All the optical properties of materials are derived from dielectric function. In spectral region where the dielectric permittivity approaches zero, known as epsilon-near-zero (ENZ) region, the propagating light within the material attains a very high phase velocity, and meanwhile the material exhibits strong optical nonlinearity. The interplay between the linear and nonlinear optical response in these materials thus offers unprecedented pathways for all-optical control and device design. Here the authors demonstrate ultrafast all-optical modulation based on a typical ENZ material of indium tin oxide (ITO) nanocrystals (NCs), accessed by a wet-chemistry route. In the ENZ region, the authors find that the optical response in these ITO NCs is associated with a strong nonlinear character, exhibiting sub-picosecond response time (corresponding to frequencies over 2 THz) and modulation depth up to ≈160%. This large optical nonlinearity benefits from the highly confined geometry in addition to the ENZ enhancement effect of the ITO NCs. Based on these ENZ NCs, the authors successfully demonstrate a fiber optical switch that allows switching of continuous laser wave into femtosecond laser pulses. Combined with facile processibility and tunable optical properties, these solution-processed ENZ NCs may offer a scalable and printable material solution for dynamic photonic and optoelectronic devices.

We report herein a novel fluorescent probe based on α,β-unsaturated acyl sulfonamide to detect thiols. The probe has good water solubility and reacts with thiols under aqueous conditions. It reacts selectively with cysteine but not with the other natural amino acids. The probe was subsequently applied to detect intracellular thiols.

We report two organocatalysts for CO2 hydroboration to methylborylethers, which upon hydrolysis can produce methanol. These organocatalysts feature carbon-centered reversible CO2 binding, broad borane scopes, and high catalytic activities.

Radar is the only sensor that can realize target imaging at all time and all weather, which would be a key technical enabler for future intelligent society. Poor resolution and large size are the two critical issues for radar to gain ground in civil applications. Conventional electronic radars are difficult to address due to both issues, especially in the Ka band or lower. In this work, a chip-based microwave-photonic radar based on silicon photonic platform, which can implement high-resolution imaging with very small footprint, is proposed and experimentally demonstrated. Both the wideband signal generator and the de-chirp receiver are integrated on the chip. A broadband microwave-photonic imaging radar occupying the full Ku band is experimentally established. A high-precision range measurement with a resolution of 2.7 cm and an error of less than 2.75 mm is obtained. Inverse synthetic aperture imaging of multiple targets with complex profiles is also implemented.

Remote sensing image change detection (RSICD) is a technique that explores the change of surface coverage in a certain time series by studying the difference between multiple remote sensing images (RSIs) collected over the same area. Traditional RSICD algorithms exhibit poor performance on complex change detection (CD) tasks. In recent years, deep learning (DL) techniques have achieved outstanding results in the fields of RSI segmentation and target recognition. In CD research, most of the methods treat multitemporal remote sensing data as one input and directly apply DL-based image segmentation theory on it while ignoring the spatio-temporal information in these images. In this article, a new siamese neural network is designed by combing an attention mechanism (Siamese_AUNet) with UNet to solve the problems of RSICD algorithms. SiameseNet encodes the feature extraction of RSIs by two branches in the siamese network, respectively. The weights are shared between these two branches in siamese networks. Subsequently, an attention mechanism is added to the model in order to improve its detection ability for changed objects. The models are then compared with conventional neural networks using three benchmark datasets. The results show that the Siamese_AUNet newly proposed in this article exhibits better performance than other standard methods when solving problems related to weak CD and noise suppression.

Joint estimation of the human body is suitable for many fields such as human–computer interaction, autonomous driving, video analysis and virtual reality. Although many depth-based researches have been classified and generalized in previous review or survey papers, the point cloud-based pose estimation of human body is still difficult due to the disorder and rotation invariance of the point cloud. In this review, we summarize the recent development on the point cloud-based pose estimation of the human body. The existing works are divided into three categories based on their working principles, including template-based method, feature-based method and machine learning-based method. Especially, the significant works are highlighted with a detailed introduction to analyze their characteristics and limitations. The widely used datasets in the field are summarized, and quantitative comparisons are provided for the representative methods. Moreover, this review helps further understand the pertinent applications in many frontier research directions. Finally, we conclude the challenges involved and problems to be solved in future researches.

The abundance of atmospheric CO presents both an opportunity and a challenge for synthetic chemists to transform CO into value-added products. A promising strategy involves CO reduction driven by the energy stored in chemical bonds and promoted by molecules containing nucleophilic carbon sites. This approach allows the synthesis of new C-C or C-H bonds from CO-derived carbon. The first part of this Feature article deals with uncatalyzed reductions of CO such as insertion into metal-carbon bonds and reactivity towards multidentate actor ligands and metal-free compounds. The second part covers catalytic reduction of CO in which a nucleophilic C-site is involved. This review brings together two general approaches in the chemical CO reduction field, showing how the discovery of fundamental reactivity of CO leads to synthetic applications, and proposes directions for further development.

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