Explore the vast range of titles available.

Iontronic power source harnesses ion-electron coupling effect for energy conversion, offering great potential in wearable electronics and bioelectronics. However, its limited power density and insufficient current hinder the progress toward commercialization. Here we report an iontronic electricity generator based on intrinsic asymmetric interfaces and controllable energy-release, which reaches a maximum volumetric power density of up to 3680 W m -3 , area power density of 18.4 W m -2 , and peak current of more than 5 A. Cycling power generation is ascribed to reversible electron-coupled ion-oscillation at interface. Synergistically, the intrinsic asymmetric interfaces facilitate ion-electron coupling interaction, and the energy-release gate optimizes the energy conversion pathway, thus ensuring giant power output. The developed generator shows large-scale manufacturability and good flexibility. These exceptional performances in iontronic power source not only offer useful paradigms for wearable electronics, but also indicate a strategy and direction into high-power energy conversion from low-grade environmental source. An iontronic electricity generator is developed based on intrinsic asymmetric interfaces and controllable energy release, which achieves giant power generation through reversible electron-coupled ion oscillation at interfaces.

The high-resolution visualization of atomic structures is significant for understanding the relationship between the microscopic configurations and macroscopic properties of materials. However, a rapid, accurate, and robust approach to automatically resolve complex patterns in atomic-resolution microscopy remains difficult to implement. Here, we present a Trident strategy-enhanced disentangled representation learning method (a generative model), which utilizes a few unlabelled experimental images with abundant low-cost simulated images to generate a large corpus of annotated simulation data that closely resembles experimental results, producing a high-quality large-volume training dataset. A structural inference model is then trained via a residual neural network which can directly deduce the interlayer slip and rotation of diversified and complicated stacking patterns at van der Waals (vdW) interfaces with picometer-scale accuracy across various materials (e.g. MoS 2 , WS 2 , ReS 2 , ReSe 2 , and 1 T’-MoTe 2 ) with different layer numbers (bilayer and trilayers), demonstrating robustness to defects, imaging quality, and surface contaminations. The framework can also identify pattern transition interfaces, quantify subtle motif variations, and discriminate moiré patterns that are difficult to distinguish in frequency domains. Finally, the high-throughput processing ability of our method provides insights into a vdW epitaxy mode where various thermodynamically favorable slip stackings can coexist. Here, the authors report a Trident strategy-enhanced generative model able to produce a large volume of high-quality simulated atomic microscopy images, which can be used to automatically resolve the interlayer sliding and twist angle of few-layered van der Waals materials with improved robustness to defects and image quality.

The efficient removal of lignin is crucial for process optimization, as it enhances the exposure of polar groups in wood and provides interfacial binding sites for subsequent material modifications. In this study, an environmentally friendly peracetic acid/ hydrogen peroxide system was employed to delignify fast-growing wood. The results indicated mass loss rates of 30.7% for poplar and 31.3% for Chinese fir, with corresponding decreases in relative lignin content by 95.3% and 87.2%, respectively. Additionally, the specific surface area increased by 6.4% in poplar and 30.9% in Chinese fir. The relative crystallinity was enhanced by 31.2% in poplar and 15.7% in Chinese fir, and the O/C ratio increased by 29.6% and 19.7%, respectively. Microsocopic morphological analysis revealed noticeably thinner and slightly collapsed cell walls in the treated samples. The disappearance of lignin-specific peaks at 1507 cm −1 , 1460 cm −1 , and 1264 cm −1 confirmed the effective removal of lignin. Additionally, delignification resulted in a lower pyrolysis temperature, increased surface brightness, and reduced color variation. Due to the differences in internal structures and chemical compositions between poplar and Chinese fir, the effects of lignin removal varied, leading to significant changes in their physicochemical properties. These findings provide a theoretical foundational for lignin removal from wood and support future efforts in wood functionalization.

Accurate and fast identification of wood at the species level is critical for protecting and conserving tree species resources. The current identification methods are inefficient, costly, and complex. A wood species identification model based on wood anatomy and using the genus wood cell geometric dataset was proposed. The model was enhanced by the CTGAN deep learning algorithm and used a simulated cell geometric feature dataset. The machine learning models BPNN and SVM were trained respectively for recognition of three species with simulated vessel cells and simulated wood fiber cells. The SVM model and BPNN model achieved recognition accuracy of 96.4% and 99.6%, respectively, on the real dataset, using the CTGAN-generated vessel dataset. The BPNN model and SVM model achieved recognition accuracy of 75.5% and 77.9% on real dataset, respectively, using the CTGAN-generated wood fiber dataset. The machine learning model trained based on the enhanced cell geometric feature data by CTGAN achieved good recognition of , with the SVM model having a higher prediction accuracy than BPNN. The machine learning models were interpreted based on LIME to explore how they identify tree species based on wood cell geometric features. This proposed model can be used for efficient and cost-effective identification of wood species in industrial applications.

The numerous hierarchical architectures of 2D assemblies endow them with a new dimension to realize novel properties. From theoretical perspective, freedoms stem from in‐plane and out‐plane mechanical properties of 2D materials separately, which makes 2D materials embrace more than one “persistence length” giving rise to the diverse morphologies. However, the understanding of 3D architecture formation in 2D assemblies is still in its infancy. In fact, there is even no theoretical classification or reference to help clarify structural difference among numerous experimental obtained 2D assemblies. Based on the theoretical model composed by 2D sheets and Lennard‐Jones liquids, solution concentration dependence of 2D materials conformation is systematically studied, and a ln K behavior is uncovered that can realize the theoretical conformation prediction of 2D materials. More importantly, the digital production line (solution processing procedure) is set up toward establishing the 2D assemblies’ digital factory. The obtained structures may provide a reference to 2D assemblies, which benefits the understanding of the structural difference among different experiments and even help to guide the experimental design of 2D assemblies with targeted architectures and properties. Based on the theoretical model composed by 2D sheets and Lennard–Jones liquids, the correlation among the solution concentration, bending stiffness, and conformation is systematically studied in 2D materials’ solution. More importantly, the digital production line (solution processing procedure) is set up toward establishing the 2D assemblies’ digital factory, which predicts architectures and provides structural references as well as a foundation for classification.

An in-depth analysis of urban road network distribution plays a critical role in understanding the urbanization process. However, effective ways to quantitatively analyze the spatial paradigms of road networks are still lacking, and few studies have utilized road networks to rapidly identify urban areas of a region. Thus, using a fast-developing region in the south-eastern costal region of China, Fuzhou City, as a case, we introduced kernel density estimation (KDE) to characterize road networks and quantified the area’s spatial heterogeneity using exploratory spatial data analysis (ESDA) and semivariance analysis (SA). The results show that there is an uneven spatial distribution of the networks both at the regional and downtown levels. At the regional level, there is a conspicuous polarization in the road distribution, with the KDE being much higher in the urban areas than in the rural areas; at the downtown level, the KDE gradually decreases from the center to the periphery. Quantitatively, the ranges of the spatial dependence of the networks are approximately 25 km for the entire study region and 12 km for the downtown area. Additionally, the spatial variations vary among different directions, with greater variations in the northeast–southwest and the southeast–northwest directions compared with the other directions, which is in line with the urban sprawl policy of the study area. Both the qualitative and quantitative results show that the distribution of road networks has a clear urban–rural dual structure, which indicates that road networks can be an active tool in identifying the urban areas of a region. To this end, we propose a quick and easy method to delimit urban areas using KDE. The extraction results of KDE are better than those of the index-based built-up index (IBI), indicating the effectivity and feasibility of our proposed method to identify the urban areas in the region. This research sheds new light on urbanization development research.

Bamboo fibers are very promising reinforcements for polymer composites production due to its high aspect ratio and strong mechanical performances. In order to better understand their reinforcing potential, the mechanical properties of single bamboo fibers extracted from eleven commercial bamboo species in China were measured with a newly developed microtensile technique. For comparison, the mechanical properties of mature single Chinese Fir and Masson Pine wood fibers were measured. The results show that the average longitudinal tensile modulus of the eleven kinds of bamboo fibers ranges from 25.5 to 46.3 GPa with an average value of 36.7 GPa. For tensile strength, the value ranges from 1.20 to 1.93 GPa with an average value of 1.55 GPa. The tensile strength and modulus of bamboo fibers are nearly two times of that of single Chinese Fir and Masson Pine fibers, and significantly higher than most of the published data for other softwood fibers. The average elongation at break of bamboo fibers is about 4.84 %, only a little lower than the value 5.15 % of the tested mature softwood fibers. Additionally, bamboo fibers were found to have smaller diameters and larger aspect ratio than most documented wood fibers, which favored an improved reinforcing effect. These combined mechanical and morphological advantages highlight the potential of bamboo fibers as the reinforcing phase in polymer composites for structural purpose.

Electrochemical capacitors are expected to replace conventional electrolytic capacitors in line filtering for integrated circuits and portable electronics 1 – 8 . However, practical implementation of electrochemical capacitors into line-filtering circuits has not yet been achieved owing to the difficulty in synergistic accomplishment of fast responses, high specific capacitance, miniaturization and circuit-compatible integration 1 , 4 , 5 , 9 – 12 . Here we propose an electric-field enhancement strategy to promote frequency characteristics and capacitance simultaneously. By downscaling the channel width with femtosecond-laser scribing, a miniaturized narrow-channel in-plane electrochemical capacitor shows drastically reduced ionic resistances within both the electrode material and the electrolyte, leading to an ultralow series resistance of 39 mΩ cm 2 at 120 Hz. As a consequence, an ultrahigh areal capacitance of up to 5.2 mF cm −2 is achieved with a phase angle of −80° at 120 Hz, twice as large as one of the highest reported previously 4 , 13 , 14 , and little degradation is observed over 1,000,000 cycles. Scalable integration of this electrochemical capacitor into microcircuitry shows a high integration density of 80 cells cm −2 and on-demand customization of capacitance and voltage. In light of excellent filtering performances and circuit compatibility, this work presents an important step of line-filtering electrochemical capacitors towards practical applications in integrated circuits and flexible electronics. A miniaturized narrow-channel in-plane electrochemical capacitor shows drastically reduced ionic resistances within both the electrode material and the electrolyte and an ultrahigh areal capacitance by downscaling the channel width with femtosecond-laser scribing.

In this work, a mathematical model for load distribution of planetary roller screw mechanism with pitch deviation is proposed. The load distribution coefficient on the screw–roller and nut–roller sides is determined based on the relationship between axial contact stiffness, axial contact deformation and axial load. The presented model considers the variations of the axial clearance and contact state caused by the pitch deviation and is verified by comparing with the published model. Furthermore, the effects of one-thread pitch deviation, multi-thread pitch deviation and axial applied load on the load distribution coefficient of planetary roller screw mechanism are investigated. The results show that reducing the pitch deviation and increasing the axial applied load are conducive to improve the uniformity of load distribution and avoid the empty load of thread pairs. It is found that the increase of axial applied load is beneficial to reduce the load distribution fluctuation of planetary roller screw mechanism with pitch deviation.

Natural dyes (ND) are gaining increasing interest due to their outstanding merits of being eco-friendly, biodegradable and non-toxic. Wood dyed with extracts from Dalbergia cochinchinensis residues as ND exhibited desirable color appearance and anti-UV property, while the other potential properties of ND dyed wood in terms of water fastness, mildew resistance along with the penetrability of the ND on wood blocks dyeing have not yet been exploited. The present study was aimed at exploring these aspects for multifunction assessment of the ND dyed wood. The results showed that the total color difference (Δ E * : 4.58) and color intensity reduction (PR: 14.01%) of the ND dyed wood declined slightly after washing fastness test in comparison with that of acid red (Δ E * : 32.82; RP: 76.14%) and reactive red (Δ E * : 26.85; RP: 66.52%) dyed wood, which is indicative of its preferable water fastness, which can be ascribed to the hydrophobicity improvement of the wood surface after ND dyeing. In addition, the ND ameliorated the mildew resistance against Aspergillus niger and Trichoderma viride infection. Interestingly, the wood blocks can be completely impregnated with ND under atmospheric pressure dyeing process without any pretreatment and auxiliary. This study provided a promising approach for multifunctional ND dyed wood preparation.