In previous work, the novel heat-transfer method (HTM) for the detection of small molecules with Molecularly Imprinted Polymers (MIP)-type receptors was presented. In this study we focus on optimization of this sensor performance, with as final aim to lower the detection limit by reducing the noise level. It was determined that the noise originates foremost from the power supply, which can be controlled by varying the PID parameters. Therefore, the effect of the individual parameters was evaluated by tuning P, I and D separately at a temperature of 37 °C, giving a first indication of the optimal configuration. Next, a temperature profile was programmed and the standard deviation of the heat-transfer resistance over the entire regime was studied for a set of parameters. The optimal configuration, P1-I6-D0, reduced the noise level with nearly a factor of three compared to the original parameters of P10-I5-D0. With the optimized settings, the detection of L-nicotine in buffer solutions was studied and the detection limit improved significantly from 100 nM to 35 nM. Summarizing, optimization of the PID parameters and thereby improving the detection limit is a key parameter for first applications of the HTM-method for MIP receptors in analytical research.

An alternative energy source that has appeared beyond expectations and has seen a lot of progress is the fuel cell. A proton exchange membrane (PEM) fuel cell is chosen for analysis and requires a DC-DC boost converter as an interface between the fuel cell and the load to provide a high-gain regulated voltage. Although great effort towards developing different converter topologies has been made during recent decades, less attention has been devoted to the reliability and thermal performance assessment of the present converters. In this paper, five non-isolated DC-DC converters are analyzed in terms of both thermal behavior and reliability. The temperature estimation of semiconductor devices as a critical part of the thermal analysis has been made via a detailed thermal model and the reliability is evaluated by means of a power cycling test. Finally, a performance score has been attributed using the TOPSIS ranking methodology and considering all the criteria (e.g., the number of components and cost) at the same time. The results indicated that the floating interleaved boost converter is always at the top of the list, even if the weight of the indicators is changed. When the weight of the cost criterion is higher than the reliability criterion, the multi-switch boost converter will be in second place. If the weight of the reliability criterion is greater than cost, the interleaved and multi-switch converter are ranked second and third, respectively. Additionally, the Cuk converter with a closeness coefficient of zero is always associated with the most unfavorable performance.

In this work we present the first steps towards a molecularly imprinted polymer (MIP)-based biomimetic sensor array for the detection of small organic molecules via the heat-transfer method (HTM). HTM relies on the change in thermal resistance upon binding of the target molecule to the MIP-type receptor. A flow-through sensor cell was developed, which is segmented into four quadrants with a volume of 2.5 μL each, allowing four measurements to be done simultaneously on a single substrate. Verification measurements were conducted, in which all quadrants received a uniform treatment and all four channels exhibited a similar response. Subsequently, measurements were performed in quadrants, which were functionalized with different MIP particles. Each of these quadrants was exposed to the same buffer solution, spiked with different molecules, according to the MIP under analysis. With the flow cell design we could discriminate between similar small organic molecules and observed no significant cross-selectivity. Therefore, the MIP array sensor platform with HTM as a readout technique, has the potential to become a low-cost analysis tool for bioanalytical applications.

Serotonin is an important neurotransmitter that plays a major role in the pathogenesis of a variety of conditions, including psychiatric disorders. The detection of serotonin typically relies on high-performance liquid chromatography (HPLC), an expensive technique that requires sophisticated equipment and trained personnel, and is not suitable for point-of-care applications. In this contribution, we introduce a novel sensor platform that can measure spiked neurotransmitter concentrations in whole blood samples in a fast and low-cost manner by combining synthetic receptors with a thermal readout technique—the heat-transfer method. In addition, the design of a miniaturized version of the sensing platform is presented that aims to bridge the gap between measurements in a laboratory setting and point-of-care measurements. This fully automated and integrated, user-friendly design features a capillary pumping unit that is compatible with point-of-care sampling techniques such as a blood lancet device (sample volume—between 50 µL and 300 µL). Sample pre-treatment is limited to the addition of an anti-coagulant. With this fully integrated setup, it is possible to successfully discriminate serotonin from a competitor neurotransmitter (histamine) in whole blood samples. This is the first demonstration of a point-of-care ready device based on synthetic receptors for the screening of neurotransmitters in complex matrices, illustrating the sensor’s potential application in clinical research and diagnosis of e.g., early stage depression.

This study investigates the thermal behavior of Insulated Gate Bipolar Transistors (IGBTs) with a focus on the influence of solder voids within the device. Utilizing a combination of Finite Element Method (FEM) simulations, X-ray imaging, and SEM-EDX analysis, we accurately modeled the internal structure of IGBTs to assess the impact of void characteristics on thermal resistance. The findings reveal that the presence and characteristics of solder voids—particularly their size, number, and distribution—significantly affect the thermal resistance of IGBT devices. Experimental measurements validate the FEM model’s accuracy, confirming that voids disrupt the heat flow path, which can lead to increased thermal resistance and potential device failure. Five regression models, including Gaussian process regression (GPR) and neural networks, were employed to predict the thermal resistance based on void characteristics, with the GPR models demonstrating superior performance. The optimal GPR RQ model consistently provided accurate predictions with an RMSE of 0.0050 and R² of 0.9728. Using the void percentage as the only input parameter for the regression models significantly impacted the prediction accuracy, showing the importance of the void extraction method. This study shows the necessity of minimizing solder voids and offers a robust methodological framework for a better prediction of the reliability of IGBTs.