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Plasmonic devices offer the possibility of passively detecting changes in local chemistry that opens up a wide range of applications from molecular sensing to monitoring water quality. Conventional plasmonics have previously shown great promise as nanoscale chemical sensors through detection of small variations in the local refractive index (RI). The motivation behind using plasmonics for these applications includes the fact that detection is entirely passive and the devices themselves can be readily miniaturized. Previously, a lack of any control over the output of these devices, has fundamentally limited their application to chemicals which produce clearly identifiable resonances within the range of detection. Here it is demonstrated that microfluidic devices, incorporating polarization-controlled plasmonic nanopixels, allow the device response to be tuned to the particular analyte of interest, anywhere within the visible spectrum. This dramatically increases the effective dynamic range and allows local variations in RI to be perceived directly as color changes by the human eye. Active control over the output of the device also enables clear differentiation between a number of different analytes, paving the way for plasmonics to be used for a wide range of real-world chemical sensing applications.

The human eye can distinguish as many as 10,000 different colours but is far less sensitive to variations in intensity1, meaning that colour is highly desirable when interpreting images. However, most biological samples are essentially transparent, and nearly invisible when viewed using a standard optical microscope2. It is therefore highly desirable to be able to produce coloured images without needing to add any stains or dyes, which can alter the sample properties. Here we demonstrate that colorimetric histology images can be generated using full-sized plasmonically active microscope slides. These slides translate subtle changes in the dielectric constant into striking colour contrast when samples are placed upon them. We demonstrate the biomedical potential of this technique, which we term histoplasmonics, by distinguishing neoplastic cells from normal breast epithelium during the earliest stages of tumorigenesis in the mouse MMTV-PyMT mammary tumour model. We then apply this method to human diagnostic tissue and validate its utility in distinguishing normal epithelium, usual ductal hyperplasia, and early-stage breast cancer (ductal carcinoma in situ). The colorimetric output of the image pixels is compared to conventional histopathology. The results we report here support the hypothesis that histoplasmonics can be used as a novel alternative or adjunct to general staining. The widespread availability of this technique and its incorporation into standard laboratory workflows may prove transformative for applications extending well beyond tissue diagnostics. This work also highlights opportunities for improvements to digital pathology that have yet to be explored.

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Microfluidic devices which integrate both rapid mixing and liquid jetting for sample delivery are an emerging solution for studying molecular dynamics via X-ray diffraction. Here we use finite element modelling to investigate the efficiency and time-resolution achievable using microfluidic mixers within the parameter range required for producing stable liquid jets. Three-dimensional simulations, validated by experimental data, are used to determine the velocity and concentration distribution within these devices. The results show that by adopting a serpentine geometry, it is possible to induce chaotic mixing, which effectively reduces the time required to achieve a homogeneous mixture for sample delivery. Further, we investigate the effect of flow rate and the mixer microchannel size on the mixing efficiency and minimum time required for complete mixing of the two solutions whilst maintaining a stable jet. In general, we find that the smaller the cross-sectional area of the mixer microchannel, the shorter the time needed to achieve homogeneous mixing for a given flow rate. The results of these simulations will form the basis for optimised designs enabling the study of molecular dynamics occurring on millisecond timescales using integrated mix-and-inject microfluidic devices.

Cholesterol is believed to induce the formation of membrane domains, "rafts", which are implicated in a range of natural and pathologic membrane processes. Therefore, it is important to understand the role that cholesterol plays in the formation of these structures. Here, we use label-free spectroscopic imaging to investigate cholesterol fractioning in supported bilayer membranes at nanoscale. Scattering-Type scanning near-field optical microscopy (s-SNOM) was used to visualize the formation of cholesterol-induced domains in 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) membranes. Our results revealed the coexistence of phase separated domains in DMPC lipids with 10 mol % cholesterol content, whereas a mostly homogeneous bilayer was found at low (5 mol %) and high (15 mol %) cholesterol content. Near-field nano-FTIR spectroscopy was used to identify the cholesterol-rich domains based on their qualitative chemical compositions. It was determined that cholesterol binds to phosphodiester and alkyl glycerol ester moieties, likely via hydrogen bonding of the alcohol to either of the ester oxygens. The results also confirm the existence of an ideal cholesterol-lipid mixture ratio (â 15:85) with a geometrically defined packing. At lower cholesterol content there is phase separation between liquid ordered and almost neat DMPC domains. Thus, the liquid ordered phase exists at an energy minimum at a given lipid-cholesterol ratio.

A peak-finding algorithm for serial crystallography (SX) data analysis based on the principle of `robust statistics' has been developed. Methods which are statistically robust are generally more insensitive to any departures from model assumptions and are particularly effective when analysing mixtures of probability distributions. For example, these methods enable the discretization of data into a group comprising inliers (i.e. the background noise) and another group comprising outliers (i.e. Bragg peaks). Our robust statistics algorithm has two key advantages, which are demonstrated through testing using multiple SX data sets. First, it is relatively insensitive to the exact value of the input parameters and hence requires minimal optimization. This is critical for the algorithm to be able to run unsupervised, allowing for automated selection or `vetoing' of SX diffraction data. Secondly, the processing of individual diffraction patterns can be easily parallelized. This means that it can analyse data from multiple detector modules simultaneously, making it ideally suited to real-time data processing. These characteristics mean that the robust peak finder (RPF) algorithm will be particularly beneficial for the new class of MHz X-ray free-electron laser sources, which generate large amounts of data in a short period of time.

© 2020, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply. Single Particle Imaging (SPI) with intense coherent X-ray pulses from X-ray free-electron lasers (XFELs) has the potential to produce molecular structures without the need for crystallization or freezing. Here we present a dataset of 285,944 diffraction patterns from aerosolized Coliphage PR772 virus particles injected into the femtosecond X-ray pulses of the Linac Coherent Light Source (LCLS). Additional exposures with background information are also deposited. The diffraction data were collected at the Atomic, Molecular and Optical Science Instrument (AMO) of the LCLS in 4 experimental beam times during a period of four years. The photon energy was either 1.2 or 1.7 keV and the pulse energy was between 2 and 4 mJ in a focal spot of about 1.3 μm x 1.7 μm full width at half maximum (FWHM). The X-ray laser pulses captured the particles in random orientations. The data offer insight into aerosolised virus particles in the gas phase, contain information relevant to improving experimental parameters, and provide a basis for developing algorithms for image analysis and reconstruction.