Nanjing University of Aeronautics and Astronautics

Unlike conventional materials removal methods, additive manufacturing (AM) is based on a novel materials incremental manufacturing philosophy. Additive manufacturing implies layer by layer shaping and consolidation of powder feedstock to arbitrary configurations, normally using a computer controlled laser. The current development focus of AM is to produce complex shaped functional metallic components, including metals, alloys and metal matrix composites (MMCs), to meet demanding requirements from aerospace, defence, automotive and biomedical industries. Laser sintering (LS), laser melting (LM) and laser metal deposition (LMD) are presently regarded as the three most versatile AM processes. Laser based AM processes generally have a complex non-equilibrium physical and chemical metallurgical nature, which is material and process dependent. The influence of material characteristics and processing conditions on metallurgical mechanisms and resultant microstructural and mechanical properties of AM processed components needs to be clarified. The present review initially defines LS/LM/LMD processes and operative consolidation mechanisms for metallic components. Powder materials used for AM, in the categories of pure metal powder, prealloyed powder and multicomponent metals/alloys/MMCs powder, and associated densification mechanisms during AM are addressed. An in depth review is then presented of material and process aspects of AM, including physical aspects of materials for AM and microstructural and mechanical properties of AM processed components. The overall objective is to establish a relationship between material, process, and metallurgical mechanism for laser based AM of metallic components.

Making recognition more reliable under uncontrolled lighting conditions is one of the most important challenges for practical face recognition systems. We tackle this by combining the strengths of robust illumination normalization, local texture-based face representations, distance transform based matching, kernel-based feature extraction and multiple feature fusion. Specifically, we make three main contributions: 1) we present a simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition; 2) we introduce local ternary patterns (LTP), a generalization of the local binary pattern (LBP) local texture descriptor that is more discriminant and less sensitive to noise in uniform regions, and we show that replacing comparisons based on local spatial histograms with a distance transform based similarity metric further improves the performance of LBP/LTP based face recognition; and 3) we further improve robustness by adding Kernel principal component analysis (PCA) feature extraction and incorporating rich local appearance cues from two complementary sources--Gabor wavelets and LBP--showing that the combination is considerably more accurate than either feature set alone. The resulting method provides state-of-the-art performance on three data sets that are widely used for testing recognition under difficult illumination conditions: Extended Yale-B, CAS-PEAL-R1, and Face Recognition Grand Challenge version 2 experiment 4 (FRGC-204). For example, on the challenging FRGC-204 data set it halves the error rate relative to previously published methods, achieving a face verification rate of 88.1% at 0.1% false accept rate. Further experiments show that our preprocessing method outperforms several existing preprocessors for a range of feature sets, data sets and lighting conditions.

Recently, Kovalenko and co‐workers and Li and co‐workers developed CsPbX 3 (X = Cl, Br, I) inorganic perovskite quantum dots (IPQDs), which exhibited ultrahigh photoluminescence (PL) quantum yields (QYs), low‐threshold lasing, and multicolor electroluminescence. However, the usual synthesis needs high temperature, inert gas protection, and localized injection operation, which are severely against applications. Moreover, the so unexpectedly high QYs are very confusing. Here, for the first time, the IPQDs' room‐temperature (RT) synthesis, superior PL, underlying origins and potentials in lighting and displays are reported. The synthesis is designed according to supersaturated recrystallization (SR), which is operated at RT, within few seconds, free from inert gas and injection operation. Although formed at RT, IPQDs' PLs have QYs of 80%, 95%, 70%, and FWHMs of 35, 20, and 18 nm for red, green, and blue emissions. As to the origins, the observed 40 meV exciton binding energy, halogen self‐passivation effect, and CsPbX 3 @X quantum‐well band alignment are proposed to guarantee the excitons generation and high‐rate radiative recombination at RT. Moreover, such superior optical merits endow them with promising potentials in lighting and displays, which are primarily demonstrated by the white light‐emitting diodes with tunable color temperature and wide color gamut.

In this paper a new frequency domain technique is introduced for the modal identification of output-only systems, i.e. in the case where the modal parameters must be estimated without knowing the input exciting the system. By its user friendliness the technique is closely related to the classical approach where the modal parameters are estimated by simple peak picking. However, by introducing a decomposition of the spectral density function matrix, the response spectra can be separated into a set of single degree of freedom systems, each corresponding to an individual mode. By using this decomposition technique close modes can be identified with high accuracy even in the case of strong noise contamination of the signals. Also, the technique clearly indicates harmonic components in the response signals.

Abstract A facile two‐step method is developed for large‐scale growth of ultrathin mesoporous nickel cobaltite (NiCo 2 O 4 ) nanosheets on conductive nickel foam with robust adhesion as a high‐performance electrode for electrochemical capacitors. The synthesis involves the co‐electrodeposition of a bimetallic (Ni, Co) hydroxide precursor on a Ni foam support and subsequent thermal transformation to spinel mesoporous NiCo 2 O 4 . The as‐prepared ultrathin NiCo 2 O 4 nanosheets with the thickness of a few nanometers possess many interparticle mesopores with a size range from 2 to 5 nm. The nickel foam supported ultrathin mesoporous NiCo 2 O 4 nanosheets promise fast electron and ion transport, large electroactive surface area, and excellent structural stability. As a result, superior pseudocapacitive performance is achieved with an ultrahigh specific capacitance of 1450 F g −1 , even at a very high current density of 20 A g −1 , and excellent cycling performance at high rates, suggesting its promising application as an efficient electrode for electrochemical capacitors.

Laser-metal additive manufacturing capabilities have advanced from single-material printing to multimaterial/multifunctional design and manufacturing. Material-structure-performance integrated additive manufacturing (MSPI-AM) represents a path toward the integral manufacturing of end-use components with innovative structures and multimaterial layouts to meet the increasing demand from industries such as aviation, aerospace, automobile manufacturing, and energy production. We highlight two methodological ideas for MSPI-AM-"the right materials printed in the right positions" and "unique structures printed for unique functions"-to realize major improvements in performance and function. We establish how cross-scale mechanisms to coordinate nano/microscale material development, mesoscale process monitoring, and macroscale structure and performance control can be used proactively to achieve high performance with multifunctionality. MSPI-AM exemplifies the revolution of design and manufacturing strategies for AM and its technological enhancement and sustainable development.

While the synthesis of hollow structures of transition metal oxides is well established, it is extremely challenging to fabricate complex hollow structures for mixed transition metal sulfides. Here we report an anion exchange method to synthesize a complex ternary metal sulfides hollow structure, namely nickel cobalt sulfide ball-in-ball hollow spheres. Uniform nickel cobalt glycerate solid spheres are first synthesized as the precursor and subsequently chemically transformed into nickel cobalt sulfide ball-in-ball hollow spheres. When used as electrode materials for electrochemical capacitors, these nickel cobalt sulfide hollow spheres deliver a specific capacitance of 1,036 F g−1 at a current density of 1.0 A g−1. An asymmetric supercapacitor based on these ball-in-ball structures shows long-term cycling performance with a high energy density of 42.3 Wh kg−1 at a power density of 476 W kg−1, suggesting their potential application in high-performance electrochemical capacitors. While the synthesis of hollow structures of transition metal oxides is well established, the related sulfide chemistry remains challenging. Here, the authors report the synthesis of nickel cobalt sulfide ball-in-ball hollow spheres, via anion exchange, and evaluate their pseudocapacitive behaviour.

All-inorganic colloidal cesium lead halide perovskite quantum dots (CsPbX3, X = Cl, Br, I) are revealed to be a new class of favorable optical-gain materials, which show ­combined merits of both colloidal quantum dots and halide perovskites. Low-threshold and ­ultrastable stimulated emission is ­demonstrated under atmospheric conditions with wavelength tunability across the whole ­visible spectrum via either size or composition control. As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re-organized for online delivery, but are not copy-edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

As new members of carbon material family, carbon and graphene quantum dots (CDs, GQDs) have attracted tremendous attentions for their potentials for biological, optoelectronic, and energy related applications. Among these applications, bio‐imaging has been intensively studied, but optoelectronic and energy devices are rapidly rising. In this Feature Article, recent exciting progresses on CD‐ and GQD‐based optoelectronic and energy devices, such as light emitting diodes (LEDs), solar cells (SCs), photodetctors (PDs), photocatalysis, batteries, and supercapacitors are highlighted. The recent understanding on their microstructure and optical properties are briefly introduced in the first part. Some important progresses on optoelectronic and energy devices are then addressed as the main part of this Feature Article. Finally, a brief outlook is given, pointing out that CDs and GQDs could play more important roles in communication‐ and energy‐functional devices in the near future.

Fuzzy c-means clustering (FCM) with spatial constraints (FCM_S) is an effective algorithm suitable for image segmentation. Its effectiveness contributes not only to the introduction of fuzziness for belongingness of each pixel but also to exploitation of spatial contextual information. Although the contextual information can raise its insensitivity to noise to some extent, FCM_S still lacks enough robustness to noise and outliers and is not suitable for revealing non-Euclidean structure of the input data due to the use of Euclidean distance (L2 norm). In this paper, to overcome the above problems, we first propose two variants, FCM_S1 and FCM_S2, of FCM_S to aim at simplifying its computation and then extend them, including FCM_S, to corresponding robust kernelized versions KFCM_S, KFCM_S1 and KFCM_S2 by the kernel methods. Our main motives of using the kernel methods consist in: inducing a class of robust non-Euclidean distance measures for the original data space to derive new objective functions and thus clustering the non-Euclidean structures in data; enhancing robustness of the original clustering algorithms to noise and outliers, and still retaining computational simplicity. The experiments on the artificial and real-world datasets show that our proposed algorithms, especially with spatial constraints, are more effective.

Feature fusion, the combination of features from different layers or branches, is an omnipresent part of modern network architectures. It is often implemented via simple operations, such as summation or concatenation, but this might not be the best choice. In this work, we propose a uniform and general scheme, namely attentional feature fusion, which is applicable for most common scenarios, including feature fusion induced by short and long skip connections as well as within Inception layers. To better fuse features of inconsistent semantics and scales, we propose a multiscale channel attention module, which addresses issues that arise when fusing features given at different scales. We also demonstrate that the initial integration of feature maps can become a bottleneck and that this issue can be alleviated by adding another level of attention, which we refer to as iterative attentional feature fusion. With fewer layers or parameters, our models outperform state-of-the-art networks on both CIFAR-100 and ImageNet datasets, which suggests that more sophisticated attention mechanisms for feature fusion hold great potential to consistently yield better results compared to their direct counterparts. Our codes and trained models are available online <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .

Abstract Laser ablation of solid targets in the liquid medium can be realized to fabricate nanostructures with various compositions (metals, alloys, oxides, carbides, hydroxides, etc.) and morphologies (nanoparticles, nanocubes, nanorods, nanocomposites, etc.). At the same time, the post laser irradiation of suspended nanomaterials can be applied to further modify their size, shape, and composition. Such fabrication and modification of nanomaterials in liquid based on laser irradiation has become a rapidly growing field. Compared to other, typically chemical, methods, laser ablation/irradiation in liquid (LAL) is a simple and “green” technique that normally operates in water or organic liquids under ambient conditions. Recently, the LAL has been elaborately developed to prepare a series of nanomaterials with special morphologies, microstructures and phases, and to achieve one‐step formation of various functionalized nanostructures in the pursuit of novel properties and applications in optics, display, detection, and biological fields. The formation mechanisms and synthetic strategies based on LAL are systematically analyzed and the reported nanostructures derived from the unique characteristics of LAL are highlighted along with a review of their applications and future challenges.

Nowadays, low-frequency electromagnetic interference (<2.0 GHz) remains a key core issue that plagues the effective attenuation performance of conventional absorption devices prepared via the component-morphology method (Strategy I). According to theoretical calculations, one fundamental solution is to develop a material that possesses a high ε' but lower ε″. Thus, it is attempted to control the dielectric values via applying an external electrical field, which inducts changes in the macrostructure toward a performance improvement (Strategy II). A sandwich-structured flexible electronic absorption device is designed using a carbon film electrode to conduct an external current. Simultaneously, an absorption layer that is highly responsive to an external voltage is selected via Strategy I. Relying on the synergistic effects from Strategies I and II, this device demonstrates an absorption value of more than 85% at 1.5-2.0 GHz with an applied voltage of 16 V while reducing the thickness to ≈5 mm. In addition, the device also shows a good absorption property at 25-150 °C. The method of utilizing an external voltage to break the intrinsic dielectric feature by modifying a traditional electronic absorption device is demonstrated for the first time and has great significance in solving the low-frequency electromagnetic interference issue.

Abstract High conductivity, large mechanical strength, and elongation are important parameters for soft electronic applications. However, it is difficult to find a material with balanced electronic and mechanical performance. Here, a simple method is developed to introduce ion‐rich pores into strong hydrogel matrix and fabricate a novel ionic conductive hydrogel with a high level of electronic and mechanical properties. The proposed ionic conductive hydrogel is achieved by physically cross‐linking the tough biocompatible polyvinyl alcohol (PVA) gel as the matrix and embedding hydroxypropyl cellulose (HPC) biopolymer fibers inside matrix followed by salt solution soaking. The wrinkle and dense structure induced by salting in PVA matrix provides large stress (1.3 MPa) and strain (975%). The well‐distributed porous structure as well as ion migration–facilitated ion‐rich environment generated by embedded HPC fibers dramatically enhances ionic conductivity (up to 3.4 S m −1 , at f = 1 MHz). The conductive hybrid hydrogel can work as an artificial nerve in a 3D printed robotic hand, allowing passing of stable and tunable electrical signals and full recovery under robotic hand finger movements. This natural rubber‐like ionic conductive hydrogel has a promising application in artificial flexible electronics.

In real-world recognition/classification tasks, limited by various objective factors, it is usually difficult to collect training samples to exhaust all classes when training a recognizer or classifier. A more realistic scenario is open set recognition (OSR), where incomplete knowledge of the world exists at training time, and unknown classes can be submitted to an algorithm during testing, requiring the classifiers to not only accurately classify the seen classes, but also effectively deal with unseen ones. This paper provides a comprehensive survey of existing open set recognition techniques covering various aspects ranging from related definitions, representations of models, datasets, evaluation criteria, and algorithm comparisons. Furthermore, we briefly analyze the relationships between OSR and its related tasks including zero-shot, one-shot (few-shot) recognition/learning techniques, classification with reject option, and so forth. Additionally, we also review the open world recognition which can be seen as a natural extension of OSR. Importantly, we highlight the limitations of existing approaches and point out some promising subsequent research directions in this field.

Hydrogels are a unique class of polymeric materials that possess an interconnected porous network across various length scales from nano- to macroscopic dimensions and exhibit remarkable structure-derived properties, including high surface area, an accommodating matrix, inherent flexibility, controllable mechanical strength, and excellent biocompatibility. Strong and robust adhesion between hydrogels and substrates is highly desirable for their integration into and subsequent performance in biomedical devices and systems. However, the adhesive behavior of hydrogels is severely weakened by the large amount of water that interacts with the adhesive groups reducing the interfacial interactions. The challenges of developing tough hydrogel-solid interfaces and robust bonding in wet conditions are analogous to the adhesion problems solved by marine organisms. Inspired by mussel adhesion, a variety of catechol-functionalized adhesive hydrogels have been developed, opening a door for the design of multi-functional platforms. This review is structured to give a comprehensive overview of adhesive hydrogels starting with the fundamental challenges of underwater adhesion, followed by synthetic approaches and fabrication techniques, as well as characterization methods, and finally their practical applications in tissue repair and regeneration, antifouling and antimicrobial applications, drug delivery, and cell encapsulation and delivery. Insights on these topics will provide rational guidelines for using nature's blueprints to develop hydrogel materials with advanced functionalities and uncompromised adhesive properties.

We report a practical and efficient strategy for synthesizing hierarchical (meso- and macro-)porous NiO nano/micro spherical superstructures. First, β-Ni(OH)2 microspheres were self-assembled based on the coalescence and Ostwald-ripening mechanisms during refluxing in an alkaline solution with Ni(NH3)x2+ at 97 °C for 1 h under vigorous stirring. Second, hierarchical porous NiO microsphere superstructures were obtained from the precursor by a simple calcination procedure. The resulting superstructures comprised two-dimensional mesoporous NiO petal building blocks. Electrochemical data demonstrated that the hierarchical porous NiO nano/micro superstructures were capable of delivering a specific capacitance of 710 F g−1 at 1 A g−1 and offered a good specific capacitance retention of ca. 98% after 2000 continuous charge-discharge cycles. This indicates that we successfully met key requirements in terms of large specific energy density, high-rate capability and good electrochemical stability. We would expect our superstructures to be good candidates for a low-cost replacement for the state-of-the-art supercapacitor material RuO2.

An advanced electrode for high-performance electrochemical capacitors has been designed by growing ultrathin mesoporous Co3O4 nanosheet arrays on the Ni foam support. This unique 3D electrode manifests exceptional supercapacitive performance with ultrahigh specific capacitance at high current densities and excellent cycling stability.

The present paper contains a critical study of a number of foundation models as well as a further development of some of the ideas involved. Among others it is shown that the Pasternak foundation is a mechanical model for the so-called “generalized” foundation. It is also demonstrated that the kernel for the Pasternak foundation in plane stress or plane strain is identical with Wieghardt’s exponential kernel, and that for the three-dimensional case the kernel is a modified Bessel function. It is also shown that the “non solvability” of the problem of a finite beam or plate resting on a continuous foundation as posed by Wieghardt and further elaborated by Pflanz is not correct, and that problems of this type are solvable for any load distribution permissible in classical plate theory. Throughout the paper, emphasis is placed on the proper mathematical formulation of the physical problems in question.

Biomass-derived carbon materials have received extensive attention as electrode materials for energy storage devices, including electrochemical capacitors, lithium–sulfur batteries, lithium-ion batteries, and sodium-ion batteries.