Unsupervised learning approaches to characterizing heterogeneous samples using X-ray single-particle imaging

One of the outstanding analytical problems in X-ray single-particle imaging (SPI) is the classification of structural heterogeneity, which is especially difficult given the low signal-to-noise ratios of individual patterns and the fact that even identical objects can yield patterns that vary greatly when orientation is taken into consideration. Proposed here are two methods which explicitly account for this orientation-induced variation and can robustly determine the structural landscape of a sample ensemble. The first, termed common-line principal component analysis (PCA), provides a rough classification which is essentially parameter free and can be run automatically on any SPI dataset. The second method, utilizing variation auto-encoders (VAEs), can generate 3D structures of the objects at any point in the structural landscape. Both these methods are implemented in combination with the noise-tolerant expand–maximize–compress (EMC) algorithm and its utility is demonstrated by applying it to an experimental dataset from gold nanoparticles with only a few thousand photons per pattern. Both discrete structural classes and continuous deformations are recovered. These developments diverge from previous approaches of extracting reproducible subsets of patterns from a dataset and open up the possibility of moving beyond the study of homogeneous sample sets to addressing open questions on topics such as nanocrystal growth and dynamics, as well as phase transitions which have not been externally triggered.

Single Particle Hopping as an Indicator for Evaluating Electrocatalysts

Design and screening electrocatalysts for gas evolution reactions suffer from scanty understanding of multi-phase processes at the electrode-electrolyte interface. Due to the complexity of multi-phase interface, it is still a great challenge to capture gas evolution dynamics under operando condition to precisely portray the intrinsic catalytic performance of interface. Here, we establish a single particle imaging method to real time monitor a potential-dependent vertical motion or hopping of electrocatalysts induced by electrogenerated gas nanobubbles. The hopping feature of single particle is closely correlated with intrinsic activities of electrocatalysts, thus is developed to be an indicator to evaluate gas evolution performance of various electrocatalysts. This optical indicator diminishes interferences from heterogeneous morphologies, non-Faradaic processes and parasitic side reactions that are unavoidable in conventional electrochemical measurements, therefore enables precise evaluation and high-throughput screening of catalysts for gas evolution systems.

Photoelectron Spectroscopy in Molecular Physical Chemistry

Photoelectron spectroscopy has long been a powerful method in the toolbox of experimental physical chemistry and molecular physics. Recent improvements in coincidence methods, charged-particle imaging, and electron energy resolution have...

Magnetic particle imaging: tracer development and the biomedical applications of a radiation-free, sensitive, and quantitative imaging modality

Magnetic particle imaging (MPI) is an emerging tracer-based modality that enables real-time three-dimensional imaging of the non-linear magnetisation produced by superparamagnetic iron oxide nanoparticles (SPIONs), in the presence of an...

Sensitive and Specific Detection of Breast Cancer Lymph Node Metastasis Through Dual-Modality Magnetic Particle Imaging and Fluorescence Molecular Imaging: a Preclinical Evaluation

Purpose: A sensitive and specific imaging method to detect metastatic cancer cells in lymph nodes (LNs) to detect the early-stage breast cancer is urgently needed. The purpose of this study was to investigate a novel breast cancer-targeting and tumour microenvironment ATP-responsive superparamagnetic iron oxide (SPIOs) imaging probe that was developed to detect lymph node metastasis (LNMs) through fluorescence molecular imaging (FMI) and magnetic particle imaging (MPI). The imaging nanoprobe comprised of SPIOs conjugated with breast cancer-targeting peptides (CREKA) and an ATP-responsive DNA aptamer (dsDNA-Cy5.5), abbreviated as SPIOs@A-T. Methods: SPIOs@A-T was synthesised and characterized for its imaging properties, targeting ability and toxicity in vitro. Mice with metastatic lymph node (MLN) of breast cancer were established to evaluate the FMI and MPI imaging strategy in vivo. Healthy mice with normal lymph node (NLN) were used as control group. Histological examination and biosafety evaluation were performed for further assessment. Results: After injection with SPIO@A-T, the obvious high fluorescent intensity and MPI signal were observed in MLN group than those in NLN group. MPI could also complement the limitation of imaging depth from FMI, thus could detect MLN more sensitively. The combination of the imaging strengths of FMI and MPI ensured the detection of breast cancer metastases with high sensitivity and specificity, thereby facilitating the precision differentiation of malignant from benign LNs. Besides, the biosafety evaluation results showed SPIO@A-T had good biocompatibility. Conclusion: Due to the superior properties of tumour-targeting, detection specificity, and biosafety, the SPIOs@A-T imaging probe in combination with FMI and MPI can provide a promising novel method for the early and precise detection of LNMs in clinical practice.

Velocity Field Measurements of the California Sea Lion Propulsive Stroke Using Bubble PIV

California sea lions are among the most agile of swimming mammals. Most marine mammals swim with their hind appendages—flippers or flukes, depending on the species—whereas sea lions use their foreflippers for propulsion and maneuvering. The sea lion’s propulsive stroke generates thrust by forming a jet between the flippers and the body and by dragging a starting vortex along the suction side of the flipper. Prior experiments using robotic flippers have shown these mechanisms to be possible, but no flow measurements around live sea lions previously existed with which to compare. In this study, the flow structures around swimming sea lions were observed using an adaptation of particle imaging velocimetry. To accommodate the animals, it was necessary to use bubbles as seed particles and sunlight for illumination. Three trained adult California sea lions were guided to swim through an approximately planar sheet of bubbles in a total of 173 repetitions. The captured videos were used to calculate bubble velocities, which were processed to isolate and inspect the flow velocities caused by the swimming sea lion. The methodology will be discussed, and measured flow velocities will be presented.

Modelling of Dynamic Behaviour in Magnetic Nanoparticles

The efficient development and utilisation of magnetic nanoparticles (MNPs) for applications in enhanced biosensing relies on the use of magnetisation dynamics, which are primarily governed by the time-dependent motion of the magnetisation due to externally applied magnetic fields. An accurate description of the physics involved is complex and not yet fully understood, especially in the frequency range where Néel and Brownian relaxation processes compete. However, even though it is well known that non-zero, non-static local fields significantly influence these magnetisation dynamics, the modelling of magnetic dynamics for MNPs often uses zero-field dynamics or a static Langevin approach. In this paper, we developed an approximation to model and evaluate its performance for MNPs exposed to a magnetic field with varying amplitude and frequency. This model was initially developed to predict superparamagnetic nanoparticle behaviour in differential magnetometry applications but it can also be applied to similar techniques such as magnetic particle imaging and frequency mixing. Our model was based upon the Fokker–Planck equations for the two relaxation mechanisms. The equations were solved through numerical approximation and they were then combined, while taking into account the particle size distribution and the respective anisotropy distribution. Our model was evaluated for Synomag®-D70, Synomag®-D50 and SHP-15, which resulted in an overall good agreement between measurement and simulation.