Editorial Cite This: ACS Sens. 2018, 3, 1431−1431
pubs.acs.org/acssensors
So, You Have a Great New Sensor. How Will You Validate It? s an Associate Editor of ACS Sensors, I feel and appreciate the excitement of researchers who want to share their newest findings in the field. In this issue, we find a Sensor Issues article on the importance of interconnected sensors for the (IoT) Internet of things (DOI: 10.1021/acssensors.8b00276); and among the many excellent research articles, a probe that can correctly identify all natural amino acids (DOI: 10.1021/acssensors.8b00371); an optical sensor for explosives detection (DOI: 10.1021/acssensors.8b00113); a microfluidic channel design to identify human cancer cells (DOI: 10.1021/acssensors.8b00301); a single-molecule approach for FRET-based biosensor design (DOI: 10.1021/ acssensors.8b00143); a phosphate sensor for environmental monitoring (DOI: 10.1021/acssensors.8b00343); and a nanostructured electrode for redox potential measurements in blood (DOI: 10.1021/acssensors.8b00498). So, how do we know that the sensor is really doing what it is supposed to? Of course, a characterization of selectivity, measuring range, and detection limit accompanies any good sensor paper, but the real test is in applying the sensor to the intended problem. Sometimes we see researchers simply producing a table that lists earlier work and some associated figures of merit as comparison. This is often not sufficiently useful. Quoted detection limits may not be meaningful if they simply reflect a multiple of the background noise, as so often is reported. Calibration curves should be recorded all the way to this concentration for this value to have any meaning, and it should be done in the intended sample matrix. Reported sensitivities are often not comparable because the methods used to determine them differ depending on authorship and analytical technique. Most researchers understand that a new sensor should be tested in the intended complex sample. This is often done by spiking the sample with a known concentration of analyte and reporting the recovery. Spiking allows one to subtract the background signal. We see this with electrochemical experiments, where background subtraction is a popular way to clean up voltammograms. It is also widespread with optical probes where a change in signal is easier to quantify than just an isolated signal for one sample. However, spiking experiments are not equivalent to measuring a sample of unknown composition, which is really what we would like to see. If there is an interfering substance, it will often contribute to the background signal (which is held constant), and spiking simply removes the effect of the interference. But in an unknown sample of complex composition, the level of interference will vary, and result in an unknown systematic error. For characterizing matrix effects, spiking experiments may be more useful because they allow one to evaluate the change in sensitivitysignal change vs concentration changein different sample backgrounds. But of course, the authors are required to critically evaluate this aspect with a range of matrices. It is also not convincing to essentially eliminate the matrix by massive dilution and additional filtering or
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centrifugation, as is done by numerous prospective authors. A sensor that does not work in undiluted samples will likely not have a practical impact. The best way to validate a new sensor is to perform experiments on a wide range of unmodified, unspiked samples, and to cross correlate the data with a reference method. Gas sensors may be cross-correlated with GC-MS, and a new ion sensing principle with an established probe for the same ion. For in situ sensor measurements, where no reference method is available, discrete samples may be taken and analyzed in the laboratory with a more traditional method. An important caveat here is that sampling and transport to a traditional laboratory may introduce significant errors as well. Cross-correlation experiments may reveal such challenges. A researcher may also need to think deeply about what each analytical technique is reporting, as they may not be the same. Typical laboratory instrumentation generally provides total concentration, because the different chemical species in equilibrium with each other, and with other sample constituentsligands, polymers, and colloidsare disrupted by the measurement. Your sensor may report on an equilibrium species instead. So, you have developed a great new sensor for an important application. At ACS Sensors, we expect you to be able to demonstrate the analysis of an unknown sample with this new sensor, and to compare your results with an established method. Please show us that you are deeply concerned about the sensing problem, and that you are willing to invest time to critically evaluate your new sensor for its intended application. Only this mindset will move the field, and the world, forward.
Eric Bakker, Associate Editor
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The University of Geneva, Geneva, Switzerland
AUTHOR INFORMATION
ORCID
Eric Bakker: 0000-0001-8970-4343 Notes
Views expressed in this editorial are those of the author and not necessarily the views of the ACS.
Received: August 7, 2018 Published: August 24, 2018 1431
DOI: 10.1021/acssensors.8b00798 ACS Sens. 2018, 3, 1431−1431
