Thermogravimetric analysis (TGA) is a powerful technique for evaluating the purity of carbon-based nanomaterials by monitoring their mass loss as a function of temperature under controlled atmospheres. The method is particularly useful for distinguishing between different forms of carbon, including crystalline structures like graphene and carbon nanotubes (CNTs), amorphous carbon, and functionalized species. By analyzing oxidation profiles, TGA provides quantitative insights into impurity levels, structural defects, and surface modifications, making it indispensable for quality control in nanomaterial production.

When carbon-based nanomaterials are heated in an oxidative environment, typically air or oxygen, their combustion behavior varies depending on their structural order and chemical composition. Highly ordered materials such as pristine graphene or defect-free CNTs exhibit sharp, well-defined oxidation peaks at higher temperatures due to their thermal stability. In contrast, amorphous carbon, which lacks long-range order, oxidizes at significantly lower temperatures, often appearing as a broad mass loss event before the main decomposition of the crystalline phase. Defects in the carbon lattice, such as vacancies or edge sites, also lower the oxidation temperature, as these regions are more reactive toward oxygen. Functional groups, such as carboxyl or hydroxyl moieties introduced during synthesis or processing, decompose at intermediate temperatures, further contributing to the complexity of the TGA curve.

The differentiation between these components is achieved by deconvoluting the derivative thermogravimetric (DTG) curve, which highlights the rate of mass loss. A typical DTG plot for a carbon nanomaterial sample may reveal multiple peaks, each corresponding to a distinct component. For example, a CNT sample containing amorphous carbon may show a low-temperature peak around 300–400°C, followed by the main CNT oxidation peak at 500–700°C. The relative areas under these peaks allow for the quantification of amorphous content. Similarly, functionalized graphene oxide exhibits mass loss steps corresponding to the decomposition of oxygen-containing groups (150–300°C) before the combustion of the carbon backbone (400–600°C).

Quantifying impurities in carbon nanomaterials using TGA relies on careful calibration and interpretation. The residual mass at high temperatures (often 800–1000°C) is typically attributed to non-combustible impurities such as metal catalysts or inorganic supports. For instance, CNTs synthesized using iron catalysts may leave behind iron oxide residues, while graphene samples contaminated with silica nanoparticles show residual mass proportional to the impurity content. The percentage of residual ash directly correlates with the inorganic impurity level, providing a straightforward metric for purity assessment. In cases where the impurity is carbonaceous, such as amorphous carbon, its concentration is derived from the relative mass loss in the corresponding temperature range.

The sensitivity of TGA allows for the detection of low-level impurities, often down to 1–5 wt%, depending on the instrument and experimental conditions. For high-purity applications, such as electronics or energy storage, even trace amounts of amorphous carbon or metal residues can significantly impact performance. By comparing TGA profiles of batches from different synthesis routes or post-processing treatments, manufacturers can optimize production protocols to minimize undesirable components. For example, post-synthesis annealing of graphene may reduce amorphous content, shifting the DTG peak to higher temperatures and narrowing its width, indicating improved crystallinity.

Applications of TGA in quality control extend beyond impurity quantification. The technique is also used to evaluate the effectiveness of purification methods, such as acid treatment or thermal annealing, by tracking changes in the oxidation profile. A successful purification process should reduce or eliminate low-temperature mass loss events associated with defects or functional groups. Additionally, TGA can assess the degree of functionalization in modified carbon nanomaterials. For instance, the mass loss attributed to grafted polymer chains in a functionalized CNT sample can be calculated by comparing the decomposition steps before and after modification.

Despite its advantages, TGA-based purity analysis has limitations. Overlapping decomposition events can complicate data interpretation, particularly in samples with multiple impurity types. In such cases, complementary techniques like Raman spectroscopy or X-ray photoelectron spectroscopy may be employed to validate TGA findings. Furthermore, the heating rate and gas flow conditions must be standardized to ensure reproducibility, as these parameters influence the oxidation kinetics.

In summary, TGA serves as a critical tool for assessing the purity of carbon-based nanomaterials by exploiting differences in their oxidative stability. Through detailed analysis of mass loss profiles, the method enables the quantification of amorphous carbon, structural defects, functional groups, and inorganic residues. This information is vital for optimizing synthesis and purification processes, ensuring consistent material quality for applications ranging from nanocomposites to biomedical devices. By integrating TGA into routine characterization workflows, researchers and manufacturers can achieve greater control over nanomaterial properties, ultimately enhancing their performance in real-world applications.