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Abstract Solution‐processed 2D materials hold promise for their scalable applications. However, the random, fragmented nature of the solution‐processed nanoflakes and the poor percolative conduction through their discrete networks limit the performance of the enabled devices. To overcome the problem, conduction modulation of the solution‐processed 2D materials is reported via Stark effect. Using liquid‐phase exfoliated molybdenum disulfide (MoS2) as an example, nonlinear conduction switching with a ratio of >105 is demonstrated by the local fields from the interfacial ferroelectric P(VDF‐TrFE). Through density‐functional theory calculations and in situ Raman scattering and photoluminescence spectroscopic analysis, the modulation is understood to arise from a charge redistribution in the solution‐processed MoS2. Beyond MoS2, the modulation may be shown effective for the other solution‐processed 2D materials and low‐dimensional materials. The modulation can open their electronic device applications, for instance, thin‐film nonlinear electronics and non‐volatile memories.

Efficient airport airside ground movement (AAGM) is key to successful operations of urban air mobility. Recent studies have introduced the use of multi-objective multigraphs (MOMGs) as the conceptual prototype to formulate AAGM. Swift calculation of the shortest path costs is crucial for the algorithmic heuristic search on MOMGs, however, previous work chiefly focused on single-objective simple graphs (SOSGs), treated cost enquires as search problems, and failed to keep a low level of computational time and storage complexity. This paper concentrates on the conceptual prototype MOMG, and investigates its node feature extraction, which lays the foundation for efficient prediction of shortest path costs. Two extraction methods are implemented and compared: a statistics-based method that summarises 22 node physical patterns from graph theory principles, and a learning-based method that employs node embedding technique to encode graph structures into a discriminative vector space. The former method can effectively evaluate the node physical patterns and reveals their individual importance for distance prediction, while the latter provides novel practices on processing multigraphs for node embedding algorithms that can merely handle SOSGs. Three regression models are applied to predict the shortest path costs to demonstrate the performance of each. Our experiments on randomly generated benchmark MOMGs show that (i) the statistics-based method underperforms on characterising small distance values due to severe overestimation; (ii) A subset of essential physical patterns can achieve comparable or slightly better prediction accuracy than that based on a complete set of patterns; and (iii) the learning-based method consistently outperforms the statistics-based method, while maintaining a competitive level of computational complexity.

As a multi-factorial disease, obesity has become one of the major health problems in the world, and it is still increasing rapidly. Konjac supplementation, as a convenient dietary therapy, has been shown to be able to regulate gut microbiota and improve obesity. However, the specific mechanism by which konjac improves obesity through gut microbiota remains to be studied. In this study, a high-fat diet (HFD) was used to induce a mouse obesity model, and 16S rDNA sequencing and an untargeted metabolomics were used to investigate the impact of konjac on gut microbiota and gut metabolites in HFD-induced obese mice. The results show that konjac can reduce the body weight, adipose tissue weight, and lipid level of high-fat diet induced obese mice by changing the gut microbiota structure and gut metabolic profile. Association analysis revealed that konjac supplementation induced changes in gut microbiota, resulting in the up-regulation of 7-dehydrocholesterol and trehalose 6-phosphate, as well as the down-regulation of glycocholic acid and ursocholic acid within the Secondary bile acid biosynthesis pathway, ultimately leading to improvements in obesity. Among them, g_Acinetobacter (Greengene ID: 911888) can promote the synthesis of 7-dehydrocholesterol by synthesizing ERG3. g_Allobaculum (Greengene ID: 271516) and g_Allobaculum (Greengene ID: 259370) can promote the breakdown of trehalose 6-phosphate by synthesizing glvA. Additionally, the down-regulation of glycocholic acid and ursocholic acid may be influenced by the up-regulation of Lachnospiraceae_NK4A136_group. In conclusion, konjac exerts an influence on gut metabolites through the regulation of gut microbiota, thereby playing a pivotal role in alleviating obesity induced by a high-fat diet.

Abstract Implementing and integrating spiking neurons for neuromorphic hardware realization conforming to spiking neural networks holds great promise in enabling efficient learning and decision‐making. The spiking neurons, however, may lack the spiking dynamics to encode the dynamical information in complex real‐world problems. Herein, using filamentary memristors from solution‐processed hexagonal boron nitride, this study assembles leaky integrate‐and‐fire spiking neurons and, particularly, harnesses the common switching stochasticity feature in the memristors to allow key neural dynamics, including Poisson‐like spiking and adaptation. The neurons, with the dynamics fitted via hardware‐algorithm codesign, suggest a potential in realizing spike‐based neuromorphic hardware capable of handling complex problems. Simulation of an autoencoder for anomaly detection of time‐series real analog and digital data from physical systems is demonstrated, underscoring its promising prospect in applications, especially, at the edges with limited computation resources, for instance, auto‐pilot, manufacturing, wearables, and Internet of things.

Matcha shows promise for diabetes, obesity, and gut microbiota disorders. Studies suggest a significant link between gut microbiota, metabolites, and obesity. Thus, matcha may have a positive impact on obesity by modulating gut microbiota and metabolites. This study used 16S rDNA sequencing and untargeted metabolomics to examine the cecal contents in mice. By correlation analysis, we explored the potential mechanisms responsible for the positive effects of matcha on obesity. The results indicated that matcha had a mitigating effect on the detrimental impacts of a high-fat diet (HFD) on multiple physiological indicators in mice, including body weight, adipose tissue weight, serum total cholesterol (TC), and low-density lipoprotein (LDL) levels, as well as glucose tolerance. Moreover, it was observed that matcha had an impact on the structural composition of gut microbiota and gut metabolites. Specifically, matcha was able to reverse the alterations in the abundance of certain obesity-improving bacteria, such as Alloprevotella, Ileibacterium, and Rikenella, as well as the abundance of obesity-promoting bacteria Romboutsia, induced by a HFD. Furthermore, matcha can influence the levels of metabolites, including formononetin, glutamic acid, pyroglutamic acid, and taurochenodeoxycholate, within the gastrointestinal tract. Additionally, matcha enhances caffeine metabolism and the HIF-1 signaling pathway in the KEGG pathway. The results of the correlation analysis suggest that formononetin, theobromine, 1,3,7-trimethyluric acid, and Vitamin C displayed negative correlation with both the obesity phenotype and microbiota known to exacerbate obesity, while demonstrating positive correlations with microbiota that alleviated obesity. However, glutamic acid, pyroglutamic acid, and taurochenodeoxycholate had the opposite effect. In conclusion, the impact of matcha on gut metabolites may be attributed to its modulation of the abundance of Alloprevotella, Ileibacterium, Rikenella, and Romboutsia within the gastrointestinal tract, thereby potentially contributing to the amelioration of obesity.

The arrival management (AMAN) system is a decision support tool for air traffic controllers to establish and maintain the landing sequence for arrival aircraft. The original intention of designing the AMAN system is to improve the efficiency of air traffic management (ATM), but few studies are investigating the operational benefits of this system based on key performance indicators (KPIs) and evaluating actual data in a real-time environment. The main purpose of this paper is to propose a KPI based transferable comparative analysis method for identifying the operational benefits of the AMAN through radar trajectories. Firstly, six KPIs are established from a joint study of the mainstream ATM performance frameworks worldwide. Secondly, appropriate evaluation technique approaches are determined according to the characteristics of each KPI. Finally, a Chinese metropolitan airport is taken for the case study, and three periods are defined to form data samples with high similarity for comparative experiments. The results validate the feasibility of the proposed method and find comprehensive performance improvements in arrival operations under the effects of the AMAN system.

Nanocrystal solar cells (NCs) allow for large scale solution processing under ambient conditions, permitting a promising approach for low-cost photovoltaic products. Although an up to 10% power conversion efficiency (PCE) has been realized with the development of device fabrication technologies, the open circuit voltage (Voc) of CdTe NC solar cells has stagnated below 0.7 V, which is significantly lower than most CdTe thin film solar cells fabricated by vacuum technology (around 0.8 V~0.9 V). To further improve the NC solar cells’ performance, an enhancement in the Voc towards 0.8–1.0 V is urgently required. Given the unique processing technologies and physical properties in CdTe NC, the design of an optimized band alignment and improved junction quality are important issues to obtain efficient solar cells coupled with high Voc. In this work, an efficient method was developed to improve the performance and Voc of solution-processed CdTe nanocrystal/TiO2 hetero-junction solar cells. A thin layer of solution-processed CdS NC film (~5 nm) as introduced into CdTe NC/TiO2 to construct hetero-junction solar cells with an optimized band alignment and p-n junction quality, which resulted in a low dark current density and reduced carrier recombination. As a result, devices with improved performance (5.16% compared to 2.63% for the control device) and a Voc as high as 0.83 V were obtained; this Voc value is a record for a solution-processed CdTe NC solar cell.

We propose Sb-doped TiO2 as electron acceptor material for depleted CdTe nanocrystal (NC) hetero-junction solar cells. Novel devices with the architecture of FTO/ZnO/Sb:TiO2/CdTe/Au based on CdTe NC and TiO2 precursor are fabricated by rational ambient solution process. By introducing TiO2 with dopant concentration, we are able to tailor the optoelectronic properties of NC solar cells. Our novel devices demonstrate a very high open circuit voltage of 0.74 V, which is the highest Voc reported for any CdTe NC based solar cells. The power conversion efficiency (PCE) of solar cells increases with the increase of Sb-doped content from 1% to 3%, then decreases almost linearly with further increase of Sb content due to the recombination effect. The champion device shows Jsc, Voc, FF, and PCE of 14.65 mA/cm2, 0.70 V, 34.44, and 3.53% respectively, which is prospective for solution processed NC solar cells with high Voc.

Nanocrystal solar cells (NCs) allowfor large scale solution processing under ambient conditions, permitting a promising approach for low-cost photovoltaic products. Although an up to 10% power conversion efficiency (PCE) has been realized with the development of device fabrication technologies, the open circuit voltage (Voc) of CdTeNC solar cells has stagnated below0.7 V,which is significantly lower thanmost CdTe thin film solar cells fabricated by vacuumtechnology (around 0.8 V~0.9 V). To further improve theNC solar cells' performance, an enhancement in the Voc towards 0.8-1.0 V is urgently required. Given the unique processing technologies and physical properties in CdTeNC, the design of an optimized band alignment and improved junction quality are important issues to obtain efficient solar cells coupled with high Voc. In this work, an efficient method was developed to improve the performance and Voc of solution-processed CdTe nanocrystal/TiO2 hetero-junction solar cells. A thin layer of solution-processed CdS NC film (~5 nm) as introduced into CdTe NC/TiO2 to construct hetero-junction solar cells with an optimized band alignment and p-n junction quality, which resulted in a low dark current density and reduced carrier recombination. As a result, devices with improved performance (5.16% compared to 2.63% for the control device) and a Voc as high as 0.83 V were obtained; this Voc value is a record for a solution-processed CdTe NC solar cell. [ABSTRACT FROM AUTHOR]

We propose Sb-doped TiO2 as electron acceptor material for depleted CdTe nanocrystal (NC) hetero-junction solar cells. Novel devices with the architecture of FTO/ZnO/Sb:TiO2/CdTe/Au based on CdTe NC and TiO2 precursor are fabricated by rational ambient solution process. By introducing TiO2 with dopant concentration, we are able to tailor the optoelectronic properties of NC solar cells. Our novel devices demonstrate a very high open circuit voltage of 0.74 V, which is the highest Voc reported for any CdTe NC based solar cells. The power conversion efficiency (PCE) of solar cells increases with the increase of Sb-doped content from 1% to 3%, then decreases almost linearly with further increase of Sb content due to the recombination effect. The champion device shows Jsc, Voc, FF, and PCE of 14.65 mA/cm², 0.70 V, 34.44, and 3.53% respectively, which is prospective for solution processed NC solar cells with high Voc. [ABSTRACT FROM AUTHOR]