potentiometric [ 10 , 11 ] as well as strain [ 12 , 13 ] detection, which make them
particularly attractive for preparing hybrid active materials. We havefirst charac-
terized photoconductivity changes (i.e., photon detection) of nanowire elements
(Figs.3.2and3.3). As the focused laser is scanned across a sample (Fig.3.2a, I), an
increase of conductance due to the photocurrent [ 14 ] in semiconductor nanowire is
recorded at the positions of the nanowire devices. We extend this approach as a
general imaging technique to precisely determine the position of nanoelectronic
sensing elements in a 3D network through imaging the nanoelectronic networks
with a confocal microscope, recording photocurrent as a function of x-y-z coor-
dinates and overlapping with simultaneously acquiredfluorescence images. The
resolution of this approach can be assessed in two ways. Conventionally, the plot of
conductance versus position (Fig.3.2a, II) can befit with a Gaussian function and
its full-width at half-maximum (FWHM) reflects the diffraction limited resolution
of the illuminating light spot. Second and recognizing that the nanowire diameter
(30 nm) is line-like; we can use methods similar to super-resolution imaging
technologies [ 15 , 16 ] to locate the nanowire to much higher precision by identifying
the peak position from the Gaussianfit. We note that a similar concept as exploited
in stochastic super-resolutionfluorescence imaging to resolve close points can be
implemented in our photoconductivity maps because individual semiconductor
nanowire devices can be turned on and off as needed [ 15 ].
A typical high-resolution photoconductivity image of a single nanowire device
(Fig.3.2b, I) shows clearly the position of the nanowire. The conductance change
versus x-position is perpendicular to the nanowire axis (Figs.3.2b, II and3.3b)
yielding a FWHM is 314±32 nm (n = 20) resolution consistent with confocal
microscopy imaging resolution (202 nm) in this experiment. Moreover, the nano-
wire position determined from the peaks of Gaussianfits (Fig.3.3c) yielded a
standard deviation of 14 nm (n = 20), and shows that the position of devices can be
localized with a precision better than the diffraction limit. In addition, we have
acquired simultaneous photoconductivity andfluorescence confocal microscopy
images to map the positions of nanowire devices in 3D macroporous nanoelectronic
Fig. 3.1 Strategy for integration of 3D macroporous nanoelectronics with host materials.
aFreestanding 2D macroporous nanowire nanoelectronic “precursor”. Blue: nanoelectronic
element, orange: passivation polymer, black: metal contact and input/output (I/O).b3D
macroporous nanoelectronic networks integrated with host materials (Gray)
3.3 Results and Discussion 29