We present a comparative study aiming to determine the most efficient multivariate model screening for the main drugs of abuse based on their ATR-FTIR spectra. A preliminary statistical analysis of selected spectra data extracted from the public SWGDRUG IR Library was first performed. The results corroborated those of an exploratory analysis that was based on several dimensionality reduction methods, i.e., Principal Component Analysis (PCA), Independent Component Analysis (ICA), and autoencoders. Then, several machine learning methods, i.e., Support Vector Machines (SVM), eXtreme Gradient Boosting (XGB), Random Forest, Gradient Boosting, and K-Nearest Neighbors (KNN), were used to assign the drug class membership. In order to account for the stochastic nature of these machine learning methods, both models were evaluated 10 times on a randomly distributed subset of the whole SWGDRUG IR Library, and the results were compared in detail. Finally, their performance in assigning the class identity of three classes of drugs of abuse, i.e., hallucinogenic (2C-x, DOx, and NBOMe) amphetamines, cannabinoids, and opioids, were compared based on confusion matrices and various classification parameters, such as balanced accuracy, sensitivity, and specificity. The advantages of each of the illicit drug-detecting systems and their potential as forensic screening tools used in field scenarios are also discussed.

Chromium nitride thin films are known for their good mechanical properties. We present the characteristics of ultrathin chromium nitride films under 400 nm thickness deposited on silicon substrates by direct current and high-power impulse magnetron sputtering techniques. The methods of investigation of the CrN films were scanning electron microscopy, atomic force microscopy, and nanoindentation. Qualitative and quantitative analyses were performed using AFM and SEM images by fractal dimension, surface roughness and gray-level co-occurrence matrix methods. Our results show that using magnetron techniques, ultrathin CrN films with excellent mechanical properties were obtained, characterized by values of Young’s modulus between 140 GPa and 250 GPa for the samples obtained using high-power impulse magneton sputtering (HiPIMS) and between 240 GPa and 370 GPa for the samples obtained using direct current sputtering (DC). Stiffness measurements also reveal the excellent mechanical properties of the investigated samples, where the samples obtained using HiPIMS sputtering had stiffness values between 125 N/m and 132 N/m and the samples obtained using DC sputtering had stiffness values between 110 N/m and 119 N/m.

This study explores the mechanical and optical properties of nanoporous copper (NPC) films dealloyed in acid and base using qualitative and quantitative measurements. Two types of NPC films (NPC 1 and NPC 2) were prepared by dealloying precursor films in different corrosive solutions. The films were characterized using atomic force microscopy (AFM) and scattering scanning near-field optical microscopy (s-SNOM). The AFM analysis revealed distinct nanoporous structures in both films, with NPC 2 showing larger pore sizes and the presence of copper aggregations on the surface. Rugosity measures showed that NPC 2 had higher rugosity than NPC 1 and indicated fundamental differences in the height distributions of the surface nano-topography. The Minkowski connectivity analysis highlighted differences in connectedness and topological characteristics between the two samples. The analysis of the GLCM features showed clear distinctions in ASM, Contrast, Dissimilarity, Energy, and Homogeneity between NPC 1 and NPC 2. The s-SNOM results demonstrated altered optical properties, with NPC 1 showing higher contrast at 1550 nm, while NPC 2 exhibited reversed-phase contrast at 561 nm. The phase contrast at 638 nm indicated a red-shifted optical absorption peak in NPC 2 compared to NPC 1.