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The 2D VDW heterojunction MoC@NG@CN shows 13.4 times higher photocatalytic activity than CN. Abundant active sites, enhanced charge separation and numerous charge transport channels all contribute to the excellent photocatalytic performance.

The type II heterojunction MIL-68(In)@ZIS with hierarchical loose structure is successfully synthesized by in-situ growth. The optimized photocatalyst shows an excellent photocatalytic performance of 9.09 mmol g−1 h−1 with great stability.

The Mott–Schottky heterojunction MoC@NG@ZIS exhibits an excellent photocatalytic hydrogen evolution performance of 32.96 mmol g−1 h−1 with great stability. The enhanced charge and reaction kinetics explain the remarkable photocatalytic activity.

Gas-in-liquid foam templating is considered as a promising method for commercial-scale production of porous materials. However, Ostwald ripening occurs frequently during the preparation, which could bring about inhomogeneous bubbles with large sizes and deteriorate the performance of resultant products. To tackle this challenging task, we report a gelation network-triggered morphology-regulation strategy during the foaming process. As an exemplar, boehmite sol was incorporated into the andalusite suspension to stabilize the foam framework. After casting, the wet foam was able to maintain its overall shape, since andalusite particles were anchored onto the gas-liquid interface by the gelation network during mechanical frothing. The samples calcined at 1400°C feature homogeneous bubble with hierarchical porous architecture, smaller pore size with a mean value of 5.32 μm and robust compressive strength of 11.97MPa at a porosity level of 84 %. Our approach substantially broadens the design philosophy of high performance porous ceramics.

A compact imaging-based metasurface nanophotonic sensor consisting of graphene nanoribbon arrays is proposed. It provides a chemically specific technique to resolve absorption fingerprints without the need for spectrometry and frequency scanning.

The structural design of photocatalysts is highly related to the separation and transfer of photogenerated carriers, which is essential for the improvement of photocatalytic hydrogen evolution performance. Here, the hybrid photocatalyst M@NCNT@ZIS (M: Fe, Co, Ni; NCNT: nitrogen-doped carbon nanotube; ZIS: ZnIn

Mechanical metamaterials are of great interest due to their counterintuitive deformation under various physical fields. However, the research on metamaterials responding to moisture is still rare and controllable hygroscopic deformation is vital for sensoring, actuating, and stress elimination in a moisture environment. Inspired by the hygroscopic deformation of pinecones, this work studies 2D moisture-sensitive mechanical metamaterials exploiting bi-material curved strips as building blocks by simulations and experiments, which especially demonstrates repeatable programming ability to realize customized unusual hygroscopic deformations. Depending on the structural design of geometrical parameters and material configurations, the metamaterials exhibit a tunable coefficient of hygroscopic expansion from negative to positive, and unusual hygroscopic deformation modes including anisotropic, shearing, gradient, bending, and 3D deformation of 2D structures. Programmable metamaterials of arbitrary hygroscopic deformation are achieved by pixelated design and coding the building blocks. More importantly, the hygroscopic deformation is re-programmable by adopting erasable moisture-proof coatings on specific areas of metamaterials

Piezocatalysis, converting mechanical vibration into chemical energy, is an emerging technology to address environmental issues. In this work, we propose an efficient method to significantly improve the piezocatalytic activity by morphology engineering rather than composition design. The catalytic property in BaTiO3 nanocrystallites with diverse morphologies is investigated by dye degradation and hydrogen production under ultrasonic vibration. The BaTiO3 nanosheets exhibit an excellent piezocatalytic activity with a degradation rate of 0.1279 min−1 for Rhodamine B, far beyond those in previous piezocatalytic literature and even comparable to excellent photocatalysts, and also a high hydrogen production rate of 92 μmol g−1 h−1. Compared with nanowires and nanoparticles, the 2D morphology greatly enhances the piezocatalytic activity in nanosheets owing to much larger piezoelectric potential. This proves that the piezocatalytic property is dominated by the morphology-dependent piezoelectricity, rather than specific surface area as other catalysis. Dominated by bending vibrating mode, the piezocatalytic activity reaches a maximum at the piezoelectric resonating frequency, and it increases with the ultrasonic power. Moreover, it has good reusability and wide versatility for catalytic degradation. This work gives an in-depth understanding of piezocatalytic mechanism and provides a way to develop high performance and eco-friendly piezocatalysts.

Mechanical metamaterials pave a way for designing and optimizing microstructure topology to achieve counterintuitive deformation including negative Poisson's ratio (NPR) and negative thermal expansion (NTE). Previous studies were always limited to single anomalous mechanical or thermal deformation, but current applications for high-precision mechanical or optical equipment always require their combination and customized and anisotropic deformation parameters. This work develops programmable two-dimensional (2D) mechanical metamaterials based on chiral and antichiral structures constructed with curved bimaterial strips to produce tailorable NPR and arbitrary thermal deformation. The coefficient of thermal expansion of the mechanical metamaterials is tunable on a large scale across negative, near-zero, and positive values depending on the bimaterial configurations and geometrical parameters of curved strips, while the value of NPR is mainly determined by the radian. Furthermore, it is programmable by coding the unit cells to exhibit customized and anisotropic thermal deformation combining homogeneous, gradient, and shear modes. The proposed mechanical metamaterials are fabricated by multimaterial three-dimensional (3D) printing, and the unusual deformation modes are verified experimentally, which is well in agreement with the results of finite element analysis. This work demonstrates a feasible approach to achieving customized mechanical and thermal deformation through easy block building for specific engineering applications including eliminating thermal stress, shape morphing, and smart actuators.

Multifunctional polarization controlling plays an important role in modern photonics, but their designs toward broad bandwidths and high efficiencies are still rather challenging. Here, by applying the inverse design method of model-based theoretical paradigm, we design cascaded chiral metamaterials for different polarization controls in oppositely propagating directions and demonstrate their broadband and high-efficiency performance theoretically and experimentally. Started with the derivation of scattering matrix towards specified polarization control, a chiral metamaterial is designed as a meta-quarter-wave plate for the forward propagating linearly polarized wave, which converts the x- or y-polarized wave into a nearly perfect left- or right-handed circularly polarized wave; intriguingly, it also serves as a 45° polarization rotator for the backward propagating linearly polarized waves. This bifunctional metamaterial shows a high transmission as well as a broad bandwidth due to the Fabry–Perot-like interference effect. Using the similar approach, an abnormal broadband meta-quarter-wave plate is achieved to convert the forward x- and y-polarized or the backward y- and x-polarized waves into left- and right-handed circularly polarized waves with high transmission efficiencies. The integration of multiple functions in a single structure endows the cascaded chiral metamaterials with great interests for the high-efficiency polarization-controlled applications.

This study demonstrates the pore structures and mechanical properties of porous aluminum titanate‒strontium feldspar‒mullite fiber (ASM) composite ceramics. Samples were prepared using two different processes. A traditional reactive sintering method, with Al2O3 and TiO2 as raw materials, was used to prepare one group of samples, and an improved method, using aluminum titanate (AT) clinkers, was used to prepare another group of samples. The effects of the processes and raw materials on the pore structure and mechanical properties of the composite ceramics were investigated. The properties of the sintered porous ceramics, including the microstructure, density, porosity, pore size, and mechanical properties, were analyzed. After sintered at 1400 °C, the ASM ceramics that were prepared using the improved method had a porosity level of 70% and a pore size of 24 µm, which were twice that of the traditional ASM ceramics, while both samples had identical flexural strength values of 2.27 MPa. The improved process endowed the porous ASM ceramics with excellent pore structures and mechanical properties, promoting their potential use in filter applications.

Piezocatalysis, converting mechanical vibration into chemical energy, is an emerging technology to address environmental issues. In this work, we propose an efficient method to significantly improve the piezocatalytic activity by morphology engineering rather than composition design. The catalytic property in BaTiO

Guided mode resonances (GMRs) and bounded states in the continuum (BICs), both supported by dielectric gratings, can realize ultrahigh Q-factors and strong localized field enhancements, beneficial to high-performance sensing applications. In this paper, based on GMR theory and numerical simulations, we systematically investigate the relationship between different order GMRs and BICs/quasi-BICs in Si-based dielectric gratings with symmetric, singly, and doubly asymmetric profiles. The introduction of broken-symmetry in adjacent gaps or Si nanobeams brings about new GRM and symmetry-protected BIC and can transform the fundamental BIC into a resonant state with finite Q-factor as high diffraction orders. A Friedric-Wintgen BIC is also achieved under normal incidence by breaking symmetries of both gaps and Si nanobeams. Further, the asymmetric dielectric gratings with high Q-factor quasi-BICs are designed as a refractive index sensor. Although the Q-factor and localized electric field penetrating into the vacuum are greatly improved with the decreasing asymmetry parameter, the sensitivity is almost unchanged while the FOM demonstrates an inverse square dependence on the asymmetry parameter. To further improve the sensitivity, we construct an asymmetric dielectric grating with a low fill factor and a big period, which manifests an excellent sensing performance with a near theoretical sensitivity limit of ∼1506 nm/RIU and an ultrahigh FOM of ∼5000.

Large magnetocaloric effect (MCE) and magnetic properties in R3Ni6Al2 (R = Dy, Ho and Er) compounds were systematically investigated. Large magnetic moment and low magnetic transition temperature play a decisive role in the magnetocaloric performance for R3Ni6Al2 (R = Dy, Ho and Er) compounds. Ho3Ni6Al2 compound shows the largest magnetic entropy change with the value of 19.7 J/kg K at ~20 K under field change of 0–5 T because of the large magnetic moment and moderate transition temperature. For Dy3Ni6Al2 compound, it exhibits the largest refrigeration capacity with the value of 394.5 J/kg due to the successive magnetic transitions. The Er3Ni6Al2 compound has a large MCE at ultra-low temperatures around 4 K. Furthermore, the second-order phase transition in R3Ni6Al2 (R = Dy, Ho, Er) compounds were confirmed according to Arrott plots, rescaled universal magnetic entropy change curves and mean field theory with negligible thermal and magnetic hysteresis. The large reversible MCE with low working temperatures indicates that R3Ni6Al2 compounds are potential excellent MCE materials for low-temperature magnetic refrigeration.

The technology of photocatalytic hydrogen production that converts abundant yet intermittent solar energy into an environmentally friendly alternative energy source is an attractive strategy to mitigate the energy crisis and environmental pollution. Graphitic carbon nitride (g-C3N4), as a promising photocatalyst, has gradually received focus in the field of artificial photosynthesis due to its appealing optical property, high chemical stability and easy synthesis. However, the limited light absorption and massive recombination of photoinduced carriers have hindered the photocatalytic activity of bare g-C3N4. Therefore, from the perspective of theoretical calculations and experiments, many valid approaches have been applied to rationally design the photocatalyst and ameliorate the hydrogen production performance, such as element doping, defect engineering, morphology tuning, and semiconductor coupling. This review summarized the latest progress of g-C3N4-based photocatalysts from two perspectives, modification of pristine g-C3N4 and interfacial engineering design. It is expected to offer feasible suggestions for the fabrication of low-cost and high-efficiency photocatalysts and the photocatalytic mechanism analyses assisted by calculation in the near future. Finally, the prospects and challenges of this exciting research field are discussed.

With aid of good materials descriptors, machine learning algorithms are able to accelerate the design of new materials and uncover underlying mechanisms. In the present study, we adopt machine learning methods to discover the most important materials descriptors for properties of ferroelectric materials. A regression study, in typical BaTiO3-based and K1/2Na1/2NbO3-based lead-free ceramics and lead-contained PbMg1/3Nb2/3TiO3-PbTiO3 ceramics, screens out three important materials descriptors determining ferroelectricity from 46 potential descriptors. The three descriptors of Matyonov-Batsanov electronegativity, ratio of valence electron number to nominal charge and core electron distance (Schubert) are confirmed to be dominant as well in classification of perovskite compounds into antiferroelectrics or not. The classification based on these descriptors exhibit an excellent accuracy of 96%, much higher than that of traditional criterion (89%) using tolerance factor and Pauling electronegativity. Furthermore, we propose a machine learning strategy based on our descriptors to predict the phase coexistence. The prediction probability after bootstrapping provides an effective approach to distinguish the phase boundaries and predict the phase ratio of coexisted phases. In all, we identified materials descriptors for ferroelectric materials, which is helpful to reveal the structure-property relationship of ferroelectric materials and guide the design of better ferroelectricity and piezoelectricity.

In this work, we develop a two-step sintering technology special for porous ceramics to resolve the contradiction between porosity and mechanical strength. It contains a first long-time sintering at the relatively low temperature (T1) and a second fast sintering at higher temperature (T2). Porous ceramics of aluminum titanate-strontium feldspar-mullite (ASM) ternary composite were prepared by this method, and exhibit both high porosity and high mechanical strength. The effects of T1 and T2 on the phase composition, microstructure, porosity, pore size distribution, mechanical strength and thermal expansion coefficient (TEC) of ASM porous ceramics are systematically studied. The sample sintered at T1 = 1400 °C & T2 = 1500 °C shows optimum performance: the porosity, bulk density, and mean pore diameter (D50) are 63.48%, 1.20 g/cm3 and 15.94 μm, respectively; while the flexural strength reaches 9.22 MPa and the TEC is 2.9 × 10−6/K. The two-step sintering technology provides a powerful tool to develop porous ceramics with excellent comprehensive properties.

The unusual properties of metamaterials originate from their artificial metallic structures; however, these materials indeed behave as an effective medium in the operating frequency range. Correspondingly, the unit cell is called a meta-atom, but its relationship with real atoms has not been substantially studied. In this paper, we studied a stacked fishnet metamaterial in the optical frequency range and established a basic relationship between the meta-atom and the real atom. The characteristic frequency of meta-atoms behaves just as the energy level of real atoms, which splits with the stacked meta-atom number, converges with the atom spacing and is sensitive to the features of the center dielectric layer. This work will increase the understanding of metamaterial physics in view of solid-state physics and guide metamaterial design through quantum mechanical methods.

Metasurfaces have powerful abilities to manipulate the properties of electromagnetic waves flexibly, especially the modulation of polarization state for both linearly polarized (LP) and circularly polarized (CP) waves. However, the transmission efficiency of cross-polarization conversion by a single-layer metasurface has a low theoretical upper limit of 25% and the bandwidth is usually narrow, which cannot be resolved by their simple additions. Here, we efficiently manipulate polarization coupling in multilayer metasurface to promote the transmission of cross-polarization by Fabry-Perot resonance, so that a high conversion coefficient of 80–90% of CP wave is achieved within a broad bandwidth in the metasurface with C-shaped scatters by theoretical calculation, numerical simulation and experiments. Further, fully controlling Pancharatnam-Berry phase enables to realize polarized beam splitter, which is demonstrated to produce abnormal transmission with high conversion efficiency and broad bandwidth.

Large-scale modulation of the left-handed transmission with a high quality factor is greatly desired by high-performance optical devices, but the requirements are hard to be satisfied simultaneously. This paper presents a hybrid graphene/dielectric metasurface to realize a large transmission modulation for the left-handed passband at near-infrared frequencies via tuning the Fermi energy of graphene. By splitting the nanoblocks, i.e. introducing an additional symmetry breaking in the unit cell, the metasurface demonstrates an ultrahigh quality factor (Q ≈ 550) of Fano resonance with near-unity transmission and full 2π phase coverage due to the interference between Mie-type magnetic and electric resonances, which induces the negative refraction property. Besides, the split in the nanoblock greatly enhances the local field by increasing the critical coupling area, so the light-graphene interaction is promoted intensively. When the surface conductivity of graphene is electrically tuned, the hybrid graphene/dielectric metasurface exhibits a deep modulation of 85% for the left-handed passband, which is robust even for the highest loss of graphene. Moreover, the simple configuration remarkably reduces the fabrication requirements to facilitate the widespread applications.

With the advent of advanced diesel after-treatment technologies, select catalyst reduction(SCR) becomes dominant technology for the new emission legislation in china. Because of sophisticated NOx sensors are becoming a critical cost challenge to OEMs, open loop SCR is mainly used at present, and which lead to be difficultly adaptive to control emission reduction and calibration workload was heavy. Lean combustion technique for gasoline was applied, which lead to NOx gas obvious increase and needed NOx sensor to detect NOx concentration. This paper describes an approach for replacing the engine out NOx sensor with an artificial neural network (ANN) based NOx perception. A multi-layer perception network was trained to estimate NOx concentration from engine speed, load, exhaust temperature, and oxidation factor information. This supervised learning was conducted with measured engine data. The network was validated against measured data that was excluded from the training data set. The paper details application of this technique to a heavy duty diesel engine. Results show good agreement between predictions and measured data under the steady state conditions studied. The methodology will also be evaluated for use under transient operation, and over the operating lifetime of the associated application.