The video-rate imaging of sub-10 nm plasmonic nanoparticles in a cellular medium free of background scattering

High Confidence Single Particle Analysis with Machine Learning

Single particle analysis can effectively determine the heterogeneity between particles based on the local information on a single particle, which is utilized extensively for monitoring chemical reactions and biological activities. However, the study of obtaining ensemble reaction information at the single particle level, which can obtain both the structural and functional heterogeneity of particles as well as the ensemble reaction information, is challenging because the selection of a single particle mainly depends on experience, which will lead to a certain randomness when analyzing the ensemble reaction with single particles. Using machine learning, it is demonstrated that the proposed intelligent single particle analysis strategy can provide single particle and ensemble analyses with high confidence. Convolutional neural network and Gaussian mixture model were utilized to develop a machine learning model for resonance scattering imaging analysis of plasmonic nanoparticles. It can identify the scattered light of single particles and select representative or diverse particles. When single particle scattering imaging is used to obtain ensemble information on the reaction, the error caused by the selection of individual particles can be significantly reduced by selecting representative particles. In addition, the real situation of the reaction can be better revealed by selecting diverse particles. These results indicate that the intelligent single particle analysis strategy has great potential for imaging analysis and biological sensing.

Machine Learning Color Feature Analysis of a High Throughput Nanoparticle Conjugate Sensing Assay

Plasmonic nanoparticles are finding applications within the single molecule sensing field in a "dimer" format, where interaction of the target with hairpin DNA causes a decrease in the interparticle distance, leading to a localized surface plasmon resonance shift. While this shift may be detected using spectroscopy, achieving statistical relevance requires the measurement of thousands of nanoparticle dimers and the timescales required for spectroscopic analysis are incompatible with point-of-care devices. However, using dark-field imaging of the dimer structures, simultaneous digital analysis of the plasmonic resonance shift after target interaction of thousands of dimer structures may be achieved in minutes. The main challenge of this digital analysis on the single-molecule scale was the occurrence of false signals caused by non-specifically bound clusters of nanoparticles. This effect may be reduced by digitally separating dimers from other nanoconjugate types. Variation in image intensity was observed to have a discernible impact on the color analysis of the nanoconjugate constructs and thus the accuracy of the digital separation. Color spaces wherein intensity may be uncoupled from the color information (hue, saturation, and value (HSV) and luminance, a* vector, and b* vector (LAB) were contrasted to a color space which cannot uncouple intensity (RGB) to train a classifier algorithm. Each classifier algorithm was validated to determine which color space produced the most accurate digital separation of the nanoconjugate types. The LAB-based learning classifier demonstrated the highest accuracy for digitally separating nanoparticles. Using this classifier, nanoparticle conjugates were monitored for their plasmonic color shift after interaction with a synthetic RNA target, resulting in a platform with a highly accurate yes/no response with a true positive rate of 88% and a true negative rate of 100%. The sensor response of tested single stranded RNA (ssRNA) samples was well above control responses for target concentrations in the range of 10 aM-1 pM.

Dark-Field Imaging Monitoring of Adenosine Triphosphate in Live Cells by Au NBPs@ZIF-8 Nanoprobes

Monitoring the fluctuation of adenosine triphosphate (ATP) level in living cells could promote the understanding of metabolic pathways and cell biology. Here, we proposed a highly sensitive, selective, and biocompatible nanoprobe with core-shell structure, namely Au NBPs@ZIF-8 composed by gold nanobipyramids (Au NBPs) and zeolitic imidazolate framework-8 (ZIF-8), for monitoring intracellular ATP level fluctuation in living cells. Because the coordination between ATP and Zn²⁺ (the metal node of ZIF-8) was much stronger than that between 2-methylimidazole and Zn²⁺, which caused the decomposition of the ZIF-8 shell and the exposure of Au NBPs in the presence of ATP, it led to the change of the localized surface plasmon resonance scattering properties of nanoprobes under dark-field microscopy. Tricolor (RGB) analysis showed that R/G value had a good linear relationship with the ATP concentrations in the range of 10 μM to 4 mM (R² = 0.999) with a detection limit of 5.28 μM. This ATP sensing platform also exhibited excellent selectivity in complex intracellular interfering substances. Besides, we realized intracellular ATP real-time imaging in HeLa cells and observed the ATP level fluctuation under dark-field microscopy. The method mentioned here could be further applied for delivery of therapeutics for biomedical applications.

Ultrafast nonlinear absorption with multiple transformations and transient dynamics of gold nanobipyramids

The process and condition of saturable absorption (SA) and reverse saturable absorption (RSA) of ultrafast nonlinear optics in metal nanoparticles are essential for applications including light generation, amplification, modulation, and switching. Here, we first discover and explore the multiple transformations (SA-RSA-SA) of ultrafast nonlinear absorption behavior of metal nanoparticles in femtosecond pulses. Correspondingly, the energy level model and fitting formula of multiple transformations are established to illustrate the process of optical response. The femtosecond transient absorption spectra provide information about their ultrafast dynamics process and vibrational mode, which further reveals the multiple transformation mechanisms of nonlinear absorption in gold nanobipyramids (Au-NBPs). Furthermore, Au-NBPs exhibit a significantly higher SA modulation depth up to 42% in the femtosecond, which is much higher than the reported values of other nanomaterials. Our results indicate that Au-NBPs can be used as broadband ultrafast Q-switching and mode-locking, and the conversion offers new opportunities for metal nanostructures in applications of optical switching.

Optical Microscopy Systems for the Detection of Unlabeled Nanoparticles

Label-free detection of nanoparticles is essential for a thorough evaluation of their cellular effects. In particular, nanoparticles intended for medical applications must be carefully analyzed in terms of their interactions with cells, tissues, and organs. Since the labeling causes a strong change in the physicochemical properties and thus also alters the interactions of the particles with the surrounding tissue, the use of fluorescently labeled particles is inadequate to characterize the effects of unlabeled particles. Further, labeling may affect cellular uptake and biocompatibility of nanoparticles. Thus, label-free techniques have been recently developed and implemented to ensure a reliable characterization of nanoparticles.

This review provides an overview of frequently used label-free visualization techniques and highlights recent studies on the development and usage of microscopy systems based on reflectance, darkfield, differential interference contrast, optical coherence, photothermal, holographic, photoacoustic, total internal reflection, surface plasmon resonance, Rayleigh light scattering, hyperspectral and reflectance structured illumination imaging. Using these imaging modalities, there is a strong enhancement in the reliability of experiments concerning cellular uptake and biocompatibility of nanoparticles, which is crucial for preclinical evaluations and future medical applications.

Responsive photonic nanopixels with hybrid scatterers

Metallic and dielectric nanoscatterers are optical pigments that offer rich resonating coloration in the subwavelength regime with prolonged material consistency. Recent advances in responsive materials, whose mechanical shapes and optical properties can change in response to stimuli, expand the scope of scattering-based colorations from static to active. Thus, active color-changing pixels are achieved with extremely high spatial resolution, in conjunction with various responsive polymers and phase-change materials. This review discusses recent progress in developing such responsive photonic nanopixels, ranging from electrochromic to other color-changing concepts. We describe what parameters permit modulation of the scattering colors and highlight superior functional devices. Potential fields of application focusing on imaging devices, including active full-color printing and flexible displays, information encryption, anticounterfeiting, and active holograms, are also discussed.

High Resolution of Plasmonic Resonance Scattering Imaging with Deep Learning

The dark-field microscopy (DFM) imaging technology has the advantage of a high signal-to-noise ratio, and it is often used for real-time monitoring of plasmonic resonance scattering and biological imaging at the single-nanoparticle level. Due to the limitation of the optical diffraction limit, it is still a challenging task to accurately distinguish two or more nanoparticles whose distance is less than the diffraction limit. Here, we propose a computational strategy based on a deep learning framework (NanoNet), which will realize the effective segmentation of the scattered light spots in diffraction-limited DFM images and obtain high-resolution plasmonic light scattering imaging. A small data set of DFM and the corresponding scanning electron microscopy (SEM) image pairs are used to learn for obtaining a highly resolved semantic imaging model using NanoNet, and thus highly resolved DFM images matching the resolution of those acquired using SEM can be obtained. Our method has the ability to transform diffraction-limited DFM images to highly resolved ones without adding a complex optical system. As a proof of concept, a highly resolved DFM image of living cells through the NanoNet technique is successfully made, opening up a new avenue for high-resolution optical nanoscopic imaging.

The unique physical shading pattern of Rayleigh scattering for the generally improved detection of scattering particles

A facile physical shading method, based on the Rayleigh scattering pattern, exhibited excellent performance detection because of its reduced background noise.

Weak Reaction Scatterometry of Plasmonic Resonance Light Scattering with Machine Learning

Weak reactions are usually overlooked due to weak detectable features and susceptibility to interference from noise signals. Strategies for detecting weak reactions are essential for exploring reaction mechanisms and exploiting potential applications. Machine learning has recently been successfully used to identify patterns and trends in the data. Here, it is demonstrated that machine learning-based techniques can offer accurate local surface plasmon resonance (LSPR) scatterometry by improving the precision of the plasmonic scattering imaging in weak chemical reactions. Dark-field microscopy (DFM) imaging technique is the most effective method for high-sensitivity plasmonic nanoparticles LSPR scatterometry. Unfortunately, deviations caused by the instrument and operating errors are inevitable, and it is difficult to effectively detect the presence of weak reactions. Thus, introducing a machine learning calibration model to automatically calibrate the scattering signal of the nanoprobe in the reaction process can greatly improve the confidence of LSPR scatterometry under DFM imaging and allow DFM imaging to effectively monitor unobvious or weak reactions. By this approach, the weak oxidation of silver nanoparticles (AgNPs) in water by dissolved oxygen was successfully monitored. Moreover, a trivial reaction between AgNPs and mercury ions was detected in a dilute mercury solution with a concentration greater than 1.0 × 10^-10 mol/L. These results suggest the great potential of the combination of LSPR scatterometry and machine learning as a method for imaging analysis and intelligent sensing.