Signal amplification strategies in photoelectrochemical sensing of carcinoembryonic antigen.

Early detection of cancer markers is critical for cancer diagnosis and cancer therapy since these markers may indicate cancer risk, incidence, and disease prognosis. Carcinoembryonic antigen (CEA) is a type of non-specific and broad-spectrum cancer biomarker commonly utilized for early cancer diagnosis. Moreover, it serves as an essential tool to assess the efficacy of cancer treatment and monitor tumor recurrence as well as metastasis, thus garnering significant attention for precise and sensitive CEA detection. In recent years, photoelectrochemical (PEC) techniques have emerged as prominent methods in CEA detection due to the advantages of PEC, such as simple equipment requirements, cost-effectiveness, high sensitivity, low interference from background signals, and easy of instrument miniaturization. Different signal amplification methods have been reported in PEC sensors for CEA analysis. Based on these, this article reviews PEC sensors based on various signal amplification strategies for detection of CEA during the last five years. The advantages and drawbacks of these sensors were discussed, as well as future challenges.

Sliding limited penetrable visibility graph for establishing complex network from time series.

This study proposes a novel network modeling approach, called sliding window limited penetrable visibility graph (SLPVG), for transforming time series into networks. SLPVG takes into account the dynamic nature of time series, which is often affected by noise disturbances, and the fact that most nodes are not directly connected to distant nodes. By analyzing the degree distribution of different types of time series, SLPVG accurately captures the dynamic characteristics of time series with low computational complexity. In this study, the authors apply SLPVG for the first time to diagnose compensation capacitor faults in jointless track circuits. By combining the fault characteristics of compensation capacitors with network topological indicators, the authors find that the betweenness centrality reflects the fault status of the compensation capacitors clearly and accurately. Experimental results demonstrate that the proposed model achieves a high accuracy rate of 99.1% in identifying compensation capacitor faults. The SLPVG model provides a simple and efficient tool for studying the dynamics of long time series and offers a new perspective for diagnosing compensation capacitor faults in jointless track circuits. It holds practical significance in advancing related research fields.

ß-Cyclodextrin/CMK-8-Based Electrochemical Sensor for Sensitive Detection of Cu2.

In this work, ß-cyclodextrin (ß-CD)/mesoporous carbon (CMK-8) nanocomposite was synthesized and used as an electrochemical sensing platform for highly sensitive and selective detection of Cu2+. The morphology and structure of ß-CD/CMK-8 were characterized by scanning electron microscope (SEM) and X-ray diffraction (XRD). In addition, the dates from electrochemical impedance spectroscopy (EIS) and Cyclic voltammetry (CV) demonstrated that the ß-CD/CMK-8 possessed a fast electronic transfer rate and large effective surface area. Besides this, the ß-CD/CMK-8 composite displayed high enrichment ability toward Cu2+. As a result of these impressive features, the ß-CD/CMK-8 modified electrode provided a wide linear response ranging from 0.1 ng·L-1 to 1.0 mg·L-1 with a low detection limit of 0.3 ng·L-1. Furthermore, the repeatability, reproducibility and selectivity of ß-CD/CMK-8 towards Cu2+ were commendable. The sensor could be used to detect Cu2+ in real samples. All in all, this work proposes a simple and sensitive method for Cu2+ detection, which provides a reference for the subsequent detection of HMIs.

Highly Sensitive Electrochemical Sensor for Sunset Yellow Based on Electrochemically Activated Glassy Carbon Electrode.

Electrochemically activated glassy carbon electrode (AGCE) was fabricated and applied for sensitive and selective detection of sunset yellow (SY). The electroanalysis of SY was investigated by square wave voltammetry (SWV). Owed to the specific oxygen-contained functional groups and the outstanding conductivity of AGCE, the proposed sensor exhibits an enhanced oxidation peak current of SY when compared with non-activated glass carbon electrode (GCE). Under the optimal analytical conditions, the oxidation peak current is linear with SY concentration in the range of 0.005-1.0 µM. The low limit of detection is 0.00167 µM (S/N = 3). This method is applied for the detection of SY in the actual samples. The recovery is between 96.19 and 103.47%, indicating that AGCE is suitable for the determination of SY in beverage sample.