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With the hyperspectral sensor technology evolving and becoming more cost-effective, hyperspectral imaging offers new opportunities for robust face recognition. Hyperspectral face cubes contain much more spectral information than face images from common RGB color cameras. Hyperspectral face recognition is robust to the impacts, such as illumination, pose, occlusion, and spoofing, which can heavily avoid the limitations of the visible-image-based face recognition. In this paper, we summarize the spectrum properties of hyperspectral face cubes and survey the hyperspectral face recognition methods in the literature. We categorize them into major groups for better understanding. We overview the existing hyperspectral face datasets, and establish our own dataset. We also discuss efficient neural networks used for mobile face recognition and conduct experiments on mobile hyperspectral face recognition. Results show that under harsh conditions like large illumination changing and pose variation, hyperspectral-cube-based methods have higher recognition accuracy than visible-image-based methods. Finally, we deliver insightful discussions and prospects for future works on mobile hyperspectral face recognition.

Cationic asphalt emulsion (CAE) is a type of organic latex that could be used to modify cement hydration products in order to improve the performance of cement paste. In this study, different methodologies were used to investigate the influence of CAE on the process of cement hydration. The results showed that with an increase in the mass ratio of asphalt to cement, the setting time of the cement paste was prolonged while the compressive strength and water absorption of the cement mortar showed obvious reductions. Other techniques were also used to investigate the cement hydration process and the microstructure evolution caused by the addition of CAE, including conductivity measurements, 29Si nuclear magnetic resonance, low-field nuclear magnetic resonance, measurements of chemically bound water and field emission scanning electron microscopy. It was found that the formation of cement hydration products was delayed with an increase in CAE and the microstructure was affected by the CAE in that the pores were separated by asphalt film and the total porosity decreased.

Electrocatalytic nitrogen reduction reaction (N2RR) in an aqueous solution suffers from unsatisfactory activity and selectivity for possible practical utilization. As the emerging catalyst for N2RR among various candidate non-metal catalysts, black phosphorus (BP) is promising but hasn’t been improved enough to solve those issues due to hugely exposed but inert surface P sites. In this work, a series of metals are chosen to adjust the coordination environment of inert surface P sites in bilayered BP (BPBL-M) by subsurface intercalation. The design principles of N2RR catalysts is investigated based on proton-coupled electron transfer theory by density functional theory (DFT). The BPBL-M with low-expanded interlayer-distances shows higher activity than bare BPBL and the first hydrogenation step for NNH* formation is potential determining step, where the activated N-N bond length correlates with N2RR activity. Meanwhile, the selectivity of N2RR on BPBL-M is regulated by intercalation as well. Considering the activity and selectivity altogether, the surface sites of BP yields higher activity and selectivity than the intrinsic BPBL after intercalation of Ni, Bi, Pd, and Ga. This model provides a new strategy from a theoretical perspective to activate the inert surface P sites to realize high intrinsic activity and high P site density for N2RR and broad new insight to design efficient 2D catalysts for N2RR.

The numerical method for predicting the permeability of a laminated composite based on matrix cracks is improved to meet the application at a cryogenic temperature. The number of the parameters tha...

In this paper, the spectral efficiency (SE) of an uplink hardware-constrained cell-free massive multi-input multi-output (MIMO) system with maximal ratio combining (MRC) receiver filters in the context of superimposed pilots (SPs) is investigated. Tractable closed-form SE expressions for the considered system are derived, which share us with opportunities to explore the impacts of the hardware quality coefficient, the length of coherence interval, and the power balance factor between pilot and data signals. Numerical results indicate that the achievable SE deteriorates as the hardware quality decreases and is more susceptible to the hardware impairments at the user equipments (UEs). Besides, we observe that SPs outperform regular pilots (RPs) in terms of SE and this performance gain is heavily dependent on the values of power balance factor and coherence interval. However, the superiorities of SPs over RPs have vanished when severe hardware imperfections are considered.

Highlights The iontronic pressure sensor achieved an ultrahigh sensitivity (Smin > 200 kPa−1, Smax > 45,000 kPa−1). The iontronic pressure sensor exhibited a broad sensing range of over 1.4 MPa. Pseudocapacitive iontronic pressure sensor using MXene was proposed. ABSTRACT Flexible pressure sensors are unprecedentedly studied on monitoring human physical activities and robotics. Simultaneously, improving the response sensitivity and sensing range of flexible pressure sensors is a great challenge, which hinders the devices’ practical application. Targeting this obstacle, we developed a Ti3C2Tx-derived iontronic pressure sensor (TIPS) by taking the advantages of the high intercalation pseudocapacitance under high pressure and rationally designed structural configuration. TIPS achieved an ultrahigh sensitivity (Smin > 200 kPa−1, Smax > 45,000 kPa−1) in a broad sensing range of over 1.4 MPa and low limit of detection of 20 Pa as well as stable long-term working durability for 10,000 cycles. The practical application of TIPS in physical activity monitoring and flexible robot manifested its versatile potential. This study provides a demonstration for exploring pseudocapacitive materials for building flexible iontronic sensors with ultrahigh sensitivity and sensing range to advance the development of high-performance wearable electronics.

Agarose is a promising tool for encapsulating areas as a kind of neutral polysaccharide. The purpose of this work is to expand the application of agarose. In this work, agarose microparticles for encapsulating microorganisms were introduced to the stomach through a novel water-in-water (w/w) emulsification method. Sequencing techniques were performed for the identification and characterization of bacteria, and drug-susceptibility testing of Helicobacter pylori through gel microdroplets growth assay and traditional Oxford cup method was conducted. Results indicated the presence of three phyla, eight genera, and more than 30 species in the samples. The correlation values of the traditional Oxford cup and GMD methods were 87.5% and 90%, respectively. The proposed encapsulation technology as efficient substitution for traditional Oxford cup method promised to be applicable for the isolation and cultivation of gastric flora. Compared to other methods, this new method showed advantages when mainly due to time simplicity of the whole process. The direct drug susceptibility test based on the novel encapsulation technology is a promising tool for the rational and flexible use of drugs in clinical practice. Furthermore, this work was an early exploration for the combination of encapsulation technology and agarose.

The heterogeneous cloud radio access network (H-CRAN) is considered a promising solution to expand the coverage and capacity required by fifth-generation (5G) networks. UAV, also known as wireless aerial platforms, can be employed to improve both the network coverage and capacity. In this paper, we integrate small drone cells into a H-CRAN. However, new complications and challenges, including 3D drone deployment, user association, admission control, and power allocation, emerge. In order to address these issues, we formulate the problem by maximizing the network throughput through jointly optimizing UAV 3D positions, user association, admission control, and power allocation in H-CRAN networks. However, the formulated problem is a mixed integer nonlinear problem (MINLP), which is NP-hard. In this regard, we propose an algorithm that combines the genetic convex optimization algorithm (GCOA) and particle swarm optimization (PSO) approach to obtain an accurate solution. Simulation results validate the feasibility of our proposed algorithm, and it outperforms the traditional genetic and K-means algorithms.

This study investigates the potential of solid fuel blending as an effective approach to manipulate ash melting behaviour to alleviate ash-related problems during gasification, thus improving design, operability and safety. The ash fusion characteristics of Qinghai bituminous coal together with Fushun, Xinghua and Laoheishan oil shales (and their respective blends) were quantified using a novel picture analysis and graphing method, which incorporates conventional ash fusion study, dilatometry and sintering strength test, in a CO/CO2 atmosphere. This image-based characterisation method was used to monitor and quantify the complete melting behaviour of ash samples from room temperature to 1520 °C. The impacts of blending on compositional changes during heating were determined experimentally via X-ray diffraction and validated computationally using FactSage. Results showed that the melting point of Qinghai coal ash to be the lowest at 1116 °C, but would increase up to 1208 °C, 1161 °C and 1160 °C with the addition of 30%–50% of Laoheishan, Fushun, and Xinghua oil shales, respectively. The formation of high-melting anorthite and mullite structures inhibits the formation of low-melting hercynite. However, the sintering point of Qinghai coal ash was seen to decrease from 1005 °C to 855 °C, 834 °C, and 819 °C in the same blends due to the formation of low-melting aluminosilicate. Results also showed that blending directly influences the sintering strength during the various stages of melting. The key finding from this study is that it is possible to mitigate against the severe ash slagging and fouling issue arising from high calcium and iron coals by co-gasification with a high silica-alumina oil shale. Moreover, blending coals with oil shales can also modify the ash melting behaviour of fuels to create the optimal ash chemistry that meets the design specification of the gasifier, without adversely affecting thermal performance.

Moisture transport has a great impact on concrete durability. In this study, a hard-pulse one-dimensional frequency coding sequence of low-field nuclear magnetic resonance (LF-NMR) imaging was applied to investigate moisture transport in cement-based materials (CBMs) considering the influences of the water/binder (w/b) ratio, sand/cement (s/c) ratio and contents of silica fume (SF) and super-absorbent polymer (SAP). A weighing method was also applied and the results were compared with the LF-NMR imaging measurements. The results showed that the moisture distribution curves can be divided into three stages by height. In addition, it was found that the moisture absorption height (h) increased with time and the growth rate of water absorption proceeded from fast to slow. Additionally, as the w/b ratio of the sample was increased, h increased. The value of h decreased with increasing s/c ratio and increased with an increase in SF content. When SAP was mixed into the CBMs, the values of h increased with 0·15% SAP addition but decreased with the addition of 0·30% SAP. The LF-NMR imaging measurements were in agreement with the results of the weighing method.

Large intelligent surface (LIS) is considered as a new solution to enhance the performance of wireless networks[1]. LIS comprises low-cost passive elements which can be well controlled. In this paper, a LIS is invoked in the vehicular networks. We analyze the system performance under Weibull fading. We derive a novel exact analytical expression for outage probability in closed form. Based on the analytical result, we discuss three special scenarios including high SNR case, low SNR case, as well as weak interference case. The corresponding approximations for three cases are provided, respectively. In order to gain more insights, we obtain the diversity order of outage probability and it is proved that the outage probability at high SNR depends on the interference, threshold and fading parameters which leads to 0 diversity order. Furthermore, we investigate the ergodic achievable rate of LIS-assisted vehicular networks and present the closed-form tight bounds. Similar to the outage performance, three special cases are studied and the asymptotic expressions are provided in simple forms. A rate ceiling is shown for high SNRs due to the existence of interference which results 0 high SNR slope. Finally, we give the energy efficiency of LIS-assisted vehicular network. Numerical results are presented to verify the accuracy of our analysis. It is evident that the performance of LIS-assisted vehicular networks with optimal phase shift scheme exceeds that of traditional vehicular networks and random phase shift scheme significantly.

An atomically dispersed structure is attractive for electrochemically converting carbon dioxide (CO2) to fuels and feedstock due to its unique properties and activity. Most single-atom electrocatalysts are reported to reduce CO2 to carbon monoxide (CO). Herein, we develop atomically dispersed indium (In) on a nitrogen-doped carbon skeleton (In-N-C) as an efficient catalyst to produce formic acid/formate in aqueous media, reaching a turnover frequency as high as 26771 h-1 at -0.99 V relative to a reversible hydrogen electrode (RHE). Electrochemical measurements show that trace amounts of In loaded on the carbon matrix significantly improve the electrocatalytic behavior for the CO2 reduction reaction, outperforming conventional metallic In catalysts. Further experiments and density functional theory (DFT) calculations reveal that the formation of intermediate *OCHO on isolated In sites plays a pivotal role in the efficiency of the CO2-to-formate process, which has a lower energy barrier than that on metallic In.

Cooling uniformity has a great influence on slab solidification. To study its effect on the internal reduction efficiency, a thermo-mechanical coupling slab solidification model was studied and an ...

Electrochemical N₂ reduction reaction (NRR) is an environmentally benign and sustainable approach for NH₃ synthesis under ambient conditions, which needs efficient electrocatalyst to meet the enormous challenge of activating the inert N₂ molecule. Recently, TiO₂ emerges as an active NRR electrocatalyst with advantages of abundance, nontoxicity, and high thermal stability. Heteroatom doping is an effective oxygen vacancy introduction strategy to manufacture unique electron and structure properties of metal oxides, which will greatly influence the catalysts’ activity. In this work, we developed Mn-doped TiO₂ nanospheres (Mn-TiO₂) for electrochemical NRR. In 0.1 M Na₂SO₄, Mn-TiO₂ shows a large NH₃ yield rate of 20.05 μg h–¹ mgcₐₜ.–¹ and a high Faradaic efficiency of 11.93%, which are much higher than undoped TiO₂. The mechanism of performance improvement is uncovered by density functional theory calculations.

Dynamic principal component analysis (DPCA) and its nonlinear extension, dynamic kernel principal component analysis (DKPCA), are widely used in the monitoring of dynamic multivariate processes. In traditional DPCA and DKPCA, extended vectors through concatenating current process data point and a certain number of previous process data points are utilized for feature extraction. The dynamic relations among different variables are fixed in the extended vectors, i.e. the adoption of the dynamic information is not adaptively learned from raw process data. Although DKPCA utilizes a kernel function to handle dynamic and (or) nonlinear information, the prefixed kernel function and the associated parameters cannot be most effective for characterizing the dynamic relations among different process variables. To address these problems, this paper proposes a novel nonlinear dynamic method, called dynamic neural orthogonal mapping (DNOM), which consists of data dynamic extension, a nonlinear feedforward neural network, and an orthogonal mapping matrix. Through backpropagation and Eigen decomposition (ED) technique, DNOM can be optimized to extract key low-dimensional features from original high-dimensional data. The advantages of DNOM are demonstrated by both theoretical analysis and extensive experimental results on the Tennessee Eastman (TE) benchmark process. The results on the TE benchmark process show the superiority of DNOM in terms of missed detection rate and false alarm rate. The source codes of DNOM can be found in https://github.com/htz-ecust/DNOM .

Depth estimation is a traditional computer vision task, which plays a crucial role in understanding 3D scene geometry. Recently, algorithms that combine the multi-scale features extracted by the dilated convolution based block (atrous spatial pyramid pooling, ASPP) have gained significant improvements in depth estimation. However, the discretized and predefined dilation kernels cannot capture the continuous context information that differs in diverse scenes and easily introduce the grid artifacts. This paper proposes a novel algorithm, called attention-based context aggregation network (ACAN) for depth estimation. A supervised self-attention model is designed and utilized to adaptively learn the task-specific similarities between different pixels to model the continuous context information. Moreover, a soft ordinal inference is proposed to transform the predicted probabilities to continuous depth values which reduce the discretization error (about 1% decrease in RMSE). ACAN achieves state-of-the-art performance on public monocular depth-estimation benchmark datasets. The source code of ACAN can be found in https://github.com/miraiaroha/ACAN .

Periodic nanostructures, a group of nanomaterials consisting of single or multiple nano units/components periodically arranged into ordered patterns (e.g., vertical and lateral superlattices), have attracted tremendous attention in recent years due to their extraordinary physical and chemical properties that offer a huge potential for a multitude of applications in energy conversion, electronic and optoelectronic applications. Recent advances in the preparation strategies of periodic nanostructures, including self-assembly, epitaxy, and exfoliation, have paved the way to rationally modulate their ferroelectricity, superconductivity, band gap and many other physical and chemical properties. For example, the recent discovery of superconductivity observed in "magic-angle" graphene superlattices has sparked intensive studies in new ways, creating superlattices in twisted 2D materials. Recent development in the various state-of-the-art preparations of periodic nanostructures has created many new ideas and findings, warranting a timely review. In this review, we discuss the current advances of periodic nanostructures, including their preparation strategies, property modulations and various applications.

Catalysts for electrochemical and photochemical reactions play critical roles in energy storage and conversion as well as degradation of organic pollutants. Due to their unique structure, composition flexibility and high stability, A-site perovskite oxides are promising candidates in electrocatalysis and photocatalysis. In this article, we review the recent progress of A-site perovskite oxides as an emerging functional material in electrocatalysis and photocatalysis. Firstly, we summarize the different factors affecting the structure and composition of A-site perovskite oxides. Then, the performance of A-site perovskite oxides (bimetallic, ternary metal, multimetallic and oxynitride) in electrocatalysis and photocatalysis is systematically discussed. Particularly, the rational optimization strategies (such as doping, introducing defects, and surface modification) of A-site cation perovskite oxides (A = La, Sr, Ba, Ca, Ag, Bi, Na, K) are also described to further enhance the performance. Finally, we conclude the challenges and prospects of A-site perovskite oxides to improve the (electro) photocatalytic activity, conductivity and stability.

Imaging with different spectrums often leads to significant nonlinear differences in the intensity, which brings a great challenge to the automatic matching of multispectral images. Considering that there are often limitations to only using intensity information, this article studies multispectral image matching based on the structure consistency. First, an extended log-Gabor filter is constructed to build phase congruency maps, which encodes the structure information and provides rich and robust features. Then, a nonlinear diffusion-based algorithm is developed to detect the salient feature points on the phase congruency map, which are expected to be illumination and contrast invariant. Finally, the structure descriptors are built according to the orientation of the maximum log-Gabor filter responses, and the image matching is achieved by computing the correspondence. Extensive experiments are carried out on various multispectral image datasets. The results show that the proposed method holds a stable performance over the nonlinear intensity variance across spectrums, and outperforms the comparison methods in terms of the number of correct matches and the matching precision.

The ever-increasing demand for clean energy has attracted considerable attention on the scalable and affordable potassium-ion batteries (PIBs) while one of the challenges hampering the development of rechargeable PIBs is attributed to the lack of suitable anode materials. This problem necessitates the design of cost-effective, nontoxic, and highly stable anodes with high specific capacity. In this review, we present the recent advances in rational design of carbon materials for developing such anodes for PIBs. Firstly, graphite, graphene, and disordered carbon as anodes for PIBs and their practical problems are comprehensively summarized based on sp2 hybridization. Secondly, the potassium storage mechanism corresponding to the intercalation and adsorption mechanism for guiding rational design of carbon materials is discussed, followed by illustrations of how the design of heteroatom doping, porous structure, and carbon composites provides fundamental insights into high conductivity, structural integrity, and enhanced electrochemical activity. Finally, conclusions and perspectives are pointed out for developing the next-generation carbon anodes for PIBs with high energy density and excellent cycling stability.

The selective two-electron O2 reduction reaction (2e− ORR) represents a green, mild, and on-site means of synthesizing H2O2. However, its practical feasibility depends on the development of advanced electrocatalysts, and limited experimental work has been done on non-precious-metal-based materials for the H2O2 electrogeneration in acids. Our study here introduces NiS2 nanosheets for the first time as an efficient electrocatalyst for the 2e− ORR under acidic conditions. In 0.05 M H2SO4, the NiS2 catalyst shows an onset overpotential of ∼130 mV and offers high selectivity (H2O2 percentage up to 99%). Moreover, the NiS2 catalyst attains the largest faradaic efficiency of 98% and the highest H2O2 yield rate of 109 ppm h−1 at 0.456 V and 0.156 V in the H-cell testing, respectively. The catalytic mechanism is revealed by theoretical calculations.

Graphene, an allotrope of carbon in 2D structure, has revolutionised research, development and application in various disciplines since its successful isolation 16 years ago. The single layer of sp2-hybridised carbon atoms brings with it a string of unrivalled characteristics at a fraction of the price of its competitors, including platinum, gold and silver. More recently, there has been a growing trend in the application of graphene in catalysis, either as metal-free catalysts, composite catalysts or as catalyst supports. The unique and extraordinary properties of graphene have rendered it useful in increasing the reactivity and selectivity of some reactions. Owing to its large surface area, outstanding adsorptivity and high compatibility with various functional groups, graphene is able to provide a whole new level of possibilities and flexibilities to design and synthesise fit-for-purpose graphene-based catalysts for specific applications. This review is focussed on the progress, mechanisms and challenges of graphene application in four main reactions, i.e., oxygen reduction reaction, water splitting, water treatment and Fischer–Tropsch synthesis. This review also summarises the advantages and drawbacks of graphene over other commonly used catalysts. Given the inherent nature of graphene, coupled with its recent accelerated advancement in the synthesis and modification processes, it is anticipated that the application of graphene in catalysis will grow exponentially from its current stage of infancy.

For heat-transfer-controlled or diffusion-controlled phase transformation simulations, the movement of the interface between different phases needs to be determined, which is commonly called the Stefan problem. Various numerical methods have been developed for this and the movement of the interface is conventionally calculated by a flux balance equation, which is usually called the Stefan condition equation. However, mass conservation errors have been reported in the literature for some of the numerical methods. In this research, a control volume method based interface movement equation was proposed to achieve mass conservation during calculation. The obtained maximum relative mass conservation error for the new interface movement equation is less than 10−13 and the total mass is constant to the machine precision. This is much smaller than that of the Stefan condition discrete equation, which is in the order of 10−2. It was also found that the mass conservation accuracy of the numerical methods partly depends on the choice of the interface movement equation. The mass change term in the Stefan condition discrete equation is only a part of the whole mass change during interface movement, leading to the mass non-conservation found in the literature. In contrast, the piecewise integration treatment in the proposed interface movement equation in this research gives a more accurate mass change description during the interface movement. The correctness of the proposed interface movement equation was also proved by a comparison with the analytical solutions.

The performance of arsenic (As) poisoned catalysts in a 600 MW coal-fired power plant was tracked from 12/2013 to 10/2019, and the physicochemical characteristics and service lifetime of the cataly...

Electrochemical nitrogen reduction reaction (NRR) is considered as an alternative to the industrial Haber-Bosch process for NH3 production due to both low energy consumption and environment friendliness. However, the major problem of electrochemical NRR is the unsatisfied efficiency and selectivity of electrocatalyst. As one group of the cheapest and most abundant transition metals, iron-group (Fe, Co, Ni and Cu) electrocatalysts show promising potential on cost and performance advantages as ideal substitute for traditional noble-metal catalysts. In this minireview, we summarize recent advances of iron-group-based materials (including their oxides, hydroxides, nitrides, sulfides and phosphides, etc.) as non-noble metal electrocatalysts towards ambient N2-to-NH3 conversion in aqueous media. Strategies to boost NRR performances and perspectives for future developments are discussed to provide guidance for the field of NRR studies.

Carbon is an essential element in steel, but there are still discrepancies regarding its effect on steel hot deformation behaviours. In this research, isothermal compression tests were carried out for a low-carbon (0.05 C) and a medium-carbon (0.38 C) vanadium microalloyed steel with deformation temperatures of 900−1050 °C and strain rates of 0.01−30 s−1. It was found that carbon causes a softening effect at low strain rates (0.01−1.0 s−1), while a hardening effect at high strain rates (10.0−30 s−1). Through constitutive analysis, the hot deformation activation energy for 0.05 C steel is 305.9 kJ/mol in the whole strain rate range, while for 0.38 C steel, the activation energy is 292.3 kJ/mol in the low strain rate range (0.01−1 s−1) and 475.0 kJ/mol in the high strain rate range (10−30 s−1). It was proposed that the addition of carbon decreases the deformation activation energy at low strain rates (0.01−1 s−1), due to its positive influence on the self-diffusion coefficient of iron, which increases the rates of dislocation climb and recovery. On the other hand, carbon lowers the stacking fault energy of austenite, which could make partial dislocation collapse more difficult than dislocation climb and thus becomes the rate-controlling mechanism for 0.38 C steel at high strain rates (10−30 s−1). This gives rise to the higher activation energy and work hardening rate of 0.38C steel at high strain rates. Comparing the power-dissipation-efficiency maps, two peak domains were found in those of 0.38 C steel, while only one peak domain exists in those of 0.05 C steel.

Wireless Service Providers (WSPs) and Content Providers (CPs) charge common data price according to the flat rate, the transmission time, or the data usage, irrespective of the content characteristics. The multimedia compression parameters that determine the multimedia importance diversities among frames have largely been ignored in media pricing research. We establish a new economics-driven resource allocation framework for multimedia transmissions among WSPs, CPs, and End Users (EUs) based on Smart Media Pricing (SMP). The WSPs transmit media streams provided by CPs to the EUs to earn money. However, facing with the transaction security and privacy protection issues, we undertake promising consortium blockchain to improve resource pricing transaction security without reliance on a trusted third party. A novel Stackelberg game with consortium blockchain is firstly established to allocate transmission resources such as Transmission Times’ Limitations (TTLs) from an economic point of view. Then we investigate the utility maximization problems of the leader (WSP and CP) and the follower (EU) in terms of TTLs respectively. Finally, the optimal TTL and the optimal price are non-uniformly determined for each frame according to the game equilibria, by analyzing the payment from EUs, the channel conditions, and the media characteristics in terms of frame importance. Security analysis shows that our proposed scheme achieves better transaction security and privacy protection. Experimental comparison with uniform pricing based on equal TTL or equal frame importance validates the effectiveness of the proposed economic model.

To investigate the acoustic emission (AE) characteristics of quasi-brittle materials like rock and concrete, and to further analyze their damage and failure mechanism under seismic and other dynamic loads, the uniaxial tension test of granite cylinder specimens within the strain rate range of 10−7-10−4 s−1 was monitored by AE technology, and the typical AE characteristic parameters were analyzed using statistical and correlation analysis. The experimental results show that, with the increase of strain rate, the peak of AE hit rate appears earlier and increases; the proportion of AE hits with higher duration or amplitude increases significantly, the b-value shows a decreasing trend, and the distribution of AE frequency-amplitude is increasingly discrete. In addition, the obvious characteristic of double dominant frequency bands was observed in AE waveforms by using spectrum analysis, with the increase of strain rate, the percentage of A-type waveforms corresponding to low dominant frequency band increases, while that of D-type waveforms corresponding to high ones decreases accordingly, which is significance for the further study of the damage and failure mechanism of quasi-brittle materials.

Predicting the depth of a scene from monocular infrared images, which plays a crucial role in understanding three-dimensional structures, is one of the challenging tasks in machine learning and computer vision. Considering the lack of texture and color information in infrared images, a novel discrete convolutional conditional random field network is proposed for depth estimation. The proposed method inherits several merits of conditional random fields and deep learning. First, the pairwise features are automatically extracted and optimized through deep architectures. Second, the monocular-images-based depth regression is converted into a multi-class classification, in which the order information of different levels of depths is considered in the loss function. Our experiments demonstrate that this conversion achieves much higher accuracy and faster conversion. Third, to obtain fine-grained level details, we have further proposed a multi-scale discrete convolutional conditional random field network that computes the pairwise features of the discrete conditional random field at different spatial levels. Extensive experiments on the infrared image dataset NUSTMS demonstrate that the proposed method outperforms other depth estimation methods. Specifically, for the proposed method, the mean relative error is 0.181, the mean log10 error is 0.072, and the accuracy with a threshold (t = 1.253) is 95.3%.

Optical tweezers are versatile tools capable of sorting microparticles, yet formidable challenges are present in the separation of nanoparticles smaller than 200 nm. The difficulties arise from the controversy on the requirement of a tightly focused light spot in order to create strong optical forces while a large area is kept for the sorting. To overcome this problem, we create a near-field potential well array with connected tiny hotspots in a large scale. This situation can sort nanoparticles with sizes from 100 to 500 nm, based on the differentiated energy depths of each potential well. In this way, nanoparticles of 200, 300, and 500 nm can be selectively trapped in this microchannel by appropriately tuning the laser power. Our approach provides a robust and unprecedented recipe for optical trapping and separation of nanoparticles and biomolecules, such that it presents a huge potential in the physical and biomedical sciences. Ministry of Education (MOE) National Research Foundation (NRF) Accepted version This work was supported by the Singapore National Research Foundation under the Competitive Research Program (NRFCRP13-2014-01), EWI Research & Innovation Scheme (1102- IRIS-05- 04), and the Singapore Ministry of Education (MOE) Tier 3 grant (MOE2017-T3-1-001). M.N.- V. acknowledges Spanish Ministerio de Ciencia, Innovación y Universidades, Grants No. FIS2014-55563-REDC, No. FIS2015-69295-C3-1-P, and No. PGC2018-095777-B-C21.

Global demand for green and clean energy is increasing day by day owing to ongoing developments by the human race that are changing the face of the earth at a rate faster than ever. Exploring alter...

Depth prediction is an essential component in the research of unmanned driving. Most existing research works predict depth only based on visible light images or infrared images. However, both visible light images and infrared images have their own advantages and disadvantages, and these two kinds of images contain complementary information when the images are filmed from the same scence. In order to fuse the complementary information and predict depth under various conditions, this paper proposes a convolutional-neural-network-based architecture, called infrared and visible light images fusion network (IVFuseNet), for depth prediction. Specifically, we construct common-feature-fusion subnetwork, full-feature-fusion subnetwork, and high-resolution reconstruction subnetwork, aiming to leverage the complementarity of these two kinds of images. The common-feature-fusion subnetwork adopts a two-stream multilayer convolutional structure whose filters for each layer are partially coupled to fuse the common features extracted from infrared images and visible light images respectively. The full-feature-fusion subnetwork fuses the two-stream features generated from the common-feature-fusion subnetwork by adaptive fusion weights instead of prefixed fusion weights. Additional, residual dense convolution that can accurately map the fused low-resolution features to the corresponding high-resolution features is adopted in the high-resolution reconstruction subnetwork to enhance the reconstruction of the details for depth prediction. All three subnetworks collaborate together to conduct the depth prediction task. Our NUST-SR dataset is composed of the actual road scenes captured while unmanned vehicle driving. The proposed IVFuseNet obtains the best performances on this dataset. IVFuseNet decreases the root mean squared error to 3.4513 and the mean relative error to 0.1651 respectively and outperforms other methods. The model and dataset are available at https://github.com/liyuqi1234/IVFN.

Objective Shortage of organ donors, a critical challenge for treatment of end-stage organ failure, has motivated the development of alternative strategies to generate organs in vitro. Here, we aim to describe the hepatorganoids, which is a liver tissue model generated by three-dimensional (3D) bioprinting of HepaRG cells and investigate its liver functions in vitro and in vivo. Design 3D bioprinted hepatorganoids (3DP-HOs) were constructed using HepaRG cells and bioink, according to specific 3D printing procedures. Liver functions of 3DP-HOs were detected after 7 days of differentiation in vitro, which were later transplanted into Fah-deficient mice. The in vivo liver functions of 3DP-HOs were evaluated by survival time and liver damage of mice, human liver function markers and human-specific debrisoquine metabolite production. Results 3DP-HOs broadly acquired liver functions, such as ALBUMIN secretion, drug metabolism and glycogen storage after 7 days of differentiation. After transplantation into abdominal cavity of Fah-/-Rag2-/- mouse model of liver injury, 3DP-HOs further matured and displayed increased synthesis of liver-specific proteins. Particularly, the mice acquired human-specific drug metabolism activities. Functional vascular systems were also formed in transplanted 3DP-HOs, further enhancing the material transport and liver functions of 3DP-HOs. Most importantly, transplantation of 3DP-HOs significantly improved the survival of mice. Conclusions Our results demonstrated a comprehensive proof of principle, which indicated that 3DP-HO model of liver tissues possessed in vivo hepatic functions and alleviated liver failure after transplantation, suggesting that 3D bioprinting could be used to generate human liver tissues as the alternative transplantation donors for treatment of liver diseases.

40CrNi steel is a low alloy steel with medium hardenability and has been widely used in the manufacturing of crankshafts, wind turbine forgings, etc. As forging is usually involved in the manufacturing of these components, optimization of the hot deformation process of 40CrNi steel is quite critical. Towards this, the prediction of flow stress and the determination of optimal processing parameter windows are of great importance. In this research, isothermal uniaxial hot compression tests were carried out for 40CrNi steel with true strain rates of 0.01, 0.1, 1, 10 and 30 s−1 and temperatures of 850–1200 °C. Both the strain compensated hyperbolic sine constitutive equation and the modified Johnson-Cook model were adopted to predict the flow stress curves, with average absolute relative errors of 0.034 and 0.061, respectively. Processing maps of the tested steel were constructed to determine the optimal deformation parameter windows, and the characteristics of the prior austenite microstructure generally correspond very well to the processing map established. Furthermore, different processing map construction methods were compared and it was found that power-dissipation efficiency maps strongly rely on the σ- e ˙ relationship. Assuming a certain type of constitutive equations leads to monotonic efficiency value patterns. Calculation methods based on experimental results are helpful to reflect the meaningful information carried by the experimental σ- e ˙ relationships.

Due to the flexibility in 3-D space and high probability of line-of-sight (LoS) in air-to-ground communications, unmanned aerial vehicles (UAVs) have been considered as means to support energy-efficient data collection. However, in emergency applications, the mission completion time should be main concerns. In this article, we propose a UAV-aided data collection design to gather data from a number of ground users (GUs). The objective is to optimize the UAV’s trajectory, altitude, velocity, and data links with GUs to minimize the total mission time. However, the difficulty lies in that the formulated time minimization problem has mutual effect with trajectory variables. To tackle this issue, we first transform the original problem equivalently to the trajectory length problem and then decompose the problem into three subproblems: 1) altitude optimization; 2) trajectory optimization; and 3) velocity and link scheduling optimization. In the altitude optimization, the aim is to maximize the transmission region of GUs which can benefit trajectory designing; then, in the trajectory optimization, we propose a segment-based trajectory optimization algorithm (STOA) to avoid repeat travel; besides, we also propose a group-based trajectory optimization algorithm (GTOA) in large-scale high-density GU deployment to relieve massive computation introduced by STOA. Then, the velocity and link scheduling optimization is modeled as a mixed-integer nonlinear programming (MINLP) and block coordinate descent (BCD) is employed to solve it. Simulations show that both STOA and GTOA achieve shorter trajectory compared with the existing algorithm and GTOA has less computational complexity; besides, the proposed time minimization design is valid by comparing to the benchmark scheme.

The exploration of high-efficiency, robust and low-cost electrocatalysts for hydrogen peroxide (H2O2) production receives increasing attention due to its key role in developing a new alternative to replace the traditional anthraquinone process. In recent years, significant achievements of non-noble metal electrocatalysts for H2O2 production have been made. Herein, this review presents recent advances of noble-metal-free electrocatalysts toward H2O2 production. Specifically, we first introduce the fundamental principle of on-site electrochemical synthesis of H2O2. Then, the classification and corresponding recent advances of noble-metal-free electrocatalysts are reviewed, including carbon-based materials (modified by pore structure adjustment, topology defects, heteroatom-doping and surface oxygen functionalization), metal compounds (e.g., NiO, MnO, FeO, Bi2Te3, and CoS2), metal single atom catalysts (e.g., Fe, Co, Ni, Mn, and Mo) and metal complexes. In parallel, the synthesis strategy, component, structure, electrochemical properties and reaction mechanism are highlighted. Finally, an outlook on current advances, challenges, and potential of noble-metal-free electrocatalysts towards H2O2 production is also discussed.

As promising candidates for large-scale energy storage systems, sodium ion batteries (SIBs) are limited by their relatively lower energy density. This shortcoming necessitates the design of anodes with high specific capacity and excellent cycling durability, yet low cost. Here, we review one-dimensional structural/heterostructural carbon nanofibers (CNFs), which are generally synthesized via a simple, low cost, and scalable electrospinning technology, as a basis for developing anode materials for SIBs. The rational design in the structure and chemistry of CNFs, including construction of cross-link structures and porous structures as well as heteroatom doping, is described, followed by illustrations of how a robust understanding of CNFs as the carbon matrix within heterostructures provides fundamental insights into the electronic conductivity, electrochemical activity and cycling stability in anode materials with high theoretical capacities (alloys and metal chalcogenides). Based on the insights, we conclude the future perspectives on the critical issues, challenges and research directions in designing one-dimensional carbon nanofiber based materials via electrospinning.

Electrochemical water splitting is the most promising process to produce carbon-neutral hydrogen as an energy carrier, and hydrogen evolution reaction (HER) electrocatalysts are essential to reduce the energy barrier and improve hydrogen production efficiency. This driving force necessitates the design of HER electrocatalysts with low-cost, high-abundance, high activity and stability. In this review, we systemically summarize recent publications with critical insight into the advances of iron-based phosphides as HER electrocatalysts. Various synthesis strategies corresponding to different phosphating strategies for guiding iron phosphide design are described, followed by illustrations of how to boost FeP HER catalytic performance by enriching the accessible active sites and modifying the electronic structure. Finally, we point out several challenges and prospects, which can open up opportunities to design next-generation FeP HER electrocatalysts.

Catalysts for the N2 reduction reaction (NRR) are at the heart of key alternative technology to the Haber-Bosch process for NH3 synthesis, and are expected to optimize the interplay between efficiency, activity and selectivity. Here, we report our recent finding that P-doped graphene shows superior NRR performances in aqueous media at present, with a remarkably large NH3 yield of 32.33 μg h-1 mgcat.-1 and a high faradaic efficiency of 20.82% at -0.65 V vs. reversible hydrogen electrode. The mechanism is clarified by density functional theory calculations.

In this paper, a face spoofing detection method called the Fully Convolutional Network with Domain Adaptation and Lossless Size Adaptation (FCN-DA-LSA) is proposed. As its name suggests, the FCN-DA-LSA includes a lossless size adaptation preprocessor followed by an FCN based pixel-level classifier embedded with a domain adaptation layer. The FCN local classifier makes full use of the basic properties of face spoof distortion namely ubiquitous and repetitive. The domain adaptation (DA) layer improves generalization across different domains. The lossless size adaptation (LSA) preserves the high-frequent spoof clues caused by the face recapture process. The ablation study shows that both DA and the LSA are necessary for high-accuracy face spoofing detection. The FCN-LSA obtains competitive performance among the state-of-the-art methods. With the help of small-sample external data in the target domain (2/50, 2/50, and 1/20 subjects for CASIA-FASD, Replay-Attack, and OULU-NPU respectively), the FCN-DA-LSA further improves the performance and outperforms the existing methods.

The development of high-capacity rechargeable lithium–sulfur batteries is an ongoing challenge because of the shuttle effect, which causes rapid capacity fade, limited rate capability, and slow kinetics. Herein, conductive hollow cobalt nitride/carbon (Co5.47Nx–C) spheres with nitrogen vacancies are developed as a high-performance cathode material for use in lithium–sulfur batteries. Nitrogen vacancies are formed by annealing a zeolite imidazole framework (ZIF-67) precursor in ammonia. Benefiting from its Co–N bonds and nitrogen-vacancy sites, the Co5.47Nx–C composite achieves strong anchoring of polysulfides, fast polysulfide conversion, and accelerated lithium-ion transport. The strong anchoring effect of Co5.47Nx is confirmed by experimental measurements and density functional theory (DFT) calculations. Because of its high conductivity and nitrogen vacancies, the Co5.47Nx cathode exhibits faster redox reaction kinetics and lower polarization than a Co5.47N cathode without nitrogen vacancies, thus realizing promising rate and cycling performance. The optimized Co5.47Nx–C electrode delivers a capacity of 850 mA h g−1 at 0.5C and a rate performance of 320 mA h g−1 at 10C. This high-performance, high-rate lithium–sulfur battery is promising for widespread application in electric vehicles and intelligent devices.

Supercapacitors (SCs) are promising for bridging the power/energy gap between conventional capacitors and batteries/fuel cells. However, challenges remain in the improvement of electrode materials toward high capacitive performance. Electrospun nanofibers, prepared via facile and low-cost electrospinning techniques, exhibit superior electrochemical performance in SCs due to their unique morphology and intriguing properties, demonstrating great potential for improving the performance of SCs. Herein, we review the recent progress in electrospun one-dimensional nanofibers as electrode materials for SCs. Three categories of electrospun electrode nanomaterials (i.e., carbon nanofibers, carbon nanofiber composites, and carbon-free transition metal oxides) are discussed with design and optimization strategies. Moreover, critical insights into the electronic conductivity, electrochemical response and durability of electrospun nanofibers are reviewed. Finally, we conclude with an outlook on how these discussions and insights open opportunities for electrospun nanofibers (as electrode materials) to expand their potential application in next-generation SCs.

Batteries and supercapacitors are promising candidates for electrochemical energy storage while the development of their electrode materials is becoming a bottleneck. This limitation necessitates the design of electrode materials with high specific capacity/capacitance and excellent cycling stability, yet at a low cost. Herein, we present the research progress of magnetron sputtering enabled nanostructured materials as electrode materials for electrochemical energy storage. Firstly, magnetron sputtered anode materials (Si-based materials, metal-based materials, metal oxides, etc.) and cathode materials (i.e., transition metal oxides and phosphates) for lithium/sodium ion batteries are systematically reviewed. Secondly, magnetron sputtered electrode materials (metals and metal oxides, metal nitrides, etc.) for electrochemical supercapacitors are discussed. Furthermore, critical insights into the corresponding electrical conductivity and cycling stability of the electrode materials are provided through illustrations of how to rationally design and optimize electrode materials via magnetron sputtering technology. Finally, the emerging challenges and future directions of magnetron sputtered electrode materials for electrochemical energy storage are discussed.

We prepared a novel heterojunction containing nanocomposite (CZS/M-CoO) based on twin crystal Cd0.5Zn0.5S (CZS) and mixed-phase CoO (M-CoO) by a hydrothermal method. The high resolution transmission electron microscopy (HRTEM) examination, steady state fluorescence spectra, and time resolved fluorescence spectra revealed the formation of heterojunctions between CZS and CoO, homojunctions between zinc blende (ZB) and wurtzite (WZ) CZS, and homojunctions between cubic CoO (C-CoO) and hexagonal CoO (H-CoO) in this nanocomposite. The practical application of this composite as a photocatalyst for H2 production from water reveals that the optimized hybrid nanocomposite (per 10 mg) with 1.5% M-CoO presents the best catalytic activity with a H2 production rate of 1.78 mmol h−1, which is nearly ca. 6-fold higher than that of pristine twin crystal CZS (0.30 mmol h−1), corresponding to an apparent quantum efficiency of 37.1% at 420 nm. The nanocomposite does not show a distinctive drop in the H2 production rate after 40 h photocatalytic reaction, indicating the excellent stability of this photocatalyst. Moreover, the H2 production rate of CZS/M-CoO is much higher than that of either CZS/C-CoO or CZS/H-CoO under visible light illumination, which indicates that the homojunctions between cubic CoO and hexagonal CoO within M-CoO can enhance the separation of photogenerated electron–hole pairs. Therefore, the excellent catalytic activity of CZS/M-CoO should be attributed to the synergistic effect of heterojunctions between CoO and CZS, homojunctions in twin crystal CZS, and homojunctions in CoO, which promote remarkably the separation of photogenerated electron–hole pairs. Furthermore, M-CoO also acts as a much better co-catalyst than either pure phase H-CoO or C-CoO for photocatalytic H2 production based on other catalysts, such as g-C3N4, CdS, ZnS or TiO2. These results suggest that using a homojunction containing co-catalyst is a versatile method to promote the photocatalytic activity of a photocatalyst. We believe that this work provides a new universal approach toward more efficient photocatalysts.

Exploiting unmanned aerial vehicles (UAVs) as flying base stations (BSs) to assist the terrestrial cellular networks is promising in 5G and beyond. Despite the inherent potentials, one challenging problem is how to optimally deploy multiple UAVs to achieve on-demand coverage for ground user equipment (UE). In this article, we model the deployment problem as minimizing the number of UAVs and maximizing the load balance among them, which is subject to two main constraints, i.e., UAVs should form a robust backbone network and they should keep connected with the fixed BSs. To solve this optimization problem with low complexity, we decompose the problem into two subproblems and propose a hybrid algorithm to solve them stepwise. First, a centralized greedy search algorithm is used to heuristically obtain the minimum number of UAVs and their suboptimal positions in a discontinuous space. Then, a distributed motion algorithm is adopted which enables each UAV to autonomously control its motion toward the optimal position in a continuous space. The proposed algorithm is applicable to various scenarios where UAVs are deployed alone or with fixed BSs regardless of the UE distribution. Extensive simulations validate the proposed algorithm.

With the emergence of Internet of Things (IoT), where any device is able to connect to the Internet and monitor/control physical elements, several applications were made possible, such as smart cities, smart health care, and smart transportation. The wide range of the requirements of these applications drives traditional IoT to cognitive IoT (CIoT) that supports smart resource allocation, automatic network operation and intelligent service provisioning. To enable CIoT, there is a need for flexible and reliable wireless communication. In this paper, we propose to combine cognitive radio (CR) with a biological mechanism called reaction–diffusion to provide efficient spectrum allocation for CIoT. We first formulate the quantization of qualitative connectivity-flexibility tradeoff problem to determine the optimal cluster size (i.e., number of cluster members) that maximizes clustered throughput but minimizes communication delay. Then, we propose a bio-inspired algorithm which is used by CIoT devices to form cluster distributedly. We compute the optimal values of the algorithm’s parameters (e.g., contention window) of the proposed algorithm to increase the network’s adaption to different scenarios (e.g., spectrum homogeneity and heterogeneity) and to decrease convergence time, communication overhead, and computation complexity. We conduct a theoretical analysis to validate the correctness and effectiveness of proposed bio-inspired algorithm. Simulation results show that the proposed algorithm can achieve excellent clustering performance in different scenarios.

Real-time tracking drug release behavior is fundamentally important for avoiding adverse effects or unsuccessful treatment in personalize medical treatment. However, the development of a non-invasive drug reporting platform still remains challenging. Herein the design of a novel synthetic magnetic resonance imaging (MRI) agent for drug release tracking (SMART) is reported, which integrates photothermal core and paramagnetic ion/drug loading shell with a thermal valve in a hybrid structure. Through near-infrared (NIR)-II photothermal effect originating from inner Au-Cu9S5 nanohybrid core, burst release of drugs loaded in the mesoporous silica shell is achieved. The concomitant use of a phase change material not only prevents premature drug release, but also regulates heating effect, keeping local temperature below 45 °C, enabling synergistic chemotherapy and mild hyperthermia in vitro and in vivo. Furthermore, the drug release from SMART facilitates proton accessibility to the paramagnetic ions anchored inside mesopores channels, enhancing longitudinal T1 relaxation rate and displaying positive signal correlation to the amount of released drug, thus allowing non-invasive real-time monitoring of drug release event. The current study highlights the potential of designed MRI nanophores such as SMART for real-time and in-situ monitoring of drug delivery for precision theranostic applications.

A novel statistical fault detection method, called the global-and-local-structure-based neural network (GLSNN), is proposed for fault detection. GLSNN is a nonlinear data-driven process monitoring technique through preserving both global and local structures of normal process data. GLSNN is characterized by adaptively training a neural network which takes both the global variance information and the local geometrical structure into consideration. GLSNN is designed to extract the meaningful low-dimensional features from original high-dimensional process data. After nonlinear feature extraction, Hotelling T 2 statistic and the squared prediction error (SPE) statistic are adopted for online fault detection. The merits of the proposed GLSNN method are demonstrated by both theoretical analysis and case studies on the Tennessee Eastman (TE) benchmark process. Extensive experimental results show the superiority of GLSNN in terms of missed detection rate (MDR) and false alarm rate (FAR). The source code of GLSNN can be found in https://github.com/htzhaoecust/glsnn .

The distributed mobile robotic network consists of a group of mobile nodes, such as mobile sensors, unmanned vehicles, unmanned submarines, unmanned air vehicles, or mobile robots. The mobile robotic network keeping a regular topology can utilize efficient network protocols and is also promising in many application scenarios. We propose a distributed algorithm that controls multiple distributed robotic nodes to form regular topology, including straight line, ring, triangular lattice, and square lattice. Our algorithm generates artificial forces, including the attractive force towards a reference point to gather the distributed nodes, the repulsive force from neighboring nodes to keep the desirable distance among them, the formation force to form a specific shape, and the obstacle avoidance force to avoid possible obstacles, such that each node simply follows the resultant force to move. The algorithm works in a fully distributed manner, converges fast, and is easy to deploy, requiring only one-hop local network geometry information. And, it is effective under both 2D and 3D scenarios. A computer demo is developed to demonstrate the effectiveness of the algorithm for large numbers of robotic nodes.

The photocatalytic H2-production by solar light has been considered a promising technology to converse solar energy into carbon-free hydrogen. The development of efficient and stable catalysts is the most urgent problem in this technology. Up to now, twin crystal Cd0.5Zn0.5S solid solution has been regarded as the best efficient pristine sulphide catalysts for visible-light-driven hydrogen production. Its catalytic activity can be remarkably improved further by loading suitable co-catalyst, such as PdP~0.33S~1.67, noble metal Pt, and NiSx. However, these twin crystal Cd0.5Zn0.5S-based nanocomposites can only response to partial (wavelength less than 520 nm) visible light irradiation. Large amount of visible light and near infrared light (NIR) in solar spectrum can not be absorbed by Cd0.5Zn0.5S and, therefore, do not contribute to the H2 production. In this work, β-NaYF4:Yb3+,Tm3+,Er3+ up-conversion nanoparticles (UPNs) are prepared by a hydrothermal process and the corresponding nanocomposite photocatalyst (twin crystal Cd0.5Zn0.5S/β-NaYF4:Yb3+,Tm3+,Er3+ (T-CZS/UPNs)) based on this kind of up-conversion nanoparticles and twin crystal Cd0.5Zn0.5S nanocrystal is successfully prepared for the first time. The compositions, morphologies, and optical properties of the T-CZS/UPNs are investigated using XRD, SEM, HRTEM, UV–vis–NIR absorption spectra and photoluminescence (PL) spectrum. The photocatalytic hydrogen evolution experiments are performed under the irradiation of visible light, NIR light or simulated solar light, respectively. The H2 production rate over T-CZS/UPNs-15 nanocomposite under the irradiation of simulated solar light in the presence of Na2S/Na2SO3 as sacrificial agent is measured to be 159.3 mmol/h/g, which is 3.4 times higher than that of pristine T-CZS nanocrystals. In particular, this nanocomposite exhibits also significant photocatalytic hydrogen production rate (0.497 mmol/g/h) under NIR light irradiation (λ > 800 nm), reveals the contribution of NIR light to H2 production via an photon-up-conversion process. This work gives an innovative vision in constructing efficient photocatalysts to make the efficient use of NIR solar light.