Desmetramadol

The use of differential mobility spectrometry at low pressure coupled to liquid chromatography‐mass spectrometry (LC‐vDMS‐MS) was investigated for the analysis of 13 drugs of abuse (DoA) including the following: cocaine, ecgonine methyl ester, cocaethylene, benzoylecgonine, norcocaine, tramadol, isomeric pairs of metabolites; O‐desmethyl‐cis‐tramadol and N‐desmethyl‐cis‐tramadol, and cannabinoids: Δ9‐tetrahydrocannabinol, Δ9‐tetrahydrocannabidiol, 11‐hydroxy‐Δ9‐tetrahydrocannabinol, 11‐nor‐9carboxy‐Δ9‐tetrahydrocannabinol, and 11‐nor‐9carboxy‐Δ9‐tetrahydrocannabinol glucuronide. Different parameters were optimized for isomeric separation, such as LC mobile phase composition (20%–100% methanol acetonitrile and isopropanol, flow rate: 8–100 μL/min) and DMS separation voltage. Methanol and acetonitrile significantly affected the compensation voltage of the analytes and improved DMS separation. A short trap/elute LC‐vDMS‐SIM/MS screening method of 1 min was developed to quantify 11 drugs of abuse (except THC/CBD), in addition to a 4‐min LC‐vDMS‐SIM/MS method to identify and quantify five cannabinoids including the isomers THC/CBD and three THC metabolites. THC is the principal psychoactive constituent of cannabis and is a controlled substance in comparison to its isomeric counterpart CBD; this highlights the importance and challenges to resolve these isomeric pairs by analytical techniques. The signal responses were linear over a concentration range of 0.005–10 μg/mL for the DoA and 1–1000 ng/mL for cannabinoids. The intraday and interday precision were better than 12.2% and accuracy better than 115%. Urine samples from subjects who tested positive for THC and/or cocaine during roadside drug testing were evaluated to assess the performance of the methods LC‐vDMS‐SIM/MS and LC‐MRM/MS. Results show that the developed LC‐vDMS‐SIM/MS method presents similar performance to LC‐MRM/MS with improved sample throughput.

In recent decades, the environmental detection of various organic compounds (OCs) has highlighted the limitations of conventional soil-water sorption models, which simplify complex experimental conditions and often overlook OCs with polyfunctional and ionizable structures. To address these shortcomings, we compiled a comprehensive soil-water sorption dataset encompassing 20,945 data points for 419 OCs with various functional groups and 1037 different soils. Meta-analysis of the dataset revealed the trends of soil sorption associated with OC substructures, soil properties, and solution conditions. Machine learning models employing the XGBoost algorithm, in conjunction with MACCS fingerprints and experimental conditions, were developed to cover the entire spectrum of speciation for cationic, neutral, and anionic species. Among these, the individual models tailored to each speciation achieved an overall root-mean-square-error value of 0.32 for log K_d. Model interpretation revealed that the models correctly understood the contributions of various substructures, such as multiple aromatic rings and nitrogen or oxygen atoms, to sorption. The models were also found to accurately capture isotherm nonlinearity and the pH effect on the sorption of ionizable OCs. Finally, utilizing soil properties from the Harmonized World Soil Database, the models predicted the sorption of diverse OCs based on global soil properties under simulated environmental scenarios.