(Invited) Agriculture 4.0: Development of Chemical and Biochemical Smart Sensors Systems for Sustainable Food Production
Authors: O'Riordan Alan , Diaz Fernando , Robinson Caoimhe , Lovera Pierre , Creedon Niamh 
Published: July 2021

Introduction
With the global population expected to grow to over 9.6 billion by 2050 it is a projected that a 50-60 % increase in food production will be required. In addition, the increasing wealth of the BRIC (Brazil, Russia, India and China) countries is driving the transition from traditional to more westernized high protein diets. A key challenge then, going forward, will be to close the food gap. This must be achieved against the backdrop of climate change & desertification, labor shortages and competition for energy, land & resources. It is clear then, that addressing this challenge will require the development of more efficient and sustainable food production techniques and processes. Digitizing the entire food chain using smart sensor systems would help enable such an approach. This convergence between the Internet of Things (IoT) and the agri-food industry requires sensor systems and technologies that provide real time data to producers and processers; required for rapid, but informed, decision making. To this end, we are using microelectronic fabrication approaches to develop advanced end-to-end chemical and bio-chemical nanosensor sensor systems specifically tailored to the needs of both producers and processors [1-3]. These sensor systems are being designed to be used by non-specialists and be rugged enough for field deployment [4, 5].
Sensor Development
On-chip devices containing six gold microband electrodes, a gold counter and a platinum pseudo-reference electrodes were fabricated using standard nanofabrication processes on Si/SiO2 substrates as described in detail previously [6]. A custom built Teflon cell permit analysis of small sample volumes (~50 µL), see Fig 1. Gold contact pads were configured on-chip to permit electrical connection to the potentiostat via a microSD port. Chips could be easily plugged in/out enabling rapid analysis of multiple samples. Optical micrographs were acquired using a calibrated microscope (Axioskop II, Carl Zeiss Ltd.) with a charge-coupled detector camera (CCD; DEI-750, Optronics). Electrochemical experiments were carried out using an Autolab Potentiostat/ Galvanostat PGSTAT128N (Metrohm Ltd, Utrecht, The Netherlands) controlled by the Autolab NOVA software. All experiments employed a standard three-electrode cell configuration using a single gold nanoband as the working electrode, versus the on-chip gold counter and platinum pseudo-reference electrodes,. In this study, microband sensors were applied to the detection of Bovine IgG and anti-Fasciola antibodies in serum samples. On-chip microband electrodes were first cleaned using a mixed solvent clean process (acetone, isopropyl alcohol and DI water) for 15 minutes. Cyclic voltammetry was then employed for electropolymerisation of o-aminobenzoic acid (o-ABA, 50 mM in 0.5 M H2SO4) to create a carboxylic terminated polymer layer at the gold electrode surface. A fresh NHS/EDC mixture was prepared in 10 mM acetate buffer, pH 4.0 and subsequently deposited onto the polymerised electrodes, for approximately 30 min, to activate the surface. Concerning IgG, activated electrodes were modified with 1 ng/ml anti-Bovine IgG in 10 mM sodium acetate buffer, pH 4.0 (AB). Anti-IgG was incubated on the electrode for 15 min. Following deposition of these capture biomolecules, unbound active-sites on all electrodes were blocked with ethanolamine (1 M) for 20 minutes. A secondary blocking of 1 % NANNY care® goat based milk in PBS was used for the IgG assay, for a comparison.

Results and Conclusions

Electrochemical Impedance Spectroscopy (EIS) techniques allows for the monitoring of the increase of the Rct signal and was used to interrogate antigen binding. The reduction of the electron transfer causes an increase in the Rct as seen in Figure 2. Following exposure to igG antibodies an increase in impedance was observed. A semi-logarithmic relationship exists between the concentration of the target and the change in Rct. This correlates with the trend seen in the ELISA calibration plot. The Nyquist plot shows a much larger change in signal between each concentration of Bo IgG than the CV and thus EIS is widely used for evaluating immunosensors such as these.

References

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Figure 1