Chemical Sensing in Real-time with Plants Using a Webcam

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Chemical Sensing in Real-time with Plants Using a Webcam Xingcai Qin, Ying Zhu, Jingjing Yu, Xiaojun Xian, Chenbin Liu, Yuting Yang, and Nongjian Tao Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b03863 • Publication Date (Web): 02 Oct 2018 Downloaded from http://pubs.acs.org on October 6, 2018

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Analytical Chemistry

Chemical Sensing in Real-time with Plants Using a Webcam Xingcai Qin, † Ying Zhu, † Jingjing Yu, † Xiaojun Xian, ‡ Chenbin Liu, ‡ Yuting Yang, ‡ and Nongjian Tao*† †

State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical

Engineering, Nanjing University, Nanjing 210023 (China). ‡

Centre for Bioelectronics and Biosensors, The Biodesign Institute, Arizona State University, Tempe,

AZ 85287 (USA). E-mail: [email protected]

ABSTRACT: It has been established that plants can smell and respond to chemicals in order to adapt to and survive in a changing chemical environment. Here we show that a plant responds to chemicals in air, and the response can be detected rapidly to allow tracking of air pollution in real time. We demonstrate this capability by detecting subtle color and shape changes in the leaves of mosses upon exposure to sulfur dioxide in air with a simple webcam and an imaging-processing algorithm. The leaves of mosses consist of a monolayer of cells, providing a large surface-to-volume ratio for highly sensitive chemical sensing. The plant sensor responds linearly to sulfur dioxide within a wide concentration range (0-180 ppm), and can tolerate humidity variation (15-85% relative humidity) and chemical interference, and regenerate itself. We envision that plants can help alert chemical exposure danger as a part of our living environment using low cost CMOS imagers, and their chemical sensing capabilities may be further improved

with

genetic

engineering.

INTRODUCTION Animals, such as birds and dogs, have long been used to assist humans to detect chemicals and sense dangers in air. Dogs, today, are still widely used in law enforcement and disaster rescue

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(e.g., earthquake) due to their extraordinary chemical sensing capabilities that cannot be matched by today’s chemical sensors. Plants do not have central nervous systems like animals, but they can sense light with high sensitivity, as found by Charles Darwin more than a century ago.1,2 Plants can also sense touch (e.g. mimosa), gravity, as well as chemicals in air3-5 and in soil via their leaves and roots,6,7 allowing them to do amazing things in order to adapt to and survive in a changing environment. Compared to animals, plants are less expensive to acquire and keep, easier to control, and they are an integral part of the natural and built environment (agriculture, landscape and home or office decoration), but plants’ responses to external stimuli especially chemicals are often perceived to be slow. We show here that plants can provide sensitive monitoring of toxic chemicals in real time with a simple optical detection method. We demonstrate chemical sensing capability of mosses (atrichum undulatum8,9) (Figure 1d). Mosses usually do not have roots, and their leaves are composed of a single layer of cells, which provides a large surface-to-volume ratio for directly absorbing water and nutrients from air, and also for sensing chemicals in air. They have been previously used as bio-indicators to study the long-term impact of pollution by analysing population variability of plants in different geographic locations3,4,10,11, damage to the plants and pollutants accumulated in the plants over a period.1217

Instead of focusing on long-term and qualitative impact of pollution on plants (usually need

time-consuming process, skilled operator and expensive instruments), the present work is to explore real-time and quantitative chemical sensing capability of mosses with simple methods. Mosses may sense and respond to external stimuli in different ways, including leaf curling, growth rate, and color change of the leaves. We measure the color and shape responses of mosses to air pollution using a CMOS-imager with imaging processing algorithms, because of its simplicity, low cost, high sensitivity, and remote sensing capability.


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Plants may sense different chemicals in air, and we focus on sulphur dioxide (SO2) to demonstrate chemical sensing capability of mosses in this work. Previous works have shown that mosses are good indicators of SO2 pollution.18-23 SO2 is one of the six “criteria” air pollutants defined by EPA.24,25 The major source of SO2 in air is the burning fossil fuels by car, power plants, and other industrial processes. Direct exposure to SO2 can harm the human respiratory system, and cause life-threatening accumulation of fluid in the lungs. SO2 also harms humans by contributing to acid rain, and particulate matter (PM) in air by reacting with other compounds to form fine particles.25 Timely monitoring of SO2 is thus necessary to protect humans and environment. Various detection methods, including optical spectroscopy,26-29 electrochemical30 and semiconductor sensors,31 have been developed to detect SO2. Moss-based SO2 sensor offers a different strategy to monitor air pollution. Demonstrating this plant-based chemical sensing capability is the primary goal of the present work. We have studied the response of mosses to SO2 at various concentrations, measured the responses to the chemical optically with a CMOS imager-webcam, calibrated the dependence of the chemical response on the SO2 concentration, evaluated humidity and interference tolerances, examined the possible mechanism of the chemical response of the moss to SO2, and analysed the pros and cons of chemical sensing based on mosses.

EXPERIMENTAL SECTION Chemicals and materials Wild mosses, with fully extended green leaf (most of mosses are), were collected typically at shaded locations of trees on the campus of Nanjing University at different seasons of a year. They were transported to lab and kept wet for testing within about 1 hour. SO2, N2, O2 and CO2



from Tianze gas Co., Ltd. (Nanjing, China), and HCHO from Newradar Special Gas Co., Ltd. (Wuhan, China) were used in the experiments. Water from a Mill-Q pure water system was used to create different humidity levels to test the humidity tolerance of the moss chemical sensors. SO2 samples and interference gases at different concentrations were prepared in sample bags (Teflon FEP bag, Dalian Hede Tech., Ltd.) by mixing the chemicals with appropriate portions of clean air (20.9% O2, ~500 ppm CO2). The concentrations of the SO2 samples were confirmed with a reference SO2 sensor (SGA-600-SO2, Sing-An Co., Ltd) before and after each test. An air pump (KVP 04-1.1-12) from Kamoer (Shanghai, China) was used to deliver sample gases and the flow rate was measured with a flowmeter from Omega (FL-2012). A Nafion tubing of 1.5 m long and 1 mm in diameter (TT-050) from Perma Pure LLC was used to control the humidity. Experimental setup The detection chamber (Figures 1a and S1c) was made from PTFE with a CNC milling machine. Its length, width, and height were 40, 30 and 30 mm, respectively, with an inlet and an outlet. SO2 and other gases were introduced into the chamber and the flow path was designed such that the gases flew from inlet to outlet with minimum mechanical perturbation to the plants. Gases were introduced from the sample bag with a pump (Figure S1a) at a flow rate of 0.3 L/min and their concentrations were monitored by the reference sensor (Figure S1a). CO2, O2 and humidity levels in the test chamber were tracked with a CO2 detector (T7001) from Telaire, an O2 monitor (PGM-1600) from RAE, and a humidity detector (RH-390) from Extech, respectively. A white LED (60 mw, 508H256WC-HD, Hongli Zhihui Group Co., Ltd,) was used as light source, and a color webcam (Logitech C525) equipped with 10x macro lens (APL-0610WM, Shenzhen Apexel Techonology Co., Ltd.) was used to capture the images of the mosses at 5 frames per second (FPS). Averaging of 15 frames was used in image analysis to get lower noise level.


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Figure 1 Experimental setup and detection principle. a). Mosses are planted with water-dampened soil in a detection chamber with controlled temperature and humidity. A light emitting diode illuminated the mosses via the transparent top of the detection chamber, and a webcam with a 10x macro lens placed on top of the detection chamber to continuously image the mosses. b). Differential images were obtained by subtracting current image from the previous one. c). Response (e.g., intensity or shape change) of a moss to chemical exposure extracted from differential images. d). Snapshots of a moss upon exposure to 50 ppm SO2 at 0, 15 and 30 mins, showing color change from green to yellow (scale bar of 1 mm).

RESULTS AND DISCUSSION Mosses with soil were placed in a detection chamber with sufficient water, air circulation and light conditions to allow their healthy growth (Figure 1a). The detection chamber had an inlet and an outlet for flowing SO2 at different concentrations (0-180 ppm) in and out. It had a



transparent top to allow illumination of the mosses with a light emitting diode (LED), and imaging of the mosses with a CMOS imager (webcam) placed outside of the chamber. The images were analyzed with an imaging-processing algorithm (Matlab) to identify a moss, and track its color and shape changes over time (Figure 1b-c). The detailes of the image analysis is described with a flowchart and an example in Figure S2. Figure 1d displays three snapshots of a moss captured during exposure to SO2 at 50 ppm, showing a gradual color change from green towards yellow. There was also a subtle shape change in the leaves, which is harder to visualize by eye, but can be extracted and quantified with the imaging analysis algorithm described later. The intensities (range of 0-1) of the R (red), G (green) and B (blue) color components of the images of mosses were extracted and analyzed over time (Figure 2a). Upon exposure of the mosses to SO2, all the three-color components changed significantly, but G showed the largest intensity change, followed by R, while B displayed the least intensity change. This observation reflected the color change of the moss from green towards yellow. For this reason, we quantified the optical absorbance change of the moss using the G intensity in most of the discussions below. As a control, the background color changes captured for a region outside of the moss was also tracked (labeled as ref-R, G, B, Figure 2a), which changed little over the same time period (blue line, Figure 2b), confirming that the observed color change was indeed due to the exposure of the moss to SO2. The background region was thus used as reference (blue square in the inset of Figure 2b) to accurately determine the optical absorbance of mosses. Purging the detection chamber with ambient air stopped the color change, followed by slow recovery of the color, as shown in Figure 2a. The lag time between gas introduction and moss response was less than 10 seconds (Figure S3). This fast response was partially due to the high sensitivity of the imaging system and the single layer leaves of mosses.


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Figure 2 Real-time color change of mosses in response to SO2 exposure. a). Red (R), Green (G), and Blue (B) intensity changes of an entire moss and a background region (blue square marked by ‘ref’ in the optical moss image of b) inset.) when exposed to 100 ppm SO2 for 5 mins. G displays the most sensitive response, which reflects the color change from green towards yellow. The background region shows little intensity changes (‘ref-R, G, B’), which is used as reference to determine absorbance. The image intensity (0-1) was normalized after subtraction of the first frame for clarity. b). G intensity changes of different regions (marked by “1”and “2”) of the moss when exposed to 86 ppm SO2. For comparison, G intensity changes of a background region and the entire moss are also shown. An optical image of the moss is also shown as inset. Figure S4 shows additional analysis of responses from different regions.

To examine the variability of different regions of a moss, we plotted the color changes of several regions of the moss shown in Figures 2b and S4, and compared the responses from the individual regions with the intensity change averaged over the entire moss. Different regions of the moss do not display identical responses to the exposure of SO2, but they show similar trends. Compared to the color change averaged over the entire moss, the color changes extracted from local regions are noisier due to pixel noise of the CMOS imager. We thus used the averaged G intensity of the entire moss to track and quantify the moss response to the chemical exposure.



The results presented above show that mosses turned to yellow in response to SO2 exposure. In addition to color change, mosses could change in shape, such as leave bending, shrinking and curling (Figure S5). We tracked possible shape changes by analysing differential images from the videos. When a leaf moves, the differential images shows an increase in intensity in the leading edge (blue stripe, Figure 3c) and a decrease in intensity in the trailing edge (red stripe, Figure 3c). The relative intensity increase (or decrease) is proportional to movement. This algorithm allowed us to determine a leaf’s edge movement as small as ~1 μm (Figure S6b, supplementary Information). Figure 3d and 3e are images of a moss obtained before and after exposure to SO2 at 100 ppm, respectively. The corresponding differential image (Figure 3f) shows an increase in G intensity (Figure S6d, supplementary Information), but also reveals blue and red stripes along the edges of some leaves (marked by red arrows), which are due to slight shrinking of the leaves. The estimated shrinking is about 30 μm (Figure S6d). This analysis shows that the mosses change both color and shape when exposed to SO2. However, we focus on the color change here because it is more pronounced and easier to track in real time than the shape change.


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Figure 3 Color and shape responses to SO2 exposure. a-b). Images of a moss leaf before and after mechanical translation over 20 μm in the direction marked by arrows. c). Differential image (G component) of the leaf obtained by subtraction image b) from a), which shows a red and blue stripes in the leading and trailing edges of the moving leaf, respectively. The intensity profiles of stripes in the differential image provide quantitative information in the mechanical translation (see supplementary information Figure S4). d) and e) Images of a moss before and after exposure to SO2 at concentration of 100 ppm over 5 mins. f). Differential image (G component) obtained by subtraction image e) from d), where arrows mark red and blue stripes, corresponding to shrinking or moving leave edges. Other than the stripes at the edges of some leaves, the overall intensity difference between the region of the moss and outside of the moss is due to the color change from green towards yellow.

For quantitative chemical sensing applications, it is necessary to evaluate the absorbance response of the mosses to SO2 at different concentrations. Figure 4a shows the typical responses



of a moss over time to SO2 at different concentrations. For each concentration, the moss was exposed to SO2 for 5 mins, followed by purging the moss with clean air for 5 mins. The absorbance response increases with SO2 concentration. To quantify the absorbance response, we fitted the absorbance change vs. time plots in Figure 4a (black lines) with a linear function, and extracted the slope at each concentration. Figure 4b plots absorbance change rate (the slope) vs. SO2 concentration, showing a linear relationship with slope of -(6.0 0.2) x 10-6 per ppm, which is the sensitivity of the moss chemical sensor. The noise level of the current setup using the low cost webcam is ~2x10-4 (Figure 4a), corresponding to ~5 ppm SO2. For typical exposure time of 5 mins the limit of detection of this sensor (taken S/N=3) is ~15 ppm, which may be further lowered by increasing exposure time. This level of detection limit is adequate for many occupational and industrial safety applications and may be further improved by reducing the CMOS imager noise and light source.

Figure 4 Monitoring of the color response of a moss in real time when exposed to SO2 at different concentrations. a). Absorbance change (G component) of a moss over time when exposed to SO2 at 0 ppm, 28 ppm, 62 ppm and 101 ppm, respectively, where the solid black lines on the blue curve are linear fits to the changing absorbance vs. time as an example. b). Absorbance change rate, determined from the slope of the time dependent absorbance plots, showing a linear response within the


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concentration, where the error bars reflect measurement variability in different experiments with 8 mosses.

To evaluate the variability in the response of different mosses to SO2, we measured the absorbance responses of the individual mosses to SO2 at various concentrations. Figure 5a plots the absorbance responses of four examples of mosses to SO2 at 50 ppm, which show similar responses within the noise limit. Figure 5b showed another three mosses’ response vs. SO2 concentration. The extracted sensitivities of the mosses are within ±6% deviations (Figure 5b). This good uniformity was based on the limited study using mosses grown under similar conditions. Statistical analysis of 12 different moss sensors upon exposure to 50 ppm SO2 was performed (Figure S7), showing ~±17% variability. However, different species and different growth conditions may lead to greater variability in the mosses’ responses to SO2, and calibration will be needed (like the conventional chemical sensors) for quantitative chemical sensing applications.

Figure 5 Variability in chemical response (G component) for different mosses. a). Absorbance changes over time of different mosses when exposed to 50 ppm SO2. The inset displays the optical images of



these mosses. b). Sensitivity (absorbance change rate vs. SO2 concentration) for different mosses. The results show that the moss sensors have good uniformity.

Humidity is common interference that poses a difficult challenge for many chemical sensors that operate in ambient air. We examined humidity interference by performing tests of the moss sensor under varying levels of humidity (Figure 6a). Gas samples with different humidities were controlled by a Nafion tubing setup (Figures S1a and b). The data show that the sensitivity variation of the moss sensor is ~20% with humidity varying from 18% to 85%. This humidity tolerance is superior to many gas sensors, which might be due to self-regulation of water content of mosses.

Figure 6 Humidity interference and selectivity of moss sensor. a). Absorbance change rate of mosses upon exposure to 50 ppm SO2 at different humidity levels. The variation of the sensors with humidity is less than 20%, where the error bars are variations from 4 mosses with 3 repeated cycles for each humidity. The red dashed line is the mean response of the moss. b). Absorbance changes of mosses due to the exposure to different gases at different concentrations. The data show little responses of the mosses to CO2, O2, alcohol, BTEX and HCHO, but a positive response to NO2 compared to negative response to SO2.


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Chemical interference of the moss sensor was tested using common gases in air, which also shows good selectivity, with little responses to short-term exposure of CO2, O2 and HCHO, alcohol, BTEX (1:1:1:1) (Figures 6b, S10 and S11). Interestingly, the moss responds to 50 ppm NO2, but the response is opposite to that of SO2 (Figures 6b and S8). One possible reason of this observation is that except acidity, NO2 could also act as nitrogen fertilizer32-35 and helps synthesis of chlorophyll which is dominant compared to acidity induced response, although NO2 may also harm the moss in long term or high dose because of the increased acidity like that of SO2.35,36 Additional tests with NH3 (Figures S9, and S10), a typical nitrogen fertilizer, exhibited similar response as NO2, which supports the explanation but further studies will be need to confirm the mechanism. The green color of the mosses arises from chlorophyll (Figure S12). Exposure to SO2 lowers the pH of the leaves, which triggers the release of Mg ion from chlorophyll, and thus changes the color from green to yellow.23,37-39 Tests with different acid gases (CH3COOH and HCl in Figures S9, and S10) also support the mechanism. Chlorophyll in living plants regenerates continuously to maintain a certain level and keep the leaves green.40 In other words, plant-based chemical sensors may self-regenerate their chemical sensing capability. This is in contrast to those traditional colorimetric chemical sensors that rely on irreversible chemical reactions, which lose sensing capability once all the sensing elements are depleted. To examine this regeneration capability, we exposed a moss to SO2 (e.g., 60 ppm) for a period of time (40 mins), left the moss in ambient air for regeneration over 12 hours, and then re-tested its sensitivity (Figure 7). The sensitivity decreased by 0.7 times after exposing to the concentrated SO2, but the moss regained its initial sensitivity almost 100%.



Figure 7 Regeneration of a moss chemical sensor. Continuous exposure of the moss chemical sensor to concentrated SO2 led to gradual decrease in the sensitivity. However, the sensor regenerates and its sensitivity recovers after leaving the moss in ambient air overnight (12 hours). The relative sensitivity was defined as the ratio of sensitivity to that for the first exposure of the moss to SO2.

Plant-based chemical sensors have their limitations. Examples include variability from plant to plant, and dependence on nutrient level, light intensity, and other environmental factors. The impacts of these factors on the chemical sensing capabilities of plants require further studies. However, plants offer distinct features for air quality monitoring, especially for long-term pollution studies, and serve as a surrogate to protect us from dangerous chemical exposures. They are a part of our living environment (outdoor and indoor), solar energy “powered”, and self-regenerable (if the pollution is below the lethal levels). Furthermore, advances in genetic engineering could be used enhance their chemical sensing capability. Finally, coupling optical imaging with plant-based sensors allow remote sensing, which is particularly attractive for monitoring of large-area air pollution with satellites or drones.

CONCLUSIONS


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In summary, mosses change color and shape when exposed to SO2 in air, and the chemical responses can be monitored in real time using a low cost webcam together with an imageprocessing algorithm. The linear range of the moss environmental sensor is 0-180 ppm (typical detection range of commercial SO2 is about 0-50 ppm), and the detection limit is on the order of a few ppm with the current setup. The moss chemical sensor shows good chemical selectivity, and tolerance to humidity variation over 15% - 85% relative humidity, and can self-regenerate. The work demonstrates the possibility of real-time and quantitative chemical sensing with plants. We further anticipate remote and large-area air pollution tracking with plants, and also local chemical exposure alert in indoor settings with houseplants.

ASSOCIATED CONTENT The supporting information listed in the text is available free of charge on the ACS Publication website. The content of the supporting information includes flowchart the experiment setup, image analysis procedure, lag time between SO2 introduction and moss response, regions induced response difference, detailed image difference before and after SO2 exposure, intensity profiles of differential images, statistical analysis of 12 different sensors, response to acid and fertilizer gases, interference gases at different concentrations, interference of household chemicals to the sensor, and images of a moss leaf and chlorophyll.

AUTHOR INFORMATION Corresponding Author *E-mail: [email protected]. Phone: +01 480 965 4456

Notes



There are no conflicts of interest to declare.

ACKNOWLEDGEMENTS The work was supported by National Natural Science Foundation of China (NSFC, Grants 21327008, 21575062), and Natural Science Foundation of Jiangsu Province (BK20150574).

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Chemical Sensing in Real-time with Plants Using a Webcam

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