| 1. |
|
|
| 2. |
- Bertani, Federico, et al.
(författare)
-
Triptycene-Roofed Quinoxaline Cavitands for the Supramolecular Detection of BTEX in Air
- 2016
-
Ingår i: Chemistry - A European Journal. - : Wiley. - 0947-6539 .- 1521-3765. ; 22, s. 3312-3319
-
Tidskriftsartikel (refereegranskat)abstract
- Two novel triptycene quinoxaline cavitands (DiTriptyQxCav and MonoTriptyQxCav) have been designed, synthesized, and applied in the supramolecular detection of benzene, toluene, ethylbenzene, and xylenes (BTEX) in air. The complexation properties of the two cavitands towards aromatics in the solid state are strengthened by the presence of the triptycene moieties at the upper rim of the tetraquinoxaline walls, promoting the confinement of the aromatic hydrocarbons within the cavity. The two cavitands were used as fiber coatings for solid‐phase microextraction (SPME) BTEX monitoring in air. The best performances in terms of enrichment factors, selectivity, and LOD (limit of detection) values were obtained by using the DiTriptyQxCav coating. The corresponding SPME fiber was successfully tested under real urban monitoring conditions, outperforming the commercial divinylbenzene–Carboxen–polydimethylsiloxane (DVB–CAR–PDMS) fiber in BTEX adsorption.
|
|
| 3. |
- Bianchi, F., et al.
(författare)
-
Novel sample-substrates for the determination of new psychoactive substances in oral fluid by desorption electrospray ionization-high resolution mass spectrometry
- 2019
-
Ingår i: Talanta. - : Elsevier B.V.. - 0039-9140 .- 1873-3573. ; 202, s. 136-144
-
Tidskriftsartikel (refereegranskat)abstract
- A reliable screening and non invasive method based on the use of microextraction by packed sorbent coupled with desorption electrospray ionization-high resolution mass spectrometry was developed and validated for the detection of new psychoactive substances in oral fluid. The role of different sample substrates in enhancing signal intensity and stability was evaluated by testing the performances of two polylactide-based materials, i.e. non-functionalized and functionalized with carbon nanoparticles, and a silica-based material compared to commercially available polytetrafluorethylene supports. The best results were achieved by using the non-functionalized polylactide substrates to efficiently ionize compounds in positive ionization mode, whereas the silica coating proved to be the best choice for operating in negative ionization mode. LLOQs in the low μg/L, a good precision with CV% always lower than 16% and RR% in the 83(±4)-120(±2)% range, proved the suitability of the developed method for the determination of the analytes in oral fluid. Finally, the method was applied for screening oral fluid samples for the presence of psychoactive substances during private parties, revealing mephedrone in only one sample out of 40 submitted to analysis.
|
|
| 4. |
- Zolfaghari, Samaneh, et al.
(författare)
-
Activity Recognition in Smart Homes via Feature-Rich Visual Extraction of Locomotion Traces
- 2023
-
Ingår i: Electronics. - 2079-9292. ; 12:9, s. 1969-1969
-
Tidskriftsartikel (refereegranskat)abstract
- The proliferation of sensors in smart homes makes it possible to monitor human activities, routines, and complex behaviors in an unprecedented way. Hence, human activity recognition has gained increasing attention over the last few years as a tool to improve healthcare and well-being in several applications. However, most existing activity recognition systems rely on cameras or wearable sensors, which may be obtrusive and may invade the user’s privacy, especially at home. Moreover, extracting expressive features from a stream of data provided by heterogeneous smart-home sensors is still an open challenge. In this paper, we investigate a novel method to detect activities of daily living by exploiting unobtrusive smart-home sensors (i.e., passive infrared position sensors and sensors attached to everyday objects) and vision-based deep learning algorithms, without the use of cameras or wearable sensors. Our method relies on depicting the locomotion traces of the user and visual clues about their interaction with objects on a floor plan map of the home, and utilizes pre-trained deep convolutional neural networks to extract features for recognizing ongoing activity. One additional advantage of our method is its seamless extendibility with additional features based on the available sensor data. Extensive experiments with a real-world dataset and a comparison with state-of-the-art approaches demonstrate the effectiveness of our method.
|
|
