Co-precipitation synthesis of ZnAl2O4: Cr3+ phosphor for better light penetration in pc-LED

Cr doped ZnAl₂O₄ spinel samples were prepared by the traditional solid state reaction and co-precipitation synthetic route, and the results suggest that the co-precipitation method has some superiority in contrast to the solid state reaction method. XRD, FT-IR, and XPS spectra confirmed that the well-crystallized spinel cubic phase of ZnAl₂O₄: Cr³⁺ samples were successfully formed. The morphology of the samples was investigated by FE-SEM and FE-TEM, and the results show that the samples by the co-precipitation route can generate a smaller size of particles compared to the solid state reaction. Photoluminescence excitation spectra monitored at 686 nm are comprised of two broad excitation bands near 530 nm and 395 nm, and the emission spectra show emissions ranging from 640 to 780 nm, due to the ²E?→?⁴A₂ spin-forbidden transition of Cr³⁺ ions in spinel lattices. The optimized concentration monitored at 686 nm is 1%, while at 693 nm is 3.5%. Compared with the samples by solid state reaction method, the samples by co-precipitation method show preferable luminescent properties, such as the higher photoluminescence intensity and higher quantum efficiency. Several phosphor-converted LEDs were to investigate the applicability of the prepared samples. The results confirm that the phosphor has potential applications in plant growth and supplementing the red region in white-LEDs and the phosphors prepared by co-precipitation are more suitable to be used in phosphor-converted LED devices due to their preferable luminescent properties.

     

A blockchain-based audit approach for encrypted data in federated learning

The development of data-driven artificial intelligence technology has given birth to a variety of big data applications. Data has become an essential factor to improve these applications. Federated learning, a privacy-preserving machine learning method, is proposed to leverage data from different data owners. It is typically used in conjunction with cryptographic methods, in which data owners train the global model by sharing encrypted model updates. However, data encryption makes it difficult to identify the quality of these model updates. Malicious data owners may launch attacks such as data poisoning and free-riding. To defend against such attacks, it is necessary to find an approach to audit encrypted model updates. In this paper, we propose a blockchain-based audit approach for encrypted gradients. It uses a behavior chain to record the encrypted gradients from data owners, and an audit chain to evaluate the gradients’ quality. Specifically, we propose a privacy-preserving homomorphic noise mechanism in which the noise of each gradient sums to zero after aggregation, ensuring the availability of aggregated gradient. In addition, we design a joint audit algorithm that can locate malicious data owners without decrypting individual gradients. Through security analysis and experimental evaluation, we demonstrate that our approach can defend against malicious gradient attacks in federated learning.     

Imitative Collaboration: A mirror-neuron inspired mixed reality collaboration method with remote hands and local replicas

Mixed reality can overlay and display 3D digital content in the real world, convey abstract concepts to users, and promote the understanding of complex tasks. However, the abstract graphics overlaid on the physical space may cause a certain cognitive load for local users and reduce the efficiency of collaboration. To improve the efficiency of remote collaboration, we conducted an elicitation study on assembly tasks, explored the user needs for collaboration, and defined the design goals of our remote collaboration method. Inspired by the mirror-neuron mechanism, we present an imitative collaboration method that allows local users to imitate the interaction behavior of remote users to complete tasks. We also propose a series of interaction methods for remote users to select, copy, and interact with the local point clouds to facilitate the expression of collaboration intentions. Finally, the results of a user study evaluating our imitative collaboration method on assembly tasks are reported, confirming that our method improves collaboration efficiency while reducing the cognitive load of local users.     

DFD-SS: Document Forgery Detection using Spectral – Spatial Features for Hyperspectral Images

In the present era of machines and edge-cutting technologies, still document frauds persist. They are done intuitively by using almost identical inks, that it becomes challenging to detect them—this demands an approach that efficiently investigates the document and leaves it intact. Hyperspectral imaging is one such a type of approach that captures the images from hundreds to thousands of spectral bands and analyzes the images through their spectral and spatial features, which is not possible by conventional imaging. Deep learning is an edge-cutting technology known for solving critical problems in various domains. Utilizing supervised learning imposes constraints on its usage in real scenarios, as the inks used in forgery are not known prior. Therefore, it is beneficial to use unsupervised learning. An unsupervised feature extraction through a Convolutional Autoencoder (CAE) followed by Logistic Regression (LR) for classification is proposed (CAE-LR). Feature extraction is evolved around spectral bands, spatial patches, and spectral-spatial patches. We inspected the impact of spectral, spatial, and spectral-spatial features by mixing inks in equal and unequal proportion using CAE-LR on the UWA writing ink hyperspectral images dataset for blue and black inks. Hyperspectral images are captured at multiple correlated spectral bands, resulting in information redundancy handled by restoring certain principal components. The proposed approach is compared with eight state-of-art approaches used by the researchers. The results depicted that by using the combination of spectral and spatial patches, the classification accuracy enhanced by 4.85% for black inks and 0.13% for blue inks compared to state-of-art results. In the present scenario, the primary area concern is to identify and detect the almost similar inks used in document forgery, are efficiently managed by the proposed approach.     

智能建造技术协同创新主体互动关系研究

文章分析智能建造技术创新主体的特点，界定并划分智能建造技术协同创新主体互动关系的范畴和类别，建立智能建造协同创新的多方演化博弈模型，揭示智能建造技术协同创新主体互动关系。主要结论为：①智能建造技术协同创新呈现级联性、函数性、动态平衡性、不确定性和指数性特征；②智能建造技术协同创新主体互动关系分为同质性的内部主体互动和异质性的内部主体与外部主体互动，表现出利益共生、成长进化和共享协同的特征；③创新主体之间的互动行为有利于对创新资源进行有效配置，推动智能建造技术的应用。     

Vapor-assisted engineering heterostructure of 1D Mo3N2 nanorod decorated with nitrogen-doped carbon for rapid pH-Universal hydrogen evolution reaction

Reasonable construction of heterostructure is of significance yet a great challenge towards efficient pH-universal catalysts for hydrogen evolution reaction (HER). Herein, a facial strategy coupling gas-phase nitridation with simultaneous heterogenization has been developed to synthesize heterostructure of one-dimensional (1D) Mo₃N₂ nanorod decorated with ultrathin nitrogen-doped carbon layer (Mo₃N₂@NC NR). Thereinto, the collaborative interface of Mo₃N₂ and NC is conducive to accomplish rapid electron transfer for reaction kinetics and weaken the Mo–H_ads bond for boosting the intrinsic activity of catalysts. As expected, Mo₃N₂@NC NR delivers an excellent catalytic activity for HER with low overpotentials of 85, 129, and 162 mV to achieve a current density of 10 mA cm^?2 in alkaline, acidic, and neutral electrolytes, respectively, and favorable long-term stability over a broad pH range. As for practical application in electrocatalytic water splitting (EWS) under alkaline, Mo₃N₂@NC NR || NiFe-LDH-based EWS also exhibits a low cell voltage of 1.55 V and favorable durability at a current density of 10 mA cm^?2, even surpassing the Pt/C || RuO₂-based EWS (1.60 V). Consequently, the proposed suitable methodology here may accelerate the development of Mo-based electrocatalysts in pH-universal non-noble metal materials for energy conversion.     

Step-scheme perylenediimide supramolecular nanosheet and TiO2 nanoparticle composites for boosted water splitting performance

Adjusting the band gap of organic-inorganic composites by chemical bonding can effectively construct Step-scheme (S-scheme) heterojunctions, featuring properties of fast photogenerated charge migration and excellent photocatalytic performance. In this work, a novel perylene-3, 4, 9, 10-tetracarboxylicdiimide (PDI)-titanium dioxide (TiO₂) heterojunction is elaborately synthesized through simple solvent compounding method. The monodispersed spherical TiO₂ nanoparticles was prepared with the capping agents of oleylamine and oleic acid, and suffered by a ligand exchange process with nitrosonium tetrafluoroborate (NOBF₄) to remove oleylamine and oleic acid. The NOBF₄ ligands were further replaced by PDI super molecular nanosheets to obtain two dimensional (2D)-zero dimensional (0D) PDI-TiO₂ composites. TiO₂ nanoparticles are evenly anchored on the surface of PDI nanosheets with intimate contact. The PDI-TiO₂ composites has emerged considerably superior activity in hydrogen evolution. The highest hydrogen evolution rate for PDI-TiO₂composites with the PDI weight percentage of 2.4% was 9766 μmol h^?1 g^?1 under solar light irradiation, which is 2.56 times of TiO₂-NOBF₄ catalyst. Moreover, PDI-TiO₂ composites possess stoichiometric overall water splitting performance with H₂ and O₂ release rates of 238.20 and 114.18 μmol h^?1 g^?1. The superior photocatalytic performance of PDI-TiO₂ composites can be attributed to the dramatic increase in visible and NIR light absorption caused by π-π stacking structure of PDI, the prevented charge recombination by the S-scheme heterojunction, and the enhanced oxygen evolution by the stronger oxidation capability of PDI. PDI supramolecular nanosheets may work as a novel functional support for many types of semiconductor nanomaterials as graphene, which will display a wide range of application prospects in the energy and environmental fields.     

Synergetic photocatalytic and thermocatalytic aqueous phase reforming of methanol for hydrogen production based on noble metal/photosensitive supports catalysts

To overcome high Gibbs free energy and low reaction rate of thermal catalytic and photocatalytic hydrogen production from methanol-H₂O mixture, photo-thermal synergistic catalysis (PC-TC) reforming has proved to be an effective strategy owing to the photo-assited thermal synergistic effect to accelerate the step controlling kinetic behavior. In order to efficiently produce H₂, proper photosensitive catalysts which absorb light energy and also show efficient thermal catalytic (TC) performance need to be developed. To study the designing principle for catalysts, herein we incorporate Pt/Pd and three different supports which show similar band gaps (ZnO, CeO₂, and P25) through the in-situ photo-deposition, which act as catalysts for PC-TC methanol aqueous reforming. The resultant 0.1%Pt/P25 catalyst exhibits H₂ evolution activity ～3.1 times than that of the TC condition and ～5.5 times than that of the photocatalytic reforming (PC) condition in the proposed PC-TC process; meanwhile 0.1%Pt/ZnO and 01%Pt/CeO₂ under PC-TC condition show ～1.3 times and ～2.0 times than that of the catalytic performance under TC condition. The physical characterizations prove that the metal-support interaction and the supports may be key factors for the catalytic performance. The active intermediate trapping experiments demonstrate possible intermediates in the PC-TC process and established reaction mechanisms to explain the synergetic effect for improved efficiency of hydrogen production. These findings may open up a new avenue of designing catalysts based on semiconductors for the PC-TC reforming of methanol-water to produce hydrogen in a high-efficiency and low-cost way, serving the needs of the future hydrogen economy.     

Ferulic acid and EGCG alter the structural characteristics of ovalbumin and its application in mineral loading

This study aims to fabricate mineral-loading nanocarriers using natural materials. The interaction patterns between ovalbumin (OVA) and four water-soluble polyphenols, namely ferulic acid (FA), (-)-Epigallo-catechin 3-gallate (EGCG), gallic acid (GA) and epicatechin (EC), were investigated. Results showed that the optimised conditions for preparing stable OVA–polyphenol complexes are at the OVA–polyphenol ratio of 4:1 at pH 6, under which OVA–FA and OVA–EGCG showed the highest stability and mineral-loading capacity among four OVA–polyphenol complexes. The fluorescence results indicated that the addition of EGCG and FA induced a significant fluorescence quenching to OVA. The interaction between OVA and polyphenols involved hydrogen bonding, hydrophobic interaction and electrostatic interaction. Fourier transform infrared spectroscopy (FTIR) analysis suggested that both FA and EGCG enhanced the stability and orderliness of the structure of OVA. The transmission electron microscopy images also exhibited the spherical structure of OVA after the addition of FA and EGCG. Furthermore, scanning electron microscope–energy dispersive X-ray spectrum results suggested that OVA–FA and OVA–EGCG complexes were better mineral carriers than OVA–GA and OVA–EC. This study may serve as the theoretical support for the promising application of OVA in the fabrication of mineral-loading nanocarriers in functional food and pharmaceutic.     

Additional deepwater sampling of prey fish in Lake Michigan annual bottom trawl survey reveals new insights for depth distribution dynamics

Beginning in 2013, sites at the 128-m bottom depth contour were added to the sampling design of the annual Lake Michigan bottom trawl survey for prey fish, which has been conducted by the U.S. Geological Survey Great Lakes Science Center (GLSC) each fall since 1973, to better assess fish depth distributions in a changing ecosystem. The standard sampling design included bottom depths from 9 to 110 m, although the GLSC also sporadically conducted bottom trawl tows at the 128-m bottom depth contour during 1973–1988. Enactment of this new sampling design in 2013 revealed that mean biomass density of deepwater sculpins (Myoxocephalus thompsonii) at the 128-m depth exceeded the sum of mean biomass densities at shallower depths, indicating that the bulk of the deepwater sculpin population is residing in waters deeper than 110 m. Thus, our findings supported the hypothesis that the depth distribution of the deepwater sculpin population had shifted to deeper waters beginning in 2007, thereby explaining, at least in part, the marked decline in deepwater sculpin abundance since 2006 based on the standard sampling design. In contrast, our results did not support the hypothesis that the slimy sculpin (Cottus cognatus) population had shifted to deeper waters sometime after 2000. A portion of the burbot (Lota lota) population may have also shifted in depth distribution to waters deeper than 110 m after 2007, based on our results. Our findings have served as an impetus to further expand the range of depths sampled in our bottom trawl survey.