Modeling-based Design of Sensors for Chemical Detection in Complex Environment


DESCRIPTION: DHS and first responders need low cost, high performance sensors that can be used to
chemical materials in different environments. A persistent problem in chemical sensing is the
inability of the sensor system to reliably address complex sensing tasks and environments. Such
conditions are regularly encountered in situations involving environmental monitoring, industrial
process control, toxic chemical and fire detection. Often, these tasks are centered on the detection
of chemical signatures rather than individual chemical compounds. However, detection of individual
analytes is often complicated significantly by environmental conditions that exist in backgrounds
with multiple potentially interfering chemical species. This can lead to surprisingly poor
performance in real-world environments after excellent results have been demonstrated in the
laboratory. Hence understanding the surrounding details of a chemical sensing problem is critical
to finding a solution, together with knowing and addressing the target analytes themselves.

Different types of sensors, a large number of them being based on molecular sensing capability and
coupled with nanostructured surfaces, are being developed. However, most of these sensor
developments are empirical and their performance, particularly the interplay between sensitivity
and selectivity, cannot be predicted until the sensors are fully tested in a real-world
environment. The costs to the user are therefore quite substantial for each sensor development
before an objective assessment with regards to their usability can be made.  On the other hand, a
modeling-based approach, which would allow design of surfaces as well as the sensing device
diagnostics, could allow for an inexpensive, user friendly approach to designing sensor materials
that can be integrated with electronics to produce any type of sensor – chemical or biological,
with parts per trillion (ppt) sensitivity and fast (seconds) response times. The reduction in cost
compared to the current sensor development approaches which are empirical in design is expected to
be at least an order of magnitude.

Many current sensor developments involve different types of polymers like those used in surface
acoustic wave (SAW) mode or molecular imprinted polymer (MIP) configurations. A recurring problem
with regards to sensing of chemical vapors is the issue of addressing complex sensing tasks and
environments that are routinely encountered in most real-world situations.  Even detection of
individual analytes is almost always complicated significantly by these unavoidable environmental
conditions.  This can lead to surprisingly poor performance in environments relevant to first
responders [1,2]. The same selectivity problem exists even in the case of arrays of sensors [1].
Theoretically based strategies for design and optimization of chemical sensors are rarely adopted
by sensor developers. The same situation also exists for molecularly imprinted polymers.  Molecular
imprinting is the process whereby a polymer matrix is cross-linked in the presence of molecules
with surface sites that can bind selectively to certain ligands on the polymer. Recent theoretical
work [3,4] has discussed a model that accounts for the key features of this molecular recognition
approach. Using a combination of analytical calculations and Monte Carlo simulations, it has been
shown that the model can account for the binding of rigid particles to an imprinted polymer matrix
with valence-limited interactions. It has also been shown as to how the binding multivalency and
the polymer material properties affect the efficiency and selectivity of molecular imprinting. These
calculations also indicate pathways to formulate design criteria for optimal molecular imprinting.
While theoretical models for rational design of sensors and sensors arrays do exist, there has not
been any sensor development which are explicitly based on these models. The goal of the project is
to develop sensors based on the rational designs of the theoretical models and evaluate the sensor
performance in both pristine and complex environments relevant to the needs of the user community.

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