云南警官学院

Illicit drugs pose severe threats to social security and public health,and the governance of drug-related issues has long been a critical concern worldwide.Drug detection constitutes an indispensable component of drug control,and the development of highly sensitive,highly selective,and convenient detection techniques has emerged as a prominent research hotspot.Benefiting from fast response,intuitive visual readout,and simple operation,fluorescent sensors exhibit distinctive merits in the field of illicit drug detection.Diverse luminescent materials have been exploited to fabricate fluorescent sensors,including small-molecule fluorophores,fluorescent quantum dots,and electroluminescent materials.This paper systematically reviewed the research progress of mainstream fluorescent sensors applied to illicit drug detection over the past five years,with particular emphasis on their design strategies and sensing mechanisms.This review was expected to provide valuable references for the construction of novel high-performance fluorescent sensors and the advancement of drug detection technologies.

With the continuous development of drug detection technologies,there has been a growing variety of detection methods with increasingly high sensitivity.However,drug types are updating at an ever-faster pace,and existing detection technologies still have limitations in identifying trace drugs,drugs in complex matrices,and newly emerging drugs.Against the backdrop of accelerated global digital and intelligent transformation,machine learning has been effectively applied in numerous fields.Boasting excellent predictive and decision-making capabilities,machine learning,when combined with traditional drug detection technologies,can enhance the detection capacity for complex drug samples,compensate for the shortcomings in existing drug detection techniques,and provide more efficient technical means for drug detection.This paper introduced the machine learning algorithms commonly used in drug detection,reviewed the research progress of traditional drug detection technologies empowered by machine learning,and prospected the application prospects in this field,aiming to provide references for the future development of drug detection technologies.

Bloodstains are one of the most important forensic evidences that play a crucial role in the detection and analysis of cases.This article reviews the research progress of bloodstain detection using spectral technology,both domestically and internationally.This study also introduces forensic and spectroscopic methods for bloodstain confirmation,bloodstain species identification,and bloodstain duration prediction.The accuracy and practicality of these methods are evaluated,discussing their advantages,disadvantages,and areas of application.Spectral detection technology achieves a bloodstain confirmation accuracy of over 96%,bloodstain species identification accuracy ranging from 70%to 100%,sensitivity between 0.92 and 1,and specificity between 0.78 and 1.The error range for bloodstain duration prediction spans from several hours to several days.And the study concludes by discussing the future development of bloodstain detection technologies.This study provides new technical insights into bloodstain detection in forensic medicine.

In recent years, the misuse of synthetic cannabinoids has garnered significant societal attention. To evade regulation, synthetic cannabinoids are often illicitly mixed into e-cigarette liquids to create "heady e-cigarettes". The complex matrix of e-cigarette liquids poses a substantial challenge to the detection and regulation of synthetic cannabinoids, necessitating the development of methods suitable for rapid on-site detection of these substances in such products. A miniature ion trap mass spectrometer, with its high sensitivity, fast detection, and portability, is an ideal tool for detecting synthetic cannabinoids in e-cigarette liquids. In this study, a rapid detection method for synthetic cannabinoids in e-cigarette liquids was established based on acetone-assisted photoionization miniature ion trap mass spectrometry. The study revealed that the complex matrix present in e-cigarette liquids can mask the detection signals of synthetic cannabinoids, thereby significantly reducing detection sensitivity. Furthermore, matrix residues were found to cause severe contamination of the mass spectrometry instrument, resulting in an extended instrument recovery time of up to 360 min following contamination. To address this issue, a micro-heating desorption pre-separation module was proposed to achieve pre-separation of synthetic cannabinoids and e-cigarette liquid matrix components by exploiting the boiling point difference between them. Using 5F-EMB-PICA as a representative compound, the effects of temperature and heating time of the micro-heating desorption on the detection signal intensity were investigated. The optimal temperature was found to be 150 ℃, and the optimal heating time was 5 min. Under these conditions, the heating pre-separation strategy successfully minimized the interference from e-cigarette matrix components, achieving optimal detection sensitivity while reducing instrument contamination. The instrument recovery time was reduced to only 1 min. To demonstrate the universality of the method, three typical synthetic cannabinoids (e.g., 5F-EMB-PICA, 4F-ABUTINACA, and 4F-MMB-BUTINACA) in different flavors of e-cigarette liquids were investigated, all of which were accurately and rapidly detected. In addition, the method exhibits high sensitivity for detecting all three synthetic cannabinoids at concentrations as low as 5 mg/L. This detection limit is significantly lower than the typical synthetic cannabinoid concentration range (0.58%-2.71%) found in "heady e-cigarettes", thereby fully covering the potential content of illicit additives in e-cigarette products. This method not only offers excellent detection performance, but also features fast response, low cost and short instrument recovery time, making it suitable for on-site identification of illicitly added synthetic cannabinoids in e-cigarette liquids. With the continuous expansion of the e-cigarette market and the growing severity of related drug crimes, this method is expected to provide important support for the on-site detection of synthetic cannabinoids in e-cigarette liquids.