Highly Sensitive and Discriminative Detection of BTEX in the Vapor Phase: A Film-Based Fluorescent Approach
Source abstract
Abstract
BTEX (benzene, toluene, ethylbenzene, o-xylene, m-xylene, and p-xylene) represents a group of volatile organic compounds (VOCs) and constitutes a great threat to human health. However, sensitive, selective, and speedy detection of them on-site and in the vapor phase remains a challenge for years. Herein, we report a film-based fluorescent approach and a conceptual sensor, which shows unprecedented sensitivity, speed, and reversibility to the aromatic hydrocarbons in the vapor phase. In the studies, pentiptycene was employed to produce a nonplanar perylene bisimide (PBI) derivative, P-PBI. The compound was further utilized to fabricate the film. The novelty of the design is the combination of capillary condensation and solvent effect, which is expected to enrich the analytes from vapor phase and shows outputs at the same time. Importantly, the film permits instant response (∼3 s) and real-time identification (<1 min) of benzene and toluene from other aromatic hydrocarbons. The experimental detection limits (DLs) of the six analytes are lower than 9.2, 2.7, 1.9, 0.2, 0.4, and 0.4 ppm, which with the exception of benzene, are significantly lower than the NIOSH recommended long-term exposure limits. More importantly, the film is photochemically stable, and more than 300 repetitive tests showed no observable bleaching. In addition, the sensing is fully reversible. The superior performance of the film device is in support of the assumption that the combination of capillary condensation and solvation effect would constitute an effective way to design high-performance fluorescent films, especially for challenging chemical inert and photoelectronically inactive VOCs.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Chemical Sensor Technologies · Gas Sensing Nanomaterials and Sensors · Luminescence and Fluorescent Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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