One-Dimensional ZnO/Gold Junction for Simultaneous and Versatile Multisensing Measurements

The sensing capabilities of zinc oxide nano/micro-structures have been widely investigated and these structures are frequently used in the fabrication of cutting-edge sensors. However, to date, little attention has been paid to the multi-sensing abilities of this material. In this work, we present an efficient multisensor based on a single zinc oxide microwire/gold junction. The device is able to detect in real time three different stimuli, UV-VIS light, temperature and pH variations. This is thanks to three properties of zinc oxide its photoconductive response, pyroelectricity and surface functionalization with amino-propyl groups, respectively. The three stimuli can be detected either simultaneously or in a sequence/random order. A specific mathematical tool was also developed, together with a design of experiments (DoE), to predict the performances of the sensor. Our micro-device allows reliable and versatile real-time measurements of UV-VIS light, temperature and pH variations. Therefore, it shows great potential for use in the field of sensing for living cell cultures.


Supplementary Figures
Supplementary Figure 1| X-ray diffraction pattern of ZnO microwires. The X-ray diffraction pattern shows the reflection typical of a wurtzite crystalline structure of the microwires (JCPDS 80-0074, a = 0.3253 nm, c = 0.5215 nm, hexagonal symmetry, space group P63mc). In addition, the sharp diffraction peaks indicate that the product has a high purity and high degree of crystallinity. The legend below the column plot lists all the different tested cases.

Supplementary Tables
Supplementary Table 1  where q is the electron charge, V is the bias voltage, n is the ideality factor, k B is the Boltzmann constant and T is the temperature expressed in K.
The value of the saturation current and of the ideality factor can then be extracted from fitting parameters as shown in Supplementary Fig. 6b for the I-V measurements performed at 22 °C.
To estimate the value of the barrier height, Φ B , the Arrhenius plot of the saturation current equation (Supplementary Equation 2) is exploited 1 .
where A ** is the effective Richardson constant and S is the diode area. The behavior of the ideality factor and of the barrier height extracted from the I-V curves for different temperatures are reported in Supplementary Fig. 7. The obtained ideality factor is higher than 1, which is the reference value for an ideal diode according to the thermionic emission model.
This indicates that an important deviation from the theoretical model occurs in the experimental I-V curves. Nevertheless, the curve trends are in accordance with what reported in literature: as the temperature increases, the effective barrier height increases, since more electrons acquire enough energy to overcome the potential barrier, along with a decrease of the ideality factor 2 .

Supplementary note 2. Signal-to-Noise ratio estimation and comparison.
The signal-to-noise ratio (SNR) is estimated according to the known formula: SNR = µ / σ, where µ is the signal mean, i.e. describing what is being measured, and σ is the noise standard deviation, i.e. representing noise and other interferences. They are calculated as follows: To study the SNR behavior when multiple external stimuli are varied at the same time, the resistance curve is taken as an example. In Supplementary Fig. S18 it is outlined a column plot representing the SNR of the resistance during the multisensory measurements (red bars) and the one obtained, instead, during the individual sensing measures (greyscale bars), at under the same experimental conditions. As evident from Supplementary Fig. S18, the SNR of the multisensor measurements, despite being of the same order of magnitude, slight decreases from the value obtained during individual sensing. This may in principle be attributed to a cumulative effect of the noise associated to the variation of the three different external stimuli. Therefore, according to the known rules for error propagation, the denominator in the SNR formula, i.e. the standard deviation representing the noise, will increase leading to a reduction of the global SNR of the multisensor.

Supplementary note 3. Multiparametric sensor modeling.
The slight deviation of the multisensing equations I DC , C AC , R AC from the experimental data in Fig. 3 in the main text appears when more than one stimulus is applied simultaneously to the sensing junction. This deviation is due to the modeling linear combination, in which the fitting errors of each fitting function are linearly summed.
To prove the reconstruction capability of the presented multisensing module, an additional investigation of the proposed mathematical model was carried out.
At first, two stimuli among UV-visible irradiation, temperature and pH are fixed, e.g. UVvisible and pH, and will be then addressed as boundary conditions. The remaining one, e.g.
the temperature, is varied in the range 20 -80 °C and its behavior is reconstructed according to the multisensor model in order to predict the sensor output at different temperatures. Thus the temperature is considered as independent variable, x T , in the multiparametric sensor equations (equation 3 in the main text) and -as a representative example -we calculated the resistance output (R AC ) in the multisensory model as reported in Supplementary Equation 3 (the procedure is the same for all DC and AC outputs).
where x pH corresponds to the fixed pH value, x UV is the fixed UV irradiance expressed in mW•cm -2 , x T is the variable temperature expressed in °C. Equations f'', g'', h'' are the fitting functions reported in Supplementary Table 1. The output predicted by the multiparametric sensor model is thus shown in Supplementary Fig. 8 by the red dot curve. The obtained behavior is in line with the experimental one (blue square curve) since the applied boundary