A ‘water layer’ in the field of ISE research refers to a small water layer that can form between the conductor and transducer. This water layer then acts as an unintentional electrolyte reservoir that re-equilibrates with any change in the bulk sample composition.If the ISM and transducer layer do not have good contact with the subsequent layers and do not form a hydrophobic seal, then it is possible for the bulk solution to ‘fill in’ the space by capillary force, not unlike water soaking into a napkin or paper towel. However, if there is a good seal in different layers, it is still possible for a water layer to form. For example, if the micro-structure of the ISM contains ‘pinholes’ , water can likewise transport through these channels to the layers below. Pinholes can be avoided by careful deposition techniques or by making thicker ISM layers. For the latter, the likelihood of forming a pinhole penetrating through the entire membrane is inversely proportional to the membrane thickness. Finally, even if there is a hydrophobic seal and there are no pinholes, water will still diffuse through the membrane to some degree, as the diffusion coefficient of a typical PVC membrane is on the order of 10−8 cm2/s. This is why PVC and other hydrophobic polymers are frequently chosen as the polymer matrix – their high level of hydrophobicity and small diffusion coefficients make it so the water diffusion rate through the ISM is negligible. A simple test to determine if a water layer is forming within an ISE was designed by Fibbioli et al.and is now widely used within the field of polymeric ISE research.

As it has come to be known,draining pot the’ water layer test’ is a relatively simple three-part potentiometric measurement. First, the ISE is conditioned in a concentrated solution of its primary analyte. Then, the electrodes are moved to a concentrated solution of a known interfering analyte. Finally, the electrodes are placed back in the concentrated solution of the primary analyte. The electrode potential is continuously recorded against a commercial Ag/AgCl RE following each exposure to the different solutions. The duration that the electrodes need to be soaked in each solution depends on the thickness of the membranes and the ISE response. Each exposure lasts several hours, and some experiments lasting up to 45 hours have been reported. A schematic describing the water layer test for a nitrate ISE is shown in Figure 4.14. Figure 4.15 shows the water layer test performed on the nitrate ISE. In this water layer test, 100 mM NaNO3 was used as the primary solution, and 100 mM NaCl was the interfering solution. First, the ISE was conditioned in 100 mM NaNO3 until it was stable. The final hour of stable output in NaNO3 is shown, followed by two hours in the interfering solution, and returning to NaNO3 for 24 hours. The potential shows some drift during both the NaCl step and the NaNO3 return, which could indicate the presence of a water layer on the electrode’s surface, which is not unexpected for this type of coated-wire electrode. However, the electrode’s stability is on par with values reported in the literature, which involved specific modifications for stability. The difference between the potential immediately before and the potential immediately after the NaCl step is 15 mV, the same as found by Chen et. al. for electrodes using gold nanoparticles and Polypyrrole to improve stability. Another technique for investigating the stability of an ISE is current-reversal chronopotentiometry. Recall that in Equation 4.10, potential drift is inversely proportional to the capacitance of the ISE.

Current-reversal chronopotentiometry is a technique that allows one to find the capacitance of an ISE. Current-reversal chronopotentiometry is a three-electrode electrode technique with the ISE as the working electrode , a commercial Ag/AgCl electrode as the RE, and a glassy carbon electrode as the counter electrode . The WE is polarized with a few nanoamps of current while the electrode potential is recorded. Rearranging Equation4.10 allows one to solve for the capacitance from the rate of potential change and the current input. After a short period of time, the current flow is reversed, and the bulk resistance of the electrode can be calculated from the ohmic drop when the current is reversed by rearrangement of Equation 4.9. A nitrate ISEs was configured into the three-electrode system described above and submerged in 100 mM NaNO3. A +1 nA current was applied for 60s, at which point the current was reversed to -1 nA for another 60s. The potential is plotted over time in Figure 4.16. EIS is an electrochemical technique that provides in-depth information about the dielectric properties of solid-state ISE sensors. EIS can also identify water layers, pockets of water in membrane pores, and pinholes. Finally, EIS characterizes the contact resistance of the boundaries between layers, which should be minimized to ensure a hydrophobic seal and reduce the ISE impedance. The nitrate ISEs were configured in a three-electrode system, with the ISE as the WE, a commercial Ag/AgCl electrode as the RE, and a glassy carbon electrode as the CE. The three electrodes were immersed in 100 mM NaNO3 solution and the impedance spectra were recorded in the frequency range of 0.5 Hz – 200 kHz. The Bode plot is shown in Figure 4.17A, and the Nyquist plot is shown in Figure 4.17B. The electrode demonstrated a bulk impedance of 1.72 MΩ. Higher bulk resistance of ISEs with PVC and DBF-based membranes has been previously reported, which could be accounted for by membrane thickness and the lack of a transducer layer in our device.Precision agriculture offers a pathway to increase crop yield while reducing water consumption, carbon footprint, and chemicals leaching into groundwater.

Precision agriculture is the practice of collecting spatial and temporal data in an agricultural field to match the inputs to the site-specific conditions. While industrial agriculture seeks to maximize crop yield, there is also the consideration of maintaining a healthy ecosystem. Fortunately, these are not competing interests; Numerous case studies have demonstrated that adopting precision agriculture techniques increases crop yield while lessening detrimental environmental effects. Consider first the use of irrigation in agriculture, which accounts for approximately 36.7% of the freshwater consumption in the U.S., 65% in China, and77% in New Zealand. Part of why so much water is used in agriculture is, quite simply, because crops need a lot of water to grow. For example, high-production maize crops require 600,000 gallons of water per acre per season – that’s an Olympic swimming pool’s worth of fresh water per acre! Adopting precision agriculture practices – such as variable-rate irrigation – have proven to reduce water consumption by 26.3% . Meanwhile, fixing nitrogen from the air to produce fertilizers is an extraordinarily energy-intensive process and accounts for nearly 2% of the U.S.’s annual CO2 emissions. Crops recover only 30-50% of nitrogen in fertilizers, which means that over half of the nitrogen becomes a potential source of environmental pollution, such as groundwater contamination, eutrophication, acid rain, ammonia redeposition, and greenhouse gases. Fortunately, precision agriculture practices have demonstrated an increase in nitrogen use efficiency, thereby reducing both the production volume of fertilizer as well as the amount that is polluted into the environment. We began this exploration from the ground-up. First, we investigated how many sensors are needed to inform a precision agriculture system. The results of that work informed the design of nitrate sensor nodes to fulfill those specifications,round plastic planters and lab-scale versions of those nodes were fabricated and tested in greenhouse experiments. After these WiFi-enabled nitrate sensor nodes were validated, we replaced the components of the nitrate sensor node with naturally-degradable alternatives to realize a no-maintenance version of the sensor node. The fabrication methods were scalable and low cost, while the sensors were comparable to their non-degradable twins. Such sensors could be widely distributed throughout a landscape to map nitrate movement through the watershed, inform the efficient application of fertilizer, or alert residents to elevated nitrate levels in drinking water.Accurate soil data is crucial information for precision agriculture. In particular, the moisture content and the concentration of various chemical analytes in soil have a significant influence on crop health and yield.

These properties vary considerably over short distances, which begs the question: What spatial density does soil need to be sampled to capture soil variability? Half of the spatial range, referred to hereafter as the ‘half-variogram range’, can be used as a “rule-of-thumb” to account for the spatial dependency of agricultural measurements.Similar to how an agricultural field can be defined in the real world as a geographic area at a location, a digital representation – or ’simulation’ – of an agricultural field can be defined as many discrete pixels, where each pixel’s position corresponds to a geographic coordinate and its size to an area. Here, we briefly discuss three methods of expressing an agricultural field in a digital format. For agricultural fields that a simple geometric shape can approximate – such as a rectangular farm or a central-pivot farm – expressing the farm digitally is trivial. For a rectangular-shaped field, we discretize the space into a grid of uniform pixels with dimensions proportional to the length and width of the physical domain. For a central-pivot field, we bound the field in a square grid of uniform pixels, loop through each pixel in the grid, and add the pixel to a list if that pixel’s coordinates are equal to or less than the field’s radius. This technique is demonstrated in Figure 5.2A for a rectangular-shaped field and in Figure 5.2B for a central-pivot field. When the boundaries of the agricultural field are not regularly shaped, we define the field by a list of consecutive coordinate points that, when piece wise connected by polynomial curves, form an enclosed shape. Here, we adopt a simple ray tracing algorithm to determine whether or not a pixel is inside or outside of this boundary. Given an enclosed boundary and a point in space, if one were to draw an infinite vector in any direction originating from that point, it will intersect the boundary an odd-numbered amount of times if-and-only-if the point is within the enclosed space, which is shown in Figure 5.2C. This holds for all points in space except for points on the boundary, which must be determined explicitly. In this way, we use the coordinates of each pixel as a point to determine if a pixel is inside the boundary and append it to a list. Finally, satellite or drone visible-spectra images of agricultural land are already stored in a digital, pixelized format. Such images and datasets are widely available from Google Earth, NASA Earth Observatory, or the USDA cropland data layer. Computer vision techniques can differentiate the arable land on a field from obstructions and store those pixels in a list. This process is visualized in Figure 5.2D. In all cases, it is essential to note the physical dimensions that a single pixel represents. It should also be noted that because each method requires discretization of the field, the results are approximations whose accuracy increases proportionally to the number of pixels used.The optimal layout of sensors in an agricultural field is achieved when, using the fewest number of sensors possible, all points in the field are statistically represented by the data collected by sensors in that field. For a given sensor, the data collected from that sensor is statistically significant for all points within a radial distance equal to the half-variogram range of that sensor. Thus, if we consider an agricultural field a two-dimensional collection of pixels, we can model sensors as circles with a radius equal to the half-variogram range. Using this definition for placement, our problem is similar to the circle packing problem. Circle packing is a well-researched area in mathematics that has many practical applications. Object packing aims to fit as many of some objects within a domain as possible without any overlap. There are several algorithms that aim to optimize object packing, such as random sequential addition, the Metropolis algorithm, and various particle growth schemes. The limit of packing efficiency for equal-size circles in two dimensions is about 91% for a hexagonal grid.