From Single-Technique Analysis to Multimodal Characterization: Recent Advances and Future Perspectives for Carbon Nanotubes and Graphene

Carbon nanotubes and graphene represent two archetypal sp²-hybridized carbon nanomaterials whose exceptional electrical, mechanical, thermal, optical, and surface properties arise directly from their atomic structure, dimensionality, defect population, chemical state, and processing history. Reliable characterization is therefore not a supporting step but a central requirement for understanding, comparing, and translating these materials into practical technologies. Over the past three decades, characterization has evolved from basic morphological confirmation by electron microscopy to a multidimensional analytical framework that combines Raman spectroscopy, transmission and scanning electron microscopy, atomic force microscopy, scanning tunneling microscopy, X-ray photoelectron spectroscopy, X-ray diffraction, thermogravimetric analysis, infrared spectroscopy, optical spectroscopy, surface-area analysis, and local electrical, thermal, and mechanical measurements. This review critically examines the progress in the characterization of carbon nanotubes and graphene, emphasizing how each technique probes a different aspect of structure–property relationships. Raman spectroscopy remains the most widely used nondestructive method because it can identify graphene layer number, disorder, strain, doping, edge structure, nanotube diameter, and electronic resonance effects. However, Raman analysis alone is insufficient for complete material qualification, particularly in heterogeneous, functionalized, oxidized, bundled, or industrially produced samples. Electron and scanning probe microscopies provide essential information on morphology, layer structure, chirality, defects, grain boundaries, and nanoscale topography, whereas XPS, FT-IR, XRD, TGA, and gas adsorption techniques clarify surface chemistry, oxidation level, crystallinity, purity, thermal stability, and accessible surface area. Despite major progress, persistent research gaps remain in cross-laboratory reproducibility, statistical sampling, quantitative defect metrology, industrial quality control, functional-group assignment, nanoscale–macroscale correlation, and standardization of reporting practices. Future advances should focus on multimodal workflows, automated high-throughput mapping, uncertainty-aware data analysis, standardized reference materials, machine-learning-assisted spectral interpretation, and application-specific characterization protocols. Addressing these gaps will be essential for moving carbon nanotubes and graphene from laboratory-scale materials toward reproducible, scalable, and application-ready technologies.

Carbon nanotubes and graphene have shaped modern nanoscience because they provide model systems in which atomic arrangement, dimensional confinement, and bonding symmetry are directly linked to macroscopic material performance. Carbon nanotubes can be regarded as rolled graphene sheets with one-dimensional electronic and phononic behavior, whereas graphene is a two-dimensional honeycomb lattice of carbon atoms that serves as the parent structure for graphite, fullerenes, and nanotubes. Because small variations in diameter, chirality, number of walls, number of graphene layers, defect density, oxygen content, residual catalyst, strain, doping, aggregation state, and flake-size distribution can significantly change performance, characterization is fundamental to every stage of synthesis, purification, processing, device fabrication, and application development.

Early CNT characterization reviews emphasized the need for complementary methods such as transmission electron microscopy, scanning tunneling microscopy, Raman spectroscopy, X-ray diffraction, neutron scattering, and thermal analysis.¹ Later work established Raman spectroscopy as one of the most powerful shared tools for graphene, graphite, and CNT characterization.^2-4 Analytical reviews also showed that CNT determination in environmental and biological samples requires not only structural characterization but also quantification, separation, and matrix-specific validation.⁵ Comparative studies of multiwalled CNTs demonstrated that TEM, gas adsorption, thermogravimetry, and X-ray scattering provide different but complementary descriptions of the same material.⁶ More recent work has highlighted high-throughput homogeneity evaluation and AFM-IR nanoscale chemical mapping.^7,8 Application-oriented CNT Raman protocols and broader Raman interpretation frameworks have also been reviewed.^9,10 Additional studies have examined TGA-based thermal stability analysis, AFM-based topography, and conductive-AFM measurements,^11-13 as well as high-energy XRD and combined electron microscopy/Raman characterization.^14,15

The central argument of this review is that no single method can fully characterize carbon nanotubes or graphene. Raman spectroscopy may reveal disorder and layer number, but it cannot alone determine all aspects of flake-size distribution, surface chemistry, oxidation state, catalyst residue, or bulk homogeneity. TEM can image atomic structure but often probes a small sample area and may induce beam damage. XPS reveals surface chemistry but averages over selected analysis areas and can miss buried heterogeneity. TGA quantifies thermal stability and residue but cannot identify defect type. Therefore, high-quality characterization requires an integrated strategy that links local, statistical, chemical, structural, thermal, optical, and functional measurements.

Unlike reviews that focus mainly on individual characterization techniques, this review evaluates characterization as a reliability problem. It emphasizes where common methods can lead to incomplete or misleading conclusions, especially when single-point Raman spectra, selected microscopy images, or surface-averaged chemical measurements are used to represent heterogeneous CNT and graphene materials. The review therefore frames characterization as a multimodal quality-control challenge that requires statistical sampling, cross-method validation, and application-specific reporting. Figure 1 summarizes this transition from single-technique analysis to multimodal quality control.

Core Characterization Framework: Characterization Methods, Data Collection, and Data Analysis for CNTs and Graphene

Introduction to CNTs and Graphene: Structure and Properties

The modern CNT field was accelerated by Iijima’s observation of graphitic microtubules, which provided direct evidence for nanoscale tubular carbon structures.¹⁶ Graphene was later isolated and electrically characterized as an atomically thin carbon material, opening a new class of two-dimensional crystals.¹⁷ The rapid rise of graphene research was driven by its unusual electronic structure, mechanical strength, optical transparency, and compatibility with nanoscale device concepts.^18–20 These foundational advances also made clear that structure and property cannot be separated: CNT chirality controls whether a tube is metallic or semiconducting, while graphene layer number, stacking order, substrate interaction, and defect concentration control transport, optical, mechanical, and chemical behavior.

From a characterization perspective, CNTs and graphene present different challenges. CNTs require determination of diameter, length, wall number, chirality, bundling, catalyst residue, amorphous carbon content, and functionalization. Graphene requires determination of monolayer/few-layer content, lateral flake size, stacking, wrinkles, grain boundaries, cracks, point defects, oxygen-containing groups, doping, strain, and contamination. In both materials, the measured property is often scale-dependent: atomic-resolution microscopy may reveal a nearly perfect local lattice, while large-area Raman mapping or industrial sampling may show substantial heterogeneity. This mismatch between nanoscale ideality and macroscale variability is one of the main reasons why rigorous characterization remains a central research priority. Table 1 provides a material-specific protocol for linking these descriptors to suitable characterization methods.

Data Collection: Measured Properties and Characterization Methods

Data collection in CNT and graphene characterization involves selecting complementary techniques that measure morphology, layer number, wall number, defects, chemical state, purity, thermal stability, surface area, and functional properties. Raman spectroscopy, electron microscopy, scanning probe microscopy, XPS, XRD, TGA, FTIR, optical spectroscopy, BET analysis, and local electrical, thermal, and mechanical measurements collect different forms of evidence, so reliable assessment depends on combining local and bulk measurements rather than relying on one method alone. Figure 2 maps the major characterization techniques to the material features they help evaluate, while Table 2 summarizes each method, its strengths, limitations, and complementary use.

Raman and Optical Spectroscopy

Raman spectroscopy is the most widely used nondestructive characterization method for graphene and CNTs because it is sensitive to bonding, disorder, phonons, strain, doping, layer number, edge structure, and electronic resonance. The Raman spectrum of graphene and graphitic carbon is dominated by the G band, D band, and 2D band. The G band is associated with in-plane sp² carbon vibrations, the D band is activated by disorder or edges, and the 2D band arises from a second-order double-resonance process. The landmark Raman study of graphene and graphene layers showed that the 2D-band shape, width, and intensity can distinguish monolayer graphene from bilayer and few-layer graphene.²¹ Broader Raman reviews have clarified the role of double resonance, electron–phonon coupling, doping, strain, defects, and stacking in graphene spectra.²²

The use of the D band as a disorder marker has deep roots in the Raman analysis of graphite, where the D-to-G intensity relationship was linked to crystallite size.²³ Ferrari and Robertson later developed a framework for interpreting Raman spectra of disordered and amorphous carbon, which remains important for distinguishing graphitic order, nanocrystallinity, and amorphization trajectories.²⁴ Double-resonance Raman scattering provided the theoretical basis for understanding the excitation-energy dependence of D and 2D features in graphite and graphene.^25,26 Quantitative defect analysis in graphene was further advanced by studies showing that the D/G ratio depends on excitation energy and that the same D/G ratio can correspond to different defect-density regimes if the sample crosses from low to high disorder.^27,28

Raman spectroscopy is also sensitive to charge doping and strain. Electrochemical top-gating experiments demonstrated that the G and 2D bands shift and change intensity with carrier concentration, making Raman spectroscopy a practical probe of doping.²⁹ Charged impurities can produce Raman signatures even in nominally undoped graphene, revealing nanoscale electronic inhomogeneity.³⁰ Spatially resolved Raman spectroscopy made it possible to map layer number, edges, and local disorder across graphene flakes.³¹ Thus, Raman spectroscopy is not merely a fingerprinting tool; it is a spatially resolved diagnostic for structural and electronic nonuniformity.

In CNTs, Raman spectroscopy provides even richer information because of resonance effects and one-dimensional electronic density of states. Optical absorption studies established the relationship between nanotube diameter and electronic transition energies, often represented through Kataura-type plots.³² Photoluminescence excitation spectroscopy enabled assignment of optical transitions to specific semiconducting SWCNT structures,³³ while band-gap fluorescence from individual SWCNTs provided a direct optical method for probing isolated semiconducting nanotubes.³⁴ Empirical transition-energy maps further improved structure assignment in aqueous suspensions.³⁵ Resonant Raman measurements can identify nanotube diameter through radial breathing modes and can help assign chirality through electronic transition energies.^36,37 Comprehensive CNT Raman reviews have shown that radial breathing modes, G-band line shapes, D-band intensity, and resonance conditions together provide structural, electronic, and defect-related information.³⁸ Representative Raman signatures of sp² carbon nanostructures are shown in Figure 3.

Despite its importance, Raman spectroscopy has limitations. Laser wavelength, power, substrate, temperature, polarization, focus, aggregation, and processing history can influence spectra. For CNTs, resonance enhancement means that Raman spectra may overrepresent nanotube species resonant with the chosen laser energy. For graphene, D/G ratios must be interpreted carefully because edges, vacancies, functional groups, grain boundaries, and amorphization can all activate disorder bands differently. Therefore, Raman spectroscopy should be used as part of a multimodal workflow rather than as a stand-alone quality certificate.³⁹

A common limitation in the CNT and graphene literature is the overinterpretation of Raman spectra as complete quality indicators. For graphene, a low D/G ratio does not necessarily prove high material quality if sampling is limited, edges are excluded, or oxidation and contamination are not independently measured. For CNTs, resonance effects can bias the spectrum toward specific tube populations, so Raman intensity may not represent the full diameter, chirality, or metallic/semiconducting distribution of the sample. Therefore, Raman-based claims about quality, purity, defect density, or uniformity should be supported by microscopy, surface chemistry, and statistically meaningful mapping.

Electron Microscopy and Scanning Probe Characterization

Transmission electron microscopy is essential for direct visualization of CNT and graphene structure. For graphene, TEM confirmed that suspended monolayers are not perfectly flat but exhibit intrinsic rippling and corrugation.⁴⁰ Atomic-resolution TEM and electron energy-loss analysis later confirmed free-standing single-layer graphene and provided insight into its morphology and electronic excitations.⁴¹ Aberration-corrected TEM allowed direct imaging of lattice atoms and topological defects in graphene membranes, including Stone–Wales defects.⁴² TEM also revealed atomic defects in graphene layers and helped establish how vacancy-type and bond-rotation defects alter nanoscale carbon networks.⁴³ Real-time atomic-resolution imaging of graphene edges showed edge reconstruction and atom dynamics under the electron beam.⁴⁴ Grain-boundary imaging in CVD graphene demonstrated that polycrystallinity can strongly affect mechanical behavior and must be considered in large-area graphene applications.⁴⁵ Reviews of graphene defects and beam-induced engineering have further shown that defects can be harmful for electronics but useful for functionalization, catalysis, membranes, and sensing.^46,47 Atomic-resolution TEM evidence of graphene lattice imaging is illustrated in Figure 4.

Scanning probe microscopy complements electron microscopy by providing topography, local electronic structure, and mechanical or electrical maps under different environments. STM revealed the atomic structure and substrate-induced corrugation of graphene on SiO₂, showing that supported graphene is strongly influenced by surface roughness and contamination.⁴⁸ High-resolution STM imaging of mesoscopic graphene sheets on insulating substrates demonstrated that atomically resolved imaging can be combined with Raman identification of monolayers.⁴⁹ Scanning tunneling spectroscopy of epitaxial graphene revealed quasiparticle interference and scattering, linking defects and electronic structure.⁵⁰

AFM is widely used to measure CNT diameter, graphene flake thickness, surface roughness, and film morphology. However, apparent AFM height can depend on substrate, humidity, tip condition, contamination, and scanning mode. For graphene, AFM thickness values are often larger than the ideal interlayer spacing because of adsorbates and tip–sample interactions. For CNTs, AFM can estimate diameter and length but may underestimate bundled structures or distort soft assemblies. Conductive AFM adds local current mapping and contact-resistance information, while AFM-IR extends scanning probe analysis into nanoscale chemical spectroscopy. These methods are particularly valuable for heterogeneous films, composites, and device structures where spatially averaged techniques obscure local behavior.

A major weakness of microscopy-based characterization is the tendency to present selected representative images without sufficient statistical sampling. High-resolution TEM can demonstrate the presence of well-ordered regions, but it cannot alone prove batch-level uniformity, purity, or processing consistency. Similarly, AFM and STM provide valuable local information but can be affected by substrate roughness, contamination, tip artifacts, and small field-of-view bias. For reliable material assessment, microscopy should be combined with automated image analysis, large-area sampling, and complementary chemical or spectroscopic measurements.

Chemical, Surface, Diffraction, and Thermal Characterization

Chemical characterization is especially important for graphene oxide, reduced graphene oxide, functionalized CNTs, oxidized CNTs, and solution-processed graphene. Chemically reduced graphene nanosheets have been characterized using elemental analysis, TGA, SEM, XPS, NMR, Raman spectroscopy, and conductivity measurements, illustrating the need for multiple techniques to evaluate reduction and residual oxygen content.⁵¹ Reviews of graphene oxide chemistry emphasize that the material is structurally heterogeneous and that its properties depend strongly on oxidation protocol, functional-group distribution, water content, and reduction pathway.^52-54 Solid-state NMR studies led to influential graphite oxide structural models involving aromatic regions, hydroxyl groups, epoxides, and oxidized domains.^55,56 XRD, TEM, XPS, and electron energy-loss spectroscopy have been used to compare graphene oxide and reduced graphene oxide, revealing changes in interlayer spacing, oxygen content, and electronic structure after reduction.⁵⁷

XPS is one of the most important tools for quantifying surface chemistry in CNTs and graphene derivatives. In CNTs, oxidation introduces carboxyl, hydroxyl, carbonyl, and other oxygen-containing groups, but harsh treatments can damage nanotube sidewalls and shorten tubes.⁵⁸ For graphene oxide, improved synthesis protocols have been developed to control oxidation efficiency and reduce hazardous by-products.⁵⁹ However, functional-group assignment by XPS is not always straightforward because peak deconvolution depends on fitting constraints, reference binding energies, sample charging, and overlapping chemical states. Chemistry-focused reviews have therefore emphasized that controlled functionalization of graphene and graphene oxide requires sophisticated analytical methods rather than simple single-spectrum interpretation.⁶⁰

XRD is useful for probing interlayer spacing, stacking order, crystallinity, and oxidation-driven expansion. Graphene oxide typically shows an expanded interlayer distance compared with graphite because of oxygen functional groups and intercalated water. Reduced graphene oxide often shows partial restacking and broad peaks associated with disordered graphitic domains. For CNTs, XRD and wide-angle scattering provide information on graphitic wall ordering, bundle packing, and crystallinity, but peak broadening and sample heterogeneity can complicate interpretation. TGA is used to assess purity, amorphous carbon content, catalyst residue, oxidation temperature, and thermal stability. BET gas adsorption provides specific surface area and porosity information and is especially useful for powders, aerogels, electrodes, adsorbents, and catalyst supports. Theoretical work on CNT surface area showed how diameter, wall number, and bundling control accessible surface area.⁶¹

Surface-sensitive and bulk chemical methods also require caution. XPS can estimate elemental composition and chemical-state distribution, but it cannot fully resolve the spatial arrangement of oxygen groups or distinguish all overlapping carbon-oxygen environments without fitting assumptions. FTIR identifies functional groups qualitatively but is often less reliable for quantitative assignment in heterogeneous carbon materials. TGA can estimate residue and thermal stability, but overlapping mass-loss steps cannot uniquely identify specific defect or functional-group types. These limitations show why chemical characterization should be interpreted through converging evidence rather than isolated spectra.

Property-Oriented Characterization

Beyond structural and chemical analysis, CNTs and graphene often require direct property measurements. AFM nanoindentation of suspended graphene membranes provided quantitative elastic stiffness and intrinsic strength values, demonstrating the extraordinary mechanical properties of monolayer graphene.⁶² Tensile testing of individual MWCNTs inside an SEM revealed high strength and a “sword-in-sheath” failure mechanism in which the outer shell bears much of the load.⁶³ Electromechanical resonance measurements of CNTs in TEM enabled extraction of bending modulus and dynamic mechanical behavior.⁶⁴ These examples show that property measurements are themselves characterization tools because they reveal how defects, geometry, interfaces, and processing affect functional performance.

Thermal characterization is similarly important. Raman thermometry of suspended graphene demonstrated extremely high thermal conductivity, linking phonon transport to crystal quality and substrate isolation.⁶⁵ Electrical self-heating measurements of suspended SWCNTs enabled extraction of thermal conductance and thermal conductivity at elevated temperature.⁶⁶

However, thermal measurements are sensitive to contact resistance, sample geometry, defect density, isotope content, substrate coupling, laser absorption, and environmental conditions. Therefore, thermal characterization must be interpreted alongside structural and chemical data.

Electrical measurements, including field-effect mobility, sheet resistance, Hall effect, four-point probe conductivity, and local conductive-AFM mapping, are essential for electronic applications.

However, electrical performance depends strongly on contact resistance, substrate disorder, adsorbates, grain boundaries, tube–tube junctions, interflake resistance, and percolation pathways. A graphene film with high-quality flakes may still show poor conductivity if flake junctions are resistive. A CNT film may show high conductivity but poor semiconducting purity. Therefore, electrical characterization should be tied to morphology, chemistry, and statistical sampling.

Property measurements are essential but should not be treated as direct substitutes for structural characterization. High electrical conductivity, mechanical strength, or thermal performance may result from processing, alignment, percolation, contact quality, or film densification rather than intrinsic nanotube or graphene quality alone. Conversely, a structurally high-quality material may perform poorly if interfaces, junctions, or processing conditions are unfavorable. Therefore, functional performance should be interpreted together with morphology, chemistry, defect density, and statistical uniformity. Figure 5 links nanoscale structure and processing variables to macroscopic performance.

Data Analysis and Interpretation

Data analysis translates raw characterization outputs into useful material descriptors. Raman spectra are analyzed through the positions, widths, shapes, and intensity ratios of the D, G, 2D, and radial breathing mode features to infer disorder, layer number, strain, doping, nanotube diameter, and resonance behavior. Microscopy data are interpreted through particle or flake dimensions, wall number, layer count, defect type, edge structure, surface roughness, and image statistics. XPS and FTIR data are analyzed to estimate surface composition and functional groups, while XRD, TGA, and BET data provide information about stacking, crystallinity, thermal stability, residue, and accessible surface area.

For reliable interpretation, data reduction steps must be reported clearly. Important examples include Raman baseline correction and peak fitting, XPS background subtraction and peak deconvolution, TEM/SEM image thresholding, AFM height calibration, XRD peak fitting, TGA derivative analysis, and BET model selection. Because each method has method-specific bias, the most useful information is obtained when independent techniques converge on the same conclusion. Figure 6 and Table 3 summarize common interpretation pitfalls and cross-validation routes, while Table 4 lists minimum reporting standards for reproducible characterization.

Research Gaps

Lack of Universal Characterization Protocols

A major gap is the absence of universally adopted protocols for classifying CNT and graphene quality. Although roadmaps have identified characterization and processing as central challenges for graphene technologies,^67,68 different laboratories still use different definitions of “graphene,” “few-layer graphene,” “reduced graphene oxide,” “high-quality CNTs,” and “defect density.” This inconsistency makes it difficult to compare results across studies. As a result, materials with very different layer number, defect density, oxidation state, or purity may be described using the same label, which weakens reproducibility and slows technology translation.

Insufficient Statistical Sampling

Many studies report representative TEM images, Raman spectra, or AFM scans, but carbon nanomaterials are often heterogeneous over millimeter, centimeter, or batch scales. Industrial CNT powders and graphene flakes may show broad distributions of length, diameter, wall number, layer number, flake size, defect density, and oxygen content. Large-scale commercial CNT applications and graphene production both require sampling strategies that reflect real batch variability.^69,70 This creates a risk that conclusions are based on local best-case regions rather than true batch-level material quality.

Ambiguity in Defect Quantification

Raman D/G ratios are widely used, but defect quantification remains context-dependent. Vacancies, edges, grain boundaries, functional groups, sp³ sites, ion-damage tracks, and amorphous carbon can all alter Raman spectra differently. For CNTs, resonance effects complicate defect analysis because the measured spectrum may be biased toward certain tube species. Future defect metrology must combine Raman, TEM, XPS, electrical measurements, and controlled reference materials.

Incomplete Functional-Group Identification

Graphene oxide, reduced graphene oxide, and functionalized CNTs are chemically heterogeneous. XPS, FTIR, NMR, TGA, and Raman can each identify aspects of functionalization, but none alone fully determines the spatial distribution and bonding configuration of functional groups. Quantitative differentiation of hydroxyl, epoxide, carbonyl, carboxyl, lactone, ether, and adsorbed contaminant species remains challenging.

Weak Nanoscale-Macroscale Correlation

Atomic-resolution TEM may show nearly perfect regions, but device or composite performance is governed by large-area uniformity, interfaces, percolation, grain boundaries, and processing-induced damage. A persistent gap is the lack of strong correlation between nanoscale metrics and macroscale performance. Future characterization must bridge single-flake, single-tube, thin-film, powder, composite, and device scales.

Limited Standardization for Commercial Materials

Commercial graphene and CNT materials are often marketed using inconsistent terminology and incomplete data sheets. Independent analysis of commercial graphene flakes has shown that many products labeled as graphene contain thick graphite-like platelets or broad quality distributions.⁷⁰ This highlights a major translational gap: without standardized descriptors, customers cannot reliably select materials for electronics, composites, membranes, sensors, or energy devices. Consequently, reported performance differences may reflect inconsistent material quality rather than genuine differences in device design or application strategy.

Data Processing and Reporting Variability

Raman baseline correction, XPS peak fitting, TEM image thresholding, AFM height analysis, TGA derivative analysis, and XRD peak fitting all involve user choices. Without transparent reporting of acquisition parameters and analysis workflows, reproducibility suffers. Automated and machine-learning-assisted analysis could help, but only if trained on curated, well-labeled datasets.

Table 5 summarizes the main research gaps, their consequences, and future metrology solutions.

Future Directions

Future characterization of CNTs and graphene should move toward integrated, standardized, and statistically meaningful workflows. First, application-specific characterization protocols are needed. A CNT sample for structural composites requires different metrics than a semiconducting CNT ink for transistors or a graphene oxide dispersion for membranes. Characterization should therefore be linked to intended function rather than reported as a generic checklist.

Table 6 links major application areas to the characterization priorities they require.

Second, high-throughput mapping should become routine. Raman mapping, automated SEM/TEM image analysis, AFM statistical height mapping, and spatially resolved XPS or spectromicroscopy can reveal heterogeneity that single-point analysis misses. High-throughput methods are particularly important for CVD graphene, roll-to-roll films, liquid-exfoliated graphene, and industrial CNT powders.

Third, reference materials and calibration standards are urgently needed. Standard graphene and CNT reference samples with known layer-number distribution, defect density, oxygen content, and catalyst residue would improve interlaboratory comparability. Shared protocols for Raman acquisition, XPS fitting, TGA atmosphere and heating rate, AFM height analysis, and TEM sampling would reduce ambiguity.

Fourth, multimodal correlative characterization should be expanded. The same region of a sample should be examined by Raman, AFM, SEM/TEM, XPS, and electrical mapping when possible. Correlative workflows can connect local structure to local performance and reduce the risk of drawing conclusions from nonrepresentative regions.

Fifth, machine learning can improve analysis if used carefully. Automated peak fitting, image segmentation, flake classification, defect identification, and spectral clustering could accelerate characterization. However, models must report uncertainty, avoid overfitting to narrow datasets, and preserve physical interpretability.

Finally, production-oriented reviews and protocols emphasize that scalable processing and reliable characterization must develop together.⁷¹ CNTs and graphene will not reach their full technological value merely through better synthesis; they require transparent, reproducible, and application-relevant metrology. Figure 7 presents a future roadmap for standardized, application-specific characterization.

Conclusion

The characterization of carbon nanotubes and graphene has progressed from basic structural confirmation to a sophisticated, multimodal discipline that connects atomic structure, chemistry, defects, morphology, and functional performance. Raman spectroscopy remains the most versatile and accessible method, but it cannot provide complete material qualification by itself. Electron microscopy, scanning probe microscopy, XPS, XRD, TGA, FTIR, optical spectroscopy, BET analysis, and property-specific measurements all contribute essential and complementary information.

The central challenge for the field is no longer the availability of characterization techniques but the development of reproducible, standardized, statistically meaningful, and application-specific characterization workflows. Research gaps remain in defect metrology, functional-group assignment, interlaboratory comparison, commercial quality control, and nanoscale–macroscale correlation. Future progress will require reference materials, automated mapping, transparent data-processing protocols, and integrated characterization pipelines. With these advances, CNTs and graphene can be evaluated more reliably and translated more effectively into electronics, composites, sensors, membranes, energy devices, biomedical platforms, and industrial materials.

Therefore, the future value of CNT and graphene characterization will depend less on the availability of advanced instruments and more on how rigorously these tools are combined, standardized, and linked to application-level performance.

Declaration of Generative AI Use in the Writing Process

During the preparation of this work, the author used ChatGPT (GPT- 5.5, OpenAI) for paraphrasing, editing, and proofreading the overall manuscript. All AI-assisted content was subsequently reviewed, edited, and verified by the author.

Acknowledgement

The author expresses deep gratitude to Dr. Collin D. Wick, Dean and Daniel D. Reneau Eminent Scholar Chair of the College of Engineering and Science at Louisiana Tech University, for his invaluable administrative support and strategic vision that facilitated this academic endeavor.

Special thanks are extended to Dr. Shengnian Wang, Professor of Chemical Engineering, who played a pivotal role in fostering the author's intellectual engagement with this field of research; his mentorship provided the fundamental inspiration that ultimately motivated the development of this critical perspective paper.

Additionally, the author sincerely appreciates Dr. Kevin Nixon, Assistant Professor of Chemical Engineering, for providing the research autonomy to foster an independent scholarly environment, as well as for his insightful guidance and overall scholarly stewardship throughout the publication process.

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