Excessive levels of Pb²⁺ and Cd²⁺ in seawater pose significant combined toxicity to marine organisms, resulting in harmful effects and further threatening human health through biomagnification in the food chain. Traditional methods for detecting marine Pb²⁺ and Cd²⁺ rely on laboratory analyses, which are hindered by limitations such as sample degradation during transport and complex operational procedures. In this study, we present an electrochemical sensor based on intelligent mobile detection devices. By combining G-COOH-MWCNTs/ZnO with differential pulse voltammetry, the sensor enables the efficient, simultaneous detection of Pb²⁺ and Cd²⁺ in seawater. The G-COOH-MWCNTs/ZnO composite film is prepared via drop-coating and is applied to a glassy carbon electrode. The film is characterized using cyclic voltammetry, electrochemical impedance spectroscopy, and scanning electron microscopy, while Pb²⁺ and Cd²⁺ are quantified using differential pulse voltammetry. Using a 0.1 mol/L sodium acetate buffer (pH 5.5), a deposition potential of −1.1 V, and an accumulation time of 300 s, a strong linear correlation was observed between the peak response currents of Pb²⁺ and Cd²⁺ and their concentrations in the range of 25–450 µg/L. The detection limits were 0.535 µg/L for Pb²⁺ and 0.354 µg/L for Cd²⁺. The sensor was applied for the analysis of seawater samples from Maowei Sea, achieving recovery rates for Pb²⁺ ranging from 97.7% to 103%, and for Cd²⁺ from 97% to 106.1%. These results demonstrate that the sensor exhibits high sensitivity and stability, offering a reliable solution for the on-site monitoring of heavy metal contamination in marine environments.

Copper is an essential element in living organisms and is crucial in marine ecosystems. However, excessive concentrations can lead to seawater pollution and pose a risk of toxicity to marine organisms, as it is a heavy metal. In addition, it can enter the human body through the food chain, potentially endangering human health. Consequently, there is increasing focus on the rapid and highly sensitive detection of copper ions (Cu²⁺). We prepared a graphite carbon electrode modified with graphitised multi-walled carbon nanotubes/copper(II) ion carrier IV (GMWCNT/copper(II) ion carrier IV/glassy carbon electrode (GCE)) using a drop-coating method. Scanning electron microscopy (SEM) analysis revealed that the composite material film possessed a large surface area. Incorporating this composite material significantly enhanced the adsorption capacity for ions on the electrode surface and greatly improved conductivity. Differential pulse anodic stripping voltammetry (DPASV) was employed to quantify copper levels in seawater. Under optimal experimental conditions, a strong linear relationship was observed between the Cu²⁺ response peak current and its concentration within a range of 50–500 µg L⁻¹, with a correlation coefficient of 0.996. The GMWCNT/copper(II) ion carrier IV/GCE exhibited excellent stability and reproducibility, achieving a low detection limit for Cu²⁺ at 0.74 µg L⁻¹ when applied to copper detection in seawater. Furthermore, spiked recovery rates ranging from 98.6% to 102.8% demonstrated the method’s high sensitivity, convenient operation, and practical value for real-world applications in detecting Cu²⁺ levels in seawater.

Hg(II), as an extremely hazardous heavy metal contaminant in the environment, poses a significant potential hazard to human health and ecosystems. A GR-MWCNTs-COOH/CeO₂/Nafion composite film-modified glassy carbon electrode was prepared using the drop-casting method in this study. The GR-MWCNTs-COOH/CeO₂/Nafion/GCE was electrochemically investigated through cyclic voltammetry (CV) and differential pulse stripping voltammetry (DPSV). Additionally, the surface morphologies of the composite film were evaluated using scanning electron microscopy (SEM). The conditions, such as buffer solution, pH, deposition potential, deposition time, modified film thickness, and Nafion content, were optimized. Under optimal experimental conditions, a good linear relationship between the peak current response of Hg(II) and its concentration in the range of 5–100 μg·L⁻¹ was observed, with a detection limit of 0.389 μg·L⁻¹. When it was used to detect Hg(II) in offshore seawater, the recovery rate ranged from 94.72% to 103.8%, with RSDs ≤ 5.79%.

Considering that heavy-metal contamination of seawater is getting worse, building a quick, accurate and portable device for detecting trace zinc in seawater in real time would be very beneficial. In this work, a microfluidic system was developed that includes a planar disc electrode, a micro-cavity for detection, an electrochemical workstation, a computer, a container for waste liquid reprocessing, an external pipeline and other components as well as a graphene/cerium oxide/nano-cerium oxide/Nafion composite membrane was used to modify the planar disc electrode (GR/CeO₂/Nafion/Au) to investigate the electrochemical behaviour of Zn(II) using cyclic voltammetry, square-wave voltammetry and orthogonal test methods. Under optimal experimental conditions, the peak reaction current of Zn(II) showed a good linear relationship with the concentration of Zn(II) in the range of 1–900 μg/L with a correlation coefficient of 0.998, and the detection limit of the method was 0.87 μg/L. In addition, the microfluidic system had good stability, reproducibility and anti-interference. The system was used for determining zinc ions in real seawater samples, and the results were very similar to those of inductively coupled plasma–emission spectrometry, demonstrating the practicality of the system for the detection of trace zinc.

7,12-Dimethylbenzo[a]anthracene (DMBA-7,12), a highly toxic and environmentally persistent polycyclic aromatic hydrocarbon (PAH), poses significant threats to marine biodiversity and human health due to its bioaccumulation through the food chain. Conventional chromatographic methods, while achieving comparable detection limits, are hindered by the need for expensive instrumentation and prolonged analysis times, rendering them unsuitable for rapid on-site monitoring of DMBA-7,12 in marine environments. Therefore, the development of novel, efficient detection techniques is imperative. In this study, we have successfully developed an electrochemical immunosensor based on a polydopamine (PDA)–chitosan (CTs) composite interface to overcome existing technical limitations. PDA provides a robust scaffold for antibody immobilization due to its strong adhesive properties, while CTs enhances signal amplification and biocompatibility. The synergistic integration of these materials combines the high efficiency of electrochemical detection with the specificity of antigen–antibody recognition, enabling precise qualitative and quantitative analysis of the target analyte through monitoring changes in the electrochemical properties at the electrode surface. By systematically optimizing key experimental parameters, including buffer pH, probe concentration, and antibody loading, we have constructed the first electrochemical immunosensor for detecting DMBA-7,12 in seawater. The sensor achieved a detection limit as low as 0.42 ng/mL. In spiked seawater samples, the recovery rates ranged from 95.53% to 99.44%, with relative standard deviations (RSDs) ≤ 4.6%, demonstrating excellent accuracy and reliability. This innovative approach offers a cost-effective and efficient solution for the in situ rapid monitoring of trace carcinogens in marine environments, potentially advancing the field of marine pollutant detection technologies.

This study combines an experimental semi-physical simulation model of an electric propulsion tugboat with four different neural networks to create a real-time simulation model for forecasting total power loads with small samples. The results of repeated experiments demonstrate that the BP neural network effectively forecasts the power load. Subsequently, addressing the limitations of traditional BP neural networks, an optimization approach employing an enhanced particle swarm algorithm and attention mechanism was developed, thereby improving the model’s prediction accuracy and robustness. The experiment shows that the improved prediction model achieves an R² value of 97.42%, demonstrating its effectiveness in forecasting changes in the short-term power load of ships as parameters change. In actual operation, ships can allocate power reasonably and in a timely manner according to the load forecast results, thereby improving the efficiency of the power grid.