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Environmental humidity variations are ubiquitous and high humidity characterizes fuel cell and electrolyzer operation conditions. Since hydrogen-air mixtures are highly flammable, humidity tolerant H2 sensors are important from safety and process monitoring perspectives. Here, we report an optical nanoplasmonic hydrogen sensor operated at elevated temperature that combined with Deep Dense Neural Network or Transformer data treatment involving the entire spectral response of the sensor enables a 100 ppm H2 limit of detection in synthetic air at 80% relative humidity. This significantly exceeds the <1000 ppm US Department of Energy performance target. Furthermore, the sensors pass the ISO 26142:2010 stability requirement in 80% relative humidity in air down to 0.06% H2 and show no signs of performance loss after 140 h continuous operation. Our results thus demonstrate the potential of plasmonic hydrogen sensors for use in high humidity and how neural-network-based data treatment can significantly boost their performance.
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Traditional single-nanoparticle sizing using optical microscopy techniques assesses size via the diffusion constant, which requires suspended particles to be in a medium of known viscosity. However, these assumptions are typically not fulfilled in complex natural sample environments. Here, we introduce dual-angle interferometric scattering microscopy (DAISY), enabling optical quantification of both size and polarizability of individual nanoparticles (radius <170 nm) without requiring a priori information regarding the surrounding media or super-resolution imaging. DAISY achieves this by combining the information contained in concurrently measured forward and backward scattering images through twilight off-axis holography and interferometric scattering (iSCAT). Going beyond particle size and polarizability, single-particle morphology can be deduced from the fact that the hydrodynamic radius relates to the outer particle radius, while the scattering-based size estimate depends on the internal mass distribution of the particles. We demonstrate this by differentiating biomolecular fractal aggregates from spherical particles in fetal bovine serum at the single-particle level.
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Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences.
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Object detection is a fundamental task in digital microscopy, where machine learning has made great strides in overcoming the limitations of classical approaches. The training of state-of-the-art machine-learning methods almost universally relies on vast amounts of labeled experimental data or the ability to numerically simulate realistic datasets. However, experimental data are often challenging to label and cannot be easily reproduced numerically. Here, we propose a deep-learning method, named LodeSTAR (Localization and detection from Symmetries, Translations And Rotations), that learns to detect microscopic objects with sub-pixel accuracy from a single unlabeled experimental image by exploiting the inherent roto-translational symmetries of this task. We demonstrate that LodeSTAR outperforms traditional methods in terms of accuracy, also when analyzing challenging experimental data containing densely packed cells or noisy backgrounds. Furthermore, by exploiting additional symmetries we show that LodeSTAR can measure other properties, e.g., vertical position and polarizability in holographic microscopy.
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Holografía , Microscopía , Algoritmos , Aprendizaje AutomáticoRESUMEN
The marine microbial food web plays a central role in the global carbon cycle. However, our mechanistic understanding of the ocean is biased toward its larger constituents, while rates and biomass fluxes in the microbial food web are mainly inferred from indirect measurements and ensemble averages. Yet, resolution at the level of the individual microplankton is required to advance our understanding of the microbial food web. Here, we demonstrate that, by combining holographic microscopy with deep learning, we can follow microplanktons throughout their lifespan, continuously measuring their three-dimensional position and dry mass. The deep-learning algorithms circumvent the computationally intensive processing of holographic data and allow rapid measurements over extended time periods. This permits us to reliably estimate growth rates, both in terms of dry mass increase and cell divisions, as well as to measure trophic interactions between species such as predation events. The individual resolution provides information about selectivity, individual feeding rates, and handling times for individual microplanktons. The method is particularly useful to detail the rates and routes of organic matter transfer in micro-zooplankton, the most important and least known group of primary consumers in the oceans. Studying individual interactions in idealized small systems provides insights that help us understand microbial food webs and ultimately larger-scale processes. We exemplify this by detailed descriptions of micro-zooplankton feeding events, cell divisions, and long-term monitoring of single cells from division to division.
Picture a glass of seawater. It looks clear and empty, but in reality, it contains one hundred million bacteria, about one hundred thousand other single-celled organisms, and a few microscopic animals. In fact, the majority of life in the ocean is microscopic and we know relatively little about it. Nevertheless, these microbes have a major impact on our lives. Microscopic algae known as phytoplankton, for example, produce half of the oxygen we breathe. For animals, birds and other large organisms in the ocean, we have a good understanding of who eats who and where the material ends up. However, for phytoplankton and other microbes, we depend on bulk measurements and averages of large groups. Bachimanchi et al. developed a method to follow individual microbes living in seawater and to observe how they move, grow, consume each other and reproduce. The team combined holographic microscopy with artificial intelligence to follow multiple planktons, diatoms and other microbes throughout their life span and continuously measured their three-dimensional location and mass. This made it possible to estimate how fast the organisms were growing and moving, and to observe what they ate. The experiments revealed new insights into how micro-zooplankton, diatoms and other microbes in the ocean interact with each other. This new method may be useful for researchers who would like to track the movements and whereabouts of microscopic planktons, bacteria or other microbes for extended periods of time. It is also a rapid method for counting, sizing, and weighing cells in suspension. The hardware used in this method is relatively cheap, and Bachimanchi et al. have shared all the computer code with examples and demonstrations in a public database to enable other researchers to use it.
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Aprendizaje Profundo , Fitoplancton , Animales , Microscopía , Zooplancton , Océanos y Mares , Agua de MarRESUMEN
Label-free characterization of single biomolecules aims to complement fluorescence microscopy in situations where labeling compromises data interpretation, is technically challenging or even impossible. However, existing methods require the investigated species to bind to a surface to be visible, thereby leaving a large fraction of analytes undetected. Here, we present nanofluidic scattering microscopy (NSM), which overcomes these limitations by enabling label-free, real-time imaging of single biomolecules diffusing inside a nanofluidic channel. NSM facilitates accurate determination of molecular weight from the measured optical contrast and of the hydrodynamic radius from the measured diffusivity, from which information about the conformational state can be inferred. Furthermore, we demonstrate its applicability to the analysis of a complex biofluid, using conditioned cell culture medium containing extracellular vesicles as an example. We foresee the application of NSM to monitor conformational changes, aggregation and interactions of single biomolecules, and to analyze single-cell secretomes.
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Nanopartículas , Nanotecnología , Difusión , Microscopía FluorescenteRESUMEN
During diffusion of nanoparticles bound to a cellular membrane by ligand-receptor pairs, the distance to the laterally mobile interface is sufficiently short for their motion to depend not only on the membrane-mediated diffusivity of the tethers but also in a not yet fully understood manner on nanoparticle size and interfacial hydrodynamics. By quantifying diffusivity, velocity, and size of individual membrane-bound liposomes subjected to a hydrodynamic shear flow, we have successfully separated the diffusivity contributions from particle size and number of tethers. The obtained diffusion-size relations for synthetic and extracellular lipid vesicles are not well-described by the conventional no-slip boundary condition, suggesting partial slip as well as a significant diffusivity dependence on the distance to the lipid bilayer. These insights, extending the understanding of diffusion of biological nanoparticles at lipid bilayers, are of relevance for processes such as cellular uptake of viruses and lipid nanoparticles or labeling of cell-membrane-residing molecules.
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Membrana Dobles de Lípidos , Liposomas , Membrana Celular , Difusión , MembranasRESUMEN
Characterization of suspended nanoparticles in their native environment plays a central role in a wide range of fields, from medical diagnostics and nanoparticle-enhanced drug delivery to nanosafety and environmental nanopollution assessment. Standard optical approaches for nanoparticle sizing assess the size via the diffusion constant and, as a consequence, require long trajectories and that the medium has a known and uniform viscosity. However, in most biological applications, only short trajectories are available, while simultaneously, the medium viscosity is unknown and tends to display spatiotemporal variations. In this work, we demonstrate a label-free method to quantify not only size but also refractive index of individual subwavelength particles using 2 orders of magnitude shorter trajectories than required by standard methods and without prior knowledge about the physicochemical properties of the medium. We achieved this by developing a weighted average convolutional neural network to analyze holographic images of single particles, which was successfully applied to distinguish and quantify both size and refractive index of subwavelength silica and polystyrene particles without prior knowledge of solute viscosity or refractive index. We further demonstrate how these features make it possible to temporally resolve aggregation dynamics of 31 nm polystyrene nanoparticles, revealing previously unobserved time-resolved dynamics of the monomer number and fractal dimension of individual subwavelength aggregates.
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Quantitative analysis of cell structures is essential for biomedical and pharmaceutical research. The standard imaging approach relies on fluorescence microscopy, where cell structures of interest are labeled by chemical staining techniques. However, these techniques are often invasive and sometimes even toxic to the cells, in addition to being time consuming, labor intensive, and expensive. Here, we introduce an alternative deep-learning-powered approach based on the analysis of bright-field images by a conditional generative adversarial neural network (cGAN). We show that this is a robust and fast-converging approach to generate virtually stained images from the bright-field images and, in subsequent downstream analyses, to quantify the properties of cell structures. Specifically, we train a cGAN to virtually stain lipid droplets, cytoplasm, and nuclei using bright-field images of human stem-cell-derived fat cells (adipocytes), which are of particular interest for nanomedicine and vaccine development. Subsequently, we use these virtually stained images to extract quantitative measures about these cell structures. Generating virtually stained fluorescence images is less invasive, less expensive, and more reproducible than standard chemical staining; furthermore, it frees up the fluorescence microscopy channels for other analytical probes, thus increasing the amount of information that can be extracted from each cell. To make this deep-learning-powered approach readily available for other users, we provide a Python software package, which can be easily personalized and optimized for specific virtual-staining and cell-profiling applications.
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Determination of size and refractive index (RI) of dispersed unlabeled subwavelength particles is of growing interest in several fields, including biotechnology, wastewater monitoring, and nanobubble preparations. Conventionally, the size distribution of such samples is determined via the Brownian motion of the particles, but simultaneous determination of their RI remains challenging. This work demonstrates nanoparticle tracking analysis (NTA) in an off-axis digital holographic microscope (DHM) enabling determination of both particle size and RI of individual subwavelength particles from the combined information about size and optical phase shift. The potential of the method to separate particle populations is demonstrated by analyzing a mixture of three types of dielectric particles within a narrow size range, where conventional NTA methods based on Brownian motion alone would fail. Using this approach, the phase shift allowed individual populations of dielectric beads overlapping in either size or RI to be clearly distinguished and quantified with respect to these properties. The method was furthermore applied for analysis of surfactant-stabilized micro- and nanobubbles, with RI lower than that of water. Since bubbles induce a phase shift of opposite sign to that of solid particles, they were easily distinguished from similarly sized solid particles made up of undissolved surfactant. Surprisingly, the dependence of the phase shift on bubble size indicates that only those with 0.15-0.20 µm radius were individual bubbles, whereas larger bubbles were actually clusters of bubbles. This label-free means to quantify multiple parameters of suspended individual submicrometer particles offers a crucial complement to current characterization strategies, suggesting broad applicability for a wide range of nanoparticle systems.
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Aire , Nanopartículas/química , Tamaño de la Partícula , Poliestirenos/química , Refractometría , Dióxido de Silicio/química , Hexosas/química , Microburbujas , Polisorbatos/química , Tensoactivos/químicaRESUMEN
Microorganisms adapt their biophysical properties in response to changes in their local environment. However, quantifying these changes at the single-cell level has only recently become possible, largely relying on fluorescent labeling strategies. In this work, we utilize yeast (Saccharomyces cerevisiae) to demonstrate label-free quantification of changes in both intracellular osmolarity and macromolecular concentration in response to changes in the local environment. By combining a digital holographic microscope with a millifluidic chip, the temporal response of cellular water flux was successfully isolated from the rate of production of higher molecular weight compounds, in addition to identifying the produced compounds in terms of the product of their refractive index increment [Formula: see text] and molar mass. The ability to identify, quantify and temporally resolve multiple biophysical processes in living cells at the single cell level offers a crucial complement to label-based strategies, suggesting broad applicability in studies of a wide-range of cellular processes.
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Citosol/metabolismo , Saccharomyces cerevisiae/química , Análisis de la Célula Individual/métodos , Agua/metabolismo , Transporte Biológico , Citosol/química , Citosol/ultraestructura , Holografía , Dispositivos Laboratorio en un Chip , Concentración Osmolar , Presión Osmótica , Refractometría , Saccharomyces cerevisiae/metabolismo , Saccharomyces cerevisiae/ultraestructura , Análisis de la Célula Individual/instrumentación , Agua/químicaRESUMEN
Quartz crystal microbalance with dissipation monitoring and total internal reflection fluorescence microscopy have been used to investigate binding of norovirus-like particles (noroVLPs) to a supported (phospho)lipid bilayer (SLB) containing a few percent of H or B type 1 glycosphingolipid (GSL) receptors. Although neither of these GSLs spontaneously form domains, noroVLPs were observed to form micron-sized clusters containing typically up to about 30 VLP copies, especially for B type 1, which is a higher-affinity receptor. This novel finding is explained by proposing a model implying that VLP-induced membrane deformation promotes VLP clustering, a hypothesis that was further supported by observing that functionalized gold nanoparticles were able to locally induce SLB deformation. Because similar effects are likely possible also at cellular membranes, our findings are interesting beyond a pure biophysicochemical perspective as they shed new light on what may happen during receptor-mediated uptake of viruses as well as nanocarriers in drug delivery.
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Glicoesfingolípidos/química , Membrana Dobles de Lípidos/metabolismo , Nanopartículas del Metal/química , Norovirus/química , Carbocianinas/química , Fluorescencia , Colorantes Fluorescentes/química , Oro/química , Humanos , Membrana Dobles de Lípidos/química , Microscopía Fluorescente , Fosfatidilcolinas/química , Fosfatidilcolinas/metabolismoRESUMEN
A multi-scale approach for the theoretical description of deformed phosphorene is presented. This approach combines a valence-force model to relate macroscopic strain to microscopic displacements of atoms and a tight-binding model with distance-dependent hopping parameters to obtain electronic properties. The resulting self-consistent electromechanical model is suitable for large-scale modeling of phosphorene devices. We demonstrate this for the case of inhomogeneously deformed phosphorene drum, which may be used as an exciton funnel.
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Matrix proteins from enveloped viruses play an important role in budding and stabilizing virus particles. In order to assess the role of the matrix protein M1 from influenza C virus (M1-C) in plasma membrane deformation, we have combined structural and in vitro reconstitution experiments with model membranes. We present the crystal structure of the N-terminal domain of M1-C and show by Small Angle X-Ray Scattering analysis that full-length M1-C folds into an elongated structure that associates laterally into ring-like or filamentous polymers. Using negatively charged giant unilamellar vesicles (GUVs), we demonstrate that M1-C full-length binds to and induces inward budding of membrane tubules with diameters that resemble the diameter of viruses. Membrane tubule formation requires the C-terminal domain of M1-C, corroborating its essential role for M1-C polymerization. Our results indicate that M1-C assembly on membranes constitutes the driving force for budding and suggest that M1-C plays a key role in facilitating viral egress.
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Membrana Celular/metabolismo , Membrana Celular/virología , Gammainfluenzavirus/fisiología , Proteínas de la Matriz Viral/metabolismo , Sitios de Unión , Concentración de Iones de Hidrógeno , Interacciones Hidrofóbicas e Hidrofílicas , Modelos Moleculares , Conformación Molecular , Unión Proteica , Dominios y Motivos de Interacción de Proteínas , Multimerización de Proteína , Proteínas Recombinantes , Electricidad Estática , Relación Estructura-Actividad , Proteínas de la Matriz Viral/química , Proteínas de la Matriz Viral/genéticaRESUMEN
In order to understand the relation of strain and material properties, both a microscopic model connecting a given strain to the displacement of atoms, and a macroscopic model relating applied stress to induced strain, are required. Starting from a valence-force model for black phosphorous [Kaneta et al., Solid State Communications, 1982, 44, 613] we use recent experimental and computational results to obtain an improved set of valence-force parameters for phosphorene. From the model we calculate the phonon dispersion and the elastic properties of single-layer phosphorene. Finally, we use these results to derive a complete continuum model, including the bending rigidities, valid for long-wavelength deformations of phosphorene. This continuum model is then used to study the properties of pressurized suspended phosphorene sheets.
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We compare the simplified valence-force model for single-layer black phosphorus with the original model and recent ab initio results. Using an analytic approach and numerical calculations we find that the simplified model yields Young's moduli that are smaller compared to the original model and are almost a factor of two smaller than ab initio results. Moreover, the Poisson ratios are an order of magnitude smaller than values found in the literature.
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Cells can enter into a dormant state when faced with unfavorable conditions. However, how cells enter into and recover from this state is still poorly understood. Here, we study dormancy in different eukaryotic organisms and find it to be associated with a significant decrease in the mobility of organelles and foreign tracer particles. We show that this reduced mobility is caused by an influx of protons and a marked acidification of the cytoplasm, which leads to widespread macromolecular assembly of proteins and triggers a transition of the cytoplasm to a solid-like state with increased mechanical stability. We further demonstrate that this transition is required for cellular survival under conditions of starvation. Our findings have broad implications for understanding alternative physiological states, such as quiescence and dormancy, and create a new view of the cytoplasm as an adaptable fluid that can reversibly transition into a protective solid-like state.
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Citoplasma/química , Citoplasma/efectos de los fármacos , Dictyostelium/fisiología , Transición de Fase/efectos de los fármacos , Saccharomyces cerevisiae/fisiología , Supervivencia Celular , Concentración de Iones de Hidrógeno , Estrés FisiológicoRESUMEN
Vacancies in graphene lead to the appearance of localized electronic states with non-vanishing spin moments. Using a mean-field Hubbard model and an effective double-quantum dot description we investigate the influence of strain on localization and magnetic properties of the vacancy-induced states in semiconducting armchair nanoribbons. We find that the exchange splitting of a single vacancy and the singlet-triplet splitting for two vacancies can be widely tuned by applying uniaxial strain, which is crucial for spintronic applications.
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Phononic crystals and acoustic metamaterials are used to tailor phonon and sound propagation properties by utilizing artificial, periodic structures. Analogous to photonic crystals, phononic band gaps can be created, which influence wave propagation and, more generally, allow engineering of the acoustic properties of a system. Beyond that, nonlinear phenomena in periodic structures have been extensively studied in photonic crystals and atomic Bose-Einstein condensates in optical lattices. However, creating nonlinear phononic crystals or nonlinear acoustic metamaterials remains challenging and only few examples have been demonstrated. Here, we show that atomically thin and periodically pinned membranes support coupled localized modes with nonlinear dynamics. The proposed system provides a platform for investigating nonlinear phononics.