The current approach of Therapeutic Drug Monitoring (TDM) relies on blood analysis to closely monitor drugs with a narrow therapeutic window. This method is uncomfortable for the patient and time-consuming and therefore challenging for frequent monitoring. Electrochemical analysis in sweat is a promising alternative, as sweat sensors are non-invasive and can continuously measure drug concentrations. This study explores novel techniques to improve the analytical performance of voltammetric sensors for TDM in a sweat matrix. Methotrexate (MTX) is selected as the model analyte as it is a widely used therapeutic drug for treatment of cancer, rheumatoid arthritis, among other disorders. Changes in pH and interference from amino acids originating from sweat have been shown to impact the measurement of target drugs such as MTX. Herein, an algorithm is developed to compensate for potential pH fluctuations in sweat by using the relation between the pH level and the peak potential of the electro-oxidized analyte to estimate the pH and calculate the concentration of the analyte. Additionally, an algorithm was developed to separate peaks of distinct amino acids with a similar oxidation potential as MTX. The algorithm uses Gaussian fitting for subtracting and linear discriminant analysis (LDA) to identify the peak related to the analyte. The results demonstrate that the algorithms are effective for the detection of MTX and present an approach to compensating for sweat matrix-related interferences in wearable sweat sensors, driving development for low-cost continuous therapeutic drug monitoring.

To maintain experimental lab course work during the COVID-19 lockdowns, we chose a hybrid approach for our electronic instrumentation course and developed thereto the Advanced Learning Platform for Analog Circuits and Automation (ALPACA). To further meet our goals and standards, the ALPACA platform has been updated, using a Raspberry Pi Pico with Python instead of an Arduino. Our educational materials and approach are illustrated here through the typical example of a relaxation oscillator assignment. As student's feedback was overall positive and grades remained comparable, we continue the use of the ALPACA in the non-COVID era.

This manuscript presents the first practical guide to build a Raspberry Pi Pico based potentiostat for electrical and electrochemical instrumentation education. The circuit enables us to perform different types of voltammetry such as cyclic and square wave voltammetry. Voltammograms of paracetamol tablets in a neutral buffer solution were successfully recorded and compared to lab equipment. Thereafter, the effect of different scan rates and different concentrations was studied as a proof of concept. Furthermore, the experiments were expanded with measurements of other pharmaceutical tablets such as vitamin C. Over 80 nanobiology bachelor students successfully built their own potentiostat in an electronic instrumentation course. They validated their systems successfully with electrochemical experiments using paracetamol as a conventional pharmaceutical that can be performed in a classroom. The students acquired a valuable understanding of the electronic building blocks and system architecture within electrochemical instrumentation, equipping them with the requisite knowledge to effectively optimize instrumentation parameters in their future research work.

One of the major challenges associated with e-textiles is the connection between flexible fabric-integrated wires and rigid electronics. This work aims to increase the user experience and mechanical reliability of these connections by foregoing conventional galvanic connections in favor of inductively coupled coils. The new design allows for some movement between the electronics and the wires, and it relieves the mechanical strain. Two pairs of coupled coils continuously transmit power and bidirectional data across two air gaps of a few millimeters. A detailed analysis of this double inductive link and associated compensation network is presented, and the sensitivity of the network to changing conditions is explored. A proof of principle is built that demonstrates the system’s ability to self-tune based on the current–voltage phase relation. A demonstration combining 8.5 kbit/s of data transfer with a power output of 62 mW DC is presented, and the hardware is shown to support data rates of up to 240 kbit/s. This is a significant improvement of the performance of previously presented designs.

Sweat sensors allow for new unobtrusive ways to continuously monitor an athlete's performance and health status. Significant advances have been made in the optimization of sensitivity, selectivity, and durability of electrochemical sweat sensors. However, comparing the in situ performance of these sensors in detail remains challenging because standardized sweat measurement methods to validate sweat sensors in a physiological setting do not yet exist. Current collection methods, such as the absorbent patch technique, are prone to contamination and are labor-intensive, which limits the number of samples that can be collected over time for offline reference measurements. We present an easy-to-fabricate sweat collection system that allows for continuous electrochemical monitoring, as well as chronological sampling of sweat for offline analysis. The patch consists of an analysis chamber hosting a conductivity sensor and a sequence of 5 to 10 reservoirs that contain level indicators that monitor the filling speed. After testing the performance of the patch in the laboratory, elaborate physiological validation experiments (3 patch locations, 6 participants) were executed. The continuous sweat conductivity measurements were compared with laboratory [Na+] and [Cl-] measurements of the samples, and a strong linear relationship (R2 = 0.97) was found. Furthermore, sweat rate derived from ventilated capsule measurement at the three locations was compared with patch filling speed and continuous conductivity readings. As expected from the literature, sweat conductivity was linearly related to sweat rate as well. In short, a successfully validated sweat collection patch is presented that enables sensor developers to systematically validate novel sweat sensors in a physiological setting.

Ammonium levels in sweat can potentially be used to measure muscle fatigue and to diagnose particular metabolic myopathies. To research the potential use of ammonia in sweat as a biomarker, a new real-time monitoring system is developed. This system consists of a capsule that is placed on the skin and ventilated with dry air. A metal-oxide gas sensor in the capsule detects the ammonia that is evaporated from sweat. The sensor system was built, and calibration experiments were performed. The sensors show good sensitivity from 27 mV/ppm to 1.1 mV/ppm in the desired measurement range of 1 to 30 ppm, respectively. A temperature and humidity sensor is integrated to compensate for temperature and humidity effects on the NH ₃ sensor.

This paper presents a method to continuously collect and reliably measure sweat analyte concentrations during exercise. The method can be used to validate newly developed sweat sensors and to obtain insight into intraindividual variations of sweat analytes in athletes. First, a novel design of a sweat collection system is created. The sweat collection patch, that is made from hydrophilized foil and a double-sided acrylate adhesive, consists of a reservoir array that collects samples consecutively in time. During a physiological experiment, sweat can be collected from the back of a participant and the filling speed of the collector is monitored by using a camera. After the experiment, Na+, Cl- and K+ levels are measured with ion chromatography. Sweat analyte variations are measured during exercise for an hour at three different locations on the back. The Na+ and Cl- variations show a similar trend and the absolute concentrations vary with the patch location. Na+ and Cl- concentrations increase and K+ concentrations seem to decrease during this exercise. With this new sweat collection system, sweat Na+, Cl- and K+ concentrations can be collected over time during exercise at medium to high intensity, to analyse the trend in electrolyte variations per individual.

A wide variety of electrochemical sweat sensors are recently being developed for real-time monitoring of biomarkers. However, from a physiological perspective, little is known about how sweat biomarkers change over time. This paper presents a method to collect and analyze sweat to identify inter and intraindividual variations of electrolytes during exercise. A new microfluidic sweat collection system is developed which consists of a patch covering the collection surface and a sequence of reservoirs. Na⁺, Cl^- and K⁺ are measured with ion chromatography afterwards. The measurements show that with the new collector, variations in these ion concentrations can be measured reliably over time.

A capacitive probe is generally used in a flex-fuel engine for measuring the ethanol content in biofuel. However, the water content in biofuel of high ethanol content cannot be disregarded or considered constant and the full composition measurement of ethanol, gasoline and water in biofuel is required. Electrical impedance spectroscopy with a customized capacitive probe operating in the 10 kHz to 1 MHz frequency range is combined with optical absorption spectroscopy in the UV spectral range between 230 and 300 nm for a full composition measurement. This approach is experimentally validated using actual fuels and the results demonstrate that electrical impedance spectroscopy when supplemented with optical impedance spectroscopy can be used to fully determine the composition of the biofuel and applied for a more effective engine management. A concept for a low-cost combined measurement system in the fuel line is presented.