Fourier-transform infrared spectroscopy (FTIR) is a widely used analytical technique for characterizing nanomaterials, including complex blends such as carbon nanotube (CNT) and graphene mixtures. Accurate quantification of component ratios in such blends relies on systematic approaches involving baseline correction, peak deconvolution, and calibration curves derived from characteristic infrared absorption bands. However, challenges such as scattering effects and overlapping peaks necessitate careful methodology and advanced data processing techniques.

Baseline selection is a critical first step in FTIR quantification. Nanomaterial blends often exhibit baseline distortions due to scattering effects, particularly when particles are larger than the wavelength of incident IR radiation. Mie scattering and diffuse reflectance can skew absorbance measurements, leading to inaccurate quantifications. To address this, baseline correction techniques such as polynomial fitting or linear interpolation between selected anchor points are employed. For CNT/graphene blends, the baseline is typically adjusted to minimize interference from broad scattering artifacts while preserving the integrity of characteristic peaks. Automated algorithms or manual baseline fitting can be used, but consistency in application is essential for reproducible results.

Peak deconvolution is necessary when overlapping bands obscure individual component contributions. In CNT/graphene blends, the C=C stretching vibrations (around 1580 cm⁻¹) and defect-related D-band (around 1350 cm⁻¹) are key regions for analysis. Gaussian or Lorentzian peak fitting is applied to separate overlapping bands, with constraints based on known peak positions and widths from pure component spectra. For example, graphene oxide exhibits a prominent C=O stretch near 1730 cm⁻¹, while CNTs may show additional peaks from functional groups introduced during synthesis. The relative areas of deconvoluted peaks are then used to determine component ratios.

Calibration curves are constructed using standards of known composition. Pure CNT and graphene samples are measured to identify their unique spectral fingerprints, and mixtures with predetermined ratios are analyzed to establish a correlation between peak intensity or area and composition. For instance, the ratio of the D-band to G-band intensities (ID/IG) can serve as a metric for graphene content when calibrated against reference samples. Linear or nonlinear regression models are fitted to the data, with care taken to ensure the calibration range covers expected experimental conditions. The accuracy of these curves depends on the homogeneity of reference samples and the reproducibility of sample preparation, such as dispersion quality in KBr pellets or thin films.

Scattering effects pose significant limitations in FTIR quantification of nanomaterial blends. Larger agglomerates or uneven particle distributions can cause spectral distortions, complicating baseline correction and peak integration. Strategies to mitigate scattering include sonication to improve dispersion, the use of thinner sample films, or alternative measurement techniques such as attenuated total reflectance (ATR)-FTIR, which minimizes scattering by pressing samples against a high-refractive-index crystal. Additionally, background subtraction of scattering profiles from blank substrates can improve signal clarity.

Multivariate analysis offers a powerful solution for complex nanocomposites where univariate methods fall short. Techniques like partial least squares regression (PLSR) or principal component analysis (PCA) leverage the entire spectral dataset rather than relying on isolated peaks. These methods can disentangle overlapping contributions from multiple components and account for scattering effects by incorporating spectral variations into the model. For CNT/graphene blends, PLSR models trained on a diverse set of reference spectra can predict unknown compositions with higher accuracy than single-peak approaches. However, multivariate models require large, well-characterized training datasets and validation to avoid overfitting.

Limitations of FTIR quantification include sensitivity to sample preparation and environmental conditions. Moisture absorption, for example, can introduce interfering O-H stretches near 3400 cm⁻¹, while oxidation during processing may alter spectral features. Functionalized nanomaterials present additional complexity, as introduced groups (e.g., carboxyl or epoxy) contribute new peaks that may overlap with matrix signals. In such cases, complementary techniques like Raman spectroscopy or X-ray photoelectron spectroscopy (XPS) may be necessary to cross-validate results.

For industrial or research applications, robust protocols must account for these variables. Standardized dispersion methods, controlled atmospheric conditions during measurement, and iterative model refinement improve reliability. Advanced software tools for automated baseline correction and peak fitting further enhance throughput and reduce user bias.

In summary, FTIR spectroscopy provides a viable route for quantifying nanomaterial blends like CNT/graphene ratios, but its accuracy depends on meticulous baseline handling, peak deconvolution, and calibration. Scattering effects and spectral overlaps necessitate adaptive strategies, including multivariate analysis, to address the complexities of real-world nanocomposites. While challenges remain, ongoing advancements in instrumentation and data processing continue to expand the utility of FTIR in nanomaterial characterization.