A Megatrend Challenging Analytical Chemistry: Biosensor and Chemosensor Concepts Ready for the Internet of Things

Abstract

The Internet of Things (IoT) is a megatrend that cuts across all scientific and engineering disciplines and establishes an integrating technical evolution to improve production efficiencies and daily human life. Linked machines and sensors use decision-making routines to work toward a common product or solution. Expanding this technical revolution into the value chain of complex areas such as agriculture, food production, and healthcare requires the implementation and connection of sophisticated (bio)­analytical methods. Today, wearable sensors, monitors, and point-of-care diagnostic tests are part of our daily lives and improve patients’ medical progression or athletes’ monitoring capabilities that are already beyond imagination. Also, early contributions toward sensor networks and finally the IT revolution with wireless data collection and transmission via Bluetooth or smartphones have set the foundation to connect remote sensors and distributed analytical chemical services with centralized laboratories, cloud storage, and cloud computing. Here, we critically review those biosensor and chemosensor technologies and concepts used in an IoT setting or considered IoT-ready that were published in the period 2013–2018, while also pointing to those foundational concepts and ideas that arose over the last two decades. We focus on these sensors due to their unique ability to be remotely stationed and that easily function in networks and have made the greatest progress toward IoT integration. Finally, we highlight requirements and existing and future challenges and provide possible solutions important toward the vision of a seamless integration into a global analytical concept, which includes many more analytical techniques than sensors and includes foremost next-generation sequencing and separation principles coupled with MS detection.

Rapid and Selective NH3 Sensing by Porous CuBr

“…In addition, all process steps can be performed in situ on the chip level to minimize the number of operational units, which is attractive for industry. This can contribute to the synthesis of new low‐power gas sensors that are urgently sought in portable breath analyzers38 and detectors for distributed air39 and food quality monitoring networks 40…”

“…This can contribute to the synthesis of new low-power gas sensors that are urgently sought in portable breath analyzers [38] and detectors for distributed air [39] and food quality monitoring networks. [40]…”

Critical assessment of relevant methods in the field of biosensors with direct optical detection based on fibers and waveguides using plasmonic, resonance, and interference effects

“…Such biosensors are part of Analytics 4.0 [419]. They allow better process control, are essential for personalized medicine, will be interesting for citizen science, supply the necessary megadata for artificial intelligence [420], will help to meet challenges in analytical chemistry in the future and will prepare it for the Internet of Things [421]. It will be interesting whether for biosensors the ideas of digital twins in manufacturing or simulation processes can provide better comparable standardized information on results of biomolecular interaction analysis [422] in research and quality control .…”

Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns

“…Analytical chemistry is possibly the area corresponding to the longest history, but also one that mostly displays embryonic applications of ML. A large number of statistical analyses and ML expert systems have been implemented in analytical chemistry for a long time (e.g., comparing and classifying mass spectra, NMR, or IR through assessments on available compounds) (Lipkowitz and Boyd, 1995;Mayer and Baeumner, 2019). Until recently, ML approaches were mainly employed to explain chemical reactions and to provide valuable predictive insights.…”

“…The integration of ML-based algorithms in a chemosensor has also pointed out the future of ML and the artificial internet of things applicability, i.e., optimized sensors, linked to a central data analysis unit via wireless (Mayer and Baeumner, 2019).…”