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Abstract The advancement of microcomb sources, which serve as a versatile and powerful platform for various time–frequency measurements, have spurred widespread interest across disciplines. Their uses span coherent optical and microwave communications, atomic clocks, high-precision LiDARs, spectrometers, and frequency synthesizers. Recent breakthroughs in fabricating optical micro-cavities, along with the excitation and control of microcombs, have broadened their applications, bridging the gap between physical exploration and practical engineering systems. These developments pave the way for pioneering approaches in both classical and quantum information sciences. In this review article, we conduct a thorough examination of the latest strategies related to microcombs, their enhancement and functionalization schemes, and cutting-edge applications that cover signal generation, data transmission, quantum analysis, and information gathering, processing and computation. Additionally, we provide in-depth evaluations of microcomb-based methodologies tailored for a variety of applications. To conclude, we consider the current state of research and suggest a prospective roadmap that could transition microcomb technology from laboratory settings to broader real-world applications. 

Abstract Dissipative Kerr soliton microcombs in microresonators have enabled fundamental advances in chip-scale precision metrology, communication, spectroscopy, and parallel signal processing. Here we demonstrate polarization-diverse soliton transitions and deterministic switching dynamics of a self-stabilized microcomb in a strongly-coupled dispersion-managed microresonator driven with a single pump laser. The switching dynamics are induced by the differential thermorefractivity between coupled transverse-magnetic and transverse-electric supermodes during the forward-backward pump detunings. The achieved large soliton existence range and deterministic transitions benefit from the switching dynamics, leading to the cross-polarized soliton microcomb formation when driven in the transverse-magnetic supermode of the single resonator. Secondly, we demonstrate two distinct polarization-diverse soliton formation routes – arising from chaotic or periodically-modulated waveforms via pump power selection. Thirdly, to observe the cross-polarized supermode transition dynamics, we develop a parametric temporal magnifier with picosecond resolution, MHz frame rate and sub-ns temporal windows. We construct picosecond temporal transition portraits in 100-ns recording length of the strongly-coupled solitons, mapping the transitions from multiple soliton molecular states to singlet solitons. This study underpins polarization-diverse soliton microcombs for chip-scale ultrashort pulse generation, supporting applications in frequency and precision metrology, communications, spectroscopy and information processing. 

Reconstruction of sparsely sampled seismic data is critical for maintaining the quality of seismic images when significant numbers of shots and receivers are missing.We present a reconstruction method in the shot-receiver-time (SRT) domain based on a residual U-Net machine learning architecture, for seismic data acquired in a sparse 2-D acquisition and name it SRT2D-ResU-Net. The SRT domain retains a high level of seismic signal connectivity, which is likely the main data feature that the reconstructing algorithms rely on. We develop an “in situ training and prediction” workflow by dividing the acquisition area into two nonoverlapping subareas: a training subarea for establishing the network model using regularly sampled data and a testing subarea for reconstructing the sparsely sampled data using the trained model. To establish a reference base for analyzing the changes in data features over the study area, and quantifying the reconstructed seismic data, we devise a baseline reference using a tiny portion of the field data. The baselines are properly spaced and excluded from the training and reconstruction processes. The results on a field marine data set show that the SRT2D-ResU-Net can effectively learn the features of seismic data in the training process, and the average correlation between the reconstructed missing traces and the true answers is over 85%. 

Abstract The suppression of lithium dendrite is critical to the realization of lithium metal batteries. 3D conductive framework, among different approaches, has shown very promising results in dendrite suppression. A novel cost‐effective and versatile dip‐coating method is presented here to make 3D conductive framework. Various substrates with different geometries are coated successfully with copper, including electrically insulating glass fiber (GF) or rice paper and conducting Ni foam. In particular, the as‐prepared copper coated GF shows promising results to serve as the lithium metal substrate by the electrochemical battery tests. The method significantly broadens the candidate materials database for 3D conductive framework to include all kinds of intrinsically insulating 3D substrates.