Over the past decade, more than a thousand new psychoactive substances (NPSs) have emerged worldwide. This rapid proliferation of “designer drugs” poses significant challenges for drug control, forensic analysis, and public health. Artificial intelligence (AI) has increasingly been applied to address these challenges in NPS design and analysis. This review provides a comprehensive overview of AI methodologies—including deep learning, generative models, and quantitative structure–activity relationship (QSAR) modeling—and their applications in the synthesis, prediction, and identification of NPSs. We discuss how AI-driven generative models have been used to design novel psychoactive compounds and predict their pharmacological activity, how QSAR models can forecast potency and toxicological profiles, and how machine learning is enhancing analytical chemistry workflows for NPS identification. Special emphasis is placed on mass spectrometry (MS)-based techniques, where AI algorithms (e.g., for spectral prediction and pattern recognition) are revolutionizing the detection and characterization of unknown NPSs. A dedicated section examines the legal and regulatory implications of AI-generated psychoactive substances in the European Union (EU) and United States (USA), highlighting current policies, potential gaps, and the need for proactive regulatory responses. The review concludes with a discussion of the benefits and limitations of AI in this domain and outlines future directions for research at the intersection of AI, analytical chemistry, and drug policy.

The objective of this work is to study the anti-inflammatory effect in vitro and in vivo of microwave (MW) extracts of Thymus algeriensis. The in vitro study was performed by the human red blood cell protection test, while the in vivo study used the carrageenan-induced rat paw edema model. The experimental results were confirmed by a molecular docking calculation. The results indicated that all the microwave extracts have a moderate anti-inflammatory effect, depending on their richness in phenolic compounds. Among the extracts studied, the one obtained at 100 °C for 15 min exhibited the most pronounced anti-inflammatory effect, with an inhibition of 78.52%, which is attributed to its high flavonoid content. In particular, the flavonoids naringin and catechin showed the best affinity for the target protein, with values of −10.3 kcal/mol and −9.2 kcal/mol, respectively, as well as low inhibition constants of 0.028 μM and 0.18 μM. These results indicate that these flavonoids generate interactions that enhance the stability of the target ligand–protein complex, thus contributing to the observed anti-inflammatory effect.

With the rise in global temperatures, it is of great significance to achieve rapid and accurate detection of greenhouse gases, such as carbon dioxide and methane. Raman spectroscopy not only overcomes the weakness of absorption spectroscopy in simultaneously measuring homonuclear diatomic molecules but also enables the simultaneous detection of multiple gases using a single-wavelength laser. However, due to the small Raman scattering cross-section and weak intensity of molecules, its application in gas detection is limited. To enhance the intensity of Raman scattering, this paper designs and constructs a multi-pass enhanced Raman spectroscopy setup. This study focuses on the effects of Raman scattering collection geometry, laser multi-pass patterns, and laser polarization relative to the Raman collection direction on signal intensity. Investigations into Raman scattering collection angles of 30°, 60°, and 90° reveal that the Raman scattering signal intensity increases as the collection angle decreases. Different laser multi-pass patterns also impact the signal, with the near-concentric linear multi-pass pattern found to collect more signals. To minimize the influence of excitation light on the signal, a side collection system is employed. Experiments show that the Raman scattering signal is stronger when the laser polarization is perpendicular to the collection direction. This study achieves overall system performance enhancement through coordinated optimization of multiple physical mechanisms, including Raman scattering collection geometry, laser multi-pass patterns, and laser polarization characteristics. The optimized setup was employed to characterize the laser power dependence for nitrogen, oxygen, and carbon dioxide detection. The results showed that the Raman scattering intensity varied linearly with the laser power of the gases, with linear fitting goodness R2 values of 0.9902, 0.9848, and 0.9969, respectively. Finally, by configuring different concentrations of carbon dioxide gas using nitrogen, it was found that the Raman scattering intensity varied linearly with the concentration of carbon dioxide, with a linear fitting goodness R2 of 0.9812. The system achieves a CO2 detection limit of 500 ppm at 200 s integration time, meeting the requirements for greenhouse gas emission monitoring applications.

Electroanalysis has emerged as a critical tool in the pharmaceutical industry, offering versatile and sensitive methods for drug analysis. This review explores the principles, techniques, and applications of electroanalysis in pharmaceuticals, emphasizing its role in drug development, quality assurance, pharmacokinetics, and environmental monitoring. Key electroanalytical methods, including voltammetry, potentiometry, and amperometry, are detailed along with their practical applications, such as detecting active pharmaceutical ingredients, monitoring drug metabolites, and ensuring product stability. Innovations in electrode materials and biosensors have enhanced their sensitivity and specificity, paving the way for advanced drug screening and therapeutic monitoring. Challenges like electrode fouling, selectivity issues, and regulatory constraints are discussed, along with strategies to overcome them. Future trends highlight the integration of nanotechnology, AI, and portable sensors to facilitate real-time analysis and personalized medicine. These advancements position electroanalysis as an indispensable component of modern pharmaceutical research and healthcare. Future perspectives emphasize the integration of nanotechnology and artificial intelligence (AI) to optimize experimental processes and data interpretation. This study also predicts the increased adoption of lab-on-a-chip systems and bioelectrochemical sensors to meet the growing demand for precision medicine and sustainable pharmaceutical practices. These advancements position electroanalysis as a cornerstone of pharmaceutical research, paving the way for more efficient drug development, improved patient outcomes and better environmental management. This comprehensive review underscores the transformative potential of electroanalysis in addressing the evolving challenges of the pharmaceutical industry and provides a foundation for future innovations. This review does not explicitly define the timeframe for the considered advancements. However, it discusses recent technological developments, including innovations in nanostructured electrodes, microfluidic integration, and AI-driven data analysis, indicating a focus on advancements primarily from the last few years, i.e., from 2020 to 2025.

Green analytical chemistry represents a transformative approach to analytical science, emphasizing sustainability and environmental stewardship while maintaining high standards of accuracy and precision. This review highlights recent innovations in green analytical chemistry, including the use of green solvents, such as water, supercritical carbon dioxide, ionic liquids, and bio-based alternatives, as well as energy-efficient techniques like microwave-assisted, ultrasound-assisted, and photo-induced processes. Advances in green instrumentation, including miniaturized and portable devices, and the integration of automation and chemometric tools, have further enhanced efficiency and reduced the environmental footprint of analytical workflows. Despite these advancements, challenges remain, including the need to balance analytical performance with eco-friendliness and the lack of global standards to measure and promote sustainable practices consistently. However, the future of green analytical chemistry looks promising, with emerging technologies like artificial intelligence and digital tools offering new ways to optimize workflows, minimize waste, and streamline analytical processes. By focusing on these areas, green analytical chemistry is transforming analytical methodologies into tools that not only achieve high performance but also align with global sustainability goals. This review underscores how green analytical chemistry is more than just a scientific discipline, but a pathway for reducing the ecological impact of analytical processes while driving innovation in science and industry. With the continued commitment to research, collaboration, and the adoption of cutting-edge technologies, green analytical chemistry has the potential to shape a greener and more sustainable future for analytical chemistry and its diverse applications.

This study investigated the effect of surface treatments on the electrochemical performance of 3D-printed electrodes for versatile applications. The conductive filament was obtained from a mixture of polylactic acid (PLA) and carbon black (CB) at a 7:3 ratio (PLA/CB) dispersed in acetic acid and dichloroethane (3:1) medium. The treatments used were HNO3, NaOH, DMF (immersion for 30, 30, and 15 min, respectively), and electrochemical activation (amperometry 150 s, 1.8 V). In general, the treatments allow greater exposure of the conductive material and active sites present on the sensor surface. This was confirmed using cyclic voltammetry and electrochemical impedance spectroscopy. The analyses were conducted with a 0.10 M KCl solution containing the redox pair ferricyanide/ferrocyanide 5.00 mmol L−1. Based on the results obtained, the electroactive area, kinetic constant and resistance to electron transfer were determined for each treatment. The treatment in basic medium stood out as the treatment that was most appropriate for the device used in this work. The device was also tested for its potential in the analysis of acetaminophen, demonstrating satisfactory results permitting the application of 3D-SBasic in the analysis of acetaminophen.

GLANCE (Graphical Layout Tool for Analytical Chemistry Evaluation) is an innovative and adaptable free, editable template specifically designed to help researchers visually summarize their analytical chemistry methods in a structured and clear manner. It provides an accessible solution to the challenge of presenting complex scientific data, offering a significant advantage over traditional reporting methods, which often lack visual clarity. This is crucial because no previous tool has been developed to summarize analytical methods in such a comprehensive and concise visual format, significantly enhancing the process of gathering and presenting key information, particularly in review articles. The GLANCE template (bit.ly/409cwDd) is composed of twelve distinct attributes, each targeting critical aspects of method development (novelty, analytes, sample preparation, reagents, instrumentation, method validation, matrix effects and recoveries, application to real samples, analytical metrics, main results, limitations, and additional information). By filling out each block with keywords or short phrases, authors can provide a concise yet thorough overview of their method. Once completed, the template can be easily downloaded and included in scientific articles. This straightforward integration enhances both the clarity and accessibility of publications, providing the scientific community with a quick snapshot of the principal features of research.