Ambient temperature and relative humidity-based drift correction in frequency domain electromagnetics using machine learning

Electromagnetic instrument responses suffer from signal drift that results in a variable response at a given location over time. If left uncorrected, spatiotemporal aliasing can manifest and global trends or abrupt changes might be observed in the data, which are independent of subsurface electromagnetic variations. By performing static ground measurements, we characterized drift patterns of different electromagnetic instruments. Next, we performed static measurements at an elevated height, approximately 4 metre above ground level, to collect a data set that forms the basis of a new absolute calibration methodology. By additionally logging ambient temperature variations, battery voltage and relative humidity, a relation between signal drift and these parameters was modelled using a machine learning (ML) approach. The results show that it was possible to mitigate the effects of signal drift; however, it was not possible to completely eliminate them. The reason is three-fold: (1) the ML algorithm is not yet sufficiently adapted for accurate prediction; (2) signal instability is not explained sufficiently by ambient temperature, relative humidity and battery voltage; and (3) the black-box internal (factory) calibration impeded direct access to raw data,which prevents accurate evaluation of the proposed methodology. However, the results suggest that these challenges are not insurmountable and thatML can form a viable approach in tackling the drift problem instrument specific in the near future.

Passive Electrical Damping of a Quartz Tuning Fork as a Path to Fast Resonance Tracking in QEPAS

In Quartz-Enhanced PhotoAcoustic Spectroscopy (QEPAS) gas sensors, the acoustic wave is detected by the piezoelectric Quartz Tuning Fork (QTF). Due to its high-quality factor, the QTF can detect very low-pressure variations, but its resonance can also be affected by the environmental variations (temperature, humidity, …), which causes an unwanted signal drift. Recently, we presented the RT-QEPAS technique that consistently corrects the signal drift by continuously measuring the QTF resonance. In this article, we present an improvement of RT-QEPAS to fasten the QTF characterization time by adding a passive electronic circuit, which causes the damping of the QTF resonance. The damping circuit is optimized analytically and through SPICE simulation. The results are supported by experimental observations, showing a 70 times improvement of the relaxation times compared to the lone QTF, which opens the way to a fast and drift-free QEPAS sensor.

Water Nitrate Remote Monitoring System with Self-Diagnostic Function for Ion-Selective Electrodes

The detection of nitrate pollutants is a widely used strategy for protecting water sources. Although ion-selective electrodes (ISEs) have been considered for the determination of ion concentrations in water, the accuracy of ISE technology decreases owing to the signal drift and decreasing sensitivity over time. The objectives of the present study were: (1) to develop an online water monitoring system mainly consisting of an Arduino board-based Internet-of-Things (IoT) device and nitrate ISEs; and (2) to propose a self-diagnostic function for monitoring and reporting the condition of the ISEs. The developed system communicates with the cloud server by using the message queuing telemetry transport (MQTT) protocol and provides monitoring information through the developed cloud-based webpage. In addition, the online monitoring system provides information on the electrode status, which is determined based on a self-diagnostic index (SDI, with a range of 0–100) of the electrode drift and sensitivity. The diagnostic method for monitoring and reporting the electrode status was validated in a one-month-long laboratory test followed by a field test in a stream near an agricultural facility. Moreover, a self-diagnostic index (SDI) was applied in the final field experiments with an accuracy of 0.77.

Ratiometric Electrochemistry: Improving the Robustness, Reproducibility and Reliability of Biosensors

Electrochemical biosensors are an increasingly attractive option for the development of a novel analyte detection method, especially when integration within a point-of-use device is the overall objective. In this context, accuracy and sensitivity are not compromised when working with opaque samples as the electrical readout signal can be directly read by a device without the need for any signal transduction. However, electrochemical detection can be susceptible to substantial signal drift and increased signal error. This is most apparent when analysing complex mixtures and when using small, single-use, screen-printed electrodes. Over recent years, analytical scientists have taken inspiration from self-referencing ratiometric fluorescence methods to counteract these problems and have begun to develop ratiometric electrochemical protocols to improve sensor accuracy and reliability. This review will provide coverage of key developments in ratiometric electrochemical (bio)sensors, highlighting innovative assay design, and the experiments performed that challenge assay robustness and reliability.

Sensorized tissue analogues enabled by a 3D-printed conductive organogel

State-of-the-art tissue analogues used in high-fidelity, hands-on medical simulation modules can deliver lifelike appearance and feel but lack the capability to provide quantified, real-time assessment of practitioner performance. The monolithic fabrication of hybrid printed/textile piezoresistive strain sensors in a realistic Y/V plasty suture training pad is demonstrated. A class of 3D-printable organogels comprised of inexpensive and nonhazardous feedstocks is used as the sensing medium, and conductive composite threads are used as the electrodes. These organogels are comprised of a glycol-based deep-eutectic solvent (DES) serving as the ionic conductor and 3-trimethoxysilylmethacrylate-capped fumed silica particles serving as the gelating agent. Rheology measurements reveal the influence of fumed silica particle capping group on the mixture rheology. Freestanding strain sensors demonstrate a maximum strain amplitude of 300%, negligible signal drift, a monotonic sensor response, a low degree of hysteresis, and excellent cyclic stability. The increased contact resistance of the conductive thread electrodes used in place of wire electrodes do not make a significant impact on sensor performance. This work showcases the potential of these organogels utilized in sensorized tissue analogues and freestanding strain sensors for widespread applications in medical simulation and education.

DBnorm as an R package for the comparison and selection of appropriate statistical methods for batch effect correction in metabolomic studies

As a powerful phenotyping technology, metabolomics provides new opportunities in biomarker discovery through metabolome-wide association studies (MWAS) and the identification of metabolites having a regulatory effect in various biological processes. While mass spectrometry-based (MS) metabolomics assays are endowed with high throughput and sensitivity, MWAS are doomed to long-term data acquisition generating an overtime-analytical signal drift that can hinder the uncovering of real biologically relevant changes. We developed “dbnorm”, a package in the R environment, which allows for an easy comparison of the model performance of advanced statistical tools commonly used in metabolomics to remove batch effects from large metabolomics datasets. “dbnorm” integrates advanced statistical tools to inspect the dataset structure not only at the macroscopic (sample batches) scale, but also at the microscopic (metabolic features) level. To compare the model performance on data correction, “dbnorm” assigns a score that help users identify the best fitting model for each dataset. In this study, we applied “dbnorm” to two large-scale metabolomics datasets as a proof of concept. We demonstrate that “dbnorm” allows for the accurate selection of the most appropriate statistical tool to efficiently remove the overtime signal drift and to focus on the relevant biological components of complex datasets.