![FT-IR spectra of control and treated 1,2,4-triazole (Tl and T2) samples are presented in Figure 6.The FT-IR spectrum of control 1,2,4-triazole showed characteristic absorption peaks at 3097 and 3032 cm’! due to C-H aromatic vibrations. Vibration peak at 3126 cm" was due N-H stretching of the sample. FT-IR peaks at 1529 and 1483 cm" were corresponded to C=C stretching for aromatic groups. Vibration peak for -N=N stretching was observed at 1543 cm" [31]. Likewise, the FT-IR spectrum of 1,2,4-triazole (T1) showed absorptions peaks at 3095, and 3034 cm?! that were due to C-H aromatic vibrations. N-H stretching vibration was observed at 3128 cm" in the sample. FT-IR peaks observed at 1529, and 1483 cm" were corresponded to C=C (aromatic) stretching vibration peak (Figure 6). Vibration peak for -N=N stretching was observed at 1543 cm. FT-IR spectrum of |,2,4-triazole (T2) showed absorption peaks at 3028 cm" that were due to C-H (aromatic) stretching vibrations. N-H stretching vibration peak was observed at 3128 cm" in the sample. C=C (aromatic) stretching vibrations were observed at 1529, and 1481 cm". N=N stretching peak Acknowledgement](https://figures.academia-assets.com/40056132/figure_004.jpg)
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Key research themes
1. How can areal surface topography measuring instruments be accurately calibrated and validated for improved measurement fidelity?
This research area focuses on developing and standardizing metrological characteristics (MCs) and calibration methodologies for areal surface topography measuring instruments, aiming to enhance their accuracy, traceability, and applicability in industrial and research settings. Precise calibration of lateral (x, y) and vertical (z) scales, including linearity, amplification, and squareness, is critical to obtain reliable 3D surface data, directly impacting the fidelity of surface texture assessments across diverse applications.
Key finding: The authors systematically evaluated metrological characteristics including flatness deviation, amplification, linearity, and perpendicularity for a point autofocus instrument (PAI). They demonstrated that these parameters... Read more
Key finding: This work presented methods for determining amplification coefficient, linearity, and axis squareness based on draft international specifications for areal surface texture measuring instruments. Novel approaches to z-axis... Read more
Key finding: The authors highlighted prevailing challenges such as achieving high spatial and temporal bandwidth in surface measurement instruments and emphasized the necessity of developing a toolbox of methods rather than a... Read more
2. What are effective computational and methodological approaches to extract and analyze surface area and topography data from complex or irregular surfaces?
This theme explores image processing, computational geometry, and sampling methodologies for accurate surface area estimation and topography extraction from volumetric, irregular, or anisotropic surfaces. It addresses challenges in reconstructing iso-surfaces, accounting for real-world surface irregularities, and optimizing measurement precision despite anisotropy and data discretization constraints. Such studies enable improved interpretation of surface characteristics crucial for metrological, tribological, and material science applications.
Key finding: The paper compared various iso-surface extraction techniques—particularly Marching Cubes (MC) and Marching Tetrahedra (MT) methods—evaluating their computational performance, precision, and visual quality trade-offs. It... Read more
Key finding: Through computer simulations, this study compared three surface area estimators based on the Cauchy formula using one, two, or three orthogonal spatial grids. Results showed that the spatial estimator using three orthogonal... Read more
Key finding: This research developed a technique integrating digital elevation models (DEM) with vector polygon data to approximate true surface areas accounting for terrain undulations and slopes, compensating for traditional planar... Read more
3. How can portable and software-assisted measurement systems utilizing image processing improve surface area analysis in diverse contexts?
This area investigates the design, development, and validation of portable systems and software that leverage image recognition, machine learning algorithms, and device interfacing to measure surface areas efficiently and accurately outside the laboratory setting. Such systems enable real-time, non-destructive, and automated surface area quantification applications in agriculture, textiles, and industrial metrology, expanding access and reducing reliance on costly or complex instrumentation.
Key finding: The authors designed and validated a portable acrylic device integrated with radial basis function neural networks (RBFN) for image-based leaf area measurement on mobile Android devices. The system demonstrated high... Read more
Key finding: The paper introduced URwaga software facilitating simultaneous operation and data acquisition from heterogeneous measurement instruments through RS232 and USB interfaces. The software integrates multiple device inputs,... Read more
Key finding: This study developed an image-processing algorithm capturing knitted fabric stitch density via smartphone images. The method significantly reduces manual counting errors and correlates strongly (up to 97.3%) with conventional... Read more