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.

Discovery of highly efficient dual-atom catalysts for propane dehydrogenation assisted by machine learning.

Propane dehydrogenation (PDH) is a highly efficient approach for industrial production of propylene, and the dual-atom catalysts (DACs) provide new pathways in advancing atomic catalysis for PDH with dual active sites. In this work, we have developed an efficient strategy to identify promising DACs for PDH reaction by combining high-throughput density functional theory (DFT) calculations and the machine-learning (ML) technique. By choosing the γ-Al2O3(100) surface as the substrate to anchor dual metal atoms, 435 kinds of DACs have been considered to evaluate their PDH catalytic activity. Four ML algorithms are employed to predict the PDH activity and determine the relationship between the intrinsic characteristics of DACs and the catalytic activity. The promising catalysts of CuFe, CuCo and CoZn DACs are finally screened out, which are further validated by the whole kinetic reaction calculations, and the highly efficient performance of DACs is attributed to the synergistic effects and interactions between the paired active sites.

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.

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.