Skip to Content
MDPIMDPI
SurfacesSurfaces
PDF
  • Editorial
  • Open Access

31 May 2022

Emerging Chemical Sensing Technologies: Recent Advances and Future Trends

and
Department of Engineering, Physics and Mathematics, Chemistry Institute, São Paulo State University (UNESP), Araraquara 14800-060, SP, Brazil
*
Authors to whom correspondence should be addressed.
Surfaces2022, 5(2), 318-320;https://doi.org/10.3390/surfaces5020023
This article belongs to the Special Issue Surfaces on Emerging Chemical Sensing Applications
Version Notes
Order Reprints
Contemporary chemical sensing research is rapidly growing, leading to the development of new technologies for applications in almost all areas, including environmental monitoring, disease diagnostics and food quality control, among others [1,2,3]. These relevant scientific and technological developments are intrinsically related to the emergence of new materials, synthesis methods, device manufacturing processes, and advanced characterization techniques for different chemical sensors [4,5,6].
The constant search for greener, more reliable, and cheaper syntheses methods has led to the progress on the development of smart materials for chemical sensors [7,8]. Thus, enhanced electrical, electromagnetic, electrochemical, optical or bio-activity properties have been discovered in nanostructured semiconductor metal oxides, polymers, carbon, biological materials, 2D nanomaterials, which, consequently, have enabled improving the sensing performance [9,10,11,12]. For instance, the development of hybrid organic and/or inorganic nanostructures has emerged as an effective and promising approach for the development of a new generation of biosensing devices [13,14]. Moreover, manufacturing processes, such as nanolithography or nanomanufacturing, have provided efficient tools for the development of nano-active sensing layers and, consequently, to the miniaturization of such devices [15,16,17]. Thus, the development of research furthering understanding of the relationship between optimized synthesis conditions and the enhanced properties of the material combined with new manufacturing processes are necessary and highly recommended for the development of a new generation of high-performance chemical sensors.
In addition, the emerging of in situ and/or operando characterization techniques, which contribute to monitoring of the interaction and the transduction mechanisms under real-time operating conditions, have allowed deeper insights into the chemical sensing phenomenology, contributing to more exact descriptions and understanding of the sensing mechanisms [18,19]. For instance, the use of analytical techniques, such as Raman spectroscopy, DRIFT spectroscopy, ultraviolet–visible (UV–Vis) absorption spectroscopy, X-ray absorption near-edge structure (XANES), among others, concomitantly with electrical measurements, has been used to investigate the adsorption/desorption surface kinetics on gas sensing applications [20]. This kind of approach in chemical sensing studies is very important and required because it leads to real-time monitoring of the sensing activity, providing deeper information on the dynamic sensing processes. In addition, theoretical and computational simulations have gaining great importance in sensing applications because they can help to predict material’s capability to exhibit an enhanced sensing behavior, rationalizing time and costs on the development of new sensor devices, as well as to support innovative experimental sensing studies [21,22].
As a future trend, sensors are expected to be the top five most in-demand components as the world is entering in an age of devices exchanging information on the internet, the Internet of Things (IoT), which will enable the collection of sensing data and act to propose solutions in most human life situations. [23,24]. In addition, the sensor networks generate a huge amount of data; therefore, the uses of Big Data and machine learning in the chemical sensing field are strongly increasing, aiming to predict diseases and achieve real-time environmental monitoring, food quality control, etc. [1,25,26,27]. Moreover, Big Data and machine learning technologies have been used in the development of new chemical-sensing-related technologies, which includes the synthesis prediction of new organic and inorganic materials, compound identification, the modeling of biosensing activity, manufacturing, etc. [28,29,30]. Thus, the combination of different chemical sensors with Big Data and machine learning tools will have an enormous impact on human life and the economy in the coming decades.
We hope that this Editorial and the published manuscripts in this Special Issue will stimulate the interest of readers towards the recent advances and future trends and perspectives of such a strategical and interdisciplinary field.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors thank the São Paulo Research Foundation (FAPESP) (17/26219-0), the National Council for Scientific and Technologi-cal Development (CNPq), (443138/2016-8, 305437/2018-6), and the Postdoctoral National Program of the Coordination for the Improvement of Higher Education Personnel (PNPD/CAPES).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ullo, S.L.; Sinha, G.R. Advances in Smart Environment Monitoring Systems Using IoT and Sensors. Sensors 2020, 20, 3113. [Google Scholar] [CrossRef] [PubMed]
  2. Das, S.; Pal, M. Review—Non-Invasive Monitoring of Human Health by Exhaled Breath Analysis: A Comprehensive Review. J. Electrochem. Soc. 2020, 167, 037562. [Google Scholar] [CrossRef]
  3. Galstyan, V.; Bhandari, M.; Sberveglieri, V.; Sberveglieri, G.; Comini, E. Metal Oxide Nanostructures in Food Applications: Quality Control and Packaging. Chemosensors 2018, 6, 16. [Google Scholar] [CrossRef] [Green Version]
  4. Javaid, M.; Haleem, A.; Singh, R.P.; Rab, S.; Suman, R. Exploring the potential of nanosensors: A brief overview. Sens. Int. 2021, 2, 100130. [Google Scholar] [CrossRef]
  5. Schroeder, V.; Savagatrup, S.; He, M.; Lin, S.; Swager, T.M. Carbon Nanotube Chemical Sensors. Chem. Rev. 2019, 119, 599–663. [Google Scholar] [CrossRef]
  6. Zhou, X.; Lee, S.; Xu, Z.; Yoon, J. Recent Progress on the Development of Chemosensors for Gases. Chem. Rev. 2015, 115, 7944–8000. [Google Scholar] [CrossRef] [PubMed]
  7. Bahl, S.; Nagar, H.; Singh, I.; Sehgal, S. Smart materials types, properties and applications: A review. Mater. Today Proc. 2020, 28, 1302–1306. [Google Scholar] [CrossRef]
  8. Shandilya, M.; Rai, R.; Singh, J. Review: Hydrothermal technology for smart materials. Adv. Appl. Ceram. 2016, 115, 354–376. [Google Scholar] [CrossRef]
  9. Camilli, L.; Passacantando, M. Advances on Sensors Based on Carbon Nanotubes. Chemosensors 2018, 6, 62. [Google Scholar] [CrossRef] [Green Version]
  10. Grieshaber, D.; MacKenzie, R.; Vörös, J.; Reimhult, E. Electrochemical Biosensors—Sensor Principles and Architectures. Sensors 2008, 8, 1400–1458. [Google Scholar] [CrossRef]
  11. Neri, G. Thin 2D: The New Dimensionality in Gas Sensing. Chemosensors 2017, 5, 21. [Google Scholar] [CrossRef]
  12. Turner, A.P.F.; Magan, N. Electronic noses and disease diagnostics. Nat. Rev. Microbiol. 2004, 2, 160–166. [Google Scholar] [CrossRef] [PubMed]
  13. Long, D.; Tu, Y.; Chai, Y.; Yuan, R. Photoelectrochemical Assay Based on SnO2/BiOBr p–n Heterojunction for Ultrasensitive DNA Detection. Anal. Chem. 2021, 93, 12995–13000. [Google Scholar] [CrossRef] [PubMed]
  14. Batool, R.; Rhouati, A.; Nawaz, M.H.; Hayat, A.; Marty, J.L. A Review of the Construction of Nano-Hybrids for Electrochemical Biosensing of Glucose. Biosensors 2019, 9, 46. [Google Scholar] [CrossRef] [Green Version]
  15. Pimpin, A.; Srituravanich, W. Review on Micro- and Nanolithography Techniques and their Applications. Eng. J. 2012, 16, 37–56. [Google Scholar] [CrossRef] [Green Version]
  16. Maddipatla, D.; Narakathu, B.B.; Atashbar, M. Recent Progress in Manufacturing Techniques of Printed and Flexible Sensors: A Review. Biosensors 2020, 10, 199. [Google Scholar] [CrossRef]
  17. Fang, F.Z.; Zhang, X.D.; Gao, W.; Guo, Y.B.; Byrne, G.; Hansen, H.N. Nanomanufacturing—Perspective and applications. CIRP Ann. 2017, 66, 683–705. [Google Scholar] [CrossRef] [Green Version]
  18. Gurlo, A.; Riedel, R. In Situ and Operando Spectroscopy for Assessing Mechanisms of Gas Sensing. Angew. Chem. Int. Ed. 2007, 46, 3826–3848. [Google Scholar] [CrossRef]
  19. Vojinović, V.; Cabral, J.M.S.; Fonseca, L.P. Real-time bioprocess monitoring. Sens. Actuators B Chem. 2006, 114, 1083–1091. [Google Scholar] [CrossRef]
  20. Degler, D. Trends and Advances in the Characterization of Gas Sensing Materials Based on Semiconducting Oxides. Sensors 2018, 18, 3544. [Google Scholar] [CrossRef] [Green Version]
  21. Vaidyanathan, A.; Mathew, M.; Radhakrishnan, S.; Rout, C.S.; Chakraborty, B. Theoretical Insight on the Biosensing Applications of 2D Materials. J. Phys. Chem. B 2020, 124, 11098–11122. [Google Scholar] [CrossRef] [PubMed]
  22. Tang, X.; Du, A.; Kou, L. Gas sensing and capturing based on two-dimensional layered materials: Overview from theoretical perspective. WIREs Comput. Mol. Sci. 2018, 8, e1361. [Google Scholar] [CrossRef]
  23. Al Mamun, M.A.; Yuce, M.R. Sensors and Systems for Wearable Environmental Monitoring Toward IoT-Enabled Applications: A Review. IEEE Sens. J. 2019, 19, 7771–7788. [Google Scholar] [CrossRef]
  24. Shanthamallu, U.S.; Spanias, A.; Tepedelenlioglu, C.; Stanley, M. A brief survey of machine learning methods and their sensor and IoT applications. In Proceedings of the 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA), Larnaca, Cyprus, 27–30 August 2017; IEEE: Piscataway, NJ, USA, 2017; Volume 2018, pp. 1–8. [Google Scholar]
  25. Schroeder, V.; Evans, E.D.; Wu, Y.-C.M.; Voll, C.-C.A.; McDonald, B.R.; Savagatrup, S.; Swager, T.M. Chemiresistive Sensor Array and Machine Learning Classification of Food. ACS Sens. 2019, 4, 2101–2108. [Google Scholar] [CrossRef] [PubMed]
  26. Ha, N.; Xu, K.; Ren, G.; Mitchell, A.; Ou, J.Z. Machine Learning-Enabled Smart Sensor Systems. Adv. Intell. Syst. 2020, 2, 2000063. [Google Scholar] [CrossRef]
  27. Oliveira, O.N.; Iost, R.M.; Siqueira, J.R.; Crespilho, F.N.; Caseli, L. Nanomaterials for Diagnosis: Challenges and Applications in Smart Devices Based on Molecular Recognition. ACS Appl. Mater. Interfaces 2014, 6, 14745–14766. [Google Scholar] [CrossRef]
  28. Tao, H.; Wu, T.; Aldeghi, M.; Wu, T.C.; Aspuru-Guzik, A.; Kumacheva, E. Nanoparticle synthesis assisted by machine learning. Nat. Rev. Mater. 2021, 6, 701–716. [Google Scholar] [CrossRef]
  29. Syafrudin, M.; Alfian, G.; Fitriyani, N.; Rhee, J. Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing. Sensors 2018, 18, 2946. [Google Scholar] [CrossRef] [Green Version]
  30. Rodrigues, J.F.; Florea, L.; de Oliveira, M.C.F.; Diamond, D.; Oliveira, O.N. Big data and machine learning for materials science. Discov. Mater. 2021, 1, 12. [Google Scholar] [CrossRef]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Article Metrics

Citations

Article Access Statistics

Journal Statistics
Multiple requests from the same IP address are counted as one view.
Surfactants for Electrophoretic Deposition of Polyvinylidene Fluoride–Silica Composites
Previous
The Effects of Substrate Temperature on the Growth, Microstructural and Magnetic Properties of Gadolinium-Containing Films on Aluminum Nitride
Next