2D Boron Nitride Heterostructures: Recent Advances and Future Challenges

Hexagonal boron nitride (h‐BN) is one of the most attractive 2D materials because of its remarkable properties. Combining h‐BN with other components (e.g., graphene, carbonitride, semiconductors) to form heterostructures opens new perspectives to developing advanced functional devices. In this review, the state‐of‐the‐art in h‐BN heterojunctions is highlighted. The preparation of high‐quality 2D h‐BN structures with fewer defects can maximize its intrinsic properties, such as thermal conductivity and electrical insulation, which are particularly important in 2D van der Waals electronics. On the other hand, the controlled introduction in 2D h‐BN of multiple defects creates new properties and advanced functions. In this last case, only through a better understanding of the nature and function of defects, it is possible to develop advanced applications based on h‐BN heterostructures. Engineering of the heterojunctions, such as the design of bonding at the interfaces, also plays a primary role. Several applications are proposed for h‐BN heterostructures, mostly in sensing and photocatalysis, and some new perspectives worth further studies are opened. Finally, the current challenges and the rising opportunities for the future developments of next‐generation h‐BN heterostructures are discussed

Hydroxylated boron nitride materials: from structures to functional applications

Abstract Functionalization of boron nitride (BN) materials with hydroxyls has attracted great attention to accomplish better performances at micro- and nanoscale. BN surface hydroxylation, in fact, induces a change in properties and allows expanding the fields of application. In this review, we have summarized the state-of-the-art in developing hydroxylated bulk and nanoscale BN materials. The different synthesis routes to develop hydroxyl BN have been critically discussed. What emerges is the great variety of possible strategies to achieve BN hydroxylation, which, in turn, represents one of the most suitable methods to improve the solubility of BN nanomaterials. The improved stability of BN solutions creates conditions for producing high-quality nanocomposites. Furthermore, new interesting optical and electronic properties may arise from the functionalization by OH groups as displayed by a wide range of both theoretical and experimental studies. After the presentation of the most significant systems and methodologies, we question of future perspective and important trends of the next generation BN materials as well as the possible areas of advanced research. Graphical abstract Hydroxyl functionalization of boron nitride materials is a key method to control and enhance the properties and design new functional applications

Deep Reinforcement Learning for Flipper Control of Tracked Robots

The autonomous control of flippers plays an important role in enhancing the intelligent operation of tracked robots within complex environments. While existing methods mainly rely on hand-crafted control models, in this paper, we introduce a novel approach that leverages deep reinforcement learning (DRL) techniques for autonomous flipper control in complex terrains. Specifically, we propose a new DRL network named AT-D3QN, which ensures safe and smooth flipper control for tracked robots. It comprises two modules, a feature extraction and fusion module for extracting and integrating robot and environment state features, and a deep Q-Learning control generation module for incorporating expert knowledge to obtain a smooth and efficient control strategy. To train the network, a novel reward function is proposed, considering both learning efficiency and passing smoothness. A simulation environment is constructed using the Pymunk physics engine for training. We then directly apply the trained model to a more realistic Gazebo simulation for quantitative analysis. The consistently high performance of the proposed approach validates its superiority over manual teleoperation

Superelasticity in bcc Nanowires by a Reversible Twinning Mechanism

Superelasticity (SE) in bulk materials is known to originate from the structure-changing martensitic transition which provides a volumetric thermodynamic driving force for shape recovery. On the other hand, structure-invariant deformation processes, such as twinning and dislocation slip, which result in plastic deformation, cannot provide the driving force for shape recovery. We use molecular-dynamics simulations to show that some bcc metal nanowires exhibit SE by a “reversible” twinning mechanism, in contrast to the above conventional point of view. We show that this reversible twinning is driven by the surface energy change between the twinned and detwinned state. In view of similar recent findings in fcc nanowires, we suggest that SE is a general phenomenon in cubic nanowires and that the driving force for the shape recovery arises from minimizing the surface energy. Furthermore, we find that SE in bcc nanowires is unique in several respects: first, the ‹111› / {112} stacking fault generated by partial dislocation is always preferred over ‹111› / {110} and ‹111› /{123} full dislocation slip. The occurrence of ‹111› / {112} twin or full dislocation slip in bcc nanowires depends on the competition between the emission of subsequent partial dislocations in adjacent {112} planes and the emission of partial dislocations in the same plane. Second, compared to their fcc counterparts, bcc nanowires have a higher energy barrier for the nucleation of twins, but a lower energy barrier for twin migration. This results in certain unique characteristics of SE in bcc nanowires, such as low energy dissipation and low strain hardening. Third, certain refractory bcc nanowires, such as W and Mo, can show SE at very high temperatures, which are higher than almost all of the reported high-temperature shape memory alloys. Our work provides a deeper understanding of superelasticity in nanowires and refractory bcc nanowires are potential candidates for applications in nanoelectromechanical systems operating over a wide temperature range

Defect-assisted photoluminescence in hexagonal boron nitride nanosheets

The development of functional optoelectronic applications based on hexagonal boron nitride nanosheets (h-BNNs) relies on controlling the structural defects. The fluorescent emission, in particular, has been observed to depend on vacancies and substitutional defects. In the present work, few-layerh-BNNs have been obtained by sonication-assisted liquid-phase exfoliation of their bulk counterpart. The as-prepared samples exhibit a weak fluorescent emission in the visible range, centred around 400 nm. Tailored defects have been introduced by oxidation in air at different temperatures. A significant increase in the fluorescent emission of the oxidatedh-BNNs has been observed with maximum emissive intensity for the samples treated at 300 degrees C. A further increase in temperatures (>300 degrees C) determines a quenching of the fluorescence. We investigated, by means of detailed microscopic and spectroscopic analysis, the relationship between the optical properties and defects ofh-BNNs. The investigation of the optical properties as a function of treatment temperature highlights the critical role of hydroxyl groups created by the oxidation process. Onlyh-BN exfoliated in water allows introducing OH groups with consequent enhancement of fluorescence emission. Quantum chemical calculations support the experimental findings

Citric Acid Derived Carbon Dots, the Challenge of Understanding the Synthesis-Structure Relationship

Carbon dots (CDs) are highly-emissive nanoparticles obtained through fast and cheap syntheses. The understanding of CDs’ luminescence, however, is still far from being comprehensive. The intense photoluminescence can have different origins: molecular mechanisms, oxidation of polyaromatic graphene-like layers, and core-shell interactions of carbonaceous nanoparticles. The citric acid (CA) is one of the most common precursors for CD preparation because of its high biocompatibility, and this review is mainly focused on CA-based CDs. The different parameters that control the synthesis, such as the temperature, the reaction time, and the choice of solvents, were critically described. Particular attention was devoted to the CDs’ optical properties, such as tunable emission and quantum yields, in light of functional applications. The survey of the literature allowed correlating the preparation methods with the structures and the properties of CA-based CDs. Some basic rules to fabricate highly luminescent nanoparticles were selected by the metanalysis of the current literature in the field. In some cases, these findings can be generalized to other types of CDs prepared via liquid phase

From 2-D to 0-D Boron Nitride Materials, The Next Challenge

The discovery of graphene has paved the way for intense research into 2D materials which is expected to have a tremendous impact on our knowledge of material properties in small dimensions. Among other materials, boron nitride (BN) nanomaterials have shown remarkable features with the possibility of being used in a large variety of devices. Photonics, aerospace, and medicine are just some of the possible fields where BN has been successfully employed. Poor scalability represents, however, a primary limit of boron nitride. Techniques to limit the number of defects, obtaining large area sheets and the production of significant amounts of homogenous 2D materials are still at an early stage. In most cases, the synthesis process governs defect formation. It is of utmost importance, therefore, to achieve a deep understanding of the mechanism behind the creation of these defects. We reviewed some of the most recent studies on 2D and 0D boron nitride materials. Starting with the theoretical works which describe the correlations between structure and defects, we critically described the main BN synthesis routes and the properties of the final materials. The main results are summarized to present a general outlook on the current state of the art in this field

Evolutionary Hierarchical Sparse Extreme Learning Autoencoder Network for Object Recognition

Extreme learning machine (ELM), characterized by its fast learning efficiency and great generalization ability, has been applied to various object recognition tasks. When extended to the stacked autoencoder network, which is a typical symmetrical representation learning model architecture, ELM manages to realize hierarchical feature extraction and classification, which is what deep neural networks usually do, but with much less training time. Nevertheless, the input weights and biases of the hidden nodes in ELM are generated according to a random distribution and may lead to the occurrence of non-optimal and redundant parameters that deteriorate discriminative features, which will have a bad influence on the final classification effect. In this paper, a novel sparse autoencoder derived from ELM and differential evolution is proposed and integrated into a hierarchical hybrid autoencoder network to accomplish the end-to-end learning with raw visible light camera sensor images and applied to several typical object recognition problems. Experimental results show that the proposed method is able to obtain competitive or better performance than current relevant methods with acceptable or less time consumption