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. 2020 Dec 18;6(3):2045–2051. doi: 10.1021/acsomega.0c04995

Automatic Sensing Setup for Methamphetamine Based on the Reactional Wettability Variation Strategy

Yang Zhao †,*, Yahang Xie ‡, Qifan Zhou ‡, Pan Wang ‡, Ying Chang †, Changxu Lin ‡,*
PMCID: PMC7841941  PMID: 33521443

Abstract

graphic file with name ao0c04995_0011.jpg

An automatic setup for reactional wettability variation (RWV) was developed by interlinking liquid selection and transportation, object movement, and image recognition. In this way, the performance of the RWV strategy is updated to a nearly unmanned control manner with the example of methamphetamine and its aptamer. On the automatic RWV detection setup, the sensing surface acts similarly as before. The aptamer-based sensing surface resulted from the breakdown of the hydrophobic basis. The hydrophobicity is constructed on the metastable aptamer layer, which is responsive to the corresponding target. Methamphetamine interacts with its corresponding aptamer and destroys the basis of the hydrophobicity. A decrease in contact angle indicates the existence of methamphetamine. The RWV phenomenon is also affected by concentration and temperature. The development of an automatic detection ability would bring new possibilities to the surface reaction on smarter detection.

1. Introduction

Drug abuse is one of the most critical and influential problems in the field of public safety and people’s health in the current world. Methamphetamine (METH), which is a representative synthetic drug for its easy and cheap synthesis and strong addictive property, has the second largest number of drug users.1 Identification and detection of METH has attracted the attention of many researchers. The detection strategies for METH were developed following the mechanism of gas chromatography coupled with mass spectrometry (GCMS),2 high-performance liquid chromatography (HPLC),3 immunoassays,4,5 electrochemistry,6 fluorescence,7 and surface-enhanced Raman spectroscopy (SERS).8−10 However, most detection strategies and methods above required complicated procedures on large and expensive instruments, standard laboratory environment, and training for skilled personnel. In the daily investigation of antidrug police force, fast and automatic METH detection is in great need. The colorimetric method based on the colloidal gold is now used in practical environment.11

The colorimetric or fluorescence method are candidates for fast detection. Yet, the quality of detection is greatly dependent on the luminous environment, which limits the quantitative detection. It is also difficult to build an unmanned continuous operation line due to the pretreatment of this method. Some new wettability-based detection strategies have been proposed as a supplement or even a replacement to the current optical detections to work under worse ambient light.12−14 The host–guest interactions were investigated and utilized by Li and co-workers to achieve a macroscopic surface switch for sensing pesticides15 and achieving chiral discrimination of organics16 with static and dynamic wettability characterizations like contact angles (CAs) and sliding angles.

Other than the independence from ambient light environment, the application of surface wettability into sensing further delivered a possible working structure for automatic detection. CA as the signal is in the scale of optical and pseudo-real-time measurement. It is one of the most important foundations to design an automatic setup for it. Automation marks the future of analytical chemistry not only from the round-the-work efficiency and the unmanned repeatability that is desiderating in daily antidrug operation but also for the opportunity of generating massive data to feed the artificial intelligence (AI) algorithm. Since the year of Alpha Go beating human, numerous work has been reported on the assistance of AI to chemistry or material science research.17−19 However, most of the work on AI in chemistry was mainly the existing data which might be drained someday without high-throughput sources. Automatic detection setup is the best partner of AI in chemistry for its ability to greatly enlarge the data source that could be fed into the AI algorithms. There have been systematic automation approaches of the automation of mass spectrometry, step by step from sample preparation, sample delivery, and data acquisition to data processing.20 In the other report, partial automation has been applied to the sensing system of immunoassay,21 fluorescence,22,23 and electrochemistry.24 Microfluidic circuits were also good to organize detection operations in a programmed flow manner.25,26

Previously, a sensing strategy named reactional wettability variation (RWV) has been proposed by us in several works. The RWV strategy uses CA as the fundamental signal in sensing. The cornerstone of RWV is the initial hydrophobicity built on a reactive basis. It turns hydrophilic when reaction or recognition happened when analytes exist. The evolution of RWV followed this line: electrospun fabric surface27 was replaced with brucite-type cobalt hydroxide (BCH) nanoneedles,28 then nonspecific sensor array based on coordination was promoted to specific aptamer sensors.29 The RWV strategy links the macroscopic CA measurement to the sensing of molecular-scale chemical information. The overall procedure from sample handling to data processing could be accomplished in a single location. These two natures are the foundation to realize an automatic setup for the RWV-sensing strategy.

In this work, we design an automatic setup to connect all of the steps for the RWV strategy to work on the detection of METH. As shown in Scheme 1; it included the sample droplet formation and loading driven by a syringe pump and a PC-controlled digital valve, the stage movement gear driven by a robotic arm, the temperature control by a hot stage, the CA image capture by a camera, and the reading algorithm to get the CA value. Each sensing site contained three layers: the supporting glass substrate, the aptamer recognition layer, and the metastable hydrophobic layer, which finally deliver the CA variation signal of analyte droplets. Several sensing sites were merged into one sensing surface. The programmed flow of liquid, surface, temperature, and image were connected to form the chemical information that different analytes transmit to CA value continuously. Different analytes and temperatures were used. The CA value vs. time was recorded and discussed. The setup not only made the detection of METH more efficient and repeatable but also showed a good example of all-surface operation and the potential of wettability to serve as the backbone of detection automation.

Scheme 1. (a) Structure of the RWV-Sensing Surface. (b) Structure of Setup for Automatic RWV Detection of METH.

Scheme 1

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2. Results and Discussion

2.1. Design of the Automatic CA-Measuring Setup

The complete automatic CA-measuring setup is shown in Figure 1. The position was organized to make the droplet generation module could be interfered by the robotic arm. The robotic arm uplifted the hot stage, which was also the platform to place the sensing surfaces. All data line was connected to the computer on which a python program was run to control all of the movement and liquid manipulation. To be more specific, the liquid and the droplet manipulation is explained in Scheme 2. The syringe offered the driving force of the liquid, much like what a heart does. Also, the six-way valve responded to the program order to choose the liquid from a particular channel to be inhaled or exhaled. The blank liquid was used to clean the whole tube (Figure 1a). Generating a droplet onto the sensing surface consisted of three steps. Some liquid was inhaled and a small amount of it was exhaled (in this work 10 μL) to make a suspended droplet. The sensing surface moved upward to touch the droplet and received it finally. The speed should be as slow as possible to avoid possible vibration.

Figure 1.

Figure 1

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Organization of the automatic detection setup.

Scheme 2. Working Sequence of (a) Cleaning Tubes and (b–d) Generating Droplets.

Scheme 2

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The CA value calculated from the algorithm was calibrated with the contact angle meter. The average deviation is less than 5%, exhibiting the validity of the algorithm to be used in this work (Table S1).

2.2. Fabrication of the RWV-Sensing Units for METH

The fabrication process of the RWV-sensing unit shown in Figure 2 is divided into three fabrication stages. First, in the aptamer deposition stage, the aptamer layer could be observed with the naked eye especially on the edge of glass substrates. From the optical view, the growth of BCH nanoneedles turned the appearance of the sensing unit into light pink, followed by brown color after hydrophobic decoration. The generally uniform deposition of the aptamer could also be verified by the bright-field microscopy image (Figure 2-c1). After the nanoneedle growth and decoration, few dark spots from the possible aggregation appeared. In the scanning electron microscopy (SEM) image of the central part of the sensing unit, the aptamer layer was uniform. In the nanoneedle growth stage, the glass substrate was covered with a light pink layer, which could be identified to the BCH nanoneedles with the microscopy (Figures 2-c2) and SEM image (Figures 2-d2). The BCH nanoneedles provided not only the rough surfaces as the foundation of hydrophobicity but also paths for the permeation and then the reaction for the analyte solution toward the METH aptamer under nanoneedles. In the hydrophobic coating stage, no other morphological change was observed at each dimension scale except for slightly turning dark in the optical images. The color and grayscale transitions were also observable on the microscopy images. Compared to the results in our previous work in which the nanoneedles were fabricated directly on the glass substrate,28 the aptamer layer had a negligible effect on the morphology and the density of the nanoneedles.

Figure 2.

Figure 2

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Fabrication of the RWV-sensing units for METH. (a) Stages of fabrication: (a1) deposition of the METH aptamer, (a2) growth of the BCH nanoneedles, and (a3) hydrophobic decoration of nanoneedles. (b) Optical images corresponding to stages 1–3. (c) Bright-filed microscopy images corresponding to stages 1–3. (d) SEM images corresponding to stages 1–3.

The reservation of the aptamer after the last two stages of operation to fabricate the sensing unit was verified by fluorescence microscopy (Figure 3). A fluorescently labeled aptamer (3′-Cy3-METH_Aptamer) was used in the deposition stage. Finally, the distribution of aptamer was confirmed by fluorescence microscopy. We chose the areal density of the aptamer in this work as 0.03 OD/cm2. The fluorescent density of the sensing unit was between 0.01 and 0.1 OD/cm2, exhibiting the repeatability of the fabrication of the sensing unit. In the fluorescent image, some aggregation of the fluorescent active site was observed. It was assumed that the growth of nanoneedles could break the uniformity of the aptamer layer based on their heterophase nature. The aptamer was adsorbed on the glass surface with an intermolecular force. During the growth of the BCH nanoneedles, an inorganic crystallization process occurred. To the best of our knowledge, the combined action of nucleation and growth of the BCH precursor may affect the distribution of aptamers. In other words, the newly formed discontinuous phase of the bottom of nanoneedles also created new domains for aptamer aggregation of dispersion.

Figure 3.

Figure 3

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(a) Fluorescence intensity histogram of cy3 labeled as aptamer-based hydrophobic surface (AHS-MA-x, x = 0.01, 0.03, 0.1, 0.3); the shadowed bars are the three data from our previous work.29 (b) Fluorescent image of the cy3-labeled AHS-MA-0.03.

2.3. Performance of the RWV Sensor Units

The RWV-sensing mechanism works based on the recognition between the analyte and the response compositions. The response composition also served as the foundation to support or be part of the hydrophobicity. When the recognition with the response composition broke the metastable state of hydrophobicity, the CA decreased as the signal out. In this work, the interaction playing a crucial role was the interaction between the METH aptamer and the target METH. The METH aptamer also supported the hydrophobically modified nanoneedles in a metastable nature. Figure 4 shows the schematic illustration of the above mechanism. It also shows the CA results of 0.1 M METH droplet on the sensing surface of AHS-MA-0.03 at 25 °C at the two ends of the process.

Figure 4.

Figure 4

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(a) Schematic illustration of the RWV mechanism based on the METH aptamer. (b, c) Images of measurement of CA of the 0.1 M METH droplet on the AHS-MA-0.03 at 25 °C at the start and the end.

5The time-related behavior of the METH droplet on the AHS-MA-0.03 surface in the terms of CA is shown in Figures 5 and 6. The process of the METH droplet that caused a decrease in CA response occurred for nearly an hour. This made it possible for the current automatic setup to reach the proper time resolution but made the effect of the evaporation of droplets non-negligible. Thus, we used the net contact angle (nCA, deducted by the CA of deionized water as the background) as the key performance parameter to investigate the RWV response. When the RWV detection system worked at 25 °C, only the METH droplet of a higher concentration to 0.1 M had an evident decreased response. The 0.01 M METH droplets and the 0.1 M ephedrine (EPH) droplets showed no major difference in the almost static variation trend, only every CA of 0.01 M METH was slightly lower than that of EPH. EPH was chosen to work in parallel as a reference due to its structural similarity and the frequent usage as the reduction precursor in the production of illegal drugs. Looking into the 0.1 M METH data at 25 °C, we found the major deviation of the 0.1 M METH vs. the other two appeared after the 40th min. Such an induction period of various lengths also existed in the three curves at 30 °C. It is 10 min for both 0.01 and 0.1 M METH, as well as 22 min for 0.1 M EPH. Other than sharing the existence of the induce period, all three analytes at 30°C showed an enhanced response (lower nCA) compared to 25 °C. The most typical analyte was still 0.1 M METH. A plateau period when the CA stayed unchanged appeared after the 38th min, which could also be marked as the endpoint of the nCA response. A similar endpoint appeared at the 42nd min for 0.01 M METH. The rising temperature also sped up the recognition process. The mechanism on temperature effect was raised as below. The diffusion rate through the hydrophobic barrier and the gap between nanoneedles; the recognition ability between analyte and aptamer; the background of evaporation which could shorten the live time span of the droplets to avoid a full expression of sensing interactions.

Figure 5.

Figure 5

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Graph of nCA vs time for AHS-MA-0.03 at (a) 25 °C and (b) 30 °C.

Figure 6.

Figure 6

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SEM picture of the nanoneedle surface AHS-MA-0.03 after contacting with (a) deionized water droplets, (b) 0.01 M METH, (c) 0.1 M METH, and (d) 0.1 M EPH. All were at 25 °C.

From the microscopic observation through SEM, a possible reason could be deducted. For the two METH cases, some sediment was found to exudate around the nanoneedles. All of the solutions started with large CAs according to the hydrophobically modified nanoneedles. They all acted as the hydrophobic barrier to the aptamer layer, which is compact due to the passage between the nanoneedles. Over time, a trace amount of METH permeated through the barrier and combined with the MA aptamer. The recognition generally dissolved its product to the surface, promoted further permeation and recognition, finally shook the foundation of the hydrophobicity. Hydrocarbon and fluorocarbon chains on the surface of nanoneedles, together with air in the space between nanoneedles, supported the droplet to show a large contact angle. Once the recognition dissolved its product onto the surface. Hydrocarbon and fluorocarbon chains were covered. Also, space was filled too. These were the two causes that shook the foundation of hydrophobicity. The EPH droplet could not react with the MA aptamer. The bulk might be the residues that resulted from evaporation.

Figure 7 exhibits the status of the analyte and AHS at the end of the whole recognition. From the qualitatively visual evaluation, a higher amount of sediment exudated at higher temperatures. It is supposed that hydrophobicity was destroyed at every timepoint in the whole recognition process at 30 °C. After the endpoint, no hydrophobicity was left to support a further increase in nCA. The nCA curve turned into a plateau. For a higher METH concentration, the acceleration is more sensitive to temperature.

Figure 7.

Figure 7

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SEM picture of the nanoneedle surface AHS-MA-0.03 after contacting with (a) 0.01 M METH; (b) 0.1 M METH; and (c) 0.1 M EPH. All were at 30 °C.

3. Conclusions

We developed an automatic setup based on macroscopic object transportation, liquid and droplet manipulation, and image acquisition and recognition. The RWV strategy evolved into a sequential style by organizing the flow of liquid/solution, the sensing surface, and the chemical information that existed or was generated. On the automatic RWV detection setup, very similar behaviors as those reported previously reported for METH-aptamer-based sensing surface were observed with the interaction of all three analytes. The hydrophobicity breakdown of the sensing surface indicated the existence of METH. Such an indication was correlated to the concentration of METH. The EPH solution showed a much less response on the sensing surface, although the rise of temperature narrowed the distinction. The development of the automatic detection ability would inspire similar design to further the possibility of surface reaction for efficient detection. For a more generalized application, any chemical process involved in the manipulation of liquid or droplet could be accelerated with this instrumental setup. Reactions could be performed on the surface as in this work, or into the liquid phase, which could be switched with robotic arms. In this way, a high-throughput data flow is built and serves as possible algorithm solutions to chemistry problems.

4. Experimental Section

4.1. Chemicals and DNA Strand

Ultrapure (Milli-Q) water was from Milli-Q water system (Millipore). Ethanol and sodium hydroxide were purchased from Xilong Chemical. Cobalt chloride hexahydrate was obtained from Sigma Aldrich. Triethoxy-1H,1H,2H,2H-trifluorooctylsilane and urea were obtained from Aladdin Biochemical Technology. Methamphetamine and ephedrine were legal samples from the Institute of Forensic Science, Ministry of Public Security of China. All reagents were analytical grade and used without further purification.

The single-strand DNA oligomer of the METH aptamer was synthesized by Sangon Biotech (Shanghai) with the sequence (5′-ACG GTT GCA AGT GGG ACT CTG GTA GGC TGG GTT AAT TTG G-3′).

4.2. Fabrication of Sensing Surfaces

4.2.1. Growth of Aptamers

After centrifuging the oligo at 4000 rpm for 30–60 s. After the lid of the centrifuge tube was opened slowly and carefully, an appropriate amount of water was added. The tube was shaken to mix the liquid with the lid covered. The glass slide substrates were immersed in 10 M sodium hydroxide solution for 24 h, taken out and rinsed with distilled water, and dried for use. The oligomer solution in the previous centrifugation tube was evenly dropped on a clean glass slide (2.5 cm × 0.8 cm) and dried at room temperature. The areal density of the METH aptamer on the glass substrate was controlled and calculated.

4.2.2. Fabrication of the Nanoneedle Surfaces

CoCl2·6H2O (0.9018 g) and (NH2)2CO (5 g) were dissolved and mixed in DI water (25 mL) as Solution A. Glass substrates were washed with 10 M sodium hydroxide solution before the deposition of the aptamer. The substrates were soaked in Solution A in a 15 mL centrifuge tube and kept at 60 °C for 24 h to perform the hydrothermal growth of the brucite-type cobalt hydroxide (BCH, Co(OH)1.13Cl0.09(CO3)0.39·0.05H2O) nanoneedles.30 After the hydrothermal reaction, the obtained superhydrophilic samples were rinsed with DI water and then dried at 60 °C.

4.2.3. Hydrophobic Substance Modification

One hundred microliters of fluorosilane (triethoxy-1H,1H,2H,2H-trifluoro-n-octylsilane) was dissolved in 15 mL of ethanol/deionized water solution (1:2 v/v). The needle-like surface obtained in the previous step was immersed in the fluorosilane solution and placed in a centrifugation tube for 15 min at room temperature. The sample was taken out and dried to get the desired hydrophobic sensor units.

4.3. Automatic Setup and CA Characterization

4.3.1. Analyte Droplet Preparation and Loading

Three analyte solutions of 0.01 and 0.1 mol/L METH solutions and 0.1 mol/L EPH solution were prepared and connected to the branches of a six-way selection valve (SV06, Runze Fluid Co.) via poly(tetrafluoroethylene) (PTFE) tubes. The blank liquid was connected to the six-way selection valve. Two outlets for the main channel and the waste channel were connected to branches of the valve. A syringe pump (PHD ULTRA 4400, Harvard Co.) was connected to the center of the valve to offer the driving force for the liquid.

4.3.2. Sensing Surface Operations

The sensing surface was fixed on a heating stage (Cossim KEL-2000), which offered temperature manipulation. They were placed and fixed on the end of a robotic arm, providing the ability to move (Z-Arm 2140N0, Huiling Tech. (Shenzhen) Co.). The position of the detection spot was located in advance. The position of the droplet outlet was located, which was also the detection spot.

First, the robotic arm moved the sensing surface to meet the detection spot. The six-way valve was selected by a Python program to one of the analytes. The syringe pump inhaled the curtained volume of the analyte solution and then a droplet of 10 μL was pushed out and generated by the syringe pump. The sensing surface was moved slightly upward to receive the droplet. The location of the robotic arm was recorded by the program. Then, the circle started again to the next detection spot. Before switching to a new analyte solution, the tube and the valve must be cleaned with a blank liquid and rinsed with a new analyte solution.

4.3.3. CA Image Acquisition and Reading

Images of the droplets were taken by a digital color camera. A light-emitting diode (LED) (9W, JH-AP60-2C, Juhua China) was used to offer a white background (10.0 megapixels, 1/2.3 CMOS, U2B1000C, ZZCAT Zhuzhou Instrument Co. China). An algorithm code in Python calculated the CA value from the image.31 The CA value recognized by the algorithm was compared with that read directly from the contact angle meter from Dataphysics DSA100.

4.4. Other Characterization

4.4.1. Scanning Electron Microscopy

The sample was fixed to the sample stage with a conductive paste and then sprayed with platinum. The observation was completed on Hitachi SU-70.

4.4.2. Bright-Field Microscopy

The bright-field microscopy images were taken on a Nikon Ni–S microscope.

4.4.3. Fluorescence Microscopy

The 3′ end of the methamphetamine aptamer was modified with cy3, and the methamphetamine aptamer in the experiment was replaced with a modified fluorescent aptamer. The resulting sensor unit was then observed and photographed on the fluorescence microscope Leica DFC7000T. The excitation wavelength is 550 nm.

Acknowledgments

This research was financially supported by the Fundamental Research Funds for the Central Universities (Grant No. 20720180017), the Fujian Provincial Department of Science & Technology (Grant No. 2018J01018), and the Technical Research Project funded by the Ministry of Public Security of China (2018JSYJC09 and 2018JSYJA04).

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.0c04995.

  • Calibration of the recognized CA value; video recording of the operation of automatic RWV detection; and code to drive the robotic arm and syringe pump (PDF)

  • Operation of automatic RWV detection (MP4)

The authors declare no competing financial interest.

Supplementary Material

ao0c04995_si_001.pdf (144.5KB, pdf)
ao0c04995_si_002.mp4 (49.2MB, mp4)

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