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Atomic Mechanics, a Manchester, UK-based company, has developed a graphene-polymer-based Micro Electromechanical Systems (MEMs). This force-sensitive transparent film demonstrates prospective properties as a new design model for electronic devices (Atomic Mechanics, n.d.). Although at the prototype phase, Atomic Mechanics is working closely with several manufacturers to convert the technology into commercial applications at a competitive price while still acquiring a desirable advantage over current technologies.
Sensors have played a crucial role in medicine and life sciences as a medium to monitor vital organs and diagnose patients. However, the current conventional sensors developed by various sensing methods, such as lateral flow immunoassay (Viro Research, 2019), fluorescent microarray (Jaksik, Iwanaszko, Rzeszowska-Wolny, & Kimmel, 2015), electrochemical methods (Song, Xu, Kuroki, Liao, & Tsunoda, 2018), and polymerase chain reaction (PCR), have shown massive drawbacks of being complex and insensitive in monitoring the reactions on a real-time basis.
These voids have attracted scientists to generate novel sensors with ultra-specific sensing properties with clinical board applications in the real-time quantitative assessment. In particular, a tremendous demand for miniaturized surgical robotic systems that demonstrates real-time monitoring ability of the patients’ conditions is regarded as a next-generation breakthrough in the healthcare system (Graphene Flagship, 2019).
These surgical robots grant advantages to both doctors and patients over the use of large incisions during surgery. Although they are high cost, an annual growth rate of surgical robots is expected to reach $6.5 billion by 2023, compared to $3.9 billion in 2018.
Atomic Mechanics’ new development in MEMs using graphene-polymer gives confidence to the long and intensive search of modern composite materials with excellent electrical and thermal conductivity, large specific surface area, and outstanding mechanical flexibility needed for the development of promising sensors and implantable devices in the health monitoring system.
The Role of Graphene in Sensors
Since introduction in the late 1980s, the concept of MEMs has explored its possibility of creating a miniaturized version of sensing prototypes in day-to-day-applications (Xu, et al., 2019) with various Shape Memory Alloys (Hino & Maeno, 2004) and composite materials (Miriyev, Stack, & Lipson, 2017).
In particular, silicon-based sensors have attracted considerable attention since 1954 (Smith, 1954) due to their small size, excellent signal-to-noise ratio, low hysteresis, and high repeatability in their fabrication structure (Xu, et al., 2019). However, silicon-based sensors also bear the reputation of being expensive, producing toxic by-products, having smaller sensing surface area that reduces sensitivity, and having limited biocompatibility (Xu, et al., 2019).
These problems expanded the opportunity for scientists to transverse the magical properties of graphene to replace silicon. Graphene’s large surface area, high electrical conductivity, and unique electrical properties make them an ideal candidate to be a sensor. As a result, plenty of graphene-based sensors have been reported, including wearable or implantable sensors that can measure the body’s activities, such as temperature, heart rate, pulse oxygenation, respiration rate, blood pressure, blood glucose, and electrocardiogram signal on real-time (Choi, et al., 2020). Graphene’s spectacular property of high optical transparency means a precise observation of bio-tissues without visual disturbances can be achieved (Huang, et al., 2019).
Due to their high specific surface area, graphene layers contribute entire carbon atoms directly in contact with target molecules, which give them a superior advantage of being sensitive in comparison to silicon.
The performance of the robot sensors is attributed to their power consumption and sensitivity in detecting the target molecule or cell. Graphene-based materials are believed to be used as transducers of biosensors that convert the interactions between the receptor and the target molecules into detectable, measurable signals.
Mr. Robert Roelver presented their new graphene-based sensor based on the Hall Effect at Graphene Week 2015 on behalf of Robert Bosch company, the world’s number one supplier of microelectromechanical sensors, with €1 billion in sales at the time of Mr Roelver’s presentation (Sedgemore, 2016).
The work carried out in cooperation with scientists at the Max-Planck Institute for Solid State Research consists of a graphene-based magnetic sensor 100 times more sensitive than silicon.
Mr Roelver acclaimed that Robert Bosch has also adopted graphene in their pressure, magnetic, humidity, gas, and sound pressure devices, and their newly developed magnetic devices demonstrated a remarkable sensitivity of 7000 volts per amp-tesla (Sedgemore, 2016) in comparison to the silicon-based Hall sensor with a sensitivity of 70 volts per amp-tesla.
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Next-Generation Surgical Robots
More than 70 companies are currently developing platforms for robotic-assisted surgery in various applications (Shah, Felinski, Wilson, Bajwa, & Wilson, 2020). In 2018, over one million robotic-assisted surgical procedures were performed worldwide (Shah, Felinski, Wilson, Bajwa, & Wilson, 2020), confirming their broadened improvement and acceptability in the medical field.
Surgical placement of 75 pedicle screws on 20 patients demonstrated 98.7% accuracy with no complications (Khan, Meyers, Siasios, & Pollina, 2019). Another fascinating demonstration comes from the Institute of Microelectronics and Beijing National Research Center for Information Science and Technology (BNRist) in Beijing, China (Yancong, et al., 2019) who disclosed graphene’s ability as the sensors to detect mechanical, electrophysiological, fluid (Glucose, Micromolecular organics, Macromolecular organics) and gas (Humidity, NO2, NH3, Acetone) signals.
Another group (Yang, et al., 2018) proposed a close-fitting and wearable graphene-based textile sensor that can be knitted on clothing to detect subtle and large human motions. The sensor demonstrated incredible performance, including high sensitivity, long-term stability, and great comfort.
Robotic surgery will continue to be one of the most desirable and fastest-growing fields in medicine and life sciences. However, the next-generation surgical robots require additional consideration in biocompatibility, stability, comfort, miniaturization, cost-effectiveness, and reliability on detecting multiple and complex molecules and cells.
The current breakthrough of Atomic Mechanics has given appreciable hope in opening up a new way for humans to interface with robotics, which improves the sensitivity of the device in diagnosing the patients early enough to increase the survival rate, reducing cost and improving the quality of life. These technical innovations developed in surgical robots could potentially contribute to surgical applications in the near future.
References and Further Reading
Atomic Mechanics. (n.d.). How the technology works. [Online] Atomic Mechanics: https://www.atomic-mechanics.com/technology (Accessed on 23 September, 2020)
Choi, J. H., Lee, J., Byeon, M., Hong, T. E., Park, H., & Lee, C. Y. (2020) Graphene-Based Gas Sensors with High Sensitivity and Minimal Sensor-to-Sensor Variation. ACS Applied Nano Materials, 3(3), 2257-2265. doi:10.1021/acsanm.9b02378
Graphene Flagship. (2019) An Artificial Skin Made With Graphene That Will Revolutionize Robotic Surgery. [Online] Graphene Flagship: https://graphene-flagship.eu/news/press/Pages/Press.aspx#/news/an-artificial-skin-made-with-graphene-that-will-revolutionize-robotic-surgery-372083 (Accessed on 23 September, 2020)
Hino, T., & Maeno, T. (2004) Development of a Miniature Robot Finger with a Variable Stiffness Mechanism using Shape Memory Alloy. Proceedings of The International Simposium on Robotics and Automation, 214-218. doi:10.1299/JSMERMD.2004.108_2
Huang, H., Su, S., Wu, N., Wan, H., Wan, S., Bi, H., & Sun, L. (2019) Graphene-Based Sensors for Human Health Monitoring. Frontiers in chemistry, 399. doi:10.3389/fchem.2019.00399
Jaksik, R., Iwanaszko, M., Rzeszowska-Wolny, J., & Kimmel, M. (2015) Microarray experiments and factors which affect their reliability. Biology direct, 10(1). doi:10.1186/s13062-015-0077-2
Khan, A., Meyers, J. E., Siasios, I., & Pollina, J. (2019) Next-Generation Robotic Spine Surgery: First Reporton Feasibility, Safety, and Learning Curve. Operative Neurosurgery, 17(1), 61-69. doi:10.1093/ons/opy280
Miriyev, A., Stack, K., & Lipson, H. (2017) Soft material for soft actuators. Nature Communications . doi:10.1038/s41467-017-00685-3
Sedgemore, F. (2016) Bosch breakthrough in graphene sensor technology. [Online] Graphene Flagship: https://graphene-flagship.eu/bosch-breakthrough-in-graphene-sensor-technology (Accessed on 23 September, 2020)
Shah, S. K., Felinski, M. M., Wilson, T. D., Bajwa, K. S., & Wilson, E. B. (2020) Next-Generation Surgical Robots. Digital Surgery. doi:10.1007/978-3-030-49100-0_30
Smith, C. S. (1954) Piezoresistance Effect in Germanium and Silicon. Physical review, 94(1), 42. doi:10.1103/PhysRev.94.42
Song, Y., Xu, C., Kuroki, H., Liao, Y., & Tsunoda, M. (2018) Recent trends in analytical methods for the determination of amino acids in biological samples. Journal of pharmaceutical and biomedical analysis, 35-49. doi:10.1016/j.jpba.2017.08.050
Viro Research. (2019) Lateral Flow Assays: Advantages-Disadvantages. [Online] Viro Research: https://viroresearch.com/lateral-flow-assay-advantages-disadvantages/ (Accessed on 23 September, 2020)
Xu, Y., Hu, X., Kundu, S., Nag, A., Afsarimanesh, N., & Sapra, S. (2019) Silicon-Based Sensors for Biomedical Applications: A Review. Sensors, 2908. doi:10.3390/s19132908
Yancong, Q., Li, X., Hirtz, T., Deng, G., Wei, Y.-H., Li, M., . . . Ren, T. (2019) Graphene-based Wearable Sensors. Nanoscale, 11(41). doi:10.1039/C9NR05532K
Yang, Z., Pang, Y., Han, X.-l., Yang, Y., Ling, J., Jian, M., . . . Ren, T.-L. (2018) Graphene Textile Strain Sensor with Negative Resistance Variation for Human Motion Detection. ACS Nano, 12(9), 9134–9141. doi:10.1021/acsnano.8b03391