Earticle

Superconducting quantum devices based on Josephson junctions have evolved from highly sensitive magnetic sensors to one of the leading platforms for quantum information processing. Early developments of superconducting quantum interference devices (SQUIDs) enabled femtotesla‑level magnetic field detection, which led to applications in biomagnetic measurements such as magnetoencephalography and magnetocardiography. These technologies were further extended to ultra‑low‑field nuclear magnetic resonance (ULF‑NMR) systems, where SQUID sensors allow detection of nuclear spin signals in microtesla magnetic fields. Meanwhile, the nonlinear dynamics of Josephson junction circuits enabled the realization of artificial atoms and superconducting qubits. The development of the transmon qubit and circuit quantum electrodynamics architecture significantly improved coherence times and control fidelity, enabling the construction of multi‑qubit superconducting quantum processors. This review summarizes the physical principles, technological developments, and system‑level implementations of superconducting quantum devices, from SQUID‑based sensing technologies to modern superconducting quantum computing platforms.

A superconducting fault current limiter (SFCL) is a device designed to protect power systems from excessive fault currents. Superconductors exhibit a phenomenon known as quenching, in which they transition to a resistive state when the current exceeds a critical threshold, thereby enabling rapid fault current limitation. LS ELECTRIC, in collaboration with KEPCO, developed a 22.9 kV / 2.0 kA resistive superconducting fault current limiter (R-SFCL) and installed it at the Seogochang Substation for commissioning. Before integration into an actual power distribution system, a low-voltage load test was conducted to verify that the temperature of the liquid nitrogen inside the SFCL remained stable under various thermal load conditions. The load current profiles were established on the basis of seasonal and daily electricity consumption patterns in Korea and also included extreme scenarios involving abrupt load increases.

In this study, a high-precision isolated data acquisition (DAQ) system is designed for stable measurement of low-level voltages under high voltage common-mode environments. The proposed DAQ system is designed to employ a 24-bit Σ-Δ analog-to-digital converter, a sampling rate exceeding 1 kS/s, measurement accuracy of approximately ±0.01%, a common-mode rejection ratio (CMRR) exceeding 130 dB, and channel-to-channel isolation of ±500 Vrms. Notably, the importance of channel-to-channel isolation is experimentally verified. The results demonstrate that the absence of isolation can result in spurious voltage signals in both the specific DAQ system and other systems connected to the same measurement environments. A hybrid filtering structure is adopted to simultaneously acquire raw and filtered signals. During measurement, filtered data is used for real-time monitoring, while the stored raw data is post-processed to obtain distortion-free data. Theoretical analysis of infinite impulse response (IIR) and zero- phase filters indicate that IIR filtering causes nonlinear phase response and frequency-dependent group delay, leading to signal distortion, whereas zero-phase filtering eliminates such distortion. Based on the analysis, a post-processing-based zero-phase filtering approach is adopted. The proposed DAQ system is applicable to high temperature superconducting (HTS) magnet quench detection, cell level voltage monitoring in electric vehicle (EV) battery packs, and other high-reliability power and energy systems requiring precise low-level voltage measurements.

The distillation column is a key piece of equipment for obtaining high-purity xenon (purity ≥99.9999%). This study is focused on analyzing the dynamic relationship between pressure and impurity concentration in order to construct predictive models, optimize the operational parameters of the column, and enhance the efficiency of separation. During the research, continuous monitoring of the column pressure and data collection on impurity concentration were conducted. To address data discontinuity, time intervals of less than 1200 minutes with more than three concentration measurement points were selected. Based on the filtered data, a segmented model describing two operating modes of the column was developed. For the dynamic pressure mode, a significant polynomial linear relationship was identified between the fluctuations in impurity concentration and the absolute value of the pressure change rate. During the stable pressure period, an exponential decay model was constructed to describe the dynamics of impurity concentration reduction. The model allows for predicting the time required to achieve the target impurity concentration and the degree of product purity within the expected stable operating time interval of the column. This contributes to optimizing the equipment’s operational parameters, reducing energy consumption, and improving the efficiency of gas mixture separation.