Optimal conditions for SPR-imaging of nano-objects

Zybin, A.; Shpacovitch, Victoria; Skolnik, Julia; Hergenröder, Roland

doi:10.1016/j.snb.2016.07.124

Cited by 27 publications

(18 citation statements)

References 16 publications

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“…The schematic view of the PAMONO-sensor set up is presented in Figure 1 and is also described in [ 1 , 2 ]. In contrast to conventional SPR, the PAMONO-sensor employs local changes of reflectivity in order to image the binding events of individual biological or inorganic nano-particles [ 16 ].…”

Section: Methodsmentioning

confidence: 99%

Application of the PAMONO-Sensor for Quantiﬁcation of Microvesicles and Determination of Nano-Particle Size Distribution

Shpacovitch

Sidorenko²,

Lenssen

et al. 2017

Sensors

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Abstract:The PAMONO-sensor (plasmon assisted microscopy of nano-objects) demonstrated an ability to detect and quantify individual viruses and virus-like particles. However, another group of biological vesicles-microvesicles (100-1000 nm)-also attracts growing interest as biomarkers of different pathologies and needs development of novel techniques for characterization. This work shows the applicability of a PAMONO-sensor for selective detection of microvesicles in aquatic samples. The sensor permits comparison of relative concentrations of microvesicles between samples. We also study a possibility of repeated use of a sensor chip after elution of the microvesicle capturing layer. Moreover, we improve the detection features of the PAMONO-sensor. The detection process utilizes novel machine learning techniques on the sensor image data to estimate particle size distributions of nano-particles in polydisperse samples. Altogether, our findings expand analytical features and the application field of the PAMONO-sensor. They can also serve for a maturation of diagnostic tools based on the PAMONO-sensor platform.

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Section: Methodsmentioning

confidence: 99%

Application of the PAMONO-Sensor for Quantiﬁcation of Microvesicles and Determination of Nano-Particle Size Distribution

Shpacovitch

Sidorenko²,

Lenssen

et al. 2017

Sensors

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“…The PAMONO sensor is an optical biosensor that is used to detect nanoparticles, such as viruses or microvesicles, in liquid samples [2,10,11]. Nanoparticles are too small to be detected using light directly.…”

Section: Materials and Methodsmentioning

confidence: 99%

Nanoparticle Classification Using Frequency Domain Analysis on Resource-Limited Platforms

Yayla

Toma

Chen

et al. 2019

Sensors

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A mobile system that can detect viruses in real time is urgently needed, due to the combination of virus emergence and evolution with increasing global travel and transport. A biosensor called PAMONO (for Plasmon Assisted Microscopy of Nano-sized Objects) represents a viable technology for mobile real-time detection of viruses and virus-like particles. It could be used for fast and reliable diagnoses in hospitals, airports, the open air, or other settings. For analysis of the images provided by the sensor, state-of-the-art methods based on convolutional neural networks (CNNs) can achieve high accuracy. However, such computationally intensive methods may not be suitable on most mobile systems. In this work, we propose nanoparticle classification approaches based on frequency domain analysis, which are less resource-intensive. We observe that on average the classification takes 29 μs per image for the Fourier features and 17 μs for the Haar wavelet features. Although the CNN-based method scores 1–2.5 percentage points higher in classification accuracy, it takes 3370 μs per image on the same platform. With these results, we identify and explore the trade-off between resource efficiency and classification performance for nanoparticle classification of images provided by the PAMONO sensor.

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“…Moreover, in a study where the optimal conditions for SPR imaging of nano-objects are discussed, it has been explained that the layer on the sensor surface and its roughness have great importance in local reflectivity resonance and in signal-to-background ratio [81]. In order to have this high ratio, the reflectivity should be minimum; however, due to the roughness and inhomogeneity of the substrate and the layer on the surface, the achievable minimum reflectivity is limited, which can be obtained as minimum as 1%.…”

Section: Importance Of Surface Morphology In Single Particle Optical mentioning

confidence: 99%

“…In order to have this high ratio, the reflectivity should be minimum; however, due to the roughness and inhomogeneity of the substrate and the layer on the surface, the achievable minimum reflectivity is limited, which can be obtained as minimum as 1%. The authors were able to lower this by using different techniques to prepare the substrate surface such as magnetron sputtering of gold on glass yielding a proper thickness and 0.1% reflectivity [81].…”

Section: Importance Of Surface Morphology In Single Particle Optical mentioning

confidence: 99%

Surface chemistry and morphology in single particle optical imaging

et al. 2017

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Biological nanoparticles such as viruses and exosomes are important biomarkers for a range of medical conditions, from infectious diseases to cancer. Biological sensors that detect whole viruses and exosomes with high specificity, yet without additional labeling, are promising because they reduce the complexity of sample preparation and may improve measurement quality by retaining information about nanoscale physical structure of the bio-nanoparticle (BNP). Towards this end, a variety of BNP biosensor technologies have been developed, several of which are capable of enumerating the precise number of detected viruses or exosomes and analyzing physical properties of each individual particle. Optical imaging techniques are promising candidates among broad range of label-free nanoparticle detectors. These imaging BNP sensors detect the binding of single nanoparticles on a flat surface functionalized with a specific capture molecule or an array of multiplexed capture probes. The functionalization step confers all molecular specificity for the sensor's target but can introduce an unforeseen problem; a rough and inhomogeneous surface coating can be a source of noise, as these sensors detect small local changes in optical refractive index. In this paper, we review several optical technologies for label-free BNP detectors with a focus on imaging systems. We compare the surface-imaging methods including dark-field, surface plasmon resonance imaging and interference reflectance imaging. We discuss the importance of ensuring consistently uniform and smooth surface coatings of capture molecules for these types of biosensors and finally summarize several methods that have been developed towards addressing this challenge.

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Optimal conditions for SPR-imaging of nano-objects

Cited by 27 publications

References 16 publications

Application of the PAMONO-Sensor for Quantiﬁcation of Microvesicles and Determination of Nano-Particle Size Distribution

Application of the PAMONO-Sensor for Quantiﬁcation of Microvesicles and Determination of Nano-Particle Size Distribution

Nanoparticle Classification Using Frequency Domain Analysis on Resource-Limited Platforms

Surface chemistry and morphology in single particle optical imaging

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