Miniaturizing spectrometers: Optical spectroscopy is a widely used characterization tool in industrial and research laboratory settings for chemical fingerprinting and analysis. High-end spectrometers are typically benchtop based with bulky optical components, moving parts, and long path lengths, and they can deliver a wealth of information with ultrahigh precision and bandwidth. There is, however, a drive toward miniaturization of spectrometers, in which concepts in nanophotonics are used to control light on much smaller scales. Yang et al. reviewed recent developments in spectrometry systems, including various fabrication approaches of nanophotonics systems and the software that computationally determines the spectra, that strive to shrink their footprint and open up applications in portable spectroscopy. Science , this issue p. eabe0722

Miniaturized computational spectrometers, which can obtain incident spectra using a combination of device spectral responses and reconstruction algorithms, are essential for on-chip and implantable applications. Highly sensitive spectral measurement using a single detector allows the footprints of such spectrometers to be scaled down while achieving spectral resolution approaching that of benchtop systems. We report a high-performance computational spectrometer based on a single van der Waals junction with an electrically tunable transport-mediated spectral response. We achieve high peak wavelength accuracy (∼0.36 nanometers), high spectral resolution (∼3 nanometers), broad operation bandwidth (from ∼405 to 845 nanometers), and proof-of-concept spectral imaging. Our approach provides a route toward ultraminiaturization and offers unprecedented performance in accuracy, resolution, and operation bandwidth for single-detector computational spectrometers.

Online in situ prediction of 3-D flame evolution has been long desired and is considered to be the Holy Grail for the combustion community. Recent advances in computational power have facilitated the development of computational fluid dynamics (CFD), which can be used to predict flame behaviours. However, the most advanced CFD techniques are still incapable of realizing online in situ prediction of practical flames due to the enormous computational costs involved. In this work, we aim to combine the state-of-the-art experimental technique (that is, time-resolved volumetric tomography) with deep learning algorithms for rapid prediction of 3-D flame evolution. Proof-of-concept experiments conducted suggest that the evolution of both a laminar diffusion flame and a typical non-premixed turbulent swirl-stabilized flame can be predicted faithfully in a time scale on the order of milliseconds, which can be further reduced by simply using a few more GPUs. We believe this is the first time that online in situ prediction of 3-D flame evolution has become feasible, and we expect this method to be extremely useful, as for most application scenarios the online in situ prediction of even the large-scale flame features are already useful for an effective flame control.