Although large efforts have been made to improve the growth of hexagonal boron nitride (hBN) by heteroepitaxy, the non-native substrates remain a fundamental factor that limits the quality. This problem can be solved by homoepitaxy, which is the growth of hBN on hBN substrates. In this report, we demonstrate the homoepitaxial growth of triangular BN grains on exfoliated hBN flakes by Metal-Organic Vapor Phase Epitaxy and show by atomic force microscopy and photoluminescence that the stacking of these triangular islands can deviate from the AA\' stacking of hBN. We show that the stacking order is enforced by the crystallographic direction of the edge of the exfoliated hBN flakes, with armchair edges allowing for centrosymmetric stacking, whereas zigzag edges lead to the growth of noncentrosymmetric BN polytypes. Our results indicate pathways to grow homoepitaxial BN with tunable layer stacking, which is required to induce piezoelectricity or ferroelectricity.

OBJECTIVE: Bayesian network (BN) models were developed to explore the specific relationships between influencing factors and type 2 diabetes mellitus (T2DM), coronary heart disease (CAD), and their comorbidities. The aim was to predict disease occurrence and diagnose etiology using these models, thereby informing the development of effective prevention and control strategies for T2DM, CAD, and their comorbidities.
METHODS: Employing a case-control design, the study compared individuals with T2DM, CAD, and their comorbidities (case group) with healthy counterparts (control group). Univariate and multivariate Logistic regression analyses were conducted to identify disease-influencing factors. The BN structure was learned using the Tabu search algorithm, with parameter estimation achieved through maximum likelihood estimation. The predictive performance of the BN model was assessed using the confusion matrix, and Netica software was utilized for visual prediction and diagnosis.
RESULTS: The study involved 3,824 participants, including 1,175 controls, 1,163 T2DM cases, 982 CAD cases, and 504 comorbidity cases. The BN model unveiled factors directly and indirectly impacting T2DM, such as age, region, education level, and family history (FH). Variables like exercise, LDL-C, TC, fruit, and sweet food intake exhibited direct effects, while smoking, alcohol consumption, occupation, heart rate, HDL-C, meat, and staple food intake had indirect effects. Similarly, for CAD, factors with direct and indirect effects included age, smoking, SBP, exercise, meat, and fruit intake, while sleeping time and heart rate showed direct effects. Regarding T2DM and CAD comorbidities, age, FBG, SBP, fruit, and sweet intake demonstrated both direct and indirect effects, whereas exercise and HDL-C exhibited direct effects, and region, education level, DBP, and TC showed indirect effects.
CONCLUSIONS: The BN model constructed using the Tabu search algorithm showcased robust predictive performance, reliability, and applicability in forecasting disease probabilities for T2DM, CAD, and their comorbidities. These findings offer valuable insights for enhancing prevention and control strategies and exploring the application of BN in predicting and diagnosing chronic diseases.

The electrochemical nitrogen reduction reaction (eNRR) is a highly promising alternative to the Haber-Bosch (H-B) process, but its commercial development is limited by the high bond energy of N2 molecules and the presence of the competitive hydrogen evolution reaction (HER). Here, a metal-free composite electrocatalyst of boron nitride (h-BNNs) and carbon nanotubes (CNTs) was explored through the interfacial hybridization of h-BNNs and CNTs, which showed a highly improved eNRR Faraday efficiency (FE) of 63.9% and an NH3 yield rate of 36.5 μg h-1 mgcat.-1 at -0.691 V (vs RHE). New chemical bonds of C-B and C-N were observed, indicating a strong interaction between CNTs and h-BNNs. According to the Raman spectra and the optimized model of h-BNNs/CNTs, an obvious strain effect between h-BNNs and CNTs was supposed to play a significant role in the highly improved FE, compared with the FE of h-BNNs alone (4.7%). Density functional theory (DFT) calculations further showed that h-BNNs/CNTs had lower energy barriers in eNRR, giving them higher N2 to NH3 selectivity, while h-BNNs have lower energy barriers in the HER. This work shows the important role of the strain effect in boosting the selectivity in the eNRR process.

X-ray detectors based on conventional semiconductors with large atomic numbers are suffering from the poor stability under a high dose rate of ionizing irradiation. In this work, we demonstrate that a wide band gap ceramic-boron nitride with small atomic numbers could be used for sensitive X-ray detection. Boron nitride samples showed excellent resistance to ionizing radiation, which have been systematically studied with the neutron- and electron-aging experiments. Then, we fully analyzed the influence of these aging effects on the fundamental properties of boron nitride. Interestingly, we found that the boron nitride samples could maintain relatively good charge transport properties even after large dose of neutron irradiation. The fabricated X-ray detectors showed decent performance metrics, and the neutron-aged boron nitride even showed improved operational stability under continuous X-ray irradiation, suggesting the great potential for real applications.

To overcome the thermodynamic and kinetic impediments of the Sabatier CO2 methanation reaction, the process must be operated under very high temperature and pressure conditions, to obtain an industrially viable conversion, rate, and selectivity. Herein, we report that these technologically relevant performance metrics have been achieved under much milder conditions using solar rather than thermal energy, where the methanation reaction is enabled by a novel nickel-boron nitride catalyst. In this regard, an in situ generated HOB⋅⋅⋅B surface frustrated Lewis\'s pair is considered responsible for the high Sabatier conversion 87.68 %, reaction rate 2.03 mol gNi -1 h-1 , and near 100 % selectivity, realized under ambient pressure conditions. This discovery bodes well for an opto-chemical engineering strategy aimed at the development and implementation of a sustainable \'Solar Sabatier\' methanation process.

The thermal decomposition performance of an oxidizer directly determines the thrust and specific impulse properties of the solid propellant. Hexagonal boron nitride (h-BN) has the characteristics of high catalytic activity and good stability, which can improve the heat release and decomposition temperature of the oxidant, and then improve the energy performance of the propellant. In this study, a novel hybrid material TiO2 NPs/h-BN was successfully prepared by in situ growth, and it was found that when 5 wt.% TiO2 NPs/h-BN was added, the initial decomposition temperature of ammonium perchlorate (AP) decreased by 67.6°C. Due to the addition of TiO2, the gap between the h-BN layers as well as the specific surface increased, which optimized its thermocatalytic performance, and it also proposed a catalytic mechanism for the thermal decomposition process of AP.

Research findings strongly suggest that cognitive behavioral therapy for the eating disorders (CBT-ED) is more effective than other treatments for bulimia nervosa (BN) and for binge eating disorder (BED), although interpersonal psychotherapy appears to be equally effective for BED. Evidence for the effectiveness of CBT-ED for the persistent (adult) form of anorexia nervosa (AN) is insufficient at present and is essentially absent for AN in adolescents except for some evidence from uncontrolled trials. This article begins with an overview of the early studies in the development of CBT-ED that showed a similar effectiveness of other symptom-focused psychotherapies-a finding that was neglected at the time. Later developments are then considered, including comparisons of CBT-ED with other psychotherapies, efforts to develop Internet-based training and treatment, and electronic applications for treatment. Finally, implications of the findings for future short- and long-term research and for clinical practice are considered.

In this work, polydimethylsiloxane (PDMS)-based composites with high thermal conductivity were fabricated via a three-dimensional hybrid boron nitride@silver nanowires (BN@AgNWs) filler thermal network, and their thermal conductivity was investigated. A new thermal conductive BN@AgNWs hybrid filler was prepared by an in situ growth method. Silver ions with the different concentrations were reduced, and AgNWs crystallized and grew on the surface of BN sheets. PDMS-based composites were fabricated by the BN@AgNWs hybrid filler added. SEM, XPS, and XRD were used to characterize the structure and morphology of BN@AgNWs hybrid fillers. The thermal conductivity performances of PDMS-based composites with different silver concentrates were investigated. The results showed that the thermal conductivity of PDMS-based composite filled with 20 vol% BN@15AgNWs hybrid filler is 0.914 W/(m·K), which is 5.05 times that of pure PDMS and 23% higher than the thermal conductivity of 20 vol% PDMS-based composite with BN filled. The enhanced thermal conductivity mechanism was provided based on the hybrid filler structure. This work offers a new way to design and fabricate the high thermal conductive hybrid filler for thermal management materials.

Employing liquid organic hydrogen carriers (LOHCs) to transport hydrogen to where it can be utilized relies on methods of efficient chemical dehydrogenation to access this fuel. Therefore, developing effective strategies to optimize the catalytic performance of cheap transition metal-based catalysts in terms of activity and stability for dehydrogenation of LOHCs is a critical challenge. Here, we report the design and synthesis of ultrasmall nickel nanoclusters (∼1.5 nm) deposited on defect-rich boron nitride (BN) nanosheet (Ni/BN) catalysts with higher methanol dehydrogenation activity and selectivity, and greater stability than that of some other transition-metal based catalysts. The interface of the two-dimensional (2D) BN with the metal nanoparticles plays a strong role both in guiding the nucleation and growth of the catalytically active ultrasmall Ni nanoclusters, and further in stabilizing these nanoscale Ni catalysts against poisoning by interactions with the BN substrate. We provide detailed spectroscopy characterizations and density functional theory (DFT) calculations to reveal the origin of the high productivity, high selectivity, and high durability exhibited with the Ni/BN nanocatalyst and elucidate its correlation with nanocluster size and support-nanocluster interactions. This study provides insight into the role that the support material can have both regarding the size control of nanoclusters through immobilization during the nanocluster formation and also during the active catalytic process; this twofold set of insights is significant in advancing the understanding the bottom-up design of high-performance, durable catalytic systems for various catalysis needs.

In this study, we designed a three-dimensional structure of electrically conductive adhesives (ECAs) by adding three different kinds of nano filler, including BN, TiO2, and Al2O3 particles, into a few-layered graphene (FLG)/polymer composite to avoid FLG aggregation. Three different lateral sizes of FLG (FLG3, FLG8, and FLG20) were obtained from graphite (G3, G8, and G20) by a green, facile, low-cost, and scalable jet cavitation process. The corresponding characterizations, such as Raman spectroscopy, scanning electron microscopy (SEM), atomic force microscopy (AFM), and transmission electron microscopy (TEM), verified the successful preparation of graphene flakes. Based on the results of four-point probe measurements, FLG20 demonstrated the lowest sheet resistance value of ~0.021 Ω/■. The optimized ECAs\' composition was a 60% solid content of FLG20 with the addition 2 wt.% of Al2O3. The sheet resistance value was as low as 51.8 Ω/■, which was a reduction of 73% compared to that of pristine FLG/polymer. These results indicate that this method not only paves the way for the cheaper and safer production of graphene, but also holds great potential for applications in energy-related technologies.