Development of Highly Enantio- and Z-Selective Grubbs Catalysts via Controllable C-H Bond Activation.

Asymmetric olefin metathesis is a powerful strategy for stereocontrolled synthesis that allows the formation of chiral elements in conjunction with carbon-carbon double bonds. Here, we report a new series of cyclometalated stereogenic-at-Ru catalysts that enable highly efficient asymmetric ring opening/cross-metathesis (AROCM) and asymmetric ring-closing metathesis (ARCM) reactions. Single enantiomers of these catalysts with either right-handed or left-handed configurations at the Ru center can be easily accessed via highly stereoselective C-H bond activation-based cyclometalation. Right-handed chiral Ru catalysts enabled the Z- and enantioselective AROCM of a wide range of norbornenes and terminal alkenes, generating densely functionalized cyclopentanes with excellent stereo- and enantioselectivities (99:1 Z/E, up to 99% ee). Left-handed chiral Ru catalysts enabled the facile ARCM of sterically unhindered, all-terminal prochiral trienes, which had not been achieved by previous Ru catalysts, providing simple cyclic ethers and amides with tertiary or quaternary carbon stereocenters with excellent enantioselectivities (up to 99% ee).

Structural Transformation in a Sulfurized Polymer Cathode to Enable Long-Life Rechargeable Lithium-Sulfur Batteries.

Sulfurized polyacrylonitrile (SPAN) represents a class of sulfur-bonded polymers, which have shown thousands of stable cycles as a cathode in lithium-sulfur batteries. However, the exact molecular structure and its electrochemical reaction mechanism remain unclear. Most significantly, SPAN shows an over 25% 1st cycle irreversible capacity loss before exhibiting perfect reversibility for subsequent cycles. Here, with a SPAN thin-film platform and an array of analytical tools, we show that the SPAN capacity loss is associated with intramolecular dehydrogenation along with the loss of sulfur. This results in an increase in the aromaticity of the structure, which is corroborated by a >100× increase in electronic conductivity. We also discovered that the conductive carbon additive in the cathode is instrumental in driving the reaction to completion. Based on the proposed mechanism, we have developed a synthesis procedure to eliminate more than 50% of the irreversible capacity loss. Our insights into the reaction mechanism provide a blueprint for the design of high-performance sulfurized polymer cathode materials.

Unifying temporal phase unwrapping framework using deep learning.

Temporal phase unwrapping (TPU) is significant for recovering an unambiguous phase of discontinuous surfaces or spatially isolated objects in fringe projection profilometry. Generally, temporal phase unwrapping algorithms can be classified into three groups: the multi-frequency (hierarchical) approach, the multi-wavelength (heterodyne) approach, and the number-theoretic approach. For all of them, extra fringe patterns of different spatial frequencies are required for retrieving the absolute phase. Due to the influence of image noise, people have to use many auxiliary patterns for high-accuracy phase unwrapping. Consequently, image noise limits the efficiency and the measurement speed greatly. Further, these three groups of TPU algorithms have their own theories and are usually applied in different ways. In this work, for the first time to our knowledge, we show that a generalized framework using deep learning can be developed to perform the TPU task for different groups of TPU algorithms. Experimental results show that benefiting from the assistance of deep learning the proposed framework can mitigate the impact of noise effectively and enhance the phase unwrapping reliability significantly without increasing the number of auxiliary patterns for different TPU approaches. We believe that the proposed method demonstrates great potential for developing powerful and reliable phase retrieval techniques.

Understanding of protomers/deprotomers by combining mass spectrometry and computation.

Multifunctional compounds may form different prototropic isomers under different conditions, which are known as protomers/deprotomers. In biological systems, these protomer/deprotomer isomers affect the interaction modes and conformational landscape between compounds and enzymes and thus present different biological activities. Study on protomers/deprotomers is essentially the study on the acidity/basicity of each intramolecular functional group and its effect on molecular structure. In recent years, the combination of mass spectrometry (MS) and computational chemistry has been proven to be a powerful and effective means to study prototropic isomers. MS-based technologies are developed to discriminate and characterize protomers/deprotomers to provide structural information and monitor transformations, showing great superiority than other experimental methods. Computational chemistry is used to predict the thermodynamic stability of protomers/deprotomers, provide the simulated MS/MS spectra, infrared spectra, and calculate collision cross-section values. By comparing the theoretical data with the corresponding experimental results, the researchers can not only determine the protomer/deprotomer structure, but also investigate the structure-activity relationship in a given system. This review covers various MS methods and theoretical calculations and their devotion to isomer discrimination, structure identification, conformational transformation, and phase transition investigation of protomers/deprotomers.

Efficient photoactivatable Dre recombinase for cell type-specific spatiotemporal control of genome engineering in the mouse.

Precise genetic engineering in specific cell types within an intact organism is intriguing yet challenging, especially in a spatiotemporal manner without the interference caused by chemical inducers. Here we engineered a photoactivatable Dre recombinase based on the identification of an optimal split site and demonstrated that it efficiently regulated transgene expression in mouse tissues spatiotemporally upon blue light illumination. Moreover, through a double-floxed inverted open reading frame strategy, we developed a Cre-activated light-inducible Dre (CALID) system. Taking advantage of well-defined cell-type-specific promoters or a well-established Cre transgenic mouse strain, we demonstrated that the CALID system was able to activate endogenous reporter expression for either bulk or sparse labeling of CaMKIIα-positive excitatory neurons and parvalbumin interneurons in the brain. This flexible and tunable system could be a powerful tool for the dissection and modulation of developmental and genetic complexity in a wide range of biological systems.

Optical Flow-Based Full-Field Quantitative Blood-Flow Velocimetry Using Temporal Direction Filtering and Peak Interpolation.

The quantitative measurement of the microvascular blood-flow velocity is critical to the early diagnosis of microvascular dysfunction, yet there are several challenges with the current quantitative flow velocity imaging techniques for the microvasculature. Optical flow analysis allows for the quantitative imaging of the blood-flow velocity with a high spatial resolution, using the variation in pixel brightness between consecutive frames to trace the motion of red blood cells. However, the traditional optical flow algorithm usually suffers from strong noise from the background tissue, and a significant underestimation of the blood-flow speed in blood vessels, due to the errors in detecting the feature points in optical images. Here, we propose a temporal direction filtering and peak interpolation optical flow method (TPIOF) to suppress the background noise, and improve the accuracy of the blood-flow velocity estimation. In vitro phantom experiments and in vivo animal experiments were performed to validate the improvements in our new method.

Composite fringe projection deep learning profilometry for single-shot absolute 3D shape measurement.

Single-shot fringe projection profilometry (FPP) is essential for retrieving the absolute depth information of the objects in high-speed dynamic scenes. High-precision 3D reconstruction using only one single pattern has become the ultimate goal in FPP. The frequency-multiplexing (FM) method is a promising strategy for realizing single-shot absolute 3D measurement by compounding multi-frequency fringe information for phase unwrapping. In order to solve the problem of serious spectrum aliasing caused by multiplexing schemes that cannot be removed by traditional spectrum analysis algorithms, we apply deep learning to frequency multiplexing composite fringe projection and propose a composite fringe projection deep learning profilometry (CDLP). By combining physical model and data-driven approaches, we demonstrate that the model generated by training an improved deep convolutional neural network can directly perform high-precision and unambiguous phase retrieval on a single-shot spatial frequency multiplexing composite fringe image. Experiments on both static and dynamic scenes demonstrate that our method can retrieve robust and unambiguous phases information while avoiding spectrum aliasing and reconstruct high-quality absolute 3D surfaces of objects only by projecting a single composite fringe image.

Polarization-decoupled cavity solitons generation in Kerr resonators with flattened near-zero dispersion.

Two frequency combs emitting from a single cavity are of great potential in the field of dual-comb spectroscopy because they are mutually coherent and therefore the common mode noise can be suppressed naturally. However, it is difficult to fully and flexibly control the repetition frequency difference in most of the all-optical schemes. In this paper, a birefringence-compensation Kerr resonator is proposed for the mutual dual-comb generation. It is shown that by offset aligning the fast and slow axis with appropriate fiber length, the total birefringence of the cavity can be equalized while the local one keeps at a high level. Theoretical investigations reveal that the polarization decoupled mutual dual-comb can be generated with nearly the same power level and arbitrary repetition frequency difference. Additionally, a numerical model of polarization-maintaining fiber (PMF) with near-zero dispersion is proposed for the proof of the concept. Based on this fiber, the coherent polarization-decoupled dual-comb with 10-dB bandwidth of 33 nm can be obtained. And the repetition frequency difference can be flexibly tuned compared to the cavity without offset alignment.

Cavity soliton in a cyclic polarization permutation fiber resonator.

Cavity solitons are shape-preserving waveforms infinitely revolving around a cavity, which have numerous applications from spectroscopy to telecommunications. Although the cavity solitons have been widely studied for their special time-frequency characteristics over the past decade, the spectral flatness will be a large limitation in some applications without any extra filtering process. In this paper, we report on the generation of a distinct cavity soliton in a cyclic polarization permutation fiber resonator. With the simultaneous excitation of two orthogonal polarization modes with equally opposite dispersion, vectorial cavity solitons possessing broader and flatter spectra can be generated. In order to verify the concept, a numerical model of the polarization-maintaining fiber is proposed and the soliton with a flattened spectrum can be formed. The results enrich the soliton dynamics in the vectorial dissipation system.

Deep Learning-Based 3D Measurements with Near-Infrared Fringe Projection.

Fringe projection profilometry (FPP) is widely applied to 3D measurements, owing to its advantages of high accuracy, non-contact, and full-field scanning. Compared with most FPP systems that project visible patterns, invisible fringe patterns in the spectra of near-infrared demonstrate fewer impacts on human eyes or on scenes where bright illumination may be avoided. However, the invisible patterns, which are generated by a near-infrared laser, are usually captured with severe speckle noise, resulting in 3D reconstructions of limited quality. To cope with this issue, we propose a deep learning-based framework that can remove the effect of the speckle noise and improve the precision of the 3D reconstruction. The framework consists of two deep neural networks where one learns to produce a clean fringe pattern and the other to obtain an accurate phase from the pattern. Compared with traditional denoising methods that depend on complex physical models, the proposed learning-based method is much faster. The experimental results show that the measurement accuracy can be increased effectively by the presented method.

Single-shot 3D shape measurement using an end-to-end stereo matching network for speckle projection profilometry.

Speckle projection profilometry (SPP), which establishes the global correspondences between stereo images by projecting only a single speckle pattern, has the advantage of single-shot 3D reconstruction. Nevertheless, SPP suffers from the low matching accuracy of traditional stereo matching algorithms, which fundamentally limits its 3D measurement accuracy. In this work, we propose a single-shot 3D shape measurement method using an end-to-end stereo matching network for SPP. To build a high-quality SPP dataset for training the network, by combining phase-shifting profilometry (PSP) and temporal phase unwrapping techniques, high-precision absolute phase maps can be obtained to generate accurate and dense disparity maps with high completeness as the ground truth by phase matching. For the architecture of the network, a multi-scale residual subnetwork is first leveraged to synchronously extract compact feature tensors with 1/4 resolution from speckle images for constructing the 4D cost volume. Considering that the cost filtering based on 3D convolution is computationally costly, a lightweight 3D U-net network is proposed to implement efficient 4D cost aggregation. In addition, because the disparity maps in the SPP dataset should have valid values only in the foreground, a simple and fast saliency detection network is integrated to avoid predicting the invalid pixels in the occlusions and background regions, thereby implicitly enhancing the matching accuracy for valid pixels. Experiment results demonstrated that the proposed method improves the matching accuracy by about 50% significantly compared with traditional stereo matching methods. Consequently, our method achieves fast and absolute 3D shape measurement with an accuracy of about 100µm through a single speckle pattern.

Real-time high dynamic range 3D measurement using fringe projection.

Fringe projection profilometry (FPP) is a widely used technique for real-time three-dimensional (3D) shape measurement. However, it tends to compromise when measuring objects that have a large variation range of surface reflectivity. In this paper, we present a FPP method that can increase the dynamic range for real-time 3D measurements. First, binary fringe patterns are projected to generate grayscale sinusoidal patterns with the defocusing technique. Each pattern is then captured twice with different exposure values in one projection period. With image fusion, surfaces under appropriate exposure are retained. To improve the real-time performance of high dynamic range (HDR) 3D shape measurements, we build a binocular fringe projection profilometry system that saves the number of patterns by geometry constraint. Further, to ensure the accuracy and robustness of HDR 3D measurements, we propose a mixed phase unwrapping method that can reduce phase unwrapping errors for dense fringe patterns. Experiment results show that the proposed method can realize accurate and real-time 3D measurement for HDR scenes at 28 frames per second.

Single-shot absolute 3D shape measurement with deep-learning-based color fringe projection profilometry.

Recovering the high-resolution three-dimensional (3D) surface of an object from a single frame image has been the ultimate goal long pursued in fringe projection profilometry (FPP). The color fringe projection method is one of the technologies with the most potential towards such a goal due to its three-channel multiplexing properties. However, the associated color imbalance, crosstalk problems, and compromised coding strategy remain major obstacles to overcome. Inspired by recent successes of deep learning for FPP, we propose a single-shot absolute 3D shape measurement with deep-learning-based color FPP. Through "learning" on extensive data sets, the properly trained neural network can "predict" the high-resolution, motion-artifact-free, crosstalk-free absolute phase directly from one single color fringe image. Compared with the traditional approach, our method allows for more accurate phase retrieval and more robust phase unwrapping. Experimental results demonstrate that the proposed approach can provide high-accuracy single-frame absolute 3D shape measurement for complicated objects.

Motion-artifact-free dynamic 3D shape measurement with hybrid Fourier-transform phase-shifting profilometry.

Fourier-transform profilometry (FTP) and phase-shifting profilometry (PSP) are two mainstream fringe projection techniques widely used for three-dimensional (3D) shape measurement. The former is well known for its single-shot nature and the latter for its higher measurement resolution and precision. However, when it comes to measuring the dynamic objects, neither approach is able to produce high-resolution, high-accuracy measurement results that are free from any depth ambiguities and motion-related artifacts. Furthermore, for scenes consisting of both static and dynamic objects, a trade-off between measurement precision and efficiency has to be made, suggesting that using a single approach can yield only suboptimal results. To this end, we propose a novel hybrid Fourier-transform phase-shifting profilometry method to integrate the advantages of both approaches. The motion vulnerability of multi-shot PSP can be overcome, or at least significantly alleviated, through the combination of single-shot FTP, while the high accuracy of PSP can also be preserved when the object is motionless. We design a phase-based, pixel-wise motion detection strategy that can accurately outline the moving object regions from their motionless counterparts. The final measurement result is obtained by fusing the determined regions where the PSP or FTP is applied correspondingly. To validate the proposed hybrid approach, we develop a real-time 3D shape measurement system for measuring multiple isolated moving objects. Experimental results demonstrate that our method achieves significantly higher precision and better robustness compared with conventional approaches where PSP or FTP is applied separately.

Calibration method for panoramic 3D shape measurement with plane mirrors.

High-speed panoramic three-dimensional (3D) shape measurement can be achieved by introducing plane mirrors into the traditional fringe projection profilometry (FPP) system because such a system simultaneously captures fringe patterns from three different perspectives (i.e., by a real camera and two virtual cameras in the plane mirrors). However, calibrating such a system is nontrivial due to the complicated setup. This work introduces a flexible new technique to calibrate such a system. We first present the mathematical representation of the plane mirror, and then mathematically prove that it only requires the camera to observe a set of feature point pairs (including real points and virtual points) to generate a solution to the reflection matrix of a plane mirror. By calibrating the virtual and real camera in the same world coordinate system, 3D point cloud data obtained from real and virtual perspectives can be automatically aligned to generate a panoramic 3D model of the object. Finally, we developed a system to verify the performance of the proposed calibration technique for panoramic 3D shape measurement.

High-speed 3D shape measurement using the optimized composite fringe patterns and stereo-assisted structured light system.

In this paper, we propose a high-speed 3D shape measurement technique based on the optimized composite fringe patterns and stereo-assisted structured light system. Stereo phase unwrapping, as a new-fashioned method for absolute phase retrieval based on the multi-view geometric constraints, can eliminate the phase ambiguities and obtain a continuous phase map without projecting any additional patterns. However, in order to ensure the stability of phase unwrapping, the period of fringe is generally around 20, which limits the accuracy of 3D measurement. To solve this problem, we develop an optimized method for designing the composite pattern, in which the speckle pattern is embedded into the conventional 4-step phase-shifting fringe patterns without compromising the fringe modulation, and thus the phase measurement accuracy. We also present a simple and effective evaluation criterion for the correlation quality of the designed speckle pattern in order to improve the matching accuracy significantly. When the embedded speckle pattern is demodulated, the periodic ambiguities in the wrapped phase can be eliminated by combining the adaptive window image correlation with geometry constraint. Finally, some mismatched regions are further corrected based on the proposed regional diffusion compensation technique (RDC). These proposed techniques constitute a complete computational framework that allows to effectively recover an accurate, unambiguous, and distortion-free 3D point cloud with only 4 projected patterns. Experimental results verify that our method can achieve high-speed, high-accuracy, robust 3D shape measurement with dense (64-period) fringe patterns at 5000 frames per second.

High-resolution real-time 360° 3D model reconstruction of a handheld object with fringe projection profilometry.

The digitization of the complete shape of real objects has essential applications in fields of intelligent manufacturing, industrial detection, and reverse modeling. In order to build the full geometric models of rigid objects, the object must be moved relative to the measurement system (or the scanner must be moved relative to the object) to obtain and integrate views of the object from all sides, which not only complicates the system configuration but makes the whole process time-consuming. In this Letter, we present a high-resolution real-time 360° three-dimensional (3D) model reconstruction method that allows one to rotate an object manually and see a continuously updated 3D model during the scanning process. A multi-view fringe projection profilometry system acquires high-precision depth information about a handheld object from different perspectives and, meanwhile, the multiple views are aligned and merged together in real time. Our system employs stereo phase unwrapping and an adaptive depth constraint that can unwrap the phase of dense fringe images robustly without increasing the number of captured patterns. We then develop an efficient coarse-to-fine registration strategy to match the 3D surface segments rapidly. Our experiments demonstrate that our method can reconstruct the high-precision complete 3D model of complex objects under arbitrary rotation without any instrument assist and expensive pre/post-processing.

High-speed real-time 3D shape measurement based on adaptive depth constraint.

Stereo phase unwrapping (SPU) has been increasingly applied to high-speed real-time fringe projection profilometry (FPP) because it can retrieve the absolute phase or matching points in a stereo FPP system without projecting or acquiring additional fringe patterns. Based on a pre-defined measurement volume, artificial maximum/minimum phase maps can be created solely using geometric constraints of the FPP system, permitting phase unwrapping on a pixel-by-pixel basis. However, when high-frequency fringes are used, the phase ambiguities will increase which makes SPU unreliable. Several auxiliary techniques have been proposed to enhance the robustness of SPU, but their flexibility still needs to be improved. In this paper, we proposed an adaptive depth constraint (ADC) approach for high-speed real-time 3D shape measurement, where the measurement depth volume for geometric constraints is adaptively updated according to the current reconstructed geometry. By utilizing the spatio-temporal correlation of moving objects under measurement, a customized and tighter depth constraint can be defined, which helps enhance the robustness of SPU over a large measurement volume. Besides, two complementary techniques, including simplified left-right consistency check and feedback mechanism based on valid area, are introduced to further increase the robustness and flexibility of the ADC. Experimental results demonstrate the success of our proposed SPU approach in recovering absolute 3D geometries of both simple and complicated objects with only three phase-shifted fringe images.

High dynamic range 3D shape measurement based on the intensity response function of a camera.

Fringe projection profilometry has been widely used in many fields for its advantages such as high speed, high accuracy, and robustness to environmental illumination and surface texture. However, it is vulnerable to high dynamic range (HDR) objects. To this end, we propose a technique that can enhance the dynamic range of the fringe projection profilometry system. According to the surface reflectivities of the measured objects, several groups of fringe patterns with optimal light intensities are generated based on the intensity response function of a camera. The HDR fringe images are acquired by fusing these fringe patterns, and a three-step phase-shifting algorithm is used to obtain the unwrapped phase from the fused images. Experimental results demonstrate that the proposed technique can accurately measure objects with an HDR of surface reflectivity variation.

Active depth estimation from defocus using a camera array.

This paper introduces an active depth estimation method from defocus using a camera array. High-frequency phase-shifted sinusoidal fringe patterns are projected onto the surface of the object, making low-texture areas of the object surface easily distinguishable. Based on the light field measurement captured by a 5×5 camera array, a synthetic aperture refocusing of the fringe images can be realized after the camera array is properly calibrated and rectified. The fringe modulations at different depths are calculated based on the computationally refocused images, which are used as depth cues to reconstruct the 3D shape of the measured object. We implemented some experiments to verify the effectiveness of the proposed method.