With fiber laser induced breakdown spectroscopy, we explored the influence of pulse width (2–200 ns) and repetition rate (1 Hz to 1 MHz) on the quantitative results of elements in aluminum alloys. Moderately long pulse width was found to be beneficial to the improvement of detection sensitivity under the circumstance of limited pulse energy. Up to 40.4% enhancement of sensitivity and a 27.0% improvement in limit of detection (LOD) were achieved under high laser repetition rate, resulting in an agreeable LOD as low as 17.9 ppm considering the low laser irradiance applied here. The ranking of Mg > Mn > Cu > Fe > Zn was observed in terms of detection sensitivity, which was altered to Mg > Zn > Mn > Cu > Fe as the enhancement level was considered. The melting and boiling points of these elements were found to be highly correlated with the enhancement, which was ascribed to the efficient heat accumulation process under shortened inter-pulse delay that facilitates the melting and evaporation of elements having low melting and boiling points. Though helpful for improving detection sensitivity, high repetition rate may induce elemental fractionation and should be applied cautiously.
To minimize judgment errors, it is essential to employ diverse analytical methods to evaluate the same evidence, especially when the suspected objects share similar chemical structures. In this study, three-layered complex coatings, relevant to forensic analysis of automotive coatings involved in a hit-and-run case, were investigated using Fourier transform infrared (FT-IR) spectroscopy to determine their organic compositions. The functional groups and organic structures of the coating samples were discussed. Thorough investigations were conducted on subtle, yet distinctive spectroscopic peaks of two commonly used paints: alkyd amino paint and acrylic amino paint, utilized in the first and second layers of the coatings in this case. Key differences in third-layer coatings of the samples were also identified. A portable Raman spectroscope provided complementary data to reinforce FT-IR findings. Additionally, elemental composition analysis using a scanning electron microscope combined with energy dispersive X-ray spectrometer (SEM-EDS) produced results consistent with spectroscopic analyses. Through comprehensive instrumental analysis, the suspect was excluded, leading to the successful apprehension of the offender. The findings from this case highlight that employing multiple scientific methods delivers more precise and reliable outcomes in real-world forensic investigations.
This peer-reviewed study extends a previously modified method for human sex determination using electrothermal vaporization inductively coupled plasma optical emission spectrometry on chemically altered (dyed) hair. The impact of hair dye on elemental composition was evaluated, and model performance was tested under these conditions. Plasma loading effects were mitigated through argon-based internal standardization. This was followed by background correction, peak area integration, and multivariate analysis (principal component analysis and linear discriminant analysis (LDA)). Using new predictor elements (Ca, Fe, Li, and Sb), LDA accurately classified the sex of 10 dyed hair and 7 undyed hair samples with a model trained on 10 undyed samples analyzed under different conditions. These findings demonstrate the potential of the method for sex classification in chemically altered hair. This study serves as a proof-of-concept based on a limited and imbalanced dataset, establishing a basis for the future validation of a conclusive forensic model. The resulting model appears promising within the constraints of the dataset and adheres to green chemistry principles by employing polytetrafluoroethylene powder as a cost-effective, environmentally friendly carrier agent in place of greenhouse gas.
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.
Spectral comparison methods are widely used in various applications to compare raw or preprocessed Raman or infrared spectra with reference data for decision-making. However, they often struggle with complex samples, as dominant high-intensity and overlapping bands can mask subtle yet important differences. Recently, deep learning approaches have been proposed to replace conventional methods. In this proof-of-concept (PoC) study, a hybrid approach is explored that combines both deep learning with conventional approaches. A convolutional neural network (CNN) framework is used as a feature extraction platform to enhance spectral comparison by transforming spectra into a feature space that emphasizes localized and informative patterns. Comparison is then performed in this feature space using the conventional hit quality index (HQI). Compared to standard HQI, the CNN-augmented approach reduces spurious similarity and improves spectral discrimination. These results demonstrate that integrating learned feature extraction with established similarity metrics provides an effective and interpretable strategy for improving spectral comparison.
