Multi-resonant plasmonic nanodome arrays for label-free biosensing applications

Computational Sensing Using Low-Cost and Mobile Plasmonic Readers Designed by Machine Learning

Plasmonic sensors have been used for a wide range of biological and chemical sensing applications. Emerging nanofabrication techniques have enabled these sensors to be cost-effectively mass manufactured onto various types of substrates. To accompany these advances, major improvements in sensor read-out devices must also be achieved to fully realize the broad impact of plasmonic nanosensors. Here, we propose a machine learning framework which can be used to design low-cost and mobile multispectral plasmonic readers that do not use traditionally employed bulky and expensive stabilized light sources or high-resolution spectrometers. By training a feature selection model over a large set of fabricated plasmonic nanosensors, we select the optimal set of illumination light-emitting diodes needed to create a minimum-error refractive index prediction model, which statistically takes into account the varied spectral responses and fabrication-induced variability of a given sensor design. This computational sensing approach was experimentally validated using a modular mobile plasmonic reader. We tested different plasmonic sensors with hexagonal and square periodicity nanohole arrays and revealed that the optimal illumination bands differ from those that are "intuitively" selected based on the spectral features of the sensor, e.g., transmission peaks or valleys. This framework provides a universal tool for the plasmonics community to design low-cost and mobile multispectral readers, helping the translation of nanosensing technologies to various emerging applications such as wearable sensing, personalized medicine, and point-of-care diagnostics. Beyond plasmonics, other types of sensors that operate based on spectral changes can broadly benefit from this approach, including e.g., aptamer-enabled nanoparticle assays and graphene-based sensors, among others.

Interfacial Mode Interactions of Surface Plasmon Polaritons on Gold Nanodome Films

Hollow metallic nanodome structures were fabricated using anodized aluminum oxide (AAO) nanopores as deposition and sacrificial templates. Individual Au nanodomes inherit the unique shapes of the well-defined AAO membranes whose pedestal cells become square or hexagonal lattices with hemispheres in close proximity. Minimal contact between the hollow nanodomes and the glass substrate provide an identical dielectric medium across the film. The nanodome Au films support surface plasmon polaritons (SPPs) of strong air-Au and weak Au-glass modes in the light transmission dispersions. The mode crossings of distinct SPPs exhibit characteristic energy gaps, which depend on the periodic geometries of the nanostructures.

Localized surface plasmon resonance sensors based on wavelength-tunable spectral dips

Localized surface plasmon resonance (LSPR) sensors serve as sensitive analytical tools based on refractive index changes, which can be applied to affinity-based chemical sensing and biosensing. However, to select the monitoring wavelength, monodisperse Au or Ag nanoparticles must be synthesized. Here we developed LSPR sensors that operate at arbitrary wavelengths after preirradiation at the corresponding wavelength. Polydisperse plasmonic Ag nanospheroids or nanorods are photocatalytically deposited on TiO2. The nanoparticle ensemble shows a broad absorption band over the visible and near infrared regions, and absorption dips can be formed at desired wavelengths simply by photoexciting the ensemble at the wavelengths, on the basis of plasmon-induced charge separation. The dips redshift linearly in response to a positive change of refractive index, and the refractive index sensitivity linearly increases with increasing dip wavelength (e.g., 356 nm RIU(-1) at 1832 nm). The dip-based sensor is applied to monitoring of selective binding between biotin and streptavidin. The present system would allow development of miniaturized and cost-effective sensors that operate at the optimum wavelength at which the sensitivity is highest within the optical window of the sample.

Nanomaterials enhanced surface plasmon resonance for biological and chemical sensing applications

The main challenge for all electrical, mechanical and optical sensors is to detect low molecular weight (less than 400 Da) chemical and biological analytes under extremely dilute conditions. Surface plasmon resonance sensors are the most commonly used optical sensors due to their unique ability for real-time monitoring the molecular binding events. However, their sensitivities are insufficient to detect trace amounts of small molecular weight molecules such as cancer biomarkers, hormones, antibiotics, insecticides, and explosive materials which are respectively important for early-stage disease diagnosis, food quality control, environmental monitoring, and homeland security protection. With the rapid development of nanotechnology in the past few years, nanomaterials-enhanced surface plasmon resonance sensors have been developed and used as effective tools to sense hard-to-detect molecules within the concentration range between pmol and amol. In this review article, we reviewed and discussed the latest trend and challenges in engineering and applications of nanomaterials-enhanced surface plasmon resonance sensors (e.g., metallic nanoparticles, magnetic nanoparticles, carbon-based nanomaterials, latex nanoparticles and liposome nanoparticles) for detecting "hard-to-identify" biological and chemical analytes. Such information will be viable in terms of providing a useful platform for designing future ultrasensitive plasmonic nanosensors.