An Automated Scanning Contactless Conductivity Detection Device for Microfluidic Isoelectric Focusing

Sensors and Actuators B Chemical, Journal Year: 2025, Volume and Issue: unknown, P. 137308 - 137308

Published: Jan. 1, 2025

Language: Английский

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Advances in capillary electrophoresis for plant analysis

Talanta, Journal Year: 2025, Volume and Issue: 293, P. 128171 - 128171

Published: April 17, 2025

Language: Английский

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Advancement of microchip electrophoresis coupled with capacitively coupled contactless conductivity detection—from design to application (2018–2024)

Analytical Sciences, Journal Year: 2025, Volume and Issue: unknown

Published: April 25, 2025

Over the past three decades, microchip electrophoresis coupled with capacitively contactless conductivity detection (ME-C4D) has garnered considerable interest due to its various merits, including minimal sample consumption, compact structure, immediate detection, and high analytical precision. Continuous technological innovation improvement have significantly advanced ME-C4D in structural design, fabrication processes, experimental methodologies. As a result, application of this technology expanded into wider range electrochemical analysis fields, disease diagnosis, food safety assessment, environmental pollutant soil nutrient analysis. This review meticulously examines forefront over last five years. It methodically categorizes scrutinizes advancements from dimensions, newly emerged ME microchips, C4D electrodes, protocols, pioneering applications. Moreover, paper critically summarizes these developments, identifying prevailing limitations challenges within ME-C4D. Ultimately, it projects potential future trajectories for field ME-C4D, suggesting pathways overcome existing hurdles hinting at untapped possibilities that lie ahead.

Language: Английский

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Acoustic-Based Machine Main State Monitoring for High-Speed CNC Drilling

Machines, Journal Year: 2025, Volume and Issue: 13(5), P. 372 - 372

Published: April 29, 2025

This paper introduces an acoustic-based monitoring system for high-speed CNC drilling, aimed at optimizing processes and enabling real-time machine state detection. High-fidelity acoustic sensors capture sound signals during drilling operations, allowing the identification of critical events such as tool engagement, material breakthrough, withdrawal. Advanced signal processing techniques, including spectrogram analysis Fast Fourier Transform, extract dominant frequencies patterns, while learning algorithms like DBSCAN clustering classify operational states cutting, returning. Experimental studies on materials acrylic, PTFE, hardwood reveal distinct profiles influenced by properties conditions. Smoother patterns lower characterize PTFE whereas produces higher rougher due to its density resistance. These findings demonstrate correlation between emissions machining dynamics, non-invasive predictive maintenance. As AI power increases, it is expected in-situ process information achieve resolution, enhancing precision in data interpretation decision-making. A key contribution this project creation open library processes, fostering collaboration innovation intelligent manufacturing. By integrating big concepts algorithms, supports continuous monitoring, anomaly detection, optimization. AI-ready hardware enhances accuracy efficiency improving quality, reducing wear, minimizing downtime. The research establishes a transformative approach advancing manufacturing systems.

Language: Английский

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A potassium ion sensor with an acupuncture needle inserted into a glass capillary for small-volume sample detection

Microchemical Journal, Journal Year: 2025, Volume and Issue: 213, P. 113870 - 113870

Published: May 5, 2025

Language: Английский

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