Ministry of Industry and Information Technology

All forms of energy follow the law of conservation of energy, by which they can be neither created nor destroyed. Light-to-heat conversion as a traditional yet constantly evolving means of converting light into thermal energy has been of enduring appeal to researchers and the public. With the continuous development of advanced nanotechnologies, a variety of photothermal nanomaterials have been endowed with excellent light harvesting and photothermal conversion capabilities for exploring fascinating and prospective applications. Herein we review the latest progresses on photothermal nanomaterials, with a focus on their underlying mechanisms as powerful light-to-heat converters. We present an extensive catalogue of nanostructured photothermal materials, including metallic/semiconductor structures, carbon materials, organic polymers, and two-dimensional materials. The proper material selection and rational structural design for improving the photothermal performance are then discussed. We also provide a representative overview of the latest techniques for probing photothermally generated heat at the nanoscale. We finally review the recent significant developments of photothermal applications and give a brief outlook on the current challenges and future directions of photothermal nanomaterials.

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.

Developing efficient catalysts for nitrogen fixation is becoming increasingly important but is still challenging due to the lack of robust design criteria for tackling the activity and selectivity problems, especially for electrochemical nitrogen reduction reaction (NRR). Herein, by means of large-scale density functional theory (DFT) computations, we reported a descriptor-based design principle to explore the large composition space of two-dimensional (2D) biatom catalysts (BACs), namely, metal dimers supported on 2D expanded phthalocyanine (M2-Pc or MM′-Pc), toward the NRR at the acid conditions. We sampled both homonuclear (M2-Pc) and heteronuclear (MM′-Pc) BACs and constructed the activity map of BACs by using N2H* adsorption energy as the activity descriptor, which reduces the number of promising catalyst candidates from over 900 to less than 100. This strategy allowed us to readily identify 3 homonuclear and 28 heteronuclear BACs, which could break the metal-based activity benchmark toward the efficient NRR. Particularly, using the free energy difference of H* and N2H* as a selectivity descriptor, we screened out five systems, including Ti2-Pc, V2-Pc, TiV-Pc, VCr-Pc, and VTa-Pc, which exhibit a strong capability of suppressing the competitive hydrogen evolution reaction (HER) with favorable limiting potential of −0.75, −0.39, −0.74, −0.85, and −0.47 V, respectively. This work not only broadens the possibility of discovering more efficient BACs toward N2 fixation but also provides a feasible strategy for rational design of NRR electrocatalysts and helps pave the way to fast screening and design of efficient BACs for the NRR and other electrochemical reactions.

Abstract Unlike the unstable black phosphorous, another two-dimensional group-VA material, antimonene, was recently predicted to exhibit good stability and remarkable physical properties. However, the synthesis of high-quality monolayer or few-layer antimonenes, sparsely reported, has greatly hindered the development of this new field. Here, we report the van der Waals epitaxy growth of few-layer antimonene monocrystalline polygons, their atomical microstructure and stability in ambient condition. The high-quality, few-layer antimonene monocrystalline polygons can be synthesized on various substrates, including flexible ones, via van der Waals epitaxy growth. Raman spectroscopy and transmission electron microscopy reveal that the obtained antimonene polygons have buckled rhombohedral atomic structure, consistent with the theoretically predicted most stable β-phase allotrope. The very high stability of antimonenes was observed after aging in air for 30 days. First-principle and molecular dynamics simulation results confirmed that compared with phosphorene, antimonene is less likely to be oxidized and possesses higher thermodynamic stability in oxygen atmosphere at room temperature. Moreover, antimonene polygons show high electrical conductivity up to 10 4 S m −1 and good optical transparency in the visible light range, promising in transparent conductive electrode applications.

600 questionnaire participants were psychologically stable. Non-anxiety and non-depression rates were 93.67% and 82.83%, respectively. There were anxiety in 6.33% and depression in 17.17%. Therefore, we should pay attention to the psychological states of the public.

Phosphorene, an emerging two-dimensional material, has received considerable attention due to its layer-controlled direct bandgap, high carrier mobility, negative Poisson's ratio and unique in-plane anisotropy. As cousins of phosphorene, 2D group-VA arsenene, antimonene and bismuthene have also garnered tremendous interest due to their intriguing structures and fascinating electronic properties. 2D group-VA family members are opening up brand-new opportunities for their multifunctional applications encompassing electronics, optoelectronics, topological spintronics, thermoelectrics, sensors, Li- or Na-batteries. In this review, we extensively explore the latest theoretical and experimental progress made in the fundamental properties, fabrications and applications of 2D group-VA materials, and offer perspectives and challenges for the future of this emerging field.

The development of classical and quantum information-processing technology calls for on-chip integrated sources of structured light. Although integrated vortex microlasers have been previously demonstrated, they remain static and possess relatively high lasing thresholds, making them unsuitable for high-speed optical communication and computing. We introduce perovskite-based vortex microlasers and demonstrate their application to ultrafast all-optical switching at room temperature. By exploiting both mode symmetry and far-field properties, we reveal that the vortex beam lasing can be switched to linearly polarized beam lasing, or vice versa, with switching times of 1 to 1.5 picoseconds and energy consumption that is orders of magnitude lower than in previously demonstrated all-optical switching. Our results provide an approach that breaks the long-standing trade-off between low energy consumption and high-speed nanophotonics, introducing vortex microlasers that are switchable at terahertz frequencies.

Bio-plastics have gained tremendous attention, due to the increasing environmental pressure on global warming and plastic pollution. Among them, poly (lactic acid) (PLA) is both bio-based and bio-degradable, which has been widely used in many disposable packaging applications. The global market for PLA demand doubles every 3–4 years, as estimated by Jem's law.Compared to traditional petroleum-based plastics, PLA is more expensive and usually has less mechanical and physical properties. The recent compounding efforts and the commercialization of D(−) lactic acid and its polymer PDLA have the potential to improve the mechanical and thermal characteristics of PLA (e.g. by forming stereocomplex PLA) for applications in high-end markets. However, the usage of PLA in some other applications is still limited.With a structure similar to PLA, poly (glycolic acid) (PGA) has promising characteristics such as good biodegradability and barrier properties, which is potentially a beneficial supplement to PLA. The modification of PLA with PGA can be achieved via co-polymerization, physical blending and multilayer lamination. PGA and its combination with PLA have been widely studied in bio-medical applications, but not been well developed at large scales due to its relatively high production cost. In this case, the development of novel production technology and the advent of government regulations are the key drivers for the global transition towards bioplastics. Recently, multiple governmental regulations have been released that restrict the use of traditional plastics and facilitate bio-degradable plastic applications. PGA can be derived from industrial waste gases using an innovative production technology, which reduces carbon emissions and its production cost. By developing the production and compounding technology, PGA can be combined with PLA to play an essential role for a sustainable and environmental friendly plastic industry, especially for single-used products requiring fast degradation at room temperature or in the nature environment. Keywords: Poly (lactic acid), Poly (glycolic acid), Modification of PLA with PGA, Plastic pollution, Global warming

The widespread application of sophisticated structural health monitoring systems in civil infrastructures produces a large volume of data. As a result, the analysis and mining of structural health monitoring data have become hot research topics in the field of civil engineering. However, the harsh environment of civil structures causes the data measured by structural health monitoring systems to be contaminated by multiple anomalies, which seriously affect the data analysis results. This is one of the main barriers to automatic real-time warning, because it is difficult to distinguish the anomalies caused by structural damage from those related to incorrect data. Existing methods for data cleansing mainly focus on noise filtering, whereas the detection of incorrect data requires expertise and is very time-consuming. Inspired by the real-world manual inspection process, this article proposes a computer vision and deep learning–based data anomaly detection method. In particular, the framework of the proposed method includes two steps: data conversion by data visualization, and the construction and training of deep neural networks for anomaly classification. This process imitates human biological vision and logical thinking. In the data visualization step, the time series signals are transformed into image vectors that are plotted piecewise in grayscale images. In the second step, a training dataset consisting of randomly selected and manually labeled image vectors is input into a deep neural network or a cluster of deep neural networks, which are trained via techniques termed stacked autoencoders and greedy layer-wise training. The trained deep neural networks can be used to detect potential anomalies in large amounts of unchecked structural health monitoring data. To illustrate the training procedure and validate the performance of the proposed method, acceleration data from the structural health monitoring system of a real long-span bridge in China are employed. The results show that the multi-pattern anomalies of the data can be automatically detected with high accuracy.

and represents a versatile strategy for healing a broad range of tissue damages caused by diabetes.

Hydrogen spillover phenomenon of metal-supported electrocatalysts can significantly impact their activity in hydrogen evolution reaction (HER). However, design of active electrocatalysts faces grand challenges due to the insufficient understandings on how to overcome this thermodynamically and kinetically adverse process. Here we theoretically profile that the interfacial charge accumulation induces by the large work function difference between metal and support (∆Φ) and sequentially strong interfacial proton adsorption construct a high energy barrier for hydrogen transfer. Theoretical simulations and control experiments rationalize that small ∆Φ induces interfacial charge dilution and relocation, thereby weakening interfacial proton adsorption and enabling efficient hydrogen spillover for HER. Experimentally, a series of Pt alloys-CoP catalysts with tailorable ∆Φ show a strong ∆Φ-dependent HER activity, in which PtIr/CoP with the smallest ∆Φ = 0.02 eV delivers the best HER performance. These findings have conclusively identified ∆Φ as the criterion in guiding the design of hydrogen spillover-based binary HER electrocatalysts.

On-site production of hydrogen peroxide (H2O2) using electrochemical methods could be more efficient than the current industrial process. However, due to the existence of scaling relations for the adsorption of reaction intermediates, there is a long established trade-off between the activity and selectivity of the catalysts, as the enhancement of catalytic activity is typically accompanied by a four-electron O2 reduction reaction (ORR), leading to the reduced selectivity for the H2O2 production. Herein, by means of density functional theory (DFT) computations, we reported the feasibility of several classes of important and representative experimentally achievable single-atom catalysts (SACs) toward two-electron ORR, paying attention to their stability, selectivity, and activity at the acidic medium. Starting from 210 two-dimensional (2D) SACs, we demonstrated that 31 SACs have the potential to break the metal-based scaling relations and simultaneously achieve high activity and selectivity toward H2O2 production and screened out 7 SACs with higher activity than the PtHg4 in acidic media. Especially, a noble metal-free SAC, namely, a single Zn atom centered phthalocyanine (Zn@Pc-N4), has a remarkable activity improvement with a small overpotential of 0.15 V. Moreover, using multivariable analysis and machine-learning techniques, we provided a comprehensive understanding of the underlying origin of the selectivity and activity of SACs and unveiled the intrinsic correlations between structure and catalytic performance. This work may pave a way to the design and discovery of more promising materials for H2O2 production.

Ultracompact sources of circularly polarized light are important for classical and quantum optical information processing. Conventional approaches for generating chiral emission are restricted to excitation power ranges and fail to provide high-quality radiation with perfect polarization conversion. We used the physics of chiral quasi-bound states in the continuum to demonstrate the efficient and controllable emission of circularly polarized light from resonant metasurfaces. Exploiting intrinsic chirality and giant field enhancement, we revealed how to simultaneously modify and control spectra, radiation patterns, and spin angular momentum of photoluminescence and lasing without any spin injection. The superior characteristics of chiral emission and lasing promise multiple applications in nanophotonics and quantum optics.

Metal–organic frameworks (MOFs) have obtained increasing attention as a kind of novel electrode material for energy storage devices.

The achievement of structural color has shown advantages in large-gamut, high-saturation, high-brightness, and high-resolution. While a large number of plasmonic/dielectric nanostructures have been developed for structural color, the previous approaches fail to match all the above criterion simultaneously. Herein we utilize the Si metasurface to demonstrate an all-in-one solution for structural color. Due to the intrinsic material loss, the conventional Si metasurfaces only have a broadband reflection and a small gamut of 78% of sRGB. Once they are combined with a refractive index matching layer, the reflection bandwidth and the background reflection are both reduced, improving the brightness and the color purity significantly. Consequently, the experimentally demonstrated gamut has been increased to around 181.8% of sRGB, 135.6% of Adobe RGB, and 97.2% of Rec.2020. Meanwhile, high refractive index of silicon preserves the distinct color in a pixel with 2 × 2 array of nanodisks, giving a diffraction-limit resolution.

Recently, color generation in resonant nanostructures have been intensively studied. Despite of their exciting progresses, the structural colors are usually generated by the plasmonic resonances of metallic nanoparticles. Due to the inherent plasmon damping, such plasmonic nanostructures are usually hard to create very distinct color impressions. Here we utilize the concept of metasurfaces to produce all-dielectric, low-loss, and high-resolution structural colors. We have fabricated TiO2 metasurfaces with electron-beam lithography and a very simple lift-off process. The optical characterizations showed that the TiO2 metasurfaces with different unit sizes could generate high reflection peaks at designed wavelengths. The maximal reflectance was as high as 64% with full width at half-maximum (fwhm) around 30 nm. Consequently, distinct colors have been observed in bright field and the generated colors covered the entire visible spectral range. The detailed numerical analysis shows that the distinct colors were generated by the electric resonance and magnetic resonances in TiO2 metasurfaces. Based on the unique properties of magnetic resonances, distinct colors have been observed in bright field when the metasurfaces were reduced to a 4 × 4 array, giving a spatial resolution around 16000 dpi. Considering the cost, stability, and CMOS-compatibility, this research will be important for the structural colors to reach real-world industrial applications.

Abstract Thermal insulation under extreme conditions requires materials that can withstand complex thermomechanical stress and retain excellent thermal insulation properties at temperatures exceeding 1,000 degrees Celsius 1–3 . Ceramic aerogels are attractive thermal insulating materials; however, at very high temperatures, they often show considerably increased thermal conductivity and limited thermomechanical stability that can lead to catastrophic failure 4–6 . Here we report a multiscale design of hypocrystalline zircon nanofibrous aerogels with a zig-zag architecture that leads to exceptional thermomechanical stability and ultralow thermal conductivity at high temperatures. The aerogels show a near-zero Poisson’s ratio (3.3 × 10 −4 ) and a near-zero thermal expansion coefficient (1.2 × 10 −7 per degree Celsius), which ensures excellent structural flexibility and thermomechanical properties. They show high thermal stability with ultralow strength degradation (less than 1 per cent) after sharp thermal shocks, and a high working temperature (up to 1,300 degrees Celsius). By deliberately entrapping residue carbon species in the constituent hypocrystalline zircon fibres, we substantially reduce the thermal radiation heat transfer and achieve one of the lowest high-temperature thermal conductivities among ceramic aerogels so far—104 milliwatts per metre per kelvin at 1,000 degrees Celsius. The combined thermomechanical and thermal insulating properties offer an attractive material system for robust thermal insulation under extreme conditions.

Lithium-ion batteries, carbon anode, lithium plating, characterization techniques, sluggish intercalation kinetics.

Structural health monitoring (SHM) is used worldwide for managing and maintaining civil infrastructures. SHM systems have produced huge amounts of data, but the effective monitoring, mining, and utilization of this data still need in-depth study. SHM data generally includes multiple types of anomalies caused by sensor faults or system malfunctions that can disturb structural analysis and assessment. In the routine data pre-processing, multiple signal processing techniques are required to detect the anomalies, respectively, which is inefficient. The large variations of extracted features from massive SHM data make the data anomaly detection techniques prone to be over-processed or under-processed. Even with expert intervention, the parameter tuning, associated with multiple data preprocessing methods, is still a challenge, making the procedure expensive and inefficient. In addition, one data anomaly detection technique frequently mis-detects other types of anomaly. In this work, we focus on the anomaly detection in the stage of data pre-processing that little work has been done based on the real-world continuous SHM data with multiclass anomalies. We proposed a novel data anomaly detection method based on a convolutional neural network (CNN) that imitates human vision and decision making. First, we split raw time series data into sections, and visualized the data in time and frequency domain, respectively. Then each section's images were stacked as a single dual-channel image and labeled according to graphical features (multi-2D image space expression). Second, a CNN was designed and trained for data anomaly classification, during which the descriptions and representations of the anomalies' features were learned by convolution. To validate our work, we considered the effects of balanced and imbalanced training sets and training ratios on actual acceleration data from an SHM system for a long-span cable-stayed bridge. The results show that our method could detect the multipattern anomalies of SHM data efficiently with high accuracy. The proposed dual-information CNN-based design makes this detection process readily scalable, faster, and more accurate, thereby providing a novel perspective with strong potential for SHM data preprocessing.

Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading rapidly around the world, resulting in a massive death toll. Lung infection or pneumonia is the common complication of COVID-19, and imaging techniques, especially computed tomography (CT), have played an important role in diagnosis and treatment assessment of the disease. Herein, we review the imaging characteristics and computing models that have been applied for the management of COVID-19. CT, positron emission tomography - CT (PET/CT), lung ultrasound, and magnetic resonance imaging (MRI) have been used for detection, treatment, and follow-up. The quantitative analysis of imaging data using artificial intelligence (AI) is also explored. Our findings indicate that typical imaging characteristics and their changes can play crucial roles in the detection and management of COVID-19. In addition, AI or other quantitative image analysis methods are urgently needed to maximize the value of imaging in the management of COVID-19.