| Literature DB >> 28723190 |
Jakub W Trzciński1, Roberta Pinalli1, Nicolò Riboni1, Alessandro Pedrini1, Federica Bianchi1,2, Stefano Zampolli3, Ivan Elmi3, Chiara Massera1, Franco Ugozzoli4, Enrico Dalcanale1.
Abstract
In this work we report a comprehensive study leading to the fabrication of a prototype sensor for environmental benzene monitoring. The required high selectivity and ppb-level sensitivity are obtained by coupling a silicon-integrated concentration unit containing the specifically designed EtQxBox cavitand to a miniaturized PID detector. In the resulting stand-alone sensor, the EtQxBox receptor acts at the same time as highly sensitive preconcentrator for BTEX and GC-like separation phase, allowing for the selective desorption of benzene over TEX. The binding energies of the complexes between EtQxBox and BTX are calculated through molecular mechanics calculations. The examination of the corresponding crystal structures confirms the trend determined by computational studies, with the number of C-H···N and CH···π interactions increasing from 6 to 9 along the series from benzene to o-xylene. The analytical performances of EtQxBox are experimentally tested via SPME, using the cavitand as fiber coating for BTEX monitoring in air. The cavitand EFs are noticeably higher than those obtained by using the commercial CAR-DVB-PDMS. The LOD and LOQ are calculated in the ng/m3 range, outperforming the commercial available systems in BTEX adsorption. The desired selective desorption of benzene is achieved by applying a smart temperature program on the EtQxBox mesh, which starts releasing benzene at lower temperatures than TEX, as predicted by the calculated binding energies. The sensor performances are experimentally validated and ppbv level sensitivity toward the carcinogenic target aromatic benzene was demonstrated, as required for environmental benzene exposure monitoring in industrial applications and outdoor environment.Entities:
Keywords: MEMS device; SPME fiber; benzene sensor; cavitands; preconcentrators
Year: 2017 PMID: 28723190 DOI: 10.1021/acssensors.7b00110
Source DB: PubMed Journal: ACS Sens ISSN: 2379-3694 Impact factor: 7.711