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The swine industry annually suffers significant economic losses caused by porcine reproductive and respiratory syndrome virus (PRRSV). Because the available commercial vaccines have limited protective efficacy against epidemic PRRSV, there is an urgent need for innovative solutions. Nanoparticle vaccines induce robust immune responses and have become a promising direction in vaccine development. In this study, we designed and produced a self-assembling nanoparticle vaccine derived from thermophilic archaeal ferritin to combat epidemic PRRSV. First, multiple T cell epitopes targeting viral structural proteins were identified by IFN-γ screening after PRRSV infection. Three different self-assembled nanoparticles with epitopes targeting viral GP3, GP4, and GP5 proteins were constructed and mixed to generate a FeCocktail vaccine. Experiments showed that the FeCocktail vaccine effectively activated CD4+and CD8+T cells and effector memory T cells in mice. Piglets immunized with the FeCocktail vaccine generated specific antibodies and exhibited increased levels of PRRSV-specific IFN-γ produced by functional CD4+and CD8+cells. The FeCocktail also provided protective efficacy against PRRSV challenge, including mitigation of clinical symptoms, reduction of viral loads in serum and lungs, and the alleviation of lung tissue damage. In conclusion, this study offers a promising candidate vaccine for combating epidemic PRRSV, and affirms the utility of nanoparticle protein as a platform for next-generation PRRSV vaccine development.

Optical remote sensing images have extensive application value, but cloud contamination greatly limits their potential use in the field of geographic information. Cloud removal aims to restore clear, unobstructed images from cloud-covered ones for subsequent in-depth analysis. Due to severe cloud cover problems such as thick clouds in some areas of remote sensing images, cloud removal tasks have become challenging. Recently, many methods have attempted to incrementally fill in obscured regions by fusing cloud-free information from multitemporal data. However, most of these methods fail to effectively utilize the interaction among different temporal data, and some information of data is easily lost in the process of deep transmission, this causes problems such as inadequate cloud removal and blurred recovery of ground under the clouds. Therefore, we propose a multistream complementary generative adversarial network (MSC-GAN) for cloud removal using multitemporal data. First, it employs a multistream complementary (MSC) architecture in the down-sampling feature encoding stage to effectively promote the interaction of feature information across multitemporal data, alleviating information loss as network depth increases. Second, to reduce the feature blur, we design a group feature reweighting (GFR) module as a complementary connection of long-distance information, in which the grouping learning and multidimensional parallel architecture can cost-effectively enhance semantic fusion between low-level and high-level features. Moreover, a channel enhancement method is introduced to assist in processing the underlying transition information, minimizing the interference of invalid information. Experimental results on multiple benchmark datasets under a series of image quality assessment metrics demonstrate the effectiveness of the proposed method.

Deep Neural Networks (DNNs) have gained widespread adoption in diverse fields, including image classification, object detection, and natural language processing. However, training large-scale DNN models often encounters significant memory bottlenecks, which ask for efficient management of extensive tensors. Heterogeneous memory system, which combines persistent memory (PM) modules with traditional DRAM, offers an economically viable solution to address tensor management challenges during DNN training. However, existing memory management methods on heterogeneous memory systems often lead to low PM access efficiency, low bandwidth utilization, and incomplete analysis of model characteristics. To overcome these hurdles, we introduce an efficient tensor management approach, DeepTM, tailored for heterogeneous memory to alleviate memory bottlenecks during DNN training. DeepTM employs page-level tensor aggregation to enhance PM read and write performance and executes contiguous page migration to increase memory bandwidth. Through an analysis of tensor access patterns and model characteristics, we quantify the overall performance and transform the performance optimization problem into the framework of Integer Linear Programming. Additionally, we achieve tensor heat recognition by dynamically adjusting the weights of four key tensor characteristics and develop a global optimization strategy using Deep Reinforcement Learning. To validate the efficacy of our approach, we implement and evaluate DeepTM, utilizing the TensorFlow framework running on a PM-based heterogeneous memory system. The experimental results demonstrate that DeepTM achieves performance improvements of up to 36% and 49% compared to the current state-of-the-art memory management strategies AutoTM and Sentinel, respectively. Furthermore, our solution reduces the overhead by 18 times and achieves up to 29% cost reduction compared to AutoTM.

• Highly catalytic palladium metallene was prepared. • Initial dehydrogenation temperature of MgH 2 -Pd metallene was reduced by 141 K. • Synergistic effects of Pd single atoms and clusters were observed. Hydrogen storage plays a pivotal role in the hydrogen industry, yet its current status presents a bottleneck. Diverse strategies have emerged in recent years to address this challenge. MgH 2 has stood out as a promising solid-state hydrogen storage material due to its impressive gravimetric and volumetric hydrogen density, but its practical application is hampered by elevated thermal stability and sluggish kinetics. In this study, we introduce a solution by synthesizing Pd metallene through a one-pot solvothermal method, revealing a distinctive highly curved lamellar structure with a thickness of around 1.6 nm. Incorporating this Pd metallene into MgH 2 results in a composite system wherein the starting dehydrogenation temperature is significantly lowered to 439 K and complete dehydrogenation occurs at 583 K, releasing 6.14 wt.% hydrogen. The activation energy of dehydrogenation for MgH 2 was reduced from 170.4 kJ mol–1 to 79.85 kJ mol–1 after Pd metallene decoration. The enthalpy of dehydrogenation of the MgH 2 –10 wt.% Pd sample was calculated to be 73 kJ mol–1 H 2 –1 and decreased by 4.4 kJ mol–1 H 2 –1 from that of dehydrogenation of pure MgH 2 (77.4 kJ mol–1 H 2 –1). Theoretical calculations show that the average formation energy and average adsorption energy of hydrogen vacancies can be significantly reduced in the presence of both Pd clusters and Pd single atoms on the surface of MgH 2 /Mg, respectively. It suggests that the synergistic effect of in situ formed Pd single atoms and clusters significantly improves the hydrogenation and dehydrogenation kinetics. The identified active sites in this study hold potential as references for forthcoming multi-sized active site catalysts, underscoring a significant advancement toward resolving hydrogen storage limitations. [Display omitted] [ABSTRACT FROM AUTHOR]

Sm-Fe-N material holds immense promise as new material for developing next generation permanent magnets. However, the achievement of high coercivity in this material remains a big challenge, and moreover new coercivity theories beyond the commonly considered nucleation-type mechanism needs exploration. This study describes both the yielding of anisotropic Sm-Fe-N powders with high coercivities and the discovery of new coercivity mechanism by a systematic study using a designed milling preparation method. As a result, a random-direction cleavage behavior with unexpected XRD peak shifts and formation of new micron-sized flower-like particles were found in the yielded Sm-Fe-N powders. The grain sizes of the powders are of nanoscale dimension and they refine gradually as the milling time increases. The measured coercivity is up to 15.0 kOe, which is a substantial increase of approximately 36.4% compared to the original commercial powders and among the highest reported values via the milling preparation method (commonly below 13 kOe). The yielded powders also demonstrate a new dual-type coercivity mechanism (combing both nucleation-type and pinning-type control effect), and the improved pining effect contributes to the high coercivities. This study opens the door of utilizing pinning effect for achieving high coercivities in anisotropic Sm-Fe-N material.

Inspired by natural hierarchical self-assembly of proteins and peptides, amino acids, as the basic building units, have been shown to self-assemble to form highly ordered structures through supramolecular interactions. The fabrication of functional biomaterials comprised of extremely simple biomolecules has gained increasing interest due to the advantages of biocompatibility, easy functionalization, and structural modularity. In particular, amino acid based assemblies have shown attractive physical characteristics for various bionanotechnology applications. Herein, we propose a review paper to summarize the design strategies as well as research advances of amino acid based supramolecular assemblies as smart functional materials. We first briefly introduce bioinspired reductionist design strategies and assembly mechanism for amino acid based molecular assembly materials through noncovalent interactions in condensed states, including self-assembly, metal ion mediated coordination assembly, and coassembly. In the following part, we provide an overview of the properties and functions of amino acid based materials toward applications in nanotechnology and biomedicine. Finally, we give an overview of the remaining challenges and future perspectives on the fabrication of amino acid based supramolecular biomaterials with desired properties. We believe that this review will promote the prosperous development of innovative bioinspired functional materials formed by minimalistic building blocks.

To obtain more accurate dynamics equation parameters, this paper proposed a deep reinforcement learning (DRL) method for parameter identification. After using the least square (LS) method to identify the base parameters, we establish a training strategy where the friction coefficient serves as the DRL action. This strategy controls both the source and target manipulators, employing the concept of imitation learning. After using our strategy, the parameters of the target manipulator tend to converge to those of the source manipulator. In the experiment, we perform parameter identification of a 7-degree-of-freedom (DOF) manipulator in a real environment, and then identify friction coefficient for each joint based on the MuJoCo environment to theoretically validate the parameter identification using DRL. The identification results demonstrated that in a simulation environment, the use of DRL outperforms the traditional LS method, resulting in improved accuracy.

Despite significant progress in device performance, dye-sensitized solar cells (DSSCs) continue to fall short of their theoretical potential. Moreover, research in recent years needs to pay more attention to improving the device fabrication process. To achieve the theoretical efficiency limit, it is crucial to optimize the interface between the dye and TiO2nanoparticles in the entire device stack. Our study indicates that optimizing the structure or size of the coadsorbents and implementing a monolayer adsorption process can be an effective strategy to reduce charge recombination and enhance light-harvesting properties. Our research aims to develop a surface-coating adsorbent plan that controls the TiO2nanoparticle interface to achieve the radiative limit of power conversion efficiency (PCE). Specifically, we utilized 2-thiophenecarboxylic acid (THCA) or chenodeoxycholic acid (CDCA) as postinterfacial surface-coating adsorbents. Our results demonstrate that this approach effectively achieves the desired PCE limit. Combined with the coadsorbent structure engineering and interface optimization, the device increased the packing area on the TiO2nanoparticles’ surface, reaching an improved PCE of over 13.17% under simulated sunlight (1.5G), which is the highest efficiency of a porphyrin single dye-based DSSC. In particular, this practical approach was also applied to a large-area DSSC with an area of 3 cm2, yielding a remarkable PCE of 9.04%. Furthermore, when applied to a polymer gel electrolyte, this novel approach recorded the highest PCE of 11.16% with a long-term operational stability of up to 1000 h for the quasi-solid-state DSSCs. Our research findings provide a promising avenue for achieving high-performance DSSCs with ease of access and demonstrate practical applications as alternatives to conventional power sources.

This study aimed to develop low-cost D-π-A structured porphyrin and organic dyes with easily synthesizable donor units instead of the conventional complex multistep synthetic donor unit of Hexyloxy-BPFA[bis(7-(2,4-bis(hexyloxy)phenyl)-9,9-dimethyl-9H-fluoren-2-yl)amine] used in SGT-021and SGT-149as well-known record cosensitizers with an extremely high power conversion efficiency (PCE). The design strategy concerned the easier synthesis of low-cost donor units with inversion structures in donor groups via donor structural engineering, particularly by changing the position of the fluorene and phenylene units in the donor moiety while keeping the π-bridge and acceptor unit unchanged, leading to the synthesis of two D-π-A structured porphyrins [SGT-021(D0)and SGT-021(D)] and one D-π-A structured organic sensitizer [SGT-149(D)] for dye-sensitized solar cells (DSSCs). Specifically, porphyrin SGT-021(D0)incorporated two hexyl chains into the 9-position of each fluorene, while SGT-021(D)and SGT-149(D)substituted two hexyloxy chain units to the terminal position of each fluorene in the donor groups of porphyrin dyes. The effect of the position of the fluorene and phenylene units in the donor moiety on the photochemical and electrochemical properties, as well as the photovoltaic performance, was compared with the reference dyes of SGT-021and SGT-149, previously reported by the research group. After optimizing the DSSC devices, SGT-021(D)and SGT-021(D0)achieved a high PCE of 11.6 and 10.5%, respectively, while SGT-149(D)exhibited a little lower PCE of 10.3% under the standard AM 1.5G light intensity. The cell performance of DSSC devices based on SGT-021(D)and SGT-149(D)was inferior to the corresponding reference dyes of SGT-021and SGT-149due to their lower donating ability of Hexyloxy-BPFAthan Hexyloxy-BFPA.

Enhancing Autonomous Decision-Making (ADM) for unmanned combat aerial vehicle formations in beyond-visual-range air combat is pivotal for future battlefields, whereas the predominant reinforcement learning technique for ADM has been proven to be inadequately fitting complex tactical Unit Coordination (UC), limiting the integrity of decision-making for formations. This study proposes a knowledge-enhanced ADM method, with a focus on UC, to elevate formation combat effectiveness. The main innovation is integrating data mining technique with tactical knowledge mining and integration. Foremost, based on Frequent Event Arrangement Mining (FEAM) theory, a cross-channel UC knowledge mining method is designed by introducing data flow, which is capable of capturing dynamic coordinative action sequences. Then, a dual-mode knowledge integration method is proposed by employing the Graph Attention Network (GAT) and attenuated structural similarity, bolstering the interplay between autonomous UC tactics fitting and knowledge injection. The experimental results demonstrate that the algorithm surpasses the existing methods, providing more strategic maneuver trajectories and a win rate of more than 90% in different scenarios. The method is promising to augment the autonomous operational capabilities of unmanned formations and drive the evolution of combat effectiveness.

Point normal, as an intrinsic geometric property of 3D objects, not only serves conventional geometric tasks such as surface consolidation and reconstruction, but also facilitates cutting-edge learning-based techniques for shape analysis and generation. In this paper, we propose a normal refinement network, called Refine-Net, to predict accurate normals for noisy point clouds. Traditional normal estimation wisdom heavily depends on priors such as surface shapes or noise distributions, while learning-based solutions settle for single types of hand-crafted features. Differently, our network is designed to refine the initial normal of each point by extracting additional information from multiple feature representations. To this end, several feature modules are developed and incorporated into Refine-Net by a novel connection module. Besides the overall network architecture of Refine-Net, we propose a new multi-scale fitting patch selection scheme for the initial normal estimation, by absorbing geometry domain knowledge. Also, Refine-Net is a generic normal estimation framework: 1) point normals obtained from other methods can be further refined, and 2) any feature module related to the surface geometric structures can be potentially integrated into the framework. Qualitative and quantitative evaluations demonstrate the clear superiority of Refine-Net over the state-of-the-arts on both synthetic and real-scanned datasets.

Efficient and stable electromagnetic-wave (EMW) absorption materials have attracted great attention in the field of reducing microwave pollution. Herein, FeCoNiCuTixhigh-entropy alloys (HEAs) as electromagnetic-wave absorbing materials were prepared by a high-energy ball-milling method. The as-milled HEA powders presented a flaky shape with a high aspect ratio. Impedance matching was efficiently optimized by severe lattice distortion, which was caused by Ti incorporation. The introduced plentiful defects in FeCoNiCuTixHEAs provided abundant polarization sites for dielectric loss. By tuning Ti contents, FeCoNiCuTi0.2HEAs delivered excellent EMW absorption performances. The maximal reflection loss (RLmax) values reached −47.8 dB at 10.86 GHz as thin as 2.16 mm, and the widest bandwidth was 4.76 GHz (5.97–10.73 GHz). Furthermore, the introduction of Ti enhanced corrosion resistance via increasing the charge transfer resistance of a passivated film. Those characteristics of FeCoNiCuTixHEAs made these materials a practical gigahertz-range EMW absorber. Additionally, our findings provided a facile and tunable strategy for designing EMW absorbing materials, which was aimed at lightweight, highly efficient absorption, and resistance to harsh environments.

As a sensitive biological indicator, earthworms are widely used to monitor various pollutants of soil and provide an early warning for soil pollution. However, because many indices are involved in the exposure-induced oxidative stress response, practical applications of these indices are quite inconvenient. Therefore, it is appropriate to investigate the key monitoring index for use in early warning and pollution monitoring. Using Eisenia fetidaas an experimental model in an indoor simulation experiment, the mathematical modelling of the effect on oxidative stress in earthworms under cadmium (Cd) stress was studied. The test lasted 40 d, with the removal of one earthworm every 10 d. The Cd2+concentration gradient was set as 0, 1, 10, 20, 100, 200, 400, and 800 mg kg-1dry weight. The earthworms were divided into two sections from the clitellum for the determination of total protein (TP) and peroxidase (POD), superoxide dismutase (SOD), glutathione-S-transferase (GST), glutathione peroxidase (GPX), catalase (CAT), malondialdehyde (MDA), and acetylcholinesterase (AChE) activities. Results showed that POD was the key index of oxidative stress in head tissues after 10 d of exposure, TP was the key index at 20 d, and POD became the key index again at 30 and 40 d. By contrast, in tail tissues, MDA and SOD were the key indices at an exposure time of 10 d, GPX at 20 d, CAT and TP at 30 d, and POD and MDA at 40 d. These results contribute to establishing a scientific method for ecotoxicological diagnosis and revealing the mechanism of soil Cd toxicity.

The reliability of irrigated area (IA) information dominates the performance of irrigation water use and crop modeling accuracy. IA is typically mapped using Food and Agriculture Organization (FAO) agricultural census and remote sensing indices. Recent advances in machine learning and sampling techniques further improve IA mapping. However, the relative performances of different IA mapping approaches and their capability in capturing long‐term IA temporal variability remain unknown. Here, 1861 county‐level IA information from Government Censored Data (GCD) during 2000–2021 are collected, cross‐validated, and employed to evaluate commonly used gridded IA data sets. Results show that IA data sets based on the direct interpolation of FAO agricultural census can accurately capture the spatial distribution of IA. However, FAO statistics are only available in a particular year, which cannot capture inter‐annual irrigation variations. In contrast, IA products solely based on vegetation indices are prone to positive biases over humid regions due to the lack of contrast in vegetation dynamics. Overall, the latest GCD‐based machine learning IA data sets are relatively more accurate, but they are also problematic in estimating IA trends due to the use of temporally static training samples. Such biases are tightly related to agricultural suitability (AS calculated using precipitation and potential evapotranspiration). This suggests that AS should be employed as an endogenous variable in future machine learning based IA mapping algorithms. Understanding how much land is irrigated (Irrigated Area or IA) is crucial for accurate water use and crop modeling. Traditional IA maps are developed using data from the Food and Agriculture Organization (FAO) and remote sensing indices. Recent improvements, like machine learning, have enhanced IA mapping. However, the relative performances of these methods and their reliability in capturing long‐term changes in irrigated areas remain unknown. This study collects government‐censored county‐level IA data from 2000 to 2021, which is subsequently used for evaluating commonly used IA data sets. Results show that data sets directly using FAO data can reasonability capture the spatial distribution of IA but may miss year‐to‐year changes. On the other hand, data sets relying only on vegetation data tend to overestimate irrigated areas in humid regions. The latest machine learning‐based IA data sets are relatively more accurate but still contain significant uncertainties in IA trends. These biases are closely linked to agricultural suitability (calculated using precipitation and potential evapotranspiration), which may be useful for enhancing future IA mapping algorithms. Remote sensing vegetation index is insufficient for identifying irrigated area over humid regionsMachine learning based irrigated area is sensitive to the quality of training sampleAll the irrigated area data sets are biased in representing the temporal dynamics of irrigated area Remote sensing vegetation index is insufficient for identifying irrigated area over humid regions Machine learning based irrigated area is sensitive to the quality of training sample All the irrigated area data sets are biased in representing the temporal dynamics of irrigated area

Zn(II)-porphyrin dyes (SGT-030and SGT-031) with extended auxiliary π-spacers in the donor (D) part have been prepared and applied to dye-sensitized solar cells (DSSCs). The porphyrin dyes contained the same D–ethynyl–zinc porphyrinyl (ZnP)–ethynyl–benzothiadiazole-acceptor platform, but their donor groups varied from phenylene (Ph) in SGT-053as a reference dye to the thieno[3,2-b]benzothiophene (TBT) and 4-hexyl-4H-thieno[3,2-b]indole (TI) moieties in SGT-030and SGT-031, respectively. The effects of the extended auxiliary π-spacer in the D−π–A-structured porphyrin sensitizers on the molecular and photovoltaic properties were investigated via photophysical and electrochemical experiments as well as theoretical calculations. With the trend in conjugation length (Ph< TBT≈ TI) and the donating ability of the π-spacer (Ph< TBT< TI), the absorption maxima and molecular absorptivity increased in the order SGT-053(Ph) < SGT-030(TBT) < SGT-031(TI). The incorporation of TBTand TIpromoted significant enhancements in the light-harvesting properties by reducing the energy gap and efficiently improving electronic communication. The DSSCs based on SGT-030(10.80%) and SGT-031(10.89%) with coadsorption of 4-(3,6-bis(4-((2-ethylhexyl)oxy)phenyl)-9H-carbazol-9-yl)benzoic acid in conjunction with the [Co(bpy)3]2+/3+-based electrolyte showed better power conversion efficiency than that of SGT-053(9.10%). Electrochemical impedance spectroscopy analysis unveiled that the difference in Jscand Vocoriginates mainly from the twisted orientation between D and ZnPby the introduction of TBTand TI. This result indicated that the introduction of an extended auxiliary π-spacer in the donor part is a rational molecular design approach to improve photovoltaic performance by enhancing the light-harvesting ability and hindering charge recombination on the TiO2photoanode.

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The development of efficient and environment-benign catalytic materials to activate peroxymonosulfate (PMS) for refractory pollutant removal has been considered as an attractive yet challenging topic in the field of wastewater treatment. Herein, carbon fiber-assisted iron carbide (Fe3C@CFs) was innovatively prepared by a hydrothermal and calcination method. Fe3C@CFs could activate PMS and remove the pollutant Acid Red 1 (AR1) up to 97.3%, and even the rate constant was 12.4 times higher than that of Fe3C. The leaching of iron ions was only 0.002 ppm, which was extremely lower than the European standard. Furthermore, Fe3C@CFs still exhibited a favorable catalytic performance after 4 cycles. The catalytic mechanism was investigated by a hybrid method of various radical scavengers combined with electron spin resonance, suggesting that 1O2served as the main reaction oxidation species rather than OH or SO4−during the catalytic process, different from most of the reported radical paths for activating PMS. In addition, Fe3C@CFs possessed wide pH adaptability (3–10) and relatively low activation energy (24.47 kJ mol−1) for AR1 removal. These findings pave a promising avenue to develop highly efficient and eco-friendly catalytic materials for environmental remediation.

Among various photovoltaic devices, dye-sensitized solar cells (DSSCs) are one of the most potentially clean and renewable energy conversion devices because of their low fabrication cost, environmentally friendly nature, and high power conversion efficiency. However, the use of rare metals such as Pt counter electrodes (CEs) is one of the major drawbacks of DSSC devices for broad real-life applications. In this regard, alternative materials to Pt CEs have been long sought for DSSCs employing both cobalt and iodine redox couples. Therefore, in this study, soft-templated tellurium-doped mesoporous carbons (Te-SMCs) were synthesized for the first time by the simple pyrolysis of PAN-b-PBA block copolymer in the presence of a tellurium precursor for replacing the Pt CE. To confirm the chemical composition and porosity, the as-prepared Te-SMCmaterials were evaluated by elemental analysis (XPS and EDS) and nitrogen sorption isotherms measurement. The as-prepared Te-SMCmaterials contained mainly mesopores and retained the three-dimensional hierarchical graphite-like structure with many defect sites. They displayed doping levels with nitrogen of 9.15 atom % and tellurium of 0.15 atom % and had a specific surface area of 540 m2g–1. Therefore, these characteristics enabled the development of a high-performance CE in DSSCs with cobalt and iodine redox couples. As a result of its catalytic performance, Te-SMCexhibited outstanding electrocatalytic activity as well as a significantly improved electrochemical stability than those of Pt CE for both redox couples even after 1000 potential cycles. The results show that a maximum conversion efficiency of 11.64 and 9.67% could be achieved under one sun illumination (AM 1.5G) for SGT-021/Co(bpy)32+/3+- and N719/I–/I3–-based devices with Te-SMCCEs, and their power conversion efficiency is superior to the corresponding device with Pt CEs.

Various redox shuttle-mediated polymer gel electrolytes (PGEs) were utilized individually to fabricate quasi-solid-state (QSS) dye-sensitized solar cells (DSSCs) for outdoor and indoor applications. The power conversion efficiency (PCE) of QSS DSSCs based on I3−/I−-mediated PGEs was comparable with liquid electrolyte-devices under both 1-sun and 1000 lux compact fluorescent light (CFL) (compact fluorescent lamp) illuminations. The PCEs for QSS-DSSCs based on cobalt/copper complex-mediated PGEs were not comparable with their liquid electrolyte DSSCs under 1-sun condition, but, comparable, even higher for cobalt complex-mediated PGE devices, under CFL light. The PCE for organic dye-sensitized cobalt PGE devices was in the range of 23%–30% under 200–2000 lux CFL illuminations. The remarkable PCEs of 27.5% and 25.0% were obtained for cobalt and copper complex-mediated PGE-based QSS DSSCs using organic sensitizers, respectively, under 1000 lux CFL light. This result specifies that the mass transport limitation of Co-/Cu-complexes in PGEs can only be applicable for 1-sun illumination, not for ambient lighting. The outstanding efficiency under CFL ambient light and high stability under 50 °C make us optimistic for future commercial utilization of metal-complex-mediated PGE-QSS DSSCs as a safe power source for the internet of things and low-powered autonomous electronic devices.

The development of microwave absorption materials with good environmental adaptability is a hot topic in the electromagnetic protection field. In this research, Expanded graphite (EG)/ZIF-67 nanocubes-derived (Co@C) composites were successfully fabricated by electrostatic assembly and subsequent heat treatment. The results show that the involvement of an appropriate content of Co@C not only overcomes the problem of single EG impedance mismatch but also introduces additional magnetic loss for the absorber. At the thicknesses of 1.6 mm and 1.5 mm, an effective absorption bandwidth (EAB) of 4.52 GHz and a minimum reflection loss (RLmin) of -48.4 dB were achieved for the Z2-700 sample with a low filling ratio (20 wt%). The enhancement of EMW-absorption performance is attributed to the optimization of impedance matching and the synergistic action between multiple loss mechanisms. Electrochemical corrosion test showed that graphite nanoflakes of EG enhanced the transfer resistance during corrosion, and the corrosion resistance of pure Co@C was enhanced with the protection of EG. This work provides a reasonable attempt to design and fabricate efficient microwave absorption materials suitable for harsh environments.

Three new D−π–A-structured organic dyes, coded as SGT-138, SGT-150, and SGT-151, with the expansion of π-conjugation in the π-bridge and acceptor parts have been developed to adjust HOMO/LUMO levels and to expand the light absorption range of organic dyes. Referring to the SGT-137dye, the π-bridge group was extended from the 4-hexyl-4H-thieno[3,2-b]indole (TI) to the 9-hexyl-9H-thieno[2′,3′:4,5]thieno[3,2-b]indole (TII), and the acceptor group was extended from (E)-3-(4-(benzo[c][1,2,5]thiadiazol-4-yl)phenyl)-2-cyanoacrylic acid (BTCA) to (E)-3-(4-(benzo[c][1,2,5]thiadiazol-4-ylethynyl)phenyl)-2-cyanoacrylic acid (BTECA), where TIIwas introduced as a π-bridging unit for the first time. It was determined that both extensions are promising strategies to enhance the light-harvesting ability. They present several features, such as (i) efficiently intensifying the extinction coefficient and expanding the absorption bands; (ii) exhibiting enhanced intramolecular charge transfer in comparison with the SGT-137; and (iii) being favorable to photoelectric current generation of dye-sensitized solar cells (DSSCs) with cobalt electrolytes. In particular, the π-spacer extension from TIto TIIwas useful for modulating the HOMO energy levels, while the acceptor extension from BTCAto BTECAwas useful for modulating the LUMO energy levels. These phenomena could be explained with the aid of density functional theory calculations. Finally, the DSSCs based on new SGT-dyes with an HC-A1 co-adsorbent presented good power conversion efficiencies as high as 11.23, 11.30, 11.05, and 10.80% for SGT-137, SGT-138, SGT-150,and SGT-151, respectively. Furthermore, it was determined that the use of the bulky co-adsorbent, HC-A1, can effectively suppress the structural relaxation of dyes in the excited state, thereby enhancing the charge injection rate of SGT-dyes. The observations in time-resolved photoluminescence were indeed consistent with the variation in the PCE, quantitatively.

Engineering efficient zinc–air batteries have attracted tremendous attention because of their essential role in the field of renewable energy systems. However, the sluggish reaction kinetics of the oxygen reduction reaction (ORR) at the air cathode impair the battery performance significantly. Recently, metal–N–C-based porous carbon nanoarchitectures have emerged as promising ORR electrocatalysts in zinc–air batteries. Herein, taking advantage of metal–organic complexation and mesoporous silica templates, we successfully anchor Fe–N–C sites on hierarchically porous carbon sphere and carbon nanotube interpenetrated nanostructures (Fe–N–C/HPCS@CNT) to serve as efficient cathodes for zinc–air batteries. Benefiting from its synergistic effects between the highly active Fe–N–C sites, ultrahigh surface areas, and unique hierarchically porous nanostructures, Fe–N–C/HPCS@CNT exhibits preferable ORR performance (E1/2= 0.873 V) compared to commercial Pt/C (E1/2= 0.841 V). Most importantly, when used as a cathode catalyst for homemade zinc–air batteries, Fe–N–C/HPCS@CNT exhibits gratifying peak power density (164.0 mW cm–2), large specific capacity (762.0 mAh g–1), superior long-term stability, extraordinary rate capability, and excellent charge/discharge performance. We believe that this report will not only offer new insights into the design of Fe–N–C-based catalysts but also promote the practical utilization of Fe–N–C-based cathodes for a wide range of energy applications.

Li–S batteries are considered to be the most promising next-generation advanced energy-storage systems. However, the sluggish reaction kinetics and the “shuttle effect” of lithium polysulfides (LiPSs) severely limit their battery performances. To overcome the complex and multiphase sulfur redox chemistry of LiPSs, in this study, we propose a new type of cobalt-based double catalytic sites (DCSs) codoped mesoporous carbon to immobilize and reversibly catalyze the LiPS intermediates in the cycling process, thus eliminating the shuttle effect and improving the charge–discharge kinetics. The theoretical calculation shows that the well-designed DCS configuration endows LiPSs with both strong and weak binding capabilities, which will facilitate the synergistic and reversible catalytic conversion. Furthermore, the experimental results also confirm that the DCS structure shows significantly enhanced catalytic kinetics than the single catalytic sites. The Li–S battery equipped with the DCS structure displays an extremely high discharge capacity of 918 mA h g–1at a current density of 0.2 C and can reach a capacity of 867 mA h g–1after 200 cycles with an ultralow capacity attenuation rate of 0.028% for each cycle. This study opens new avenues to address the catalytic requirements both in discharging and charging processes.