Accurate Identification of MDMB-Type Synthetic Cannabinoids through Design of Dual Excited-State Intramolecular Proton Transfer Site Probe and Deep-Learning

Synthetic cannabinoids, a novel class of highly toxic psychoactive substances with various disguised forms, have posed significant risks to public safety, and their weak reactivity presents a substantial challenge for swift and accurate analysis. In this work, we develop an optical probe equipped with dual excited-state intramolecular proton transfer (ESIPT) sites that inhibit the intramolecular proton transfer through hydrogen bonding, facilitating the specific detection of MDMB-type synthetic cannabinoids. The probe can sensitively detect the MDMB-type synthetic cannabinoids at 1.8 × 10<sup>-3</sup> mg/mL, specifically recognizing them among 20 potential interferents with a rapid fluorescence response within 1.2 s, leading to the development of a portable, user-friendly sensor for a swift and precise analysis of these compounds in complex scenarios. Furthermore, this study combines a portable sensing chip with an advanced convolutional algorithm for image processing, enabling precise differentiation among six MDMB-type synthetic cannabinoid variants. This study successfully detected the target substance by creating a dual ESIPT optical probe, thereby offering a novel approach to detect psychoactive substances with limited reactivity.

Authors

Institutions

Publication Details

Journal
Analytical Chemistry
Published
2025-05-30
DOI
https://doi.org/10.1021/acs.analchem.5c00700
Citations
2
Primary Topic
Luminescence and Fluorescent Materials
Type
article
Field-Weighted Citation Impact
1.04

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Accurate Identification of MDMB-Type Synthetic Cannabinoids through Design of Dual Excited-State Intramolecular Proton Transfer Site Probe and Deep-Learning

Baoqing Chen, Xincun Dou, Yuwan Du, Ming Guan et al.
2 citations
Analytical Chemistry
Luminescence and Fluorescent Materials
1.04
article

Accurate Identification of MDMB-Type Synthetic Cannabinoids through Design of Dual Excited-State Intramolecular Proton Transfer Site Probe and Deep-Learning

Baoqing Chen, Xincun Dou, Yuwan Du, Ming Guan, Yudong Li, Yuan Liu, Na Wang, Chuanfang Zhao
article en
2 citations

Abstract

Synthetic cannabinoids, a novel class of highly toxic psychoactive substances with various disguised forms, have posed significant risks to public safety, and their weak reactivity presents a substantial challenge for swift and accurate analysis. In this work, we develop an optical probe equipped with dual excited-state intramolecular proton transfer (ESIPT) sites that inhibit the intramolecular proton transfer through hydrogen bonding, facilitating the specific detection of MDMB-type synthetic cannabinoids. The probe can sensitively detect the MDMB-type synthetic cannabinoids at 1.8 × 10<sup>-3</sup> mg/mL, specifically recognizing them among 20 potential interferents with a rapid fluorescence response within 1.2 s, leading to the development of a portable, user-friendly sensor for a swift and precise analysis of these compounds in complex scenarios. Furthermore, this study combines a portable sensing chip with an advanced convolutional algorithm for image processing, enabling precise differentiation among six MDMB-type synthetic cannabinoid variants. This study successfully detected the target substance by creating a dual ESIPT optical probe, thereby offering a novel approach to detect psychoactive substances with limited reactivity.

Analytical ChemistryVol. 97(22)
Xinjiang Normal University (CN), Chinese Academy of Sciences (CN), Xinjiang Technical Institute of Physics & Chemistry (CN), Shanghai Public Security Bureau (CN), University of Chinese Academy of Sciences (CN)
Natural Science Foundation of Xinjiang Province, China Postdoctoral Science Foundation
Openalex Percentile: Top 25%
Luminescence and Fluorescent Materials
1.04
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.