Reversible phase transformations between Pb nanocrystals and a viscous liquid-like phase.

Phase transformations have been a prominent topic of study for both fundamental and applied science. Solid-liquid reaction-induced phase transformations can be hard to characterize, and the transformation mechanisms are often not fully understood. Here, we report reversible phase transformations between a metal (Pb) nanocrystal and a viscous liquid-like phase unveiled by in situ liquid cell transmission electron microscopy. The reversible phase transformations are obtained by modulating the electron current density (between 1000 and 3000 electrons Å-2 s-1). The metal-organic viscous liquid-like phase exhibits short-range ordering with a preferred Pb-Pb distance of 0.5 nm. Assisted by density functional theory and molecular dynamics calculations, we show that the viscous liquid-like phase results from the reactions of Pb with the CH3O fragments from the triethylene glycol solution under electron beam irradiation. Such reversible phase transformations may find broad implementations.

Active Learning of Ligands That Enhance Perovskite Nanocrystal Luminescence.

Ligands play a critical role in the optical properties and chemical stability of colloidal nanocrystals (NCs), but identifying ligands that can enhance NC properties is daunting, given the high dimensionality of chemical space. Here, we use machine learning (ML) and robotic screening to accelerate the discovery of ligands that enhance the photoluminescence quantum yield (PLQY) of CsPbBr3 perovskite NCs. We developed a ML model designed to predict the relative PL enhancement of perovskite NCs when coordinated with a ligand selected from a pool of 29,904 candidate molecules. Ligand candidates were selected using an active learning (AL) approach that accounted for uncertainty quantified by twin regressors. After eight experimental iterations of batch AL (corresponding to 21 initial and 72 model-recommended ligands), the uncertainty of the model decreased, demonstrating an increased confidence in the model predictions. Feature importance and counterfactual analyses of model predictions illustrate the potential use of ligand field strength in designing PL-enhancing ligands. Our versatile AL framework can be readily adapted to screen the effect of ligands on a wide range of colloidal nanomaterials.

Ligand-Assisted Direct Lithography of Upconverting and Avalanching Nanoparticles for Nonlinear Photonics.

Upconverting nanoparticles (UCNPs) exhibit unique nonlinear optical properties that can be harnessed in microscopy, sensing, and photonics. However, forming high-resolution nano- and micropatterns of UCNPs with large packing fractions is still challenging. Additionally, there is limited understanding of how nanoparticle patterning chemistries are affected by the particle size. Here, we explore direct patterning chemistries for 6-18 nm Tm3+-, Yb3+/Tm3+-, and Yb3+/Er3+-based UCNPs using ligands that form either new ionic linkages or covalent bonds between UCNPs under ultraviolet (UV), electron-beam (e-beam), and near-infrared (NIR) exposure. We study the effect of UCNP size on these patterning approaches and find that 6 nm UCNPs can be patterned with compact ionic-based ligands. In contrast, patterning larger UCNPs requires long-chain, cross-linkable ligands that provide sufficient interparticle spacing to prevent irreversible aggregation upon film casting. Compared to approaches that use a cross-linkable liquid monomer, our patterning method limits the cross-linking reaction to the ligands bound on UCNPs deposited as a thin film. This highly localized photo-/electron-initiated chemistry enables the fabrication of densely packed UCNP patterns with high resolutions (∼1 µm with UV and NIR exposure; <100 nm with e-beam). Our upconversion NIR lithography approach demonstrates the potential to use inexpensive continuous-wave lasers for high-resolution 2D and 3D lithography of colloidal materials. The deposited UCNP patterns retain their upconverting, avalanching, and photoswitching behaviors, which can be exploited in patterned optical devices for next-generation UCNP applications.

Accelerating the Design of Multishell Upconverting Nanoparticles through Bayesian Optimization.

The photon upconverting properties of lanthanide-doped nanoparticles drive their applications in imaging, optoelectronics, and additive manufacturing. To maximize their brightness, these upconverting nanoparticles (UCNPs) are often synthesized as core/shell heterostructures. However, the large numbers of compositional and structural parameters in multishell heterostructures make optimizing optical properties challenging. Here, we demonstrate the use of Bayesian optimization (BO) to learn the structure and design rules for multishell UCNPs with bright ultraviolet and violet emission. We leverage an automated workflow that iteratively recommends candidate UCNP structures and then simulates their emission spectra using kinetic Monte Carlo. Yb3+/Er3+- and Yb3+/Er3+/Tm3+-codoped UCNP nanostructures optimized with this BO workflow achieve 10- and 110-fold brighter emission within 22 and 40 iterations, respectively. This workflow can be expanded to structures with higher compositional and structural complexity, accelerating the discovery of novel UCNPs while domain-specific knowledge is being developed.

Scientific Machine Learning of 2D Perovskite Nanosheet Formation.

We apply a scientific machine learning (ML) framework to aid the prediction and understanding of nanomaterial formation processes via a joint spectral-kinetic model. We apply this framework to study the nucleation and growth of two-dimensional (2D) perovskite nanosheets. Colloidal nanomaterials have size-dependent optical properties and can be observed in situ, all of which make them a good model for understanding the complex processes of nucleation, growth, and phase transformation of 2D perovskites. Our results demonstrate that this model nanomaterial can form through two processes at the nanoscale: either via a layer-by-layer chemical exfoliation process from lead bromide nanocrystals or via direct nucleation from precursors. We utilize a phenomenological kinetic analysis to study the exfoliation process and scientific machine learning to study the direct nucleation and growth and discuss the circumstances under which it is more appropriate to use phenomenological or more complex machine learning models. Data for both analysis techniques are collected through in situ spectroscopy in a stopped flow chamber, incorporating over 500,000 spectra taken under more than 100 different conditions. More broadly, our research shows that the ability to utilize and integrate traditional kinetics and machine learning methods will greatly assist in the understanding of complex chemical systems.

A Generalized Approach to Photon Avalanche Upconversion in Luminescent Nanocrystals.

Photon avalanching nanoparticles (ANPs) exhibit extremely nonlinear upconverted emission valuable for subdiffraction imaging, nanoscale sensing, and optical computing. Avalanching has been demonstrated with Tm3+-, Pr3+-, or Nd3+-doped nanocrystals, but their emission is limited to a few wavelengths and materials. Here, we utilize Gd3+-assisted energy migration to tune the emission wavelengths of Tm3+-sensitized ANPs and generate highly nonlinear emission from Eu3+, Tb3+, Ho3+, and Er3+ ions. The upconversion intensities of these spectrally discrete ANPs scale with nonlinearity factor s = 10-17 under 1064 nm excitation at power densities as low as 7 kW cm-2. This strategy for imprinting avalanche behavior on remote emitters can be extended to fluorophores adjacent to ANPs, as we demonstrate with CdS/CdSe/CdS core/shell/shell quantum dots. ANPs with rationally designed energy transfer networks provide the means to transform conventional linear emitters into a highly nonlinear ones, expanding the use of photon avalanching in biological, chemical, and photonic applications.

Triplet-Induced Singlet Oxygen Photobleaches Near-Infrared Dye-Sensitized Upconversion Nanosystems.

The rapid photobleaching of near-infrared (NIR) dye-sensitized upconversion nanosystems is one of the crucial problems that has blocked their technological applications. Uncovering the photophysical and photochemical pathways of NIR dyes would help to elucidate the photobleaching mechanism and thereby improve the photostability of the system. Here we investigate the triplet dynamics of NIR dyes and their interaction with triplet oxygen in the typically investigated IR806-sensitized upconversion nanoparticle (UCNP) nanosystem. Low-temperature fluorescence at 77 K provides direct proof of the generation of singlet oxygen (1O2) under 808 nm laser irradiation. Mass spectrometry indicates that all three double bonds in the structure of IR806 can be broken in the photochemical process. Coupling IR806 to the surface of UCNPs can accelerate its triplet dynamics, thus producing more 1O2 to photocleave IR806. Importantly, we find that the addition of ß-carotene can scavenge the generated 1O2, thereby providing a simple method to effectively inhibit photobleaching.

Indefinite and bidirectional near-infrared nanocrystal photoswitching.

Materials whose luminescence can be switched by optical stimulation drive technologies ranging from superresolution imaging1-4, nanophotonics5, and optical data storage6,7, to targeted pharmacology, optogenetics, and chemical reactivity8. These photoswitchable probes, including organic fluorophores and proteins, can be prone to photodegradation and often operate in the ultraviolet or visible spectral regions. Colloidal inorganic nanoparticles6,9 can offer improved stability, but the ability to switch emission bidirectionally, particularly with near-infrared (NIR) light, has not, to our knowledge, been reported in such systems. Here, we present two-way, NIR photoswitching of avalanching nanoparticles (ANPs), showing full optical control of upconverted emission using phototriggers in the NIR-I and NIR-II spectral regions useful for subsurface imaging. Employing single-step photodarkening10-13 and photobrightening12,14-16, we demonstrate indefinite photoswitching of individual nanoparticles (more than 1,000 cycles over 7 h) in ambient or aqueous conditions without measurable photodegradation. Critical steps of the photoswitching mechanism are elucidated by modelling and by measuring the photon avalanche properties of single ANPs in both bright and dark states. Unlimited, reversible photoswitching of ANPs enables indefinitely rewritable two-dimensional and three-dimensional multilevel optical patterning of ANPs, as well as optical nanoscopy with sub-Å localization superresolution that allows us to distinguish individual ANPs within tightly packed clusters.

Tuning Phonon Energies in Lanthanide-doped Potassium Lead Halide Nanocrystals for Enhanced Nonlinearity and Upconversion.

Optical applications of lanthanide-doped nanoparticles require materials with low phonon energies to minimize nonradiative relaxation and promote nonlinear processes like upconversion. Heavy halide hosts offer low phonon energies but are challenging to synthesize as nanocrystals. Here, we demonstrate the size-controlled synthesis of low-phonon-energy KPb2 X5 (X=Cl, Br) nanoparticles and the ability to tune nanocrystal phonon energies as low as 128 cm-1 . KPb2 Cl5 nanoparticles are moisture resistant and can be efficiently doped with lighter lanthanides. The low phonon energies of KPb2 X5 nanoparticles promote upconversion luminescence from higher lanthanide excited states and enable highly nonlinear, avalanche-like emission from KPb2 Cl5 : Nd3+ nanoparticles. The realization of nanoparticles with tunable, ultra-low phonon energies facilitates the discovery of nanomaterials with phonon-dependent properties, precisely engineered for applications in nanoscale imaging, sensing, luminescence thermometry and energy conversion.

Inverse size-dependent Stokes shift in strongly quantum confined CsPbBr3 perovskite nanoplates.

Colloidal semiconductor nanocrystals (NCs) are used as bright chromatic fluorophores for energy-efficient displays. We focus here on the size-dependent Stokes shift for CsPbBr3 nanocrystals. The Stokes shift, i.e., the difference between the wavelengths of absorption and emission maxima, is crucial for display application, as it controls the degree to which light is reabsorbed by the emitting material reducing the energetic efficiency. One major impediment to the industrial adoption of NCs is that slight deviations in manufacturing conditions may result in a wide dispersion of the product's properties. A data-driven analysis of over 2000 reactions comparing two data sets, one produced via standard colloidal synthesis and the other via high-throughput automated synthesis is discussed. We show that differences in the reaction conditions of colloidal CsPbBr3 nanocrystals yield nanocrystals with opposite Stokes shift size-dependent trends. These match the morphologies of two-dimensional nanoplatelets (NPLs) and nanocrystal cubes. The Stokes shift size dependence trend of NPLs and nanocubes is non-monotonic indicating different physics is at play for the two nanocrystal morphologies. For nanocrystals with cubic shape, with the increase of edge length, there is a significant decrease in Stokes shift values. However, for NPLs with the increase of thickness (1-4 ML), Stokes shift values will increase. The study emphasizes the transition from a spectroscopic point of view and relates the two Stokes shift trends to 2D and 0D exciton dimensionalities for the two morphologies. Our findings highlight the importance of CsPbBr3 nanocrystal morphology for Stokes shift prediction.

Dynamics of Polymer Nanocapsule Buckling and Collapse Revealed by In Situ Liquid-Phase TEM.

Nanocapsules are hollow nanoscale shells that have applications in drug delivery, batteries, self-healing materials, and as model systems for naturally occurring shell geometries. In many applications, nanocapsules are designed to release their cargo as they buckle and collapse, but the details of this transient buckling process have not been directly observed. Here, we use in situ liquid-phase transmission electron microscopy to record the electron-irradiation-induced buckling in spherical 60-187 nm polymer capsules with ∼3.5 nm walls. We observe in real time the release of aqueous cargo from these nanocapsules and their buckling into morphologies with single or multiple indentations. The in situ buckling of nanoscale capsules is compared to ex situ measurements of collapsed and micrometer-sized capsules and to Monte Carlo (MC) simulations. The shape and dynamics of the collapsing nanocapsules are consistent with MC simulations, which reveal that the excessive wrinkling of nanocapsules with ultrathin walls results from their large Föppl-von Kármán numbers around 105. Our experiments suggest design rules for nanocapsules with the desired buckling response based on parameters such as capsule radius, wall thickness, and collapse rate.

Active meta-learning for predicting and selecting perovskite crystallization experiments.

Autonomous experimentation systems use algorithms and data from prior experiments to select and perform new experiments in order to meet a specified objective. In most experimental chemistry situations, there is a limited set of prior historical data available, and acquiring new data may be expensive and time consuming, which places constraints on machine learning methods. Active learning methods prioritize new experiment selection by using machine learning model uncertainty and predicted outcomes. Meta-learning methods attempt to construct models that can learn quickly with a limited set of data for a new task. In this paper, we applied the model-agnostic meta-learning (MAML) model and the Probabilistic LATent model for Incorporating Priors and Uncertainty in few-Shot learning (PLATIPUS) approach, which extends MAML to active learning, to the problem of halide perovskite growth by inverse temperature crystallization. Using a dataset of 1870 reactions conducted using 19 different organoammonium lead iodide systems, we determined the optimal strategies for incorporating historical data into active and meta-learning models to predict reaction compositions that result in crystals. We then evaluated the best three algorithms (PLATIPUS and active-learning k-nearest neighbor and decision tree algorithms) with four new chemical systems in experimental laboratory tests. With a fixed budget of 20 experiments, PLATIPUS makes superior predictions of reaction outcomes compared to other active-learning algorithms and a random baseline.

Improving Data and Prediction Quality of High-Throughput Perovskite Synthesis with Model Fusion.

Combinatorial fusion analysis (CFA) is an approach for combining multiple scoring systems using the rank-score characteristic function and cognitive diversity measure. One example is to combine diverse machine learning models to achieve better prediction quality. In this work, we apply CFA to the synthesis of metal halide perovskites containing organic ammonium cations via inverse temperature crystallization. Using a data set generated by high-throughput experimentation, four individual models (support vector machines, random forests, weighted logistic classifier, and gradient boosted trees) were developed. We characterize each of these scoring systems and explore 66 possible combinations of the models. When measured by the precision on predicting crystal formation, the majority of the combination models improves the individual model results. The best combination models outperform the best individual models by 3.9 percentage points in precision. In addition to improving prediction quality, we demonstrate how the fusion models can be used to identify mislabeled input data and address issues of data quality. In particular, we identify example cases where all single models and all fusion models do not give the correct prediction. Experimental replication of these syntheses reveals that these compositions are sensitive to modest temperature variations across the different locations of the heating element that can hinder or enhance the crystallization process. In summary, we demonstrate that model fusion using CFA can not only identify a previously unconsidered influence on reaction outcome but also be used as a form of quality control for high-throughput experimentation.

Performance of Spherical Quantum Well Down Converters in Solid State Lighting.

We report the color conversion performance of amber and red emitting quantum dots (QDs) on InGaN solid-state lighting (SSL) light emitting diode (LED) packages. Spherical quantum well (SQW) architectures (CdS/CdSe1-xSx/CdS) were prepared using a library of thio- and selenourea synthesis reagents and high throughput synthesis robotics. CdS/CdSe1-xSx QDs with narrow luminescence bands were coated with thick CdS shells (thickness = 1.6-7.5 nm) to achieve photoluminescence quantum yields (PLQY) up to 88% at amber and red emission wavelengths (λmax = 600-642 nm, FWHM < 45 nm). The photoluminescence from SQWs encapsulated in silicone and deposited on LED packages was monitored under accelerated aging conditions (oven temperature = 85 °C, relative humidity = 5-85%, blue optical power density = 3-45 W/cm2) by monitoring the red photon output over several hundred hours of continuous operation. The growth of a ZnS shell on the SQW surface increases the stability under long-term operation but also reduces the PLQY, especially of SQWs with thick CdS shells. The results illustrate that the outer ZnS shell layer is key to optimizing the PLQY and the long-term stability of QDs during operation on SSL packages.

Giant nonlinear optical responses from photon-avalanching nanoparticles.

Avalanche phenomena use steeply nonlinear dynamics to generate disproportionately large responses from small perturbations, and are found in a multitude of events and materials1. Photon avalanching enables technologies such as optical phase-conjugate imaging2, infrared quantum counting3 and efficient upconverted lasing4-6. However, the photon-avalanching mechanism underlying these optical applications has been observed only in bulk materials and aggregates6,7, limiting its utility and impact. Here we report the realization of photon avalanching at room temperature in single nanostructures-small, Tm3+-doped upconverting nanocrystals-and demonstrate their use in super-resolution imaging in near-infrared spectral windows of maximal biological transparency. Avalanching nanoparticles (ANPs) can be pumped by continuous-wave lasers, and exhibit all of the defining features of photon avalanching, including clear excitation-power thresholds, exceptionally long rise time at threshold, and a dominant excited-state absorption that is more than 10,000 times larger than ground-state absorption. Beyond the avalanching threshold, ANP emission scales nonlinearly with the 26th power of the pump intensity, owing to induced positive optical feedback in each nanocrystal. This enables the experimental realization of photon-avalanche single-beam super-resolution imaging7 with sub-70-nanometre spatial resolution, achieved by using only simple scanning confocal microscopy and without any computational analysis. Pairing their steep nonlinearity with existing super-resolution techniques and computational methods8-10, ANPs enable imaging with higher resolution and at excitation intensities about 100 times lower than other probes. The low photon-avalanching threshold and excellent photostability of ANPs also suggest their utility in a diverse array of applications, including sub-wavelength imaging7,11,12 and optical and environmental sensing13-15.

Hybrid nanocapsules for in situ TEM imaging of gas evolution reactions in confined liquids.

Liquid cell transmission electron microscopy (TEM) enables the direct observation of dynamic physical and chemical processes in liquids at the nanoscale. Quantitative investigations into reactions with fast kinetics and/or multiple reagents will benefit from further advances in liquid cell design that facilitate rapid in situ mixing and precise control over reagent volumes and concentrations. This work reports the development of inorganic-organic nanocapsules for high-resolution TEM imaging of nanoscale reactions in liquids with well-defined zeptoliter volumes. These hybrid nanocapsules, with 48 nm average diameter, consist of a thin layer of gold coating a lipid vesicle. As a model reaction, the nucleation, growth, and diffusion of nanobubbles generated by the radiolysis of water is investigated inside the nanocapsules. When the nanobubbles are sufficiently small (10-25 nm diameter), they are mobile in the nanocapsules, but their movement deviates from Brownian motion, which may result from geometric confinement by the nanocapsules. Gases and fluids can be transported between two nanocapsules when they fuse, demonstrating in situ mixing without using complex microfluidic schemes. The ability to synthesize nanocapsules with controlled sizes and to monitor dynamics simultaneously inside multiple nanocapsules provides opportunities to investigate nanoscale processes such as single nanoparticle synthesis in confined volumes and biological processes such as biomineralization and membrane dynamics.

Elucidating the Weakly Reversible Cs-Pb-Br Perovskite Nanocrystal Reaction Network with High-Throughput Maps and Transformations.

Advances in automation and data analytics can aid exploration of the complex chemistry of nanoparticles. Lead halide perovskite colloidal nanocrystals provide an interesting proving ground: there are reports of many different phases and transformations, which has made it hard to form a coherent conceptual framework for their controlled formation through traditional methods. In this work, we systematically explore the portion of Cs-Pb-Br synthesis space in which many optically distinguishable species are formed using high-throughput robotic synthesis to understand their formation reactions. We deploy an automated method that allows us to determine the relative amount of absorbance that can be attributed to each species in order to create maps of the synthetic space. These in turn facilitate improved understanding of the interplay between kinetic and thermodynamic factors that underlie which combination of species are likely to be prevalent under a given set of conditions. Based on these maps, we test potential transformation routes between perovskite nanocrystals of different shapes and phases. We find that shape is determined kinetically, but many reactions between different phases show equilibrium behavior. We demonstrate a dynamic equilibrium between complexes, monolayers, and nanocrystals of lead bromide, with substantial impact on the reaction outcomes. This allows us to construct a chemical reaction network that qualitatively explains our results as well as previous reports and can serve as a guide for those seeking to prepare a particular composition and shape.

Controlled Assembly of Upconverting Nanoparticles for Low-Threshold Microlasers and Their Imaging in Scattering Media.

Micron-sized lasers fabricated from upconverting nanoparticles (UCNP) coupled to whispering gallery mode (WGM) microresonators can exhibit continuous-wave anti-Stokes lasing useful for tracking cells, environmental sensing, and coherent stimulation of biological activity. The integration of these microlasers into organisms and microelectronics requires even smaller diameters, however, which raises threshold pump powers beyond practical limits for biological applications. To meet the need for low lasing thresholds and high fidelity fabrication methods, we use correlative optical and electron microscopy to uncover the nanoparticle assembly process and structural factors that determine efficient upconverted lasing. We show that 5 µm microspheres with controlled submonolayer UCNP coatings exhibit, on average, 25-fold lower laser thresholds (1.7 ± 0.7 kW/cm2) compared to the mean values of the lowest threshold UCNP lasers, and variability is reduced 30-fold. WGMs are observed in the upconversion spectra for TiO2-coated microspheres as small as 3 µm, a size at which optical losses had previously prevented such observations. Finally, we demonstrate that the WGM signatures of these upconverting microlasers can be imaged and distinguished through tissue-mimicking phantoms. These advances will enable the fabrication of more efficient upconverting lasers for imaging, sensing, and actuation in optically complex environments.

Enhancing FRET biosensing beyond 10 nm with photon avalanche nanoparticles.

Förster Resonance Energy Transfer (FRET) between donor (D) and acceptor (A) molecules is a phenomenon commonly exploited to study or visualize biological interactions at the molecular level. However, commonly used organic D and A molecules often suffer from photobleaching and spectral bleed-through, and their spectral properties hinder quantitative analysis. Lanthanide-doped upconverting nanoparticles (UCNPs) as alternative D species offer significant improvements in terms of photostability, spectral purity and background-free luminescence detection, but they bring new challenges related to multiple donor ions existing in a single large size UCNP and the need for nanoparticle biofunctionalization. Considering the relatively short Förster distance (typically below 5-7 nm), it becomes a non-trivial task to assure sufficiently strong D-A interaction, which translates directly to the sensitivity of such bio-sensors. In this work we propose a solution to these issues, which employs the photon avalanche (PA) phenomenon in lanthanide-doped materials. Using theoretical modelling, we predict that these PA systems would be highly susceptible to the presence of A and that the estimated sensitivity range extends to distances 2 to 4 times longer (i.e. 10-25 nm) than those typically found in conventional FRET systems. This promises high sensitivity, low background and spectral or temporal biosensing, and provides the basis for a radically novel approach to combine luminescence imaging and self-normalized bio-molecular interaction sensing.

Ultralow-threshold, continuous-wave upconverting lasing from subwavelength plasmons.

Miniaturized lasers are an emerging platform for generating coherent light for quantum photonics, in vivo cellular imaging, solid-state lighting and fast three-dimensional sensing in smartphones1-3. Continuous-wave lasing at room temperature is critical for integration with opto-electronic devices and optimal modulation of optical interactions4,5. Plasmonic nanocavities integrated with gain can generate coherent light at subwavelength scales6-9, beyond the diffraction limit that constrains mode volumes in dielectric cavities such as semiconducting nanowires10,11. However, insufficient gain with respect to losses and thermal instabilities in nanocavities has limited all nanoscale lasers to pulsed pump sources and/or low-temperature operation6-9,12-15. Here, we show continuous-wave upconverting lasing at room temperature with record-low thresholds and high photostability from subwavelength plasmons. We achieve selective, single-mode lasing from Yb3+/Er3+-co-doped upconverting nanoparticles conformally coated on Ag nanopillar arrays that support a single, sharp lattice plasmon cavity mode and greater than wavelength λ/20 field confinement in the vertical dimension. The intense electromagnetic near-fields localized in the vicinity of the nanopillars result in a threshold of 70 W cm-2, orders of magnitude lower than other small lasers. Our plasmon-nanoarray upconverting lasers provide directional, ultra-stable output at visible frequencies under near-infrared pumping, even after six hours of constant operation, which offers prospects in previously unrealizable applications of coherent nanoscale light.