We have described a simple and inexpensive digital phase-sensitive detector using a low-cost AVR microcontroller. To store a large number of data, we have added external memory (RAM) to the microcontroller. A message passing interface daemon-based software has been implemented to control the whole operation. Performance of the system has been tested using precise signals generated through a D/A converter controlled by an 8085 microprocessor. The phase resolution of the system has been reported to be better than 0.1°. This phase-sensitive detector would be very useful for low-budget laboratories that deal with low-frequency AC signal applications.

La0.7Sr0.3MnO3 (LSMO)-ZnO nanocomposites with varying concentrations of ZnO have been synthesized using the solution combustion method. A bimodal particle size distribution has been formed in all the samples. The crystallite size increases in the composites as compared to LSMO. The study on electrical resistivity reveals that LSMO exhibits a metal-to-insulator transition at 359 K, while the inclusion of ZnO suppresses the metallic behavior in the composites and increases the resistivity. Transport behavior of the samples in metallic and semiconducting regions has been explained with a known polynomial equation and a two-channel conduction model obeying the small polaron hopping mechanism, respectively. A very low activation energy in the range of 10–12 meV is observed due to smaller-sized particles. The presence of ZnO drives the hopping mechanism from adiabatic in LSMO to become non-adiabatic in the composites and enhances the maximum temperature coefficient of resistance. 80% LSMO-20% ZnO (by weight ratio) composite shows a maximum TCR of −29.81%/K at 248 K, which makes it a potential candidate for several applications in sensing devices. The Curie temperature of the material decreases with the increase in ZnO content in the sample. The results of this study also confirm the existence of correlation between the electrical and magnetic properties of LSMO.

This article describes the use of the genetic algorithm (GA) for the optimization of the performance of a voltage measuring station. This procedure can be used for the instruments for which regular and systematic calibration is required for better performance. Here, a GA-based correction module has been used to internally store a few previous measured-value and measured-time pairs and to use these values to correct the current readings. Sanity-checking runs against zero and standard voltages from time to time and auto-calibrates itself. The genetic algorithm libraries have been developed along with the correction module. This system can also be considered as a test bed for the study of self-calibration algorithms.