Patent ID: 12228533

DETAILED DESCRIPTION OF THE INVENTION

Examples

Pt interdigitated electrodes on glass substrates were purchased from Micrux (Spain). Each interdigitated electrode contained 180 pairs of 5 μm wide electrodes separated by a gap of 5 μm. Interdigitated electrodes were cleaned with concentrated nitric acid (HNO3, 90%) then sonicated in methanol (CH3OH, 99.9%), ethanol (C2H5OH, 99.8%) and acetone (C3H6O 99.8%) for 10 minutes each prior to use. All solvents were purchased from Sigma-Aldrich UK.

Au films of 5 nm were deposited at a rate of 0.2 Å/s using a Knudsen-cell in an ultra-high vacuum chamber with a base pressure of 10−8Pa. Average layer thickness was continuously monitored throughout deposition using a quartz crystal microbalance. The layer was annealed at 200° C. for 1 h. This thermal evaporation in ultra high vacuum (UHV) followed by an annealing step that causes dewetting results in dispersed nanoparticles on the glass substrate (Lefferts et al. Appl. Phys. Lett. 112, 251602 (2018)).

All electrochemical experiments were performed using a PGSTAT204 Autolab potentiostat (Methrohm, UK) interfaced to a PC with NOVA version 1.11 software. Pyrrole (Py, 98%), lithium perchlorate (LiClO4, 95%) and acetonitrile (CH3CN, 99.8%) were purchased from Sigma-Aldrich UK. A three electrode cell was employed with a Pt coil (BASi, USA) as the counter electrode. An Ag/AgCl (CH Instruments, USA) reference electrode was used. The electrodes of the interdigitated electrode were connected and used as the working electrode.

For the electrochemical growth of polypyrrole, 0.1 M pyrrole was prepared in 0.1 M LiClO4/acetonitrile and a potentiostatic method was used. The potential was stepped up to 1 V from 0 V and held for various periods. After deposition the substrates were put back in monomer-less solution (0.1 M LiClO4/acetonitrile) and held at 1 V for 60 s to p-dope the polypyrrole.

After p-doping, the substrates were allowed to dry in air after washing with acetonitrile. DC resistance measurements were made between the interdigitated electrodes by applying a voltage of 1 V and measuring the resulting current.

FIG.8shows a schematic of the gas sensing rig used for the ammonia gas sensing experiments. Ammonia gas (10 ppm, nitrogen fill) and N2gas (for further dilution of NH3) were purchased from BOC gases UK. The flow rates from the respective gas cylinders were controlled by digital mass flow controllers (Alicat, USA) which flow into a Swagelok T-joint to ensure mixing of the gases before entering the inlet of the gas chamber. The percolation sensors are placed on a sample stage in the chamber with electrical connections running from inside the chamber to a multimeter and power supply outside. The sensing chamber is first purged with nitrogen gas for 45 minutes to remove any impurities present in the chamber or in the sensing layer on the interdigitated electrodes. Then a voltage of 1 V is applied to the two electrodes and the current is monitored as a function of time on a computer equipped with Benchvue software. Once a stable baseline is reached different concentrations of ammonia gas are introduced into the system. The ppb concentration of ammonia gas that was introduced into the sensing chamber was calculated by the relative flowrates of the two mass flow controllers while always maintaining a constant flow rate of 500 standard cubic centimetres per minute (sccm).

Example 1—Chronoamperometric Growth of Percolation Networks

To create the CP networks shown inFIG.2chronoamperometric growth for transient times between 5 s and 70 s, followed by p-type doping, was carried out for plain electrodes and electrodes decorated with gold nanoparticles.

The electrical conductance was determined by applying a dc potential of IV between the interdigitated electrodes and measuring the current (FIG.3). It will be understood that the resistance (R) of an object is defined as the ratio of voltage across it (V) to current through it (I), i.e. R=V/I while the conductance (G) is the reciprocal, i.e. G=1/R.

For the plain electrodes (FIG.3andFIG.4, circles) it can be seen that there is no appreciable increase in conductivity until 40 s, which is equivalent to the beginning of the percolation region inFIG.1andFIG.2a(panel5). As more CP is grown, so the conductivity between the electrodes increases until a continuous thin film is created.

Conversely, for the nanoparticle-decorated electrodes (FIG.3, squares) a jump in conductivity already occurs at 15 s, corresponding to panel3inFIG.2b, and there is a further significant increase at 20 s. Beyond 20 s there is no appreciable increase in conductivity, presumably because once all the nanoparticles have been electrically connected a thick film of CP is required for any further substantial impact.

Example 2—Ammonia Gas Sensing

The polypyrrole percolation networks of Example 1, having various degrees of connectivity, were tested for their performance in the gas sensing rig (FIG.8). Ammonia was used as the analyte.

Typical sensing responses from 700 parts per billion (ppb) to 100 ppb are shown inFIG.5for a 5.5 kΩ network with Au particle-decorated electrodes (FIG.5a), and a 20.9 kΩ network with plain electrodes (FIG.5b). The effect of 100 ppb ammonia exposure can be seen in both the response curves. Numerous sensors with a broad range of electrical resistances were created and these were evaluated using a testing protocol of 5 minutes of ammonia exposure in a dry nitrogen carrier gas, followed by dry nitrogen exposure until the sensor baseline was restored, which was typically in less than 30 mins, but was substantially shorter for low concentrations of ammonia.

The maximum rate of change of the sensor as a function of analyte exposure is multiplied by 3, representing 3 standard deviations, or a 99% confidence interval, to arrive at a number for the limit of detection (LOD).

The gradients of the sensor response curves are shown inFIG.5c,dwith the peaks of the spikes corresponding to the greatest rates of change. These peaks are unambiguously associated with sensor performance as they do not depend on the duration of analyte exposure.

Each sensor with a different level of network connectivity will have a different maximum rate of change response, with the networks at the percolation threshold showing the greatest sensitivity (FIG.1b). However the percolation threshold networks will also suffer from the greatest noise levels. Ultimately, we are interested in optimising the signal to noise ratio of the sensor (FIG.1c). So rather than plotting the spike heights inFIG.4c,d, we divide these heights by the root mean square of the noise measured for the individual sensors. These results are shown inFIG.6as a function of analyte concentration.

FIG.6ashows that the sensor with the steepest gradient, and hence the optimum performance, is the 5.5 kΩ network. For lower network connectivity the 25.5 kΩ and 119.4 kΩ sensors have higher sensitivity, but greater noise levels. Conversely, the networks with greater connectivity (1.1 kΩ and 49.1Ω) have lower noise levels, but also lower sensitivities. The 49.1Ω sensor is in effect operating at maximum connectivity, is not a percolation network, and can be thought of as a thin film device.

Data from the chemiresistive percolation sensors with plain electrodes is shown inFIG.6b, which also shows that the optimum sensor with the highest signal to noise ratio response lies in a region of the percolation curve that is a little beyond the percolation threshold (box inFIG.1a).

The LODs can be calculated straightforwardly from the linear fits inFIG.6and are defined as the point where the signal is a factor of 3 greater than the noise. The LOD for each sensor is plotted against resistance inFIG.7. This figure shows that a more effective percolation device is created when using an Au nanoparticle scaffold (minimum LOD of 18±2 ppb inFIG.7curve) than without (minimum LOD of 27±5 ppb inFIG.7curve).

CONCLUSION

The results demonstrate that the inventors have electrochemically grown doped PPy percolation networks between interdigitated electrodes on glass substrates, and that these networks can be used for high sensitivity ammonia sensing. The strategy of pre-patterning the glass with Au nanoparticles improves the sensitivity and it is proposed that the nanoparticles act as fresh nucleation centres, increasing the ability of the network to spread. The ideal level of network connectivity for high sensitivity gas sensing is just beyond the percolation threshold where the SNR is optimised. In this region the LOD of 18±2 ppb is better by a factor of 20 compared with thin film devices made with the same CP.