TG-DTA quantified inorganic residues and, when coupled with ATR-FTIR of residues, identified fillers not apparent from initial surface spectra. The combined approach is robust for microsample characterization in QC, failure analysis, and environmental microplastics studies.

References

1. Distinction of Polyethylene and Polypropylene by Infrared Spectrum, Shimadzu Application News No. 01-00710.
2. Estimation of Thermal History of Polymer Using DSC-60 Plus Differential Scanning Calorimeter, Shimadzu Application News No. T159.