María L. Ospina Castro

Full-spectrum Cannabis sativa resins are complex phytochemical matrices whose antimicrobial activity may vary according to the microorganism, treatment concentration, and biological phenotype evaluated. This study investigated the antibacterial and antifungal activities of Cannabis sativa resins and their ability to disrupt pre-formed biofilms of five chemically characterized cannabis resins and evaluated the relative contribution of selected experimental variables to the observed biological responses using inferential statistics and interpretable machine learning. Cannabinoid and terpene profiles were characterized by HPLC-PDA and gas chromatography, respectively. Antimicrobial activity was evaluated against Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, Acinetobacter baumannii, Candida albicans and Saccharomyces cerevisiae. The resins produced microorganism-, concentration-, and phenotype-dependent responses, with complete planktonic growth inhibition observed only for S. aureus and more heterogeneous responses against pre-formed biofilms. Generalized linear models identified significant effects of the experimental factors, whereas supervised machine-learning models achieved high predictive performance, particularly for planktonic growth (R² ≈ 0.99). Permutation Importance and SHAP analyses revealed that microbial identity provided the greatest predictive information, followed by treatment concentration, whereas resin identity exerted a smaller, phenotype-dependent contribution. These findings indicate that, within the experimental domain evaluated, microorganism identity and treatment concentration provided greater predictive information than resin identity. Integrating phytochemical characterization with microbiological phenotyping and interpretable modeling of experimental variables provides a useful framework for investigating the biological behavior of complex natural-product matrices.

Reliable analytical methods for quantifying the major phytocannabinoids cannabidiol (CBD) and Δ⁹-tetrahydrocannabinol (Δ⁹-THC) are essential to ensure quality control and regulatory compliance of cannabis-derived products. This study evaluated attenuated total reflectance Fourier-transform infrared spectroscopy (FTIR-ATR), combined with Partial Least Squares (PLS) and Classical Least Squares (CLS) chemometric models, to quantify cannabinoids in cannabis extract oils. FTIR-ATR spectra were collected over 4000–400 cm⁻¹ and processed using principal component analysis (PCA), Multiplicative Scatter Correction (MSC), and multivariate calibration. The PLS model, built on the optimal spectral regions of 1000–1800 cm⁻¹ and 3500 cm⁻¹ and validated by leave-one-out cross-validation, achieved strong predictive performance (RMSEC = 0.559%, RMSECV = 0.675%; R²cal = 0.910, R²cv = 0.869), indicating good predictive ability with no evidence of overfitting. Notably, the variables with the highest VIP scores were mainly associated with the extract-to-diluent ratio rather than CBD-specific absorbance bands. The CLS model, in turn, showed excellent agreement for pure cannabinoid standards (MSE on the order of 10⁻⁶; β coefficients between 0.98 and 0.99) but performed poorly in complex matrices, yielding negative coefficients for several samples. This limitation stems from unmodeled matrix components and the near-collinearity of the THC and CBD infrared spectra, which renders the CLS system ill-conditioned and causes noise to redistribute between the two basis vectors. Taken together, these findings indicate that the FTIR-ATR/PLS approach offers a robust, rapid, and cost-effective alternative for routine cannabinoid analysis. In contrast, the CLS model remains valuable for residual-based detection of unmodeled spectral contributions that may signal sample anomalies or contamination.

This work presents the use of an unmodified fluorine-doped tin oxide (FTO) electrode as an electrochemical sensor for the stable and highly reproducible detection of cannabidiol (CBD) in an oral pharmaceutical formulation (CBD-OS). This was achieved using an electrochemical methodology with 0.1 M LiClO₄ in acetonitrile as the electrolyte medium. The oxidation process of CBD on the FTO was extensively studied using cyclic voltammetry to analyze the influence of forced convection during the experiment. Quantification was performed by differential pulse voltammetry with standard addition to the dissolved CBD oral oil (CBD-OS), which allowed for the determination of a detection limit of 4.8 µM and a correlation coefficient R² = 0.9989. Statistical tests indicated no significant differences between the proposed electrochemical methodology and HPLC. The FTO electrode exhibited high reproducibility, withstanding more than 20 consecutive additions to the same electrode without loss of response, thanks to its intrinsic stability and the strategic use of forced convection. The use of an unmodified electrode enabled the development of a simple, reproducible, robust, and cost-effective analytical methodology for CBD analysis in pharmaceutical samples.

A hybrid material composed of IRMOF-3 and ZnO (IRMOF-3/ZnO) was synthesized to enhance photocatalytic methylene blue (MB) degradation under visible-light irradiation. Scanning electron microscopy, Fourier-transform infrared spectroscopy, X-ray diffraction, and diffuse-reflectance UV-Vis analyses confirmed the successful integration of ZnO into the IRMOF-3 framework. Compared with unmodified IRMOF-3, the hybrid demonstrated superior MB decomposition, as evidenced by faster reaction rate constants and shorter half-lives. Monitoring the MB absorbance at 670 nm (λ_max) revealed more pronounced colorant removal when IRMOF-3/ZnO was exposed to a visible-light source. Diffuse-reflectance UV-Vis spectroscopy showed that IRMOF-3 has a band gap of 2.7 eV, whereas IRMOF-3/ZnO exhibits a slightly higher band gap of 2.8 eV. This modest shift, coupled with the strong interaction between the ZnO semiconductor and the MOF’s amine functionalities, enabled two distinct energy-transfer pathways: intermolecular transfer from IRMOF-3 linkers (acting as visible-light antennas) to ZnO, and intramolecular transfer from Zn to IRMOF-3. Together, these pathways generated abundant free radicals for efficient dye degradation. Despite the necessity for careful synthesis protocols and control of operating conditions to preserve the MOF structure and optimize ZnO loading, the IRMOF-3/ZnO hybrid shows promise as a robust, cost-effective photocatalyst for water-pollutant remediation, taking advantage of the more abundant visible region of solar light.

This work presents a study investigating the inter‐ and intra‐molecular interactions within the Os ₃ (CO) ₉ (μ‐H) ₂ (μ ³ ‐η ¹ : η ¹ : η ² ‐C ₁₆ H ₈ ) crystal. The crystal‘s behavior is analyzed by comparing experimental distances, revealing intriguing interactions. In the isolated molecule, an unconventional pyrene‐C−H⋅⋅⋅CO interaction is observed, an electron transfer from σ(C−H) to π*(CO). Strikingly, the Quantum Theory of Atoms in Molecules identifies similarities to an intramolecular charge‐inverted hydrogen bond, despite its relatively low stability due to proximity to critical points. Energy surface scans demonstrate that the interaction arises from van der Waals strain induced by the crystal‘s packing structure. The proximity between carbonyl and pyrene facilitates electron transfer between σ(C−H) and π*(CO) at distances similar to the crystal structure. A significant correlation is established between total energy and the ratio (|V|/G) of potential energy density (V) to Lagrangian kinetic energy (G) at bond (BCP) and ring (RCP) critical points, underscoring the role of electron delocalization on the pseudo‐ring in determining the existence and characteristics of these interactions. In conclusion, this study provides valuable insights into molecular interactions within the Os ₃ (CO) ₉ (μ‐H) ₂ (μ ³ ‐η ¹ : η ¹ : η ² ‐C ₁₆ H ₈ ) crystal, enriching our understanding of crystal interactions and offering perspectives for further exploration in this field.

A tunable quantum cascade laser (QCL) spectrometer was used to develop methods for detecting and quantifying high explosives (HE) in soil based on multivariate analysis (MVA) and artificial intelligence (AI). For quantification, mixes of 2,4-dinitrotoluene (DNT) of concentrations from 0% to 20% w/w with soil samples were investigated. Three types of soils, bentonite, synthetic soil, and natural soil, were used. A partial least squares (PLS) regression model was generated for predicting DNT concentrations. To increase the selectivity, the model was trained and evaluated using additional analytes as interferences, including other HEs such as pentaerythritol tetranitrate (PETN), trinitrotoluene (TNT), cyclotrimethylenetrinitramine (RDX), and non-explosives such as benzoic acid and ibuprofen. For the detection experiments, mixes of different explosives with soils were used to implement two AI strategies. In the first strategy, the spectra of the samples were compared with spectra of soils stored in a database to identify the most similar soils based on QCL spectroscopy. Next, a preprocessing based on classical least squares (Pre-CLS) was applied to the spectra of soils selected from the database. The parameter obtained based on the sum of the weights of Pre-CLS was used to generate a simple binary discrimination model for distinguishing between contaminated and uncontaminated soils, achieving an accuracy of 0.877. In the second AI strategy, the same parameter was added to a principal component matrix obtained from spectral data of samples and used to generate multi-classification models based on different machine learning algorithms. A random forest model worked best with 0.996 accuracy and allowing to distinguish between soils contaminated with DNT, TNT, or RDX and uncontaminated soils.

A simple, remote-sensed method of detection of traces of petroleum in soil combining artificial intelligence (AI) with mid-infrared (MIR) laser spectroscopy is presented. A portable MIR quantum cascade laser (QCL) was used as an excitation source, making the technique amenable to field applications. The MIR spectral region is more informative and useful than the near IR region for the detection of pollutants in soil. Remote sensing, coupled with a support vector machine (SVM) algorithm, was used to accurately identify the presence/absence of traces of petroleum in soil mixtures. Chemometrics tools such as principal component analysis (PCA), partial least square-discriminant analysis (PLS-DA), and SVM demonstrated the effectiveness of rapidly differentiating between different soil types and detecting the presence of petroleum traces in different soil matrices such as sea sand, red soil, and brown soil. Comparisons between results of PLS-DA and SVM were based on sensitivity, selectivity, and areas under receiver-operator curves (ROC). An innovative statistical analysis method of calculating limits of detection (LOD) and limits of decision (LD) from fits of the probability of detection was developed. Results for QCL/PLS-DA models achieved LOD and LD of 0.2% and 0.01% for petroleum/soil, respectively. The superior performance of QCL/SVM models improved these values to 0.04% and 0.003%, respectively, providing better identification probability of soils contaminated with petroleum.

The first examples of 3-D metal–organometallic networks (MOMNs) bearing 1,1′-ferrocenyldicarboxylate ligands have been obtained. These MOMNs exhibit a diamondoid-like network topology with 3-fold interpenetration described with point symbols for a net with loops, {6⁶}{6}2. Their electrochemical properties were evaluated by solution-state differential pulse voltammetry.