While circle-packing nearly describes our model problem, there is one major caveat: no physical justification prevents the circles from overlapping one another. This ’soft boundary’ makes it possible to achieve 100% coverage of the domain by allowing overlap. If the only objective was to maximize the effective areal coverage of the field, then one could distribute sensors next to one another without discretion. However, the monetary cost of sensors, sensor operation, and sensor maintenance makes this approach unreasonable, which motivates our stated objective of maximizing field coverage with the fewest number of sensors possible.The outcome of this algorithm is that sensors are placed throughout the field such that sensors are placed in the largest gaps between sensors. This is done by incrementing the acceptable distance between sensors by a small amount and then making many attempts at placing a sensor before repeating the process. We score the fitness of each placement design by the ratio of the number of field pixels that are within the half-variogram range of a sensor to the total number of pixels in the field. In other words, what percentage of the field area is within the half-variogram range of one or more sensors? This process is repeated until it is impossible to place a sensor outside of the range of all other sensors in the design, or until the field is completely covered. The flowchart for this algorithm is shown in Figure 5.3A, and a schematic depicting the evolution of sensor placement in an arbitrary field shape is shown in Figure 5.3B.Soil is a complex environment. It is a three-phase medium containing organic matter, minerals, metals, ceramics, gases, water, and a host of biological life. This is all to say that many things could complicate potentiometric nitrate sensor readings. Although the nitrate sensor nodes are sensitive to nitrate and are largely insensitive to most other ions – soil is more complicated than aqueous solutions. Ideally, nitrate sensors should not be sensitive to soil properties ,plastic garden pot but calibration or direct measurement of the interfering property could help return accurate nitrate measurement values.
To characterize the performance of the nitrate sensor nodes in soil, the potential of the nodes was recorded over time in varying nitrate concentrations and moisture levels in three types of agricultural soils. After calibrating the nitrate sensor nodes in aqueous solutions, they were immediately cycled through several containers of soil saturated with aqueous solutions of varying nitrate concentrations. Finally, they were measured in several containers of dry soil with varying volumes of 10 mM nitrate solution. The nitrate sensor nodes were tested in sand, peat, and clay soils. Sand tests were performed with commercially available desert sand , consisting of only sand-sized soil particles and no initial nitrate concentration. Clay tests were performed with an agricultural clay soil utilized for perennial alfalfa and from Bouldin Island in the Sacramento–San Joaquin Delta, California . Peat tests were performed in Miracle-Gro Nature’s Care organic potting mix. To circumvent the possibility of hysteresis attributed to the exposure of one soil type before measuring the sensor node in another soil type, the sensors were only measured in a single kind of soil on a given day, and the stakes were gently but thoroughly cleaned with deionized water and dried between measurements. In the nitrate concentration experiment, aqueous solutions of 0.1 mM – 1 M NaNO3 were used to saturate an array of containers containing sand, clay, or peat soils, depending on which soil type the sensors were being tested in that day. After saturating the soil with a surplus of nitrate solution, the sensor nodes were placed in the 0.1 mM container by displacing the soil with a spoon, inserting the stake, and then gently redistributing the soil over the electrode surfaces. After the nitrate sensor node was inserted into the container, the program ran on the micro-controller, uploading the recorded potential over WiFi to an online spreadsheet once per minute. Once the potential of the sensor stabilized, it was then moved to the 1 mM container, and so on. Figure 5.11 shows the nitrate sensor nodes being measured in the sand. After the measurements were made, the nitrate concentration of the soils was determined by taking KCl extractions and then measuring the extractions with ionexchange chromatography. The nitrate sensor nodes’ responses to the calibrated nitrate concentrations are plotted in Figure 5.12.
We expect similar results in measuring the sensor nodes in saturated sand as we would in aqueous solutions. This is because sand has a low cation exchange capacity, so few other ionic species are present. Furthermore, uncharged solids in the soil are unlikely to interfere with the potentiometric measurement.The nitrate sensor nodes in the sand showed strong linear relationships between nitrate concentration and recorded potential, with about half of the 17 sensor nodes included in this trial showing R2 values above 0.99. Eight sensor nodes with R2 > 0.99 were used for the remaining analysis. The average sensitivity for these sensors was -42 ± 8 mV/dec. Figure 5.12A shows the linear relationship between nitrate concentration and output potential for five of the sensors in the sand, with sensitivities ranging from -40 to -42 mV/dec. In clay, five of 14 sensors had R2 values > 0.9, and the sensitivity of the sensor nodes was -39 ± 8 mV/dec mV/dec, shown in Figure 5.12C. Clay has a much finer grain size and higher cation exchange capacity than sand. This likely explains the loss in sensitivity when measuring in clay compared to an aqueous solution or saturated sand. This suggests that soil texture alone is not a primary interfering factor in soil nitrate measurements, though other soil characteristics may be. If this is the case, calibration of the sensor in different soil types would be necessary to deploy such devices in agricultural applications. Figure 5.12B shows the output potential of three nitrate sensor nodes in peat soil at varying nitrate levels. The nitrate sensor nodes demonstrated sensitivities of -31 ± 8 mV/dec,draining pots with R2 values for each sensor’s best fit line of 0.8, 0.71, and 0.99.We suspect this is because the sensors had become damaged by the time of these measurements. The sensors were measured in sand and clay soils before the measurements in peat, so by the time of these measurements, the sensors had been inserted, removed, and rinsed from soil media many times, and it is possible that despite our best efforts, the sensing element may have become damaged. Compared to Figure 4.10, which plots ‘fresh’ gold electrode nitrate sensors in the same soil type measured with a Campbell Scientific data logger, the sensitivity was -47 mV/dec, R 2=0.95, and E0 variation 30 mV . The different results depending on the age of the sensor and the electronics used to measure it highlights the importance of improved stability for real-world use cases.
To find the impact of moisture content on the sensor nodes, 10 mM nitrate solution was used to water an array of containers containing sand, clay, or peat soils, depending on which soil type the sensors were being tested in that day. The containers were watered to 0 – 50% volumetric water content in 10% increments. The sensor nodes were initially placed in 0% VWC soil while the program ran on the micro-controller, uploading the recorded potential over WiFi to an online spreadsheet once per minute. Once the potential of the sensor node stabilized, it was then moved to the 10% VWC container, and so on. Ideally, the sensors’ output signal should not depend on soil moisture content. However, potentiometric sensors require ionic contact between the two electrodes to function: ions must be able to move freely between the ISE and RE, proportional to the finite current associated with potentiometric measurements. If soil does not hold enough water to support the flow of ions, the sensor becomes an open circuit, and there would be no signal. The results are shown in Figure 5.13. The potential abruptly increases at low moisture content because the micro-controller is programmed to report high signal output at open-circuit inputs. However, above a certain moisture threshold, sand and clay soils follow the expected pattern: relatively constant potential with respect to moisture content. For sand, the threshold is between 10 and 20% volumetric water content VWC, while for clay, it is between 20 and 30%. This makes sense because clay’s matrix potential is higher than sand’s, meaning water is bound more tightly to the surfaces of solid particles in clay, lowering the likelihood of an ionic pathway forming between the ISE and the RE of the nitrate sensor. The relationship between water content and sensor signal output in peat soil is less clear. The minimum water threshold seems to be between 10-20%, but the output potential is not as stable between 30-50% VWC. This could be attributed to the possibility of damage buildup in the nitrate sensors, or it could indicate that different sensors have different minimum thresholds. It could also be explained by the variations of water retention in high-organic matter soil. Further investigation is warranted.As noted earlier, there is significant E0 variability from one sensor node to another. Up until now, the behavior of the nitrate sensor nodes has been plotted altogether. However, when we look at individual sensors, E0 is relatively consistent, indicating that the variability has to do mainly with the sensor nodes rather than the properties of the soil. Individual sensor nodes have relatively consistent E0 values across the different trials. Subsets of the same 20 sensors were used in the six plots shown in Figures 5.12 and 5.13. The sensors with low R2 in the sand also had low R2 in clay, while most sensors with high R2 in the sand also had high R2 in clay . Similarly, sensors with relatively high potential outputs had this characteristic across soil types and nitrate/moisture measurements. Figure 5.14 shows the response of two sensor nodes measured in sand and clay soils. Sensor A has a much lower potential than Sensor B in all cases. A common misconception surrounding ‘green materials’ is that if something will decompose or degrade into a naturally-occurring material , it is safe to deploy into the environment. This is an incomplete way of thinking because, by that definition, every material on the planet is ‘green.’ Stop reading for a moment and take a look around you. Everything you see would eventually break down into naturally-occurring materials on a long enough time scale devoid of human intervention. It was made of materials sourced from our planet, after all. The time it takes for something to degrade is also essential and is the basis of many different standards surrounding bio-degradation. Different organizations worldwide have developed standards that benchmark different degrees of how things degrade. Table 5.2 highlights some of the more widely known standards. Interestingly, these standards vary widely in the description, and only the compostability standards explicitly state the timescale at which those materials degrade. The standards in Table 5.2 also lack to describe of how these materials might degrade. To design devices that degrade in a controlled manner, one must first understand the different ways a material might degrade. There are three ways in which materials degrade. These are compositional or micro-structural changes, time-dependent deformation and associated damage accumulation, and environmental attack. Generally, materials degrade by some combination of several mechanisms. There are few opportunities for time-dependent deformation in soil, though several mechanisms catalyze compositional changes or attack material bonds. These primary mechanisms by which materials degrade in soil are discussed here. For most materials, microbial activity is the primary contributor to the degradation rate. Microbial and enzymatic digestion describes the degradation carried out by microorganisms and naturally-occurring enzymes. Microbes can degrade most – if not all – naturally occurring organic chemicals and convert them to inorganic end products to supply the microbes with nutrients and energy. As with other life forms, the material’s molecular bonds are broken to release energy, transformed into a less thermodynamically energetic material, and excreted from the microbe’s system. Intuitively, different materials degrade at different rates. Generally, more complex molecular structures degrade on longer time scales than simpler ones. Also, different microbes and enzymes are preferential to different molecular compounds.