The occurrence, persistence, and accumulation of antibiotics and non-steroidal anti-inflammatory drugs (NSAIDs) represent a new environmental problem due to their harmful effects on human and aquatic life. A suitable absorbent for a particular type of pollutant does not necessarily absorb other types of compounds, so knowing the compatibility between a particular pollutant and a potential absorbent before experimentation seems to be fundamental. In this work, the molecular interactions between some pharmaceuticals (amoxicillin, ibuprofen, and tetracycline derivatives) with two potential absorbers, chitosan and graphene oxide models (pyrene, GO-1, and coronene, GO-2), were studied using the ωB97X-D/6-311G(2d,p) level of theory. The energetic interaction order found was amoxicillin/chitosan > amoxicillin/GO-1 > amoxicillin/GO-2 > ibuprofen/chitosan > ibuprofen/GO-2 > ibuprofen/GO-1, the negative sign for the interaction energy in all complex formations confirms good compatibility, while the size of E_int between 24–34 kcal/mol indicates physisorption processes. Moreover, the free energies of complex formation were negative, confirming the spontaneity of the processes. The larger interaction of amoxicillin Gos, compared to ibuprofen Gos, is consistent with previously reported experimental results, demonstrating the exceptional predictability of these methods. The second-order perturbation theory analysis shows that the amoxicillin complexes are mainly driven by hydrogen bonds, while van der Waals interactions with chitosan and hydrophobic interactions with graphene oxides are modelled for the ibuprofen complexes. Energy decomposition analysis (EDA) shows that electrostatic energy is a major contributor to the stabilization energy in all cases. The results obtained in this work promote the use of graphene oxides and chitosan as potential adsorbents for the removal of these emerging pollutants from water.

This study investigated the deactivation and regeneration of hierarchical zeolites in vacuum gas oil conversion, aiming to reach the equilibrium state seen in fluidized bed catalytic cracking (FCC). The research utilized various characterization techniques to analyze the properties of zeolites before and after coking and regeneration. Zeolite Y-0.20-S was found to have the highest gasoline selectivity and quality, mirroring industrial yields, and displayed notable stability across deactivation/regeneration cycles. Higher mesopore concentration in zeolites led to increased coke selectivity and better resistance to deactivation. The study observed a dominance of aromatic coke with a higher degree of condensation in these zeolites. Despite coke deposition affecting acid and textural properties, the regeneration process effectively restored these characteristics, proving its efficiency. The zeolites with greater mesoporosity retained their fundamental properties responsible for activity and selectivity, highlighting the importance of selecting materials that provide high conversions and maintain stability and product selectivity over multiple cycles. The Y-0.20-S zeolite, in particular, was identified as a promising candidate for commercial catalyst development for gasoline production, contributing to the FCC process's energy efficiency.

A comprehensive investigation into the design and electrochemical optimization of composite electrodes consisting of poly(3,4-ethylenedioxythiophene) (PEDOT)/graphene oxide (GO)/Methanococcus deltae and reduced graphene oxide (rGO)/Methanococcus deltae hybrids, anchored onto stainless-steel (SS) substrates, has been conducted. The GO and rGO materials were synthesized using a modified Hummer method. The resulting SS/PEDOT/GO and SS/PEDOT/rGO composite electrodes were subjected to systematic electrochemical characterization, focusing on the PEDOT p-type and n-type doping/undoping processes within diverse solvent environments (CH₃CN and H₂O) and electrolyte compositions (LiClO₄ and KCl). Raman spectroscopy analysis confirmed the successful integration of graphene derivatives into the electrode structures, while field-emission scanning electron microscopy (FESEM) and atomic force microscopy (AFM) revealed increased surface roughness upon GO and rGO incorporation. This increase in surface roughness is believed to enhance the adhesion of Methanococcus deltae microorganisms and facilitate efficient electron transport. Electrochemical measurements showed that the resulting SS/PEDOT/GO and SS/PEDOT/rGO anodes exhibit remarkable electrocatalytic activity. The SS/PEDOT/GO electrode achieved a maximum power density of 1014.420 mW/cm², while the SS/PEDOT/rGO electrode reached 632.019 mW/cm².

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.

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.

Environmental pollution constitutes an increasingly complex global challenge, largely driven by industrial expansion and the consequent release of toxic species such as Cd²⁺, Pb²⁺, Cu²⁺, Hg²⁺, Fe³⁺, As³⁺, and Rh³⁺ into natural ecosystems. These contaminants pose significant risks to environmental integrity and public health, motivating the development of analytical technologies capable of sensitive, selective, and reliable detection. In this context, graphene-based electrochemical sensors have emerged as versatile platforms for monitoring a broad range of analytes, particularly in environmental applications involving heavy-metal detection. The intrinsic physicochemical properties of graphene derivatives have enabled low detection limits, rapid response times, and tunable selectivity. Despite analytical advances, critical challenges persist regarding operational stability in complex matrices, inter-batch reproducibility, and robustness to interfering species, which continue to hinder large-scale deployment and real-world applicability. However, challenges remain regarding stability and performance in complex arrays, reproducibility, and resistance to interference, necessitating innovative strategies for functionalization and molecular recognition. This review article establishes a comparative framework based on functionalization strategies (covalent, non-covalent, and hybrid), the chemical nature of graphene (GO, rGO, and doping), and various types of polymers (conductors and insulators), using statistical metrics such as the limit of detection (LOD), linear range, working potential, stability, and interferences, employing a bibliometric analysis using the PRISMA 2020 methodology. This comparative framework enables analysis and explanation of performance trends, and the generation of design and functionalization recommendations for versatile applications, including criteria for reproducibility and sustainability.