Illicit drugs and their trafficking require worldwide efforts in investigation, detection, and control. Colorimetric tests are often applied to identify drugs. Cocaine has some well-known adulterants that can provide a false positive response. Cucurbit[6]uril (CB[6]) has been suggested as a potential detector for cocaine and other illicit drugs. This work uses in silico methods to evaluate the use of CB[6] to detect cocaine and these interfering substances. More specifically, this work analyzes different possibilities of CB[6] complexation with cocaine, lidocaine, caffeine, and procaine and compares the results achieved for cocaine and its adulterants. Different methodologies were employed: quantum chemistry was investigated through DFT B3LYP/TZVP (density functional theory-Becke, three-parameter, Lee-Yang-Parr with triple zeta valence plus polarization basis set) and the semi-empirical methods Austin model 1 (AM1), parametric methods 3, 6, and 7 (PM3, PM6, PM7), and Recife model 1 (RM1). We used these methodologies intending to compare the reasonability and reproducibility of the results in the gas phase condition. Solvent influence was studied by molecular dynamics (MD) simulations. Results showed that CB[6] does not bind to these substances, as judged from the positive values of binding free energy obtained with all methods. DFT and MD were the most reliable methods whereas semiempirical ones were not reproductible in describing these systems. Results also showed that interactions are not specific, so CB[6] does not provide a good response for cocaine detection.

Glasses are common trace evidence elements in crime scenes, and the analysis of this material can be essential for evaluating different criminal dynamics. This work aimed to analyze the possibility of differentiating and classifying windshield glass using multivariate analysis methods. Automotive glass fragments from different vehicle brands were evaluated according to internal and external faces. We have collected from literature EDXRF (Energy Dispersive X-ray Fluorescence) data for different oxides concentrations. These data were organized in a matrix with 56 samples and nine variables. We applied unsupervised (PCA, Principal Component Analysis) and supervised (SIMCA, Soft Interclass Modeling Classification Analogy) methods. We assessed the classification responses through ROC (Receiver Operating Characteristics). As a result, the PCA indicated the presence of two groups of glasses in three main components. SIMCA verified the unsupervised classification, and the distances and interclass residues parameters were adequate with no outliers. The ROC analysis indicated a sensitivity of 0.793, a specificity of 0.815, and an efficiency of 0.804 for predictions. We concluded that multivariate analysis was successful in discriminating between the internal and external faces of automotive glasses.

The amount and variety of new psychoactive substances (NPS) are expanding, and there are difficulties in assessing their risks. In this regard, in silico methods are potentially useful to predict NPS properties faster and at a lower cost. In this work a quantitative structure-activity relationship (QSAR) model was used to verify the risk of drugs derived from amphetamines and cathinones. A dataset of 26 derivatives with in vitro affinity for norepinephrine transporter (NET) was selected. To ensure reproducibility of the results, only geometric molecular descriptors (AM1 (Austin model 1) level) obtained from the platform ChemDes and ordered predictors selection (OPS) were used. The model presents good internal statistics (n = 23; coefficient of determination (R2) = 0.914). The small number of samples was divided into seven training sets (n = 17) and seven test sets (n = 6). The average R2pred = 0.754 showed that the model has good predictive capacity. Based on the tests, this model can accurately predict the risk range of three previously selected derivatives: methedrone (low), ethcathinone (medium), and methamphetamine (high), even when only data referring to NET are employed. We used these data to create a simple free program in Java that focuses on the risk assessment of recreational drugs belonging to this class of compounds.

O desempenho de um reator anaeróbio compartimentado (RAC) foi avaliado para o tratamento da manipueira, resíduo tóxico. Um RAC dividido em quatro compartimentos de mesmo volume (volume total 4L) e operado a 35°C foi utilizado no tratamento da manipueira. A demanda química de oxigênio (DQO) do tanque de alimentação variou de 2000 a 7000 mg L-1 e avaliou-se o tempo de residência hidráulica (TRH) mais apropriado ao melhor desempenho do reator. O RAC foi avaliado pelas análises de DQO (método colorimétrico), pH, turbidez, sólidos totais e voláteis, alcalinidade e acidez. A Análise das Componentes Principais (PCA) foi conduzida para melhor compreensão dos dados obtidos. O sistema apresentou capacidade tamponante conforme a acidez decresceu ao longo dos compartimentos ao passo que a alcalinidade e o pH aumentaram. Houve retenção do material particulado e a remoção da DQO variou de 83 a 92% para TRH de 3,5 dias.

The performance of an anaerobic baffled reactor (ABR) was evaluated in the treatment of cassava wastewater, a pollutant residue. An ABR divided in four equal volume compartments (total volume 4L) and operated at 35°C was used in cassava wastewater treatment. Feed tank chemical oxygen demand (COD) was varied from 2000 to 7000 mg L-1 and it was evaluated the most appropriated hydraulic retention time (HRT) for the best performance on COD removal. The ABR was evaluated by analysis of COD (colorimetric method), pH, turbidity, total and volatile solids, alkalinity and acidity. Principal component analysis (PCA) was carried to better understand data obtained. The system showed buffering ability as acidity decreased along compartments while alkalinity and pH values were increased. There was particulate material retention and COD removal varied from 83 to 92% for HRT of 3.5 days.