Edge Article
Chemical Science
use one single sensor or method to realize the detection and
discrimination of all the explosives in a real complicated envi-
ronment. Mimicking the mammalian olfactory system, the
construction of sensor array using the developed sensor as
sensor element may be the best and most reliable way to achieve
the detection and discrimination in a complicated environ-
ment. The related work is ongoing in our lab. Additionally,
although our new concept was tested with CB[8], in principle it
should be used as a general and effective sensing scheme for
creating various sensors in other synthetic molecular-sized
nanocavities, for example in metal-organic frameworks (MOFs).
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4 Conclusions
In summary, based on a NT-threaded CB[8] rotaxane structure
on solid substrate, a new strategy for rapid, fully reversible, and
highly sensitive detection of a broad class of explosives was
developed by using one receptor. Due to the unique conne-
ment effect and size exclusion of CB[8] nanocavity, it is found
that, depending on the electronic structures and the sizes of
explosives, the photophysical property of the naphthalene core
in the conned CB[8] cavity shows different changes upon
contact with the tested analyte, leading to the direct detection
and discrimination of different groups of trace explosives,
including the challenging aliphatic nitro-organics (RDX, HMX
and PETN). In recent years, a uorescence-based sensing
scheme has been extensively explored for the detection of
explosives. However, chemosensors capable of detection and
discrimination of a broad range for explosives classes, espe-
cially in the vapor phase, are rare.3e Therefore, we believe that
our ndings would open a new way to design and develop a new
kind of explosive sensors enable a richer identication of
threats.
Acknowledgements
We gratefully acknowledge the nancial support from the
National Science Foundation of China (no. 21025311, 21121004,
21261130581 and 91027016), Ministry of Education
(2011Z01014), MOST Program (2011CB808403, 2013CB834502)
and the transregional project (TRR61). We also thank Prof. Feng
Liu in Perking University for the QCM characterization.
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