Advanced Sensor Research最新文献

Surface-enhanced Raman spectroscopy (SERS) has emerged as a powerful tool for biomedical diagnosis, combining heightened sensitivity with molecular precision. The integration of artificial intelligence (AI) and machine learning (ML) has further elevated its capabilities, refining data interpretation, pattern prediction, and bolstering diagnostic accuracy. This review chronicles advancements in SERS diagnostics, emphasizing the collaboration between ML and innovative nanostructures, substrates, and nanoprobes for SERS enhancement. The breakthroughs are highlighted in SERS-based point-of-care techniques and the nuanced detection of key biomarkers, from nucleic acids to proteins and metabolites. The article also addresses prevailing challenges, such as the need for standardized SERS methodologies and optimized platforms. Moreover, the potential of portable SERS systems is discussed for clinical deployment, as well as current efforts and challenges in clinical trials. In essence, this review positions the fusion of nanoengineering, AI, ML, and SERS as the frontier for next-generation biomedical diagnostics.

Ballast water, which is seawater taken onboard ships to ensure stable and maneuverable sailing, can pose a significant threat to marine ecosystems and human health when discharged owing to the presence of undesirable organisms. To mitigate this risk, ballast water treatment methods such as electrochlorination are employed, where oxidants such as hypochlorite are generated to effectively eliminate marine microorganisms. The effectiveness of an electrochlorination-based ballast water management system (BWMS) depends on the maintenance of optimal concentrations of total residual chlorine (TRC). However, excessive levels of free chlorine (Cl) can result in corrosion and environmental damage, rendering the accurate monitoring of TRC levels crucial for the safe discharge of ballast water. This review focuses on recent advancements in electrochemical sensors for free Cl measurement in BWMS. The process of free Cl generation, techniques for electrochemical detection, and factors influencing sensor performance are elucidated. In addition, materials and strategies for improving the performance of the sensors are described. Finally, perspectives on the current issues and future challenges that must be overcome to effectively utilize electrochemical detection in BWMS are discussed, thereby offering new directions for advancing this technology.

Histamine, a biogenic amine (BA), plays a significant role in various pathophysiological processes and is present in food supplies, serving as an indicator of freshness and microbial degradation. It is a major cause of food poisoning outbreaks, triggering allergic inflammatory responses. Detecting histamine in food is crucial because its toxic threshold does not affect the food's taste, making contaminated items appear normal. To address this challenge, label-free and bioactive-free electrochemical sensors utilizing molecularly imprinted polymers (MIPs) offer the desired selectivity, scalability, and efficiency. MIPs are synthetic materials designed to mimic biological receptors. This paper reviews a decade of research on MIP-assisted electrochemical sensors for histamine detection, focusing on their scalability, robustness, speed, and selectivity. The review critically analyzes the performance of these sensors in detecting histamine in food, beverages, human serum, and body diagnostics. Additionally, the current understanding of the physiological effects of endogenous and ingested histamine is reviewed, highlighting both established and emerging methods for its quantification in food and health management. The potential for transforming healthcare delivery through personalized Point-of-Care (POC) systems, integrated with Artificial Intelligence (AI) and Internet-of-Medical Things (IoMT) technologies, is also discussed.

Neural probe devices have undergone significant advancements in recent years, evolving from basic single-functional devices to sophisticated integrated systems capable of sensing, stimulating, and regulating neural activity. The neural probes have been demonstrated as effective tools for diagnosing and treating numerous neurological disorders, as well as for understanding sophisticated connections and functions of neuron circuits. The multifunctional neural probe platforms, which combine electrical, optical, and chemical sensing capabilities, hold promising potential for revolutionizing personalized healthcare through closed-loop neuromodulation, particularly in the treatment of conditions such as epilepsy, Parkinson's disease, and depression. Despite these advances, several challenges remain to be further investigated, including biocompatibility, long-term signal quality and stability, and miniaturization, all of which hinder their broader clinical application. This paper provides an overview of the design principles of the neural probe structures and sensors, fabrication strategies, and integration techniques for the advanced multi-functional neural probes. Key electrical, optical, and chemical sensing mechanisms are discussed, along with the selection of corresponding functional materials. Additionally, several representative applications are highlighted, followed by a discussion of the challenges and opportunities that lie ahead for this emerging field.

Smart Human-Machine Interface

In article 2400086, Lin Dong and co-workers introduce a self-powered human-machine interface with piezoelectric sensors for precise body motion monitoring. Enhanced sensitivity and a novel control algorithm enable the translation of muscle signals into Morse code and control of a robotic hand to perform tasks like drinking water.

Terahertz Metasurface Biosensors

In article 2400041, Minah Seo and co-workers demonstrate the sensitive detection of acetylcholine through the integration of CuO nanoflowers with Cu nanoslots at the terahertz range. The enhanced optical hotspots by the nanoflowers resulted in sufficient signal variations, highlighting their potential for sensitive detection and quantitative analysis of trace substances.

Monitoring deep tissue biometrics is crucial in various clinical settings, including internal hemorrhage. Although optical and impedance tomography techniques offer real-time monitoring with minimal medical infrastructure, they still face challenges in accurately assessing deeper tissues in wearable formats. This study introduces a novel single-electrode capacitive sensor designed to measure deep tissue capacitance changes caused by variations in dielectric constant and pressure. The sensor features a carbon nanotube-paper composite (CPC) electrode integrated with a multi-walled carbon nanotube (MWCNT)-embedded foam. The CPC electrode, with its large surface area and high-aspect-ratio fibers, generates a high electric field for deeper tissue penetration, improving deep tissue monitoring performance. Penetration depth is characterized using surrogate tissue, heart, and lung models. Additionally, the integration of pressure-sensitive MWCNT foam significantly enhances the sensitivity, enabling precise detection of regional blood volume and tissue displacement. The novel sensing mechanism is applied to detect internal hemorrhage in a porcine model. By employing a machine learning algorithm, the sensor accurately estimates the severity of internal hemorrhage, offering a noninvasive alternative to catheter-based systems. This advancement lays the foundation for a real-time wearable system that monitors deep tissue health metrics, such as blood volume, blood pressure, as well as heart and lung functions.

Soft machines such as bionic soft robotics attract tremendous interest. Environmental awareness between the “skin” of robotics and the contact surface is essential for motion control. Contact sensing requires not only bionic tactile perception but also high adaptability to their skin's soft nature. However, most tactile sensors can only measure normal pressure and are not adapted to large-area soft surfaces. Here, a multi-directional bionic tactile sensing “skin” (MBT-Skin) for soft machines is developed. The skin can detect pressure and friction simultaneously with its 3D structure. Through curvature-controlled transfer printing and multi-step 3D assembly, multiple 3D structures with a small size (1.4 mm × 1.2 mm × 4 mm) are fabricated efficiently. The sensor possesses high sensitivity (P: −0.013N⁻¹; f: 0.036 N⁻¹), good linearity (P: R² = 0.990; f: R² = 0.999), and robust repeatability (≈1000). For MBT-Skin, stretchable interconnections are designed to adapt to the large skin deformation of soft machines. It is mounted on a soft snake-like cylinder and detects multi-direction force mimicking tactile perception during soft robotics movement. The results show that MBT-Skin is capable of detecting pressure and friction with minimal interference from machine bending, which demonstrates its potential future applications in environmental awareness for bionic soft robotics.

Leaf scald (LS) caused by Xanthomonas albilineans (Xalb), is a major bacterial disease of sugarcane. The unreliable symptom expressions make traditional visual detection challenging. The molecular methods of detection require expensive equipment, labor-intensive, and time-consuming. This study proposes a novel electrochemical (EC)-approach, that is relatively easy to use and less expensive to detect Xalb DNA in LS-infected sugarcane leaves, meristematic tissue, and xylem sap samples. This method involves three key steps: i) DNA isolation from sugarcane samples via boiling lysis; ii) magnetic purification of target sequences from the lysate using magnetic bead-bound capture probes; and iii) EC detection of the target DNA. The method shows excellent detection sensitivity (10 cells µL⁻¹), reproducibility (Standard deviation, SD <5%, for n = 3), and a wide linear dynamic range (1 nM–1 fM or 10⁶–10° copies µL⁻¹, r = 0.99). The EC assay has a strong negative correlation with quantitative polymerase chain reaction (qPCR) results (r = −0.95–0.97, n = 24, p < 0.001), and weak or no correlation with the varietal resistance ratings. This EC-based assay can be a commercially viable alternative, providing a DNA isolation/purification-free solution, and can potentially be adapted into a handheld device for on-farm detection and quantification of the LS-causing pathogen.