Recent Progress of Vertical Graphene: Preparation, Structure Engineering, and Emerging Energy Applications.

Vertical graphene (VG) nanosheets have garnered significant attention in the field of electrochemical energy applications, such as supercapacitors, electro-catalysis, and metal-ion batteries. The distinctive structures of VG, including vertically oriented morphology, exposed, and extended edges, and separated few-layer graphene nanosheets, have endowed VG with superior electrode reaction kinetics and mass/electron transportation compared to other graphene-based nanostructures. Therefore, gaining insight into the structure-activity relationship of VG and VG-based materials is crucial for enhancing device performance and expanding their applications in the energy field. In this review, the authors first summarize the fabrication methods of VG structures, including solution-based, and vacuum-based techniques. The study then focuses on structural modulations through plasma-enhanced chemical vapor deposition (PECVD) to tailor defects and morphology, aiming to obtain desirable architectures. Additionally, a comprehensive overview of the applications of VG and VG-based hybrids d in the energy field is provided, considering the arrangement and optimization of their structures. Finally, the challenges and future prospects of VG-based energy-related applications are discussed.

Fundamental Understanding of Nonaqueous and Hybrid Na-CO2 Batteries: Challenges and Perspectives.

Alkali metal-CO2 batteries, which combine CO2 recycling with energy conversion and storage, are a promising way to address the energy crisis and global warming. Unfortunately, the limited cycle life, poor reversibility, and low energy efficiency of these batteries have hindered their commercialization. Li-CO2 battery systems have been intensively researched in these aspects over the past few years, however, the exploration of Na-CO2 batteries is still in its infancy. To improve the development of Na-CO2 batteries, one must have a full picture of the chemistry and electrochemistry controlling the operation of Na-CO2 batteries and a full understanding of the correlation between cell configurations and functionality therein. Here, recent advances in CO2 chemical and electrochemical mechanisms on nonaqueous Na-CO2 batteries and hybrid Na-CO2 batteries (including O2 -involved Na-O2 /CO2 batteries) are reviewed in-depth and comprehensively. Following this, the primary issues and challenges in various battery components are identified, and the design strategies for the interfacial structure of Na anodes, electrolyte properties, and cathode materials are explored, along with the correlations between cell configurations, functional materials, and comprehensive performances are established. Finally, the prospects and directions for rationally constructing Na-CO2 battery materials are foreseen.

Exfoliation of MoS2 by zero-valent transition metal intercalation.

Exfoliation of bulk molybdenum disulfide (MoS2) into few-layered nanosheets is achieved with the assistance of zero-valent transition metal (Co0, Ni0, Cu0) intercalation. The as-prepared MoS2 nanosheets are characterized to consist of 1T- and 2H-phases with an enhanced electrocatalytic hydrogen evolution reaction (HER) activity. This work provides a novel strategy to prepare 2D MoS2 nanosheets using mild reductive reagents, which is expected to avoid the undesired structural damage from conventional chemical exfoliation.

In Situ Monitored (N, O)-Doping of Flexible Vertical Graphene Films with High-Flux Plasma Enhanced Chemical Vapor Deposition for Remarkable Metal-Free Redox Catalysis Essential to Alkaline Zinc-Air Batteries.

Rechargeable zinc-air batteries (ZABs) have attracted great interests for emerging energy applications. Nevertheless, one of the major bottlenecks lies in the fabrication of bifunctional catalysts with high electrochemical activity, high stability, low cost, and free of precious and rare metals. Herein, a high-performance metal-free bifunctional catalyst is synthesized in a single step by regulating radicals within the recently invented high-flux plasma enhanced chemical vapor deposition (HPECVD) system equipped with in situ plasma diagnostics. Thus-derived (N, O)-doped vertical few-layer graphene film (VGNO) is of high areal population with perfect vertical orientation, tunable catalytic states, and configurations, thus enabling significantly enhanced electrochemical kinetic processes of oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) with reference to milestone achievements to date. Application of such VGNO to aqueous ZABs (A-ZABs) and flexible solid-state ZABs (S-ZABs) exhibited high discharge power density and excellent cycling stability, which remarkably outperformed ZABs using benchmarked precious-metal based catalysts. The current work provides a solid basis toward developing low-cost, resource-sustainable, and eco-friendly ZABs without using any metals for outstanding OER and ORR catalysis.

Multiscale space-time-frequency feature-guided multitask learning CNN for motor imagery EEG classification.

Objective. Motor imagery (MI) electroencephalography (EEG) classification is regarded as a promising technology for brain-computer interface (BCI) systems, which help people to communicate with the outside world using neural activities. However, decoding human intent accurately is a challenging task because of its small signal-to-noise ratio and non-stationary characteristics. Methods that directly extract features from raw EEG signals ignores key frequency domain information. One of the challenges in MI classification tasks is finding a way to supplement the frequency domain information ignored by the raw EEG signal.Approach. In this study, we fuse different models using their complementary characteristics to develop a multiscale space-time-frequency feature-guided multitask learning convolutional neural network (CNN) architecture. The proposed method consists of four modules: the space-time feature-based representation module, time-frequency feature-based representation module, multimodal fused feature-guided generation module, and classification module. The proposed framework is based on multitask learning. The four modules are trained using three tasks simultaneously and jointly optimized.Results. The proposed method is evaluated using three public challenge datasets. Through quantitative analysis, we demonstrate that our proposed method outperforms most state-of-the-art machine learning and deep learning techniques for EEG classification, thereby demonstrating the robustness and effectiveness of our method. Moreover, the proposed method is employed to realize control of robot based on EEG signal, verifying its feasibility in real-time applications.Significance. To the best of our knowledge, a deep CNN architecture that fuses different input cases, which have complementary characteristics, has not been applied to BCI tasks. Because of the interaction of the three tasks in the multitask learning architecture, our method can improve the generalization and accuracy of subject-dependent and subject-independent methods with limited annotated data.

Buried Interfaces in Halide Perovskite Photovoltaics.

Understanding the fundamental properties of buried interfaces in perovskite photovoltaics is of paramount importance to the enhancement of device efficiency and stability. Nevertheless, accessing buried interfaces poses a sizeable challenge because of their non-exposed feature. Herein, the mystery of the buried interface in full device stacks is deciphered by combining advanced in situ spectroscopy techniques with a facile lift-off strategy. By establishing the microstructure-property relations, the basic losses at the contact interfaces are systematically presented, and it is found that the buried interface losses induced by both the sub-microscale extended imperfections and lead-halide inhomogeneities are major roadblocks toward improvement of device performance. The losses can be considerably mitigated by the use of a passivation-molecule-assisted microstructural reconstruction, which unlocks the full potential for improving device performance. The findings open a new avenue to understanding performance losses and thus the design of new passivation strategies to remove imperfections at the top surfaces and buried interfaces of perovskite photovoltaics, resulting in substantial enhancement in device performance.

Parallel Spatial-Temporal Self-Attention CNN-Based Motor Imagery Classification for BCI.

Motor imagery (MI) electroencephalography (EEG) classification is an important part of the brain-computer interface (BCI), allowing people with mobility problems to communicate with the outside world via assistive devices. However, EEG decoding is a challenging task because of its complexity, dynamic nature, and low signal-to-noise ratio. Designing an end-to-end framework that fully extracts the high-level features of EEG signals remains a challenge. In this study, we present a parallel spatial-temporal self-attention-based convolutional neural network for four-class MI EEG signal classification. This study is the first to define a new spatial-temporal representation of raw EEG signals that uses the self-attention mechanism to extract distinguishable spatial-temporal features. Specifically, we use the spatial self-attention module to capture the spatial dependencies between the channels of MI EEG signals. This module updates each channel by aggregating features over all channels with a weighted summation, thus improving the classification accuracy and eliminating the artifacts caused by manual channel selection. Furthermore, the temporal self-attention module encodes the global temporal information into features for each sampling time step, so that the high-level temporal features of the MI EEG signals can be extracted in the time domain. Quantitative analysis shows that our method outperforms state-of-the-art methods for intra-subject and inter-subject classification, demonstrating its robustness and effectiveness. In terms of qualitative analysis, we perform a visual inspection of the new spatial-temporal representation estimated from the learned architecture. Finally, the proposed method is employed to realize control of drones based on EEG signal, verifying its feasibility in real-time applications.

Functional carbon nitride materials for water oxidation: from heteroatom doping to interface engineering.

Polymeric carbon nitrides (PCNs) are promising photocatalysts and electrocatalysts for water oxidation, as they are environmentally benign materials with an adjustable structure and facilely synthesized from inexpensive and abundant starting materials. In this minireview, we examine the state-of-the-art strategies for tailoring PCNs for efficient photocatalytic, electrocatalytic, and photoelectrochemical water oxidation, including heteroatom doping and interface engineering from band structure alignment (e.g., by coupling inorganic or organic semiconductors) to hybridization with nanoscale cocatalysts (e.g., nanosheets, nanoarrays, nanoparticles, and quantum dots) and sub-nanoscale cocatalysts (e.g., metallic molecular clusters and single-atom catalysts). Through establishing the structure-activity correlations, we aim to present a clear roadmap for providing insights into the design strategies, structure modification, and the improved catalytic performances of PCN-based materials in different catalytic water oxidation processes. For future guidance, we also propose some outlooks on the perspective and challenges of PCNs towards a better application in catalytic water oxidation.

Sputtered Ga-Doped SnOx Electron Transport Layer for Large-Area All-Inorganic Perovskite Solar Cells.

The scalability processing of all functional layers in perovskite solar cells (PSCs) is one of the critical challenges in the commercialization of perovskite photovoltaic technology. In response to this issue, a large-area and high-quality gallium-doped tin oxide (Ga-SnOx) thin film is deposited by direct current magnetron sputtering and applied in CsPbBr3 all-inorganic PSCs as an electron transport layer (ETL). It is found that oxygen defects of SnOx can be remarkably offset by regulating oxygen flux and acceptor-like Ga doping level, resulting in higher carrier mobility and suitable energy level alignment, which is beneficial in accelerating electron extraction and suppressing charge recombination at the perovskite/ETL interface. At the optimal O2 flux (12 sccm) and Ga doping level (5%), the device based on sputtered Ga-SnOx ETL without any interface modification shows a power conversion efficiency (PCE) of 8.13%, which is significantly higher than that of undoped SnOx prepared by sputtering or spin coating. Furthermore, a PCE of 5.98% for a device with an active area of 1 cm2 is obtained, demonstrating great potential in fabricating efficient and stable large-area PSCs.

Molecular Beam Epitaxy Scalable Growth of Wafer-Scale Continuous Semiconducting Monolayer MoTe2 on Inert Amorphous Dielectrics.

Monolayer MoTe2 , with the narrowest direct bandgap of ≈1.1 eV among Mo- and W-based transition metal dichalcogenides, has attracted increasing attention as a promising candidate for applications in novel near-infrared electronics and optoelectronics. Realizing 2D lateral growth is an essential prerequisite for uniform thickness and property control over the large scale, while it is not successful yet. Here, layer-by-layer growth of 2 in. wafer-scale continuous monolayer 2H-MoTe2 films on inert SiO2 dielectrics by molecular beam epitaxy is reported. A single-step Mo-flux controlled nucleation and growth process is developed to suppress island growth. Atomically flat 2H-MoTe2 with 100% monolayer coverage is successfully grown on inert 2 in. SiO2 /Si wafer, which exhibits highly uniform in-plane structural continuity and excellent phonon-limited carrier transport behavior. The dynamics-controlled growth recipe is also extended to fabricate continuous monolayer 2H-MoTe2 on atomic-layer-deposited Al2 O3 dielectric. With the breakthrough in growth of wafer-scale continuous 2H-MoTe2 monolayers on device compatible dielectrics, batch fabrication of high-mobility monolayer 2H-MoTe2 field-effect transistors and the three-level integration of vertically stacked monolayer 2H-MoTe2 transistor arrays for 3D circuitry are successfully demonstrated. This work provides novel insights into the scalable synthesis of monolayer 2H-MoTe2 films on universal substrates and paves the way for the ultimate miniaturization of electronics.