Scintillators convert ionizing radiation into visible photons, enabling applications from cosmic ray detection to medical imaging. Two independent strategies for improving scintillator performance via nanoscale patterning have recently been demonstrated: engineering material properties to enhance absorption of ionizing radiation and integrating nanophotonic structures to enhance the spontaneous emission rate ("nanophotonic scintillators"). Here, we propose a nanophotonic scintillator that simultaneously enhances both the initial energy conversion and the spontaneous emission rate, by periodically stacking a fast-emitting scintillator and a visible-light-transparent material with strong X-ray attenuation ("stopping layer") to form a one-dimensional (1D) photonic crystal (PhC) scintillator. Photoelectric absorption in the stopping layer increases the number of photoelectrons that deposit energy in neighboring scintillator layers and contribute to scintillation. At the same time, the spontaneous emission rate is enhanced by the nanophotonic structuring itself. We design a 1D PhC comprising an organic scintillator and indium tin oxide (ITO) as the stopping layer and numerically simulate the enhancement in scintillation yield and decay rate. The total detected light output is enhanced by up to a factor of 700 compared to a bulk organic scintillator of equal thickness. We further investigate a 1D PhC structure integrating inorganic and organic scintillators for time-of-flight positron emission tomography (TOF-PET): replacing the non-scintillating stopping layer with an inorganic scintillator further increases the light yield, and the coincidence time resolution (CTR) is enhanced up to 3.5 times compared to a bulk inorganic scintillator of equal thickness. Our work presents a unified approach to improve key scintillation parameters within a single nanophotonic structure.

Hanbury Brown and Twiss interferometry was a milestone experiment that transformed our understanding of the nature of light. Originally demonstrated in 1956 to measure the radii of stars through photon-correlation detection, it later became a cornerstone of modern quantum optics. Here we connect Hanbury Brown and Twiss interferometry to the physics of scintillation, the process of spontaneous light emission upon excitation by high-energy particles, such as X-rays. By revealing the underlying photon bunching in the scintillation process, we use the photon correlations $${g}^{\left(2\right)}\left(\tau \right)$$ to quantify the intrinsic light emission properties of the scintillator, specifically the emission time and the number of optical photons emitted per X-ray photon. This approach provides a characterization method that we benchmark on a wide gamut of scintillators, including several rare-earth-doped (and undoped) oxide single-crystal scintillators and perovskite nanocrystals, thereby showing the dependence of their properties on temperature and X-ray flux. Our method is particularly important for nano- and microscale scintillators, whose properties are challenging to quantify by conventional means. We extract the scintillation properties even in quantum-dot superlattices of only a few hundreds of nanometres and observed strong photon bunching ( $${g}^{\left(2\right)}\left(0\right) > 50$$ ). Our research paves the way for the broader use of methods from quantum optics for studying materials with complex optical properties in extreme regions of the electromagnetic spectrum. Photon bunching is observed in the scintillation process upon excitation by X-rays. The photon intensity correlation function is measured by using Hanbury Brown and Twiss interferometry to extract the light yield and emission lifetime in scintillator materials.

Despite advances in near-field microscopy, buried fields in complex 3D nanostructures remain challenging to resolve. We demonstrate the key steps toward attosecond electron tomography of optical near-fields using high-throughput reconstruction of massive phase-resolved datasets.

Scintillators are materials converting high-energy radiation into optical light, essential in a range of technologies such as medical imaging systems and security scanners. Scintillator development and optimization have remained limited by the complexity of their underlying physics, involving stochastic cascades of electron-electron, electron-phonon, and electron-photon interactions. Such processes are typically modeled by non-differentiable Monte Carlo simulations, limiting the applicability of machine learning for scintillator development. Here we present a physics-informed neural network that learns the scintillation cascade process from the incident high-energy particle to photon emission, substantially accelerating scintillator design and optimization. Combining this neural network with photonic simulations enables end-to-end differentiable optimization of the scintillator geometry. This allows us to optimize for arbitrary figures of merit, such as specific target emission patterns.. We demonstrate the concept and characterize it relative to previous approaches by inverse design of nanophotonic scintillators for X-ray imaging.

New techniques for imaging electromagnetic near-fields in nanostructures drive advancements in nanotechnology, optoelectronics, materials science, and biochemistry. Most existing techniques probe near-fields along surfaces, lacking the ability to extract near-fields confined within the structure. Notable exceptions use free electrons to traverse through nanostructures, integrating the field along their trajectories, extracting 2D near-field projections rather than the complete field. Here, drawing inspiration from computed tomography (CT), we present a tomography concept providing full 3D reconstruction of vectorial time-harmonic near-fields. We develop a Radon-like algorithm incorporating the electron wave-nature and the time dependency of its interaction with vector fields. To show the prospects of electron near-field tomography, we propose and analyze its ability to resolve the sub-wavelength zigzag profile of highly confined hyperbolic polaritons and to reconstruct 3D phase singularities in a chiral near-field, raising exciting goals for next-generation experiments in ultrafast transmission electron microscopes.

Fast-emitting scintillators are essential for advanced diagnostic techniques, yet many suffer from low radiation attenuation. This trade-off is particularly pronounced in polymer scintillators, which, despite their fast emission, exhibit low density and low atomic numbers, limiting the radiation attenuation factor, resulting in low detection efficiency. Here, we overcome this limitation by creating a heterostructure scintillator of alternating nanometric layers, combining fast light-emitting polymer scintillator layers and transparent stopping layers with a high radiation attenuation factor. The nanolayer thicknesses are tuned to optimize the penetration depth of recoil electrons in active emissive layers, maximizing the conversion of X-rays to visible light. This design increases light output by up to 1.5 times and enhances imaging resolution by a factor of 2 compared to homogeneous polymer scintillators due to the ability to use thinner samples. These results demonstrate the potential of heterostructure scintillators as next-generation detector materials, overcoming the limitations of homogeneous scintillators.

This study focuses on advancing metascintillators to break the 100 ps barrier and approach the 10 ps target. We exploit nanophotonic features, specifically the Purcell effect, to shape and enhance the scintillation properties of the first-generation metascintillator. We demonstrate that a faster emission is achievable along with a more efficient conversion efficiency. This results in a coincidence time resolution improved by a factor of 1.6, crucial for TOF-PET applications.

Hanbury Brown and Twiss (HBT) interferometry is a milestone experiment that transformed our understanding of the nature of light. The concept was demonstrated in 1956 to measure the radii of stars through photon coincidence detection. This form of coincidence detection later became a cornerstone of modern quantum optics. Here we connect HBT interferometry to the physics of scintillation, the process of spontaneous light emission upon excitation by high-energy particles, such as x-rays. Our work reveals intrinsic photon bunching in the scintillation process, which we utilize to elucidate its underlying light emission mechanisms. Specifically, g^((2) ) (τ) enables the quantitative extraction of scintillation lifetime and light yield, showing their dependence on temperature and X-ray flux as well. This approach provides a characterization method that we benchmark on a wide gamut of scintillators, including rare-earth-doped garnets and perovskite nanocrystals. Our method is particularly important for nano- and micro-scale scintillators, whose properties are challenging to quantify by conventional means: We extract the scintillation properties in perovskite nanocrystals of only a few hundreds of nanometers, observing strong photon bunching (g^((2) ) (0)>50). Our research paves the way for broader use of photon-coincidence measurement and methods from quantum optics in studying materials with complex optical properties in extremes regions of the electromagnetic spectrum.

We develop a framework modeling nanoscale their light yield quantitatively and comparing with new fabricated multilayer polymer-scintillators. This combined theory-experiment approach unveils the prospects of controlling secondary-electrons for future enhanced scintillators.

We propose a novel approach for extracting the 3D vectorial near-field confined in nanostructures. Our Radon-like algorithm incorporates the nonlinearity of free-electron interactions and can be realized using ultrafast transmission electron microscopes.

We measure the second-order coherence function $g^{(2)}$ of scintillators and show how this measurement enables extracting important scintillator properties: lifetime, scintillation yield, and energy resolution, all extracted using a simple X-ray tube.

Scintillation materials convert high-energy radiation to optical light through a complex multistage process. The last stage of the process is spontaneous light emission, which usually governs and limits the scintillator emission rate and light yield. For decades, scintillator research focused on developing faster-emitting materials or external photonic coatings for improving light yields. Here, we experimentally demonstrate a fundamentally different approach: enhancing the scintillation rate and yield via the Purcell effect, utilizing optical environment engineering to boost spontaneous emission. This enhancement is universally applicable to any scintillating material and dopant when the material’s nanoscale geometry is engineered. We design a thin multilayer nanophotonic scintillator, demonstrating Purcell-enhanced scintillation with 50% enhancement in emission rate and 80% enhancement in light yield. The emission is robust to fabrication disorder, further highlighting its potential for x-ray applications. Our results show prospects for bridging nanophotonics and scintillator science toward reduced radiation dosage and increased resolution for high-energy particle detection.

Converting ionizing radiation into visible light is essential in a wide range of fundamental and industrial applications, such as electromagnetic calorimeters in high-energy particle detectors, electron detectors, image intensifiers, and X-ray imaging. These different areas of technology all rely on scintillators or phosphors, i.e., materials that emit light upon bombardment by high-energy particles. In all cases, the emission is through spontaneous emission. The fundamental nature of spontaneous emission poses limitations on all these technologies, imposing an intrinsic trade-off between efficiency and resolution in all imaging applications: thicker phosphors are more efficient due to their greater stopping power, which however comes at the expense of image blurring due to light spread inside the thicker phosphors. Here, the concept of inverse-designed nanophotonic scintillators is proposed, which can overcome the trade-off between resolution and efficiency by reshaping the intrinsic spontaneous emission. To exemplify the concept, multilayer phosphor nanostructures are designed and these nanostructures are compared to state-of-the-art phosphor screens in image intensifiers, showing a threefold resolution enhancement simultaneous with a threefold efficiency enhancement. The enabling concept is applying the ubiquitous Purcell effect for the first time in a new context-for improving image resolution. Looking forward, this approach directly applies to a wide range of technologies, including X-ray imaging applications.

We demonstrate improved X-ray imaging using nanophotonic scintillators. Our scintillators rely on Purcell enhancement for brighter and faster emission. Applying this concept in radiology and nuclear medicine could enable a significant reduction of X-ray dose.

We present inverse-designed multilayer nanophotonic scintillators with optimal efficiency, directionality, and point-spread function, for applications in x-ray imaging.

Model-based neural networks provide unparalleled performance for various tasks, such as sparse coding and compressed sensing problems. Due to the strong connection with the sensing model, these networks are interpretable and inherit prior structure of the problem. In practice, model-based neural networks exhibit higher generalization capability compared to ReLU neural networks. However, this phenomenon was not addressed theoretically. Here, we leverage complexity measures including the global and local Rademacher complexities, in order to provide upper bounds on the generalization and estimation errors of model-based networks. We show that the generalization abilities of model-based networks for sparse recovery outperform those of regular ReLU networks, and derive practical design rules that allow to construct model-based networks with guaranteed high generalization. We demonstrate through a series of experiments that our theoretical insights shed light on a few behaviours experienced in practice, including the fact that ISTA and ADMM networks exhibit higher generalization abilities (especially for small number of training samples), compared to ReLU networks.

Due to its non-invasive and non-radiating nature, along with its low cost, ultrasound (US) imaging is widely used in medical applications. Typical B-mode US images have limited resolution and contrast and weak physical interpretation. Inverse US methods were developed to reconstruct the media's speed-of-sound (SoS) based on a linear acoustic model. However, the wave propagation in medical US is governed by nonlinear acoustics, which introduces more complex behaviors neglected in the linear model. In this work we propose a nonlinear waveform inversion (NWI) approach for quantitative US, that considers a nonlinear acoustics model to simultaneously reconstruct multiple material properties, including the medium's SoS, density, attenuation, and nonlinearity parameter. We thus broaden current inverse US approaches, such as the full waveform inversion (FWI) algorithm, by considering nonlinear media, and additional physical parameters. We represent the nonlinear acoustic model by means of a recurrent neural network, which enables us to apply advanced optimization algorithms borrowed from the deep learning toolbox and achieve more efficient reconstructions compared to the FWI method. We evaluate the performance of our approach on in-silico data and show that neglecting nonlinear effects may result in substantial degradation in the reconstruction, paving the way of NWI into clinical applications.

Far-UVC light can enable virus-deactivation while remaining harmless to human tissues. This triggered great efforts to create far-UVC light sources with sufficient emission power and efficiency. However, current sources, such as mercury lamps, KrCl excimer lamps, and LEDs, are made from hazardous chemicals or are limited by low efficiency. Consequently, an alternative approach for reaching the far-UVC is now receiving renewed interest: using phosphors for converting higher frequencies to the desired range of far-UVC. However, this concept is limited by the phosphor's conversion efficiency. In this paper, we propose to utilize principles of nanophotonics to create far-UVC sources. Specifically, we design a phosphor-dielectric multilayer that increases the efficiency of far-UVC light conversion and controls the intrinsic emission properties, including the angular spectrum and emission rate, by shaping the local density of photonic states. To exemplify our approach, we design an aperiodic multilayer nanostructure made of the phosphor material YPO4:Pr3+, showing an increase in light extraction by a factor of 3 compared to naïve bulk structures. Our approach can be applied to any phosphor material and any emitter geometry, opening avenues for engineering nanophotonic light sources in the far-UVC and other spectral regimes.

UV lamps are of increasing importance for safe virus disinfection. Here we propose a novel concept for UV sources based on a stack of phosphor-nanolayers, designed to maximize the output emission through photonic local-density-of-states engineering.

Adaptive beamforming can lead to substantial improvement in resolution and contrast of ultrasound images over standard delay and sum beamforming. Here we introduce the adaptive time-channel (ATC) beamformer, a data-driven approach that combines spatial and temporal information simultaneously, thus generalizing minimum variance beamformers. Moreover, we broaden the concept of apodization to the temporal dimension. Our approach reduces noises by allowing for the weights to adapt in both the temporal and spatial dimensions, thereby reducing artifacts caused by the media's inhomogeneities. We apply our method to in-silico data and show 12% resolution enhancement along with 2-fold contrast improvement, and significant noise reduction with respect to delay and sum and minimum variance beamformers.