A Robot Equipped with a High-Speed LSPR Gas Sensor Module for Collecting Spatial Odor Information from On-Ground Invisible Odor Sources

Inkjet-Printed Localized Surface Plasmon Resonance Subpixel Gas Sensor Array for Enhanced Identification and Visualization of Gas Spatial Distributions from Multiple Odor Sources

The visualization of the spatial distributions of gases from various sources is essential to understanding the composition, localization, and behavior of these gases. In this study, an inkjet-printed localized surface plasmon resonance (LSPR) subpixel gas sensor array was developed to visualize the spatial distributions of gases and to differentiate between acetic acid, geraniol, pentadecane, and cis-jasmone. The sensor array, which integrates gold nanoparticles (AuNPs), silver nanoparticles (AgNPs), and fluorescent pigments, was positioned 3 cm above the gas source. Hyperspectral imaging was used to capture the LSPR spectra across the sensor array, and these spectra were then used to construct gas information matrices. Principal component analysis (PCA) enabled effective classification of the gases and localization of their sources based on observed spectral differences. Heat maps that visualized the gas concentrations were generated using the mean squared error (MSE) between the sensor responses and reference spectra. The array identified and visualized the four gas sources successfully, thus demonstrating its potential for gas localization and detection applications. The study highlights a straightforward, cost-effective approach to gas sensing and visualization, and in future work, we intend to refine the sensor fabrication process and enhance the detection of complex gas mixtures.

Ultrasensitive Gas Detection Based on Electrically Enhanced Nanoplasmonic Sensor with Graphene-Encased Gold Nanorod

Nanoplasmonic sensors are a widely known concept and have been studied with various applications. Among them, gas detection is engaging attention in many fields. However, the analysis performance of nanoplasmonic sensors based on refractive index confined to the metal nanostructure characteristics causes challenges in gas detection. In this study, we develop a graphene-encased gold nanorod (AuNR)-based nanoplasmonic sensor to detect cadaverine gas. The graphene-encased AuNR (Gr@AuNR) presents an ultrasensitive peak wavelength shift even with tiny molecules. In addition, the external potential transmitted through graphene induces an additional shift. A chemical receptor is immobilized on Gr@AuNR (CR@Gr@AuNR) for selectively capturing cadaverine. The CR@Gr@AuNR achieves ultrasensitive detection of cadaverine gas, and the detection limit is increased to 15.99 ppb by applying a voltage to graphene. Furthermore, the experimental results of measuring cadaverine generated from spoiled pork show the practicality of CR@Gr@AuNR. The strategy of external-boosted nanoplasmonics provides new insight into plasmonic sensing and applications.

Plasmonic Biosensors with Nanostructure for Healthcare Monitoring and Diseases Diagnosis

Nanophotonics has been widely utilized in enhanced molecularspectroscopy or mediated chemical reaction, which has major applications in the field of enhancing sensing and enables opportunities in developing healthcare monitoring. This review presents an updated overview of the recent exciting advances of plasmonic biosensors in the healthcare area. Manufacturing, enhancements and applications of plasmonic biosensors are discussed, with particular focus on nanolisted main preparation methods of various nanostructures, such as chemical synthesis, lithography, nanosphere lithography, nanoimprint lithography, etc., and describing their respective advances and challenges from practical applications of plasmon biosensors. Based on these sensing structures, different types of plasmonic biosensors are summarized regarding detecting cancer biomarkers, body fluid, temperature, gas and COVID-19. Last, the existing challenges and prospects of plasmonic biosensors combined with machine learning, mega data analysis and prediction are surveyed.

A Programmable Plasmonic Gas Microsystem for Detecting Arbitrarily Combinated Volatile Organic Compounds (VOCs) with Ultrahigh Resolution

For gas sensors, the ultrasensitive and highly selective detection of multiple components is of great significance in a wide range of applications extending from environment to healthcare, which is still a long-term challenge due to the single sensing mechanism of most sensors. Here, we combine the advantages of microfluidic chips and surface-enhanced Raman spectroscopy (SERS) spectra to fabricate a smart single-chip for simultaneously detecting an arbitrary combination of VOCs that incorporates different detection units, working on either a physisorption or chemisorption mechanism. Full integration of microfluidic and multiplex nanostructure components on one chip permits programmable design for sensing multifarious volatile compounds, and enables on-chip signal amplifications with increased reproducibility. As a proof-of-principle experiment, we demonstrate the simultaneous identification of 9 different gases that belong to aromatic compounds, aldehydes, ketones, or sulfides in one mixture, with high sensitivity (ppb level), high selectivity, and high robustness (error ∼8%). We further evaluated the application of our universal gas sensor in two scenarios including indoor air pollution monitoring and exhaled breath-based disease diagnosis. We expect that our design will improve the various practical applications of gas sensors.

Optimization of Au:CuO Thin Films by Plasma Surface Modification for High-Resolution LSPR Gas Sensing at Room Temperature

In this study, thin films composed of gold nanoparticles embedded in a copper oxide matrix (Au:CuO), manifesting Localized Surface Plasmon Resonance (LSPR) behavior, were produced by reactive DC magnetron sputtering and post-deposition in-air annealing. The effect of low-power Ar plasma etching on the surface properties of the plasmonic thin films was studied, envisaging its optimization as gas sensors. Thus, this work pretends to attain the maximum sensing response of the thin film system and to demonstrate its potential as a gas sensor. The results show that as Ar plasma treatment time increases, the host CuO matrix is etched while Au nanoparticles are uncovered, which leads to an enhancement of the sensitivity until a certain limit. Above such a time limit for plasma treatment, the CuO bonds are broken, and oxygen is removed from the film's surface, resulting in a decrease in the gas sensing capabilities. Hence, the importance of the host matrix for the design of the LSPR sensor is also demonstrated. CuO not only provides stability and protection to the Au NPs but also promotes interactions between the thin film's surface and the tested gases, thereby improving the nanocomposite film's sensitivity. The optimized sensor sensitivity was estimated at 849 nm/RIU, which demonstrates that the Au-CuO thin films have the potential to be used as an LSPR platform for gas sensors.

Emission source tracing based on bionic algorithm mobile sensors with artificial olfactory system

The leakage of hazardous chemicals and toxic volatile substances in the atmosphere may cause serious consequences such as explosion and poisoning. To identify the unknown leakage locations and gas compositions, a mobile robot system to trace the leak source in the outdoor was investigated. First, two bionic searching algorithms, Zigzag and Silkworm algorithms, were tested with outdoor experiments for locating the leak source. The results showed that the locating errors of these two algorithms were within 0.5 m in 10 by 20 m search space, but the failing ratio of Zigzag and Silkworm algorithm was still high (about 40–50%). Therefore, an improved tracing algorithm combining the Silkworm and Zigzag algorithm, called as zigzag–Silkworm algorithm, was proposed. Compared with Silkworm and Zigzag algorithms, zigzag–Silkworm algorithm had a higher success ratio of 80% in outdoor source tracing tests, and the searching efficiency was enhanced, the efficiency parameter L: L0 has improved from 2.58 for Silkworm and 2.66 for Zigzag to 2.17 for zigzag–Silkworm. Then, in order to identify the composition of the leaked gases during the source tracing, an artificial olfaction system (AOS) based on the gas sensor array and support vector machine was set on the mobile robot. The test results in the source tracing experiments with ammonia and ethanol emissions indicated that the recognition accuracy of emission gases reached to 99% with AOS equipped on the robot. Therefore, the mobile robot system equipped with the zigzag–Silkworm algorithm and the AOS is feasible to trace the leakage source and identify the emission composition in the outdoor leakage event with good performance in efficiency and accuracy although some underlying problems still need to be addressed in future work.

Gas Sensors Based on Localized Surface Plasmon Resonances: Synthesis of Oxide Films with Embedded Metal Nanoparticles, Theory and Simulation, and Sensitivity Enhancement Strategies

This work presents a comprehensive review on gas sensors based on localized surface plasmon resonance (LSPR) phenomenon, including the theory of LSPR, the synthesis of nanoparticle-embedded oxide thin films, and strategies to enhance the sensitivity of these optical sensors, supported by simulations of the electromagnetic properties. The LSPR phenomenon is known to be responsible for the unique colour effects observed in the ancient Roman Lycurgus Cup and at the windows of the medieval cathedrals. In both cases, the optical effects result from the interaction of the visible light (scattering and absorption) with the conduction band electrons of noble metal nanoparticles (gold, silver, and gold–silver alloys). These nanoparticles are dispersed in a dielectric matrix with a relatively high refractive index in order to push the resonance to the visible spectral range. At the same time, they have to be located at the surface to make LSPR sensitive to changes in the local dielectric environment, the property that is very attractive for sensing applications. Hence, an overview of gas sensors is presented, including electronic-nose systems, followed by a description of the surface plasmons that arise in noble metal thin films and nanoparticles. Afterwards, metal oxides are explored as robust and sensitive materials to host nanoparticles, followed by preparation methods of nanocomposite plasmonic thin films with sustainable techniques. Finally, several optical properties simulation methods are described, and the optical LSPR sensitivity of gold nanoparticles with different shapes, sensing volumes, and surroundings is calculated using the discrete dipole approximation method.

Recent Progress in Optical Sensors for Biomedical Diagnostics

In recent years, several types of optical sensors have been probed for their aptitude in healthcare biosensing, making their applications in biomedical diagnostics a rapidly evolving subject. Optical sensors show versatility amongst different receptor types and even permit the integration of different detection mechanisms. Such conjugated sensing platforms facilitate the exploitation of their neoteric synergistic characteristics for sensor fabrication. This paper covers nearly 250 research articles since 2016 representing the emerging interest in rapid, reproducible and ultrasensitive assays in clinical analysis. Therefore, we present an elaborate review of biomedical diagnostics with the help of optical sensors working on varied principles such as surface plasmon resonance, localised surface plasmon resonance, evanescent wave fluorescence, bioluminescence and several others. These sensors are capable of investigating toxins, proteins, pathogens, disease biomarkers and whole cells in varied sensing media ranging from water to buffer to more complex environments such as serum, blood or urine. Hence, the recent trends discussed in this review hold enormous potential for the widespread use of optical sensors in early-stage disease prediction and point-of-care testing devices.

Enhancing the Sensitivity of Nanoplasmonic Thin Films for Ethanol Vapor Detection

Nanoplasmonic thin films, composed of noble metal nanoparticles (gold) embedded in an oxide matrix, have been a subject of considerable interest for Localized Surface Plasmon Resonance (LSPR) sensing. Ethanol is one of the promising materials for fuel cells, and there is an urgent need of a new generation of safe optical sensors for its detection. In this work, we propose the development of sensitive plasmonic platforms to detect molecular analytes (ethanol) through changes of the LSPR band. The thin films were deposited by sputtering followed by a heat treatment to promote the growth of the gold nanoparticles. To enhance the sensitivity of the thin films and the signal-to-noise ratio (SNR) of the transmittance–LSPR sensing system, physical plasma etching was used, resulting in a six-fold increase of the exposed gold nanoparticle area. The transmittance signal at the LSPR peak position increased nine-fold after plasma treatment, and the quality of the signal increased six times (SNR up to 16.5). The optimized thin films seem to be promising candidates to be used for ethanol vapor detection. This conclusion is based not only on the current sensitivity response but also on its enhancement resulting from the optimization routines of thin films’ architectures, which are still under investigation.

New Alternating Current Noise Analytics Enables High Discrimination in Gas Sensing

Feature analysis has been increasingly considered as an important way to enhance the discrimination performance of gas sensors. In this work, a new analytical method based on alternating current noise spectrum is developed to discriminate chemically and structurally similar gases with remarkable performance. Compared with the conventional analytics based on the maximum, integral, and time of response, the noise spectrum of electrical response introduces a new informative feature to discriminate chemical gases. In experiment, three chemically and structurally similar gases, mesitylene, toluene, and o-xylene, are tested on ZnO thin film gas sensors. The result indicated that the noise analytics assisted by the support vector machine algorithm discriminated these similar gases with 94.2% in precision, about 20% higher than those obtained by conventional methods. Such a new alternating current noise analytics is very promising for application in sensors for high discrimination precision.

A Novel Immunosensing Method Based on the Capture and Enzymatic Release of Sandwich-Type Covalently Conjugated Thionine-Gold Nanoparticles as a New Fluorescence Label Used for Ultrasensitive Detection of Hepatitis B Virus Surface Antigen

A novel ultrasensitive and simple amplified immunosensing strategy is designed based on a surface-enhanced fluorescence (SEF) nanohybrid made from covalently conjugated thionine-gold nanoparticles (GNP-Th), as a novel amplified fluorescence label, and magnetic nanoparticles (MNPs), as a biological carrier, used for hepatitis B virus surface antigen (HBsAg) detection. This immunosensing strategy operates on the basis of the capture and then release of the amplified fluorescence label. Capturing of the antiHBs-antibody (Ab)-modified GNP-thionine hybrid (GNP-Th-Ab) is carried out through the formation of a two-dimensional (sandwich) probe between this amplified label and antiHBs-antibody-modified magnetic nanoparticles (MNP-Ab), in the presence of a target antigen and using an external magnetic force. Afterward, releasing of the captured fluorescence label is performed using a protease enzyme (pepsin) by a digestion mechanism of grafted antibodies on the GNP-thionine hybrid. As a result of antibody digestion, the amplified fluorescent hybrids (labels) are released into the solution. To understand the mechanism of enhanced fluorescence, the nature of the interaction between thionine and gold nanoparticles is studied using the B3LYP density functional method. In such a methodology, several new mechanisms and structures are used simultaneously, including a SEF-based metal nanoparticle-organic dye hybrid, dual signal amplification in a two-dimensional probe between the GNP-thionine hybrid and MNPs, and a novel releasing method using protease enzymes. These factors improve the sensitivity and speed, along with the simplicity of the procedure. Under optimal conditions, the fluorescence signal increases with the increment of HBs antigen concentration in the linear dynamic range of 4.6 × 10-9 to 0.012 ng/mL with a detection limit (LOD) of 4.6 × 10-9 ng/mL. The proposed immunosensor has great potential in developing ultrasensitive and rapid diagnostic platforms.

Non-invasive plant disease diagnostics enabled by smartphone-based fingerprinting of leaf volatiles

Plant pathogen detection conventionally relies on molecular technology that is complicated, time-consuming and constrained to centralized laboratories. We developed a cost-effective smartphone-based volatile organic compound (VOC) fingerprinting platform that allows non-invasive diagnosis of late blight caused by Phytophthora infestans by monitoring characteristic leaf volatile emissions in the field. This handheld device integrates a disposable colourimetric sensor array consisting of plasmonic nanocolorants and chemo-responsive organic dyes to detect key plant volatiles at the ppm level within 1 min of reaction. We demonstrate the multiplexed detection and classification of ten individual plant volatiles with this field-portable VOC-sensing platform, which allows for early detection of tomato late blight 2 d after inoculation, and differentiation from other pathogens of tomato that lead to similar symptoms on tomato foliage. Furthermore, we demonstrate a detection accuracy of ≥95% in diagnosis of P. infestans in both laboratory-inoculated and field-collected tomato leaves in blind pilot tests. Finally, the sensor platform has been beta-tested for detection of P. infestans in symptomless tomato plants in the greenhouse setting.