Stable Interfacial Ruthenium Species for Highly Efficient Polyolefin Upcycling.

The present polyolefin hydrogenolysis recycling cases acknowledge that zerovalent Ru exhibits high catalytic activity. A pivotal rationale behind this assertion lies in the propensity of the majority of Ru species to undergo reduction to zerovalent Ru within the hydrogenolysis milieu. Nonetheless, the suitability of zerovalent Ru as an optimal structural configuration for accommodating multiple elementary reactions remains ambiguous. Here, we have constructed stable Ru0-Ruδ+ complex species, even under reaction conditions, through surface ligand engineering of commercially available Ru/C catalysts. Our findings unequivocally demonstrate that surface-ligated Ru species can be stabilized in the form of a Ruδ+ state, which, in turn, engenders a perturbation of the σ bond electron distribution within the polyolefin carbon chain, ultimately boosting the rate-determining step of C-C scission. The optimized catalysts reach a solid conversion rate of 609 g·gRu-1·h-1 for polyethylene. This achievement represents a 4.18-fold enhancement relative to the pristine Ru/C catalyst while concurrently preserving a remarkable 94% selectivity toward valued liquid alkanes. Of utmost significance, this surface ligand engineering can be extended to the gentle mixing of catalysts in ligand solution at room temperature, thus rendering it amenable for swift integration into industrial processes involving polyolefin degradation.

Layered Double Hydroxide Derivatives for Polyolefin Upcycling.

While Ru-catalyzed hydrogenolysis holds significant promise in converting waste polyolefins into value-added alkane fuels, a major constraint is the high cost of noble metal catalysts. In this work, we propose, for the first time, that Co-based catalysts derived from CoAl-layered double hydroxide (LDH) are alternatives for efficient polyolefin hydrogenolysis. Leveraging the chemical flexibility of the LDH platform, we reveal that metallic Co species serve as highly efficient active sites for polyolefin hydrogenolysis. Furthermore, we introduced Ni into the Co framework to tackle the issue of restricted hydrogenation ability associated with contiguous Co-Co sites. In-situ analysis indicates that the integration of Ni induces electron transfer and facilitates hydrogen spillover. This dual effect synergistically enhances the hydrogenation/desorption of olefin intermediates, resulting in a significant reduction in the yield of low-value CH4 from 27.1 to 12.6%. Through leveraging the unique properties of LDH, we have developed efficient and cost-effective catalysts for the sustainable recycling and valorization of waste polyolefin materials.

A Fast Method for Multidimensional Joint Parameter Estimation of Polarization-Sensitive Arrays.

The paper proposes a fast method for the multidimensional parameter estimation of a polarization-sensitive array. Compared with conventional methods (e.g., MUSIC algorithm), the proposed method applies an iterative approach based on Newton's method to obtain joint estimation results instead of a spectral search and dimension reduction. It also extends the original Newton method to the 4D scale using the Hessian matrix. To reduce the complexity of establishing the aim function, Nystrom's method is applied to process the covariance matrix. A new threshold is also proposed to select the results, which can accomplish the parameter estimation with a small number of iterations while guaranteeing a high estimation accuracy. Finally, the proposed algorithm is analyzed in detail and the numerical simulations of various algorithms are compared to verify its effectiveness.

Reweighted Off-Grid Sparse Spectrum Fitting for DOA Estimation in Sensor Array with Unknown Mutual Coupling.

In the environment of unknown mutual coupling, many works on direction-of-arrival (DOA) estimation with sensor array are prone to performance degradation or even failure. Moreover, there are few literatures on off-grid direction finding using regularized sparse recovery technology. Therefore, the scenario of off-grid DOA estimation in sensor array with unknown mutual coupling is investigated, and then a reweighted off-grid Sparse Spectrum Fitting (Re-OGSpSF) approach is developed in this article. Inspired by the selection matrix, an undisturbed array output is formed to remove the unknown mutual coupling effect. Subsequently, a refined off-grid SpSF (OGSpSF) recovery model is structured by integrating the off-grid error term obtained from the first-order Taylor approximation of the higher-order term into the underlying on-grid sparse representation model. After that, a novel Re-OGSpSF framework is formulated to recover the sparse vectors, where a weighted matrix is developed by the MUSIC-like spectrum function to enhance the solution's sparsity. Ultimately, off-grid DOA estimation can be realized with the help of the recovered sparse vectors. Thanks to the off-grid representation and reweighted strategy, the proposed method can effectively and efficiently achieve high-precision continuous DOA estimation, making it favorable for real-time direction finding. The simulation results validate the superiority of the proposed method.

A HOOI-Based Fast Parameter Estimation Algorithm in UCA-UCFO Framework.

In this paper, we introduce a Reduced-Dimension Multiple-Signal Classification (RD-MUSIC) technique via Higher-Order Orthogonal Iteration (HOOI), which facilitates the estimation of the target range and angle for Frequency-Diverse Array Multiple-Input-Multiple-Output (FDA-MIMO) radars in the unfolded coprime array with unfolded coprime frequency offsets (UCA-UCFO) structure. The received signal undergoes tensor decomposition by the HOOI algorithm to get the core and factor matrices, then the 2D spectral function is built. The Lagrange multiplier method is used to obtain a one-dimensional spectral function, reducing complexity for estimating the direction of arrival (DOA). The vector of the transmitter is obtained by the partial derivatives of the Lagrangian function, and its rotational invariance facilitates target range estimation. The method demonstrates improved operation speed and decreased computational complexity with respect to the classic Higher-Order Singular-Value Decomposition (HOSVD) technique, and its effectiveness and superiority are confirmed by numerical simulations.

Prediction of SiS2 and SiSe2 as promising anode materials for sodium-ion batteries.

In this work, we suggest SiS2 and SiSe2 as anode materials for sodium-ion batteries based on a first-principles prediction. Both SiS2 and SiSe2 have suitable adsorption energies (-1.01/-1.24 eV) and large diffusion constants (1.24 × 10-4/6.72 × 10-5 cm2 s-1) for Na ions at 300 K, resulting in low diffusion barriers (less than 0.1 eV) at high Na adsorption concentrations. As anode materials, SiS2 and SiSe2 exhibit excellent electrochemical stability with high theoretical capacities (517/864 mA h g-1) and desirable average voltages (0.19/0.25 V). Given these exceptional properties, SiS2 and SiSe2 are desired to be promising electrode materials for sodium-ion batteries.

Multi-Target Parameter Estimation of the FMCW-MIMO Radar Based on the Pseudo-Noise Resampling Method.

Subspace methods are widely used in FMCW-MIMO radars for target parameter estimations. However, the performances of the existing algorithms degrade rapidly in non-ideal situations. For example, a small number of snapshots may result in the distortion of the covariance matrix estimation and a low signal-to-noise ratio (SNR) can lead to subspace leakage problems, which affects the parameter estimation accuracy. In this paper, a joint DOA-range estimation algorithm is proposed to solve the above issues. Firstly, the improved unitary root-MUSIC algorithm is applied to reduce the influence of non-ideal terms in building the covariance matrix. Subsequently, the least squares method is employed to process the data and obtain paired range estimation. However, in a small number of snapshots and low SNR scenarios, even if the impact of non-ideal terms is reduced, there will still be cases where the estimators sometimes deviate from the true target. The estimators that deviate greatly from targets are regarded as outliers. Therefore, threshold detection is applied to determine whether outliers exist. After that, a pseudo-noise resampling (PR) technology is proposed to form a new data observation matrix, which further alleviates the error of the estimators. The proposed method overcomes performance degradation in a small number of snapshots or low SNRs simultaneously. Theoretical analyses and simulation results demonstrate the effectiveness and superiority.

A Novel Unitary ESPRIT Algorithm for Monostatic FDA-MIMO Radar.

A novel unitary estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm, for the joint direction of arrival (DOA) and range estimation in a monostatic multiple-input multiple-output (MIMO) radar with a frequency diverse array (FDA), is proposed. Firstly, by utilizing the property of Centro-Hermitian of the received data, the extended real-valued data is constructed to improve estimation accuracy and reduce computational complexity via unitary transformation. Then, to avoid the coupling between the angle and range in the transmitting array steering vector, the DOA is estimated by using the rotation invariance of the receiving subarrays. Thereafter, an automatic pairing method is applied to estimate the range of the target. Since phase ambiguity is caused by the phase periodicity of the transmitting array steering vector, a removal method of phase ambiguity is proposed. Finally, the expression of Cramér-Rao Bound (CRB) is derived and the computational complexity of the proposed algorithm is compared with the ESPRIT algorithm. The effectiveness of the proposed algorithm is verified by simulation results.

Outage Probability Performance Prediction for Mobile Cooperative Communication Networks Based on Artificial Neural Network.

This paper investigates outage probability (OP) performance predictions using transmit antenna selection (TAS) and derives exact closed-form OP expressions for a TAS scheme. It uses Monte-Carlo simulations to evaluate OP performance and verify the analysis. A back-propagation (BP) neural network-based OP performance prediction algorithm is proposed and compared with extreme learning machine (ELM), locally weighted linear regression (LWLR), support vector machine (SVM), and BP neural network methods. The proposed method was found to have higher OP performance prediction results than the other prediction methods.

Robust Angle Estimation for MIMO Radar with the Coexistence of Mutual Coupling and Colored Noise.

This paper deals with joint estimation of direction-of-departure (DOD) and direction-of- arrival (DOA) in bistatic multiple-input multiple-output (MIMO) radar with the coexistence of unknown mutual coupling and spatial colored noise by developing a novel robust covariance tensor-based angle estimation method. In the proposed method, a third-order tensor is firstly formulated for capturing the multidimensional nature of the received data. Then taking advantage of the temporal uncorrelated characteristic of colored noise and the banded complex symmetric Toeplitz structure of the mutual coupling matrices, a novel fourth-order covariance tensor is constructed for eliminating the influence of both spatial colored noise and mutual coupling. After a robust signal subspace estimation is obtained by using the higher-order singular value decomposition (HOSVD) technique, the rotational invariance technique is applied to achieve the DODs and DOAs. Compared with the existing HOSVD-based subspace methods, the proposed method can provide superior angle estimation performance and automatically jointly perform the DODs and DOAs. Results from numerical experiments are presented to verify the effectiveness of the proposed method.

Tensor-Based Angle Estimation Approach for Strictly Noncircular Sources with Unknown Mutual Coupling in Bistatic MIMO Radar.

In the paper, the estimation of joint direction-of-departure (DOD) and direction-of-arrival (DOA) for strictly noncircular targets in multiple-input multiple-output (MIMO) radar with unknown mutual coupling is considered, and a tensor-based angle estimation method is proposed. In the proposed method, making use of the banded symmetric Toeplitz structure of the mutual coupling matrix, the influence of the unknown mutual coupling is removed in the tensor domain. Then, a special enhancement tensor is formulated to capture both the noncircularity and inherent multidimensional structure of strictly noncircular signals. After that, the higher-order singular value decomposition (HOSVD) technology is applied for estimating the tensor-based signal subspace. Finally, the direction-of-departure (DOD) and direction-of-arrival (DOA) estimation is obtained by utilizing the rotational invariance technique. Due to the use of both noncircularity and multidimensional structure of the detected signal, the algorithm in this paper has better angle estimation performance than other subspace-based algorithms. The experiment results verify that the method proposed has better angle estimation performance.

Direction of Arrival Estimation for MIMO Radar via Unitary Nuclear Norm Minimization.

In this paper, we consider the direction of arrival (DOA) estimation issue of noncircular (NC) source in multiple-input multiple-output (MIMO) radar and propose a novel unitary nuclear norm minimization (UNNM) algorithm. In the proposed method, the noncircular properties of signals are used to double the virtual array aperture, and the real-valued data are obtained by utilizing unitary transformation. Then a real-valued block sparse model is established based on a novel over-complete dictionary, and a UNNM algorithm is formulated for recovering the block-sparse matrix. In addition, the real-valued NC-MUSIC spectrum is used to design a weight matrix for reweighting the nuclear norm minimization to achieve the enhanced sparsity of solutions. Finally, the DOA is estimated by searching the non-zero blocks of the recovered matrix. Because of using the noncircular properties of signals to extend the virtual array aperture and an additional real structure to suppress the noise, the proposed method provides better performance compared with the conventional sparse recovery based algorithms. Furthermore, the proposed method can handle the case of underdetermined DOA estimation. Simulation results show the effectiveness and advantages of the proposed method.

Sparsity-Aware DOA Estimation Scheme for Noncircular Source in MIMO Radar.

In this paper, a novel sparsity-aware direction of arrival (DOA) estimation scheme for a noncircular source is proposed in multiple-input multiple-output (MIMO) radar. In the proposed method, the reduced-dimensional transformation technique is adopted to eliminate the redundant elements. Then, exploiting the noncircularity of signals, a joint sparsity-aware scheme based on the reweighted l1 norm penalty is formulated for DOA estimation, in which the diagonal elements of the weight matrix are the coefficients of the noncircular MUSIC-like (NC MUSIC-like) spectrum. Compared to the existing l1 norm penalty-based methods, the proposed scheme provides higher angular resolution and better DOA estimation performance. Results from numerical experiments are used to show the effectiveness of our proposed method.

Real-Valued Covariance Vector Sparsity-Inducing DOA Estimation for Monostatic MIMO Radar.

In this paper, a real-valued covariance vector sparsity-inducing method for direction of arrival (DOA) estimation is proposed in monostatic multiple-input multiple-output (MIMO) radar. Exploiting the special configuration of monostatic MIMO radar, low-dimensional real-valued received data can be obtained by using the reduced-dimensional transformation and unitary transformation technique. Then, based on the Khatri-Rao product, a real-valued sparse representation framework of the covariance vector is formulated to estimate DOA. Compared to the existing sparsity-inducing DOA estimation methods, the proposed method provides better angle estimation performance and lower computational complexity. Simulation results verify the effectiveness and advantage of the proposed method.

A tensor-based subspace approach for bistatic MIMO radar in spatial colored noise.

In this paper, a new tensor-based subspace approach is proposed to estimate the direction of departure (DOD) and the direction of arrival (DOA) for bistatic multiple-input multiple-output (MIMO) radar in the presence of spatial colored noise. Firstly, the received signals can be packed into a third-order measurement tensor by exploiting the inherent structure of the matched filter. Then, the measurement tensor can be divided into two sub-tensors, and a cross-covariance tensor is formulated to eliminate the spatial colored noise. Finally, the signal subspace is constructed by utilizing the higher-order singular value decomposition (HOSVD) of the cross-covariance tensor, and the DOD and DOA can be obtained through the estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm, which are paired automatically. Since the multidimensional inherent structure and the cross-covariance tensor technique are used, the proposed method provides better angle estimation performance than Chen's method, the ESPRIT algorithm and the multi-SVD method. Simulation results confirm the effectiveness and the advantage of the proposed method.

Exploring causal correlations of inflammatory biomarkers in idiopathic normal-pressure hydrocephalus: insights from bidirectional Mendelian randomization analysis.

Background and objective: Neuroinflammatory processes have been identified as playing a crucial role in the pathophysiology of various neurodegenerative diseases, including idiopathic normal-pressure hydrocephalus (iNPH). iNPH, defined as a common disease of cognitive impairment in older adults, poses major challenges for therapeutic interventions owing to the stringent methodological requirements of relevant studies, clinical heterogeneity, unclear etiology, and uncertain diagnostic criteria. This study aims to assess the relationship between circulating inflammatory biomarkers and iNPH risk using bidirectional two-sample Mendelian randomization (MR) combined with meta-analysis. Methods: In our bidirectional MR study, genetic data from a genome-wide association study (GWAS) involving 1,456 iNPH cases and 409,726 controls of European ancestry were employed. Single-nucleotide polymorphisms (SNPs) associated with exposures served as instrumental variables for estimating the causal relationships between iNPH and 132 types of circulating inflammatory biomarkers from corresponding GWAS data. Causal associations were primarily examined using the inverse variance-weighted method, supplemented by MR-Egger, weighted median, simple mode, and weighted mode analyses. In the results, heterogeneity was assessed using the Cochran Q test. Horizontal pleiotropy was evaluated through the MR-Egger intercept test and the MR pleiotropy residual sum and outliers test. Sensitivity analysis was conducted through leave-one-out analysis. Reverse MR analyses were performed to mitigate bias from reverse causality. Meta-analyses of identical inflammatory biomarkers from both data sources strengthened the findings. Results: Results indicated a genetically predicted association between Interleukin-16 (IL-16) [OR: 1.228, 95% CI: 1.049-1.439, p = 0.011], TNF-related apoptosis ligand (TRAIL) [OR: 1.111, 95% CI: 1.019-1.210, p = 0.017] and Urokinase-type plasminogen activator (uPA) [OR: 1.303, 95% CI: 1.025-1.658, p = 0.031] and the risk of iNPH. Additionally, changes in human Glial cell line-derived neurotrophic factor (hGDNF) [OR: 1.044, 95% CI: 1.006-1.084, p = 0.023], Matrix metalloproteinase-1 (MMP-1) [OR: 1.058, 95% CI: 1.020, 1.098, p = 0.003] and Interleukin-12p70 (IL-12p70) [OR: 0.897, 95% CI: 0.946-0.997, p = 0.037] levels were identified as possible consequences of iNPH. Conclusion: Our MR study of inflammatory biomarkers and iNPH, indicated that IL-16, TRAIL, and uPA contribute to iNPH pathogenesis. Furthermore, iNPH may influence the expression of hGDNF, MMP-1, and IL-12p70. Therefore, targeting specific inflammatory biomarkers could be promising strategy for future iNPH treatment and prevention.

Effect of biological shells aggregate on the mechanical properties and sustainability of concrete.

The recycling bio-waste shells problem has grown more and more serious in recent years and many efforts have been made to solve this problem. One possible solution is to put these bio-shells into concrete and recycle them as building materials using the aggregate matrix concrete approach. To verify the engineering feasibility, the mechanical properties of bio-shells aggregated concrete were invested via gradient substitution rates at 10%, 30%, and 50% with a total of 78 groups of specimens in this paper. Our results show that the mechanical properties of the concrete were enhanced in maximum flexural strength and maximum compressive. Economic performance was also analyzed and found that the costs of frame-shear structure, frame structure, and tube-in-tube structure were reduced by 10.2%, 10%, and 10.3%. The carbon environmental assessment also shows superiority in the carbon reduction of a single specimen with various rates of the shell. In summary, compared with ordinary concrete materials, it is very possible to use waste bio-shells as a substitute for aggregates to develop the sustainable recycling development of concrete materials.

Bulk Trapping Organic Semiconductor with Amino-Additive Enabling Ultrasensitive NO2 Sensors.

Organic semiconductor (OSC) gas sensors have garnered considerable attention due to their promising selectivity and inherent flexibility. Introducing a functional group or modification layer is an important route to modulate the doping/trapping state of the active layer and the gas absorption/desorption process. However, the majority of the functionalization lies in the surface/interface assembling process, which is difficult to control the functional group density. This in turn brings challenges for precise modulation of the charge transport and the doping/trapping density, which will affect the repeatability and reproducibility of sensing performance. Herein, we propose a facile bulk trapping strategy incorporating amino-terminated additive molecules via the vacuum deposition process, achieving ultrahigh sensitivity of ∼2000%/ppm at room temperature to NO2 gas and approaching ∼3000%/ppm at 50 °C. Additionally, the device exhibits commendable reproducibility, stability, and low concentration detection ability, reaching down to several ppb, indicating promising potential for future applications. Comprehensive analysis of electrical properties and density functional theory calculations reveals that these exceptional properties arise from the favorable electrical characteristics of the bulk trapping structure, the high mobility of C8-BTBT, and the elevated adsorption energy of NO2. This approach enables the construction of stable and reproducible sensitive sensors and helps to understand the sensing mechanism in OSC gas sensors.

Effect of Strain Rate on Tensile Properties of Carbon Fiber-Reinforced Epoxy Laminates with Different Stacking Sequences and Ply Orientations.

In practical application situations, a carbon fiber-reinforced polymer (CFRP) is often subjected to complex dynamic loadings. The effect of the strain rate on mechanical properties is very important for the CFRP design and product development. In this work, static and dynamic tensile properties of CFRP with different stacking sequences and ply orientations were investigated. The results showed that the tensile strengths of CFRP laminates were sensitive to the strain rate, while Young's modulus was independent of the strain rate. Moreover, the strain rate effect was related to the stacking sequences and ply orientations. The experimental results showed that the strain rate effects of the cross-ply laminates and quasi-isotropic-ply laminates were lower than that of the unidirectional-ply laminates. Finally, the failure modes of CFRP laminates were investigated. Failure morphology demonstrated that the differences in strain rate effects among cross-ply laminates, quasi-isotropic-ply laminates, and unidirectional-ply laminates were caused by the mismatch between the fiber and the matrix when the strain rate increased.

Influence of dynamic load and water on energy accumulation and dissipation in sandstone.

In various engineering projects such as mineral extraction, hydropower resource utilization, railway construction, and geological hazard mitigation, rock engineering is often encountered. Furthermore, dynamic loads and moisture content exert notable influence on the energy transformation processes within rocks. Yet, the specific interplay of dynamic loading and water's impact on the energy conversion mechanism within the sandstone remains unexplored. To address this gap, this study conducted impact loading experiments on sandstone, elucidating the rock's mechanical response under these conditions and unraveling the underlying energy conversion mechanisms. It was observed that the strength of sandstone exhibits a direct correlation with impact velocity. Moreover, employing energy calculation principles, we established a connection between moisture content and the sandstone's internal energy conversion properties. The study also delved into the microscopic fracture mechanisms within the sandstone, ultimately concluding that both water content and dynamic loading have a significant impact on these microscopic fracture mechanisms.