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The generation of speckle patterns via random matrices, statistical definitions, or apertures may not always result in optimal outcomes. Issues such as correlation fluctuations in low ensemble numbers and diffraction in long-distance propagation can arise. Instead of improving results of specific applications, our solution is catching deep correlations of patterns with the framework, Speckle-Net, which is fundamental and universally applicable to various systems. We demonstrate this in computational ghost imaging (CGI) and structured illumination microscopy (SIM). In CGI with extremely low ensemble number, it customizes correlation width and minimizes correlation fluctuations in illuminating patterns to achieve higher-quality images. It also creates non-Rayleigh nondiffracting speckle patterns only through a phase mask modulation, which overcomes the power loss in the traditional ring-aperture method. Our approach provides new insights into the nontrivial speckle patterns and has great potential for a variety of applications including dynamic SIM, X-ray and photo-acoustic imaging, and disorder physics.

 

The nitrogen cycle needed for scaled agriculture relies on energy- and carbon-intensive processes and generates nitrate-containing wastewater. Here we focus on an alternative approach—the electrified co-electrolysis of nitrate and CO2 to synthesize urea. When this is applied to industrial wastewater or agricultural runoff, the approach has the potential to enable low-carbon-intensity urea production while simultaneously providing wastewater denitrification. We report a strategy that increases selectivity to urea using a hybrid catalyst: two classes of site independently stabilize the key intermediates needed in urea formation, *CO2NO2 and *COOHNH2, via a relay catalysis mechanism. A Faradaic efficiency of 75% at wastewater-level nitrate concentrations (1,000 ppm NO3− [N]) is achieved on Zn/Cu catalysts. The resultant catalysts show a urea production rate of 16 µmol h−1 cm−2. Life-cycle assessment indicates greenhouse gas emissions of 0.28 kg CO2e per kg urea for the electrochemical route, compared to 1.8 kg CO2e kg−1 for the present-day route. 

Artificial intelligence has recently been widely used in computational imaging. The deep neural network (DNN) improves the signal-to-noise ratio of the retrieved images, whose quality is otherwise corrupted due to the low sampling ratio or noisy environments. This work proposes a new computational imaging scheme based on the sequence transduction mechanism with the transformer network. The simulation database assists the network in achieving signal translation ability. The experimental single-pixel detector’s signal will be ‘translated’ into a 2D image in an end-to-end manner. High-quality images with no background noise can be retrieved at a sampling ratio as low as 2%. The illumination patterns can be either well-designed speckle patterns for sub-Nyquist imaging or random speckle patterns. Moreover, our method is robust to noise interference. This translation mechanism opens a new direction for DNN-assisted ghost imaging and can be used in various computational imaging scenarios.

 

Quantum entanglement has emerged as a great resource for spectroscopy and its importance in two-photon spectrum and microscopy has been demonstrated. Current studies focus on the two-photon absorption, whereas the Raman spectroscopy with quantum entanglement still remains elusive, with outstanding issues of temporal and spectral resolutions. Here we study the new capabilities provided by entangled photons in coherent Raman spectroscopy. An ultrafast frequency-resolved Raman spectroscopy with entangled photons is developed for condensed-phase molecules, to probe the electronic and vibrational coherences. Using quantum correlation between the photons, the signal shows the capability of both temporal and spectral resolutions not accessible by either classical pulses or the fields without entanglement. We develop a microscopic theory for this Raman spectroscopy, revealing the electronic coherence dynamics even at timescale of 50fs. This suggests new paradigms of optical signals and spectroscopy, with potential to push detection below standard quantum limit.

 

Optical imaging through scattering media has long been a challenge. Many approaches have been developed for focusing light or imaging objects through scattering media, but usually, they are either invasive, limited to stationary or slow-moving media, or require high-resolution cameras and complex algorithms to retrieve the images. By utilizing spatial–temporal encoded patterns (STEPs), we introduce a technique for the computation of imaging that overcomes these restrictions. With a single-pixel photodetector, we demonstrate non-invasive imaging through scattering media. This technique is insensitive to the motion of the media. Furthermore, we demonstrate that our image reconstruction algorithm is much more efficient than correlation-based algorithms for single-pixel imaging, which may allow fast imaging for applications with limited computing resources.

 

We propose and demonstrate a method for measuring the time evolution of the off-diagonal elements ρ n,n + k ( t ) of the reduced density matrix obtained from the quantum theory of the laser. The decay rates of the off-diagonal matrix element ρ n,n + k ( t ) (k = 2,3) are measured for the first time and compared with that of ρ n,n +1 ( t ), which corresponds to the linewidth of the laser. The experimental results agree with the Scully-Lamb quantum theory of the laser.