Over the past three decades DNA has emerged as an exceptional molecular building block for nanoconstruction due to its predictable conformation and programmable intra- and intermolecular ...Watson–Crick base-pairing interactions. A variety of convenient design rules and reliable assembly methods have been developed to engineer DNA nanostructures of increasing complexity. The ability to create designer DNA architectures with accurate spatial control has allowed researchers to explore novel applications in many directions, such as directed material assembly, structural biology, biocatalysis, DNA computing, nanorobotics, disease diagnosis, and drug delivery. This Perspective discusses the state of the art in the field of structural DNA nanotechnology and presents some of the challenges and opportunities that exist in DNA-based molecular design and programming.
Abstract
Photocatalytic reduction of CO
2
is a promising approach to achieve solar-to-chemical energy conversion. However, traditional catalysts usually suffer from low efficiency, poor stability, ...and selectivity. Here we demonstrate that a large porous and stable metal-organic framework featuring dinuclear Eu(III)
2
clusters as connecting nodes and Ru(phen)
3
-derived ligands as linkers is constructed to catalyze visible-light-driven CO
2
reduction. Photo-excitation of the metalloligands initiates electron injection into the nodes to generate dinuclear {Eu(II)}
2
active sites, which can selectively reduce CO
2
to formate in a two-electron process with a remarkable rate of 321.9 μmol h
−1
mmol
MOF
−1
. The electron transfer from Ru metalloligands to Eu(III)
2
catalytic centers are studied via transient absorption and theoretical calculations, shedding light on the photocatalytic mechanism. This work highlights opportunities in photo-generation of highly active lanthanide clusters stabilized in MOFs, which not only enables efficient photocatalysis but also facilitates mechanistic investigation of photo-driven charge separation processes.
Flexible smart surfaces with tunable wettability are promising for emerging wearable uses. However, currently, wearable superhydrophobic surfaces with dynamic wetting behaviors are rarely reported. ...Here, a skin‐like superhydrophobic elastomer surface with switchable lotus leaf and rose petal states is reported. Direct laser writing technique is employed for one‐step, programmable, large‐scale fabrication of monolithic and hierarchical micro‐nanostructures on elastomer, leading to strong water repellence. The surface topography can be finely regulated in a rapid and reversible manner by simple stretching, providing the feasibility of controlling the surface wettability by simple body motions. The ability to switch wetting states enables the surface to capture and release multiple droplets in parallel. Furthermore, the active surface can be applied to the joints of fingers and operate as a droplet manipulator under finger motions without requiring energy supply or external appliance. In this work, dynamic tuning of wetting properties is integrated into the design of skin‐like wearable surfaces, revealing great potential in versatile applications such as wearable droplet manipulator, portable actuator, adaptive adhesion control, liquid repellent skin, and smart clothing.
A skin‐like wearable superhydrophobic surface with switchable lotus leaf and rose petal states is reported. One‐step laser processing on a deformable elastomer enables rapid and reversible tailoring of hierarchical structures and wetting properties. The wearable surface can be compliant to finger joints and operates as droplet tweezers under finger motions without energy supply or external appliance.
Polarization and geometric phase shaping via a space-variant anisotropy has attracted considerable interest for fabrication of flat optical elements and generation of vector beams with applications ...in various areas of science and technology. Among the methods for anisotropy patterning, imprinting of self-assembled nanograting structures in silica glass by femtosecond laser writing is promising for the fabrication of space-variant birefringent optics with high thermal and chemical durability and high optical damage threshold. However, a drawback is the optical loss due to the light scattering by nanograting structures, which has limited the application. Here, we report a new type of ultrafast laser-induced modification in silica glass, which consists of randomly distributed nanopores elongated in the direction perpendicular to the polarization, providing controllable birefringent structures with transmittance as high as 99% in the visible and near-infrared ranges and >90% in the UV range down to 330 nm. The observed anisotropic nanoporous silica structures are fundamentally different from the femtosecond laser-induced nanogratings and conventional nanoporous silica. A mechanism of nanocavitation via interstitial oxygen generation mediated by multiphoton and avanlanche defect ionization is proposed. We demonstrate ultralow-loss geometrical phase optical elements, including geometrical phase prism and lens, and a vector beam convertor in silica glass.
Short-term load forecasting plays an indispensable role in electric power systems, which is not only an extremely challenging task but also a concerning issue for all society due to complex ...nonlinearity characteristics. However, most previous combined forecasting models were based on optimizing weight coefficients to develop a linear combined forecasting model, while ignoring that the linear combined model only considers the contribution of the linear terms to improving the model’s performance, which will lead to poor forecasting results because of the significance of the neglected and potential nonlinear terms. In this paper, a novel nonlinear combined forecasting system, which consists of three modules (improved data pre-processing module, forecasting module and the evaluation module) is developed for short-term load forecasting. Different from the simple data pre-processing of most previous studies, the improved data pre-processing module based on longitudinal data selection is successfully developed in this system, which further improves the effectiveness of data pre-processing and then enhances the final forecasting performance. Furthermore, the modified support vector machine is developed to integrate all the individual predictors and obtain the final prediction, which successfully overcomes the upper drawbacks of the linear combined model. Moreover, the evaluation module is incorporated to perform a scientific evaluation for the developed system. The half-hourly electrical load data from New South Wales are employed to verify the effectiveness of the developed forecasting system, and the results reveal that the developed nonlinear forecasting system can be employed in the dispatching and planning for smart grids.
Nanoscale surface texturing, drilling, cutting, and spatial sculpturing, which are essential for applications, including thin-film solar cells, photonic chips, antireflection, wettability, and ...friction drag reduction, require not only high accuracy in material processing, but also the capability of manufacturing in an atmospheric environment. Widely used focused ion beam (FIB) technology offers nanoscale precision, but is limited by the vacuum-working conditions; therefore, it is not applicable to industrial-scale samples such as ship hulls or biomaterials, e.g., cells and tissues. Here, we report an optical far-field-induced near-field breakdown (O-FIB) approach as an optical version of the conventional FIB technique, which allows direct nanowriting in air. The writing is initiated from nanoholes created by femtosecond-laser-induced multiphoton absorption, and its cutting "knife edge" is sharpened by the far-field-regulated enhancement of the optical near field. A spatial resolution of less than 20 nm (
/40, with
being the light wavelength) is readily achieved. O-FIB is empowered by the utilization of simple polarization control of the incident light to steer the nanogroove writing along the designed pattern. The universality of near-field enhancement and localization makes O-FIB applicable to various materials, and enables a large-area printing mode that is superior to conventional FIB processing.
Air quality early-warning plays a vital role in improving air quality and human health, especially multi-step ahead air quality early-warning, which is significant for both citizens and environmental ...protection departments. However, most previous studies have only employed simple data decomposition to perform one-step forecasting and were aimed at enhancing forecasting accuracy or stability. Little research has improved these two standards simultaneously, leading to poor forecasting performance. Because of its significance, relevant research focused on multi-step ahead air quality early-warning is especially needed. Therefore, in this paper, a novel hybrid air quality early-warning system, which consists of four modules: data preprocessing module, optimization module, forecasting module and evaluation module, is proposed to perform multi-step ahead air quality early-warning. In this system, an effective data decomposition method called the modified complete ensemble empirical mode decomposition with adaptive noise is developed to effectively extract the characteristics of air quality data and to further improve the forecasting performance. Moreover, the hybrid Elman neural network model, optimized by the multi-objective salp swarm algorithm, is successfully developed in the forecasting module and simultaneously achieves high forecasting accuracy and stability. In addition, the evaluation module is designed to conduct a reasonable and scientific evaluation for this system. Three cities in China are employed to test the effectiveness of the proposed early-warning system, and the results reveal that the proposed early-warning system has superior ability in both accuracy and stability than other benchmark models and can be used as a reliable tool for multi-step ahead air quality early-warning.
•A modified data preprocessing technique is successfully developed.•Multi-step ahead air quality early-warning system is developed for cities in China.•Accuracy and stability of the early-warning system are improved simultaneously.•The results of the hybrid system are well-validated in three cities of China.
Alzheimer’s disease (AD) is a progressive and irreversible brain degenerative disorder. Mild cognitive impairment (MCI) is a clinical precursor of AD. Although some treatments can delay its ...progression, no effective cures are available for AD. Accurate early-stage diagnosis of AD is vital for the prevention and intervention of the disease progression. Hippocampus is one of the first affected brain regions in AD. To help AD diagnosis, the shape and volume of the hippocampus are often measured using structural magnetic resonance imaging (MRI). However, these features encode limited information and may suffer from segmentation errors. Additionally, the extraction of these features is independent of the classification model, which could result in sub-optimal performance. In this study, we propose a multi-model deep learning framework based on convolutional neural network (CNN) for joint automatic hippocampal segmentation and AD classification using structural MRI data. Firstly, a multi-task deep CNN model is constructed for jointly learning hippocampal segmentation and disease classification. Then, we construct a 3D Densely Connected Convolutional Networks (3D DenseNet) to learn features of the 3D patches extracted based on the hippocampal segmentation results for the classification task. Finally, the learned features from the multi-task CNN and DenseNet models are combined to classify disease status. Our method is evaluated on the baseline T1-weighted structural MRI data collected from 97 AD, 233 MCI, 119 Normal Control (NC) subjects in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The proposed method achieves a dice similarity coefficient of 87.0% for hippocampal segmentation. In addition, the proposed method achieves an accuracy of 88.9% and an AUC (area under the ROC curve) of 92.5% for classifying AD vs. NC subjects, and an accuracy of 76.2% and an AUC of 77.5% for classifying MCI vs. NC subjects. Our empirical study also demonstrates that the proposed multi-model method outperforms the single-model methods and several other competing methods.
The rainfall distribution in a tropical cyclone (TC) is affected by many factors/processes. Most of previous studies have focused on individual TCs. Little is known about the rainfall distribution in ...binary TCs. This study examines the rainfall distribution in binary TCs over the western North Pacific based on the observational data. When two TCs become nearby, the asymmetric component of rainfall shows an increasing trend with rainfall significantly suppressed in Quadrant IV of the TC located to the west when orienting the two TCs in the west‐east direction. The suppression becomes remarkable once the separation distance between the two TCs is within about 2,050 km. Furthermore, the convective activity in one TC is related to the deep‐layer vertical wind shear (VWS) from its companion. Rainfall is enhanced downshear‐left in a TC, consistent with a single TC embedded in an environmental VWS as found in previous studies.
Plain Language Summary
The rainfall distribution in each tropical cyclone (TC) of binary‐TC systems may be more complicated than that in a single TC. Whether the rainfalls of binary TCs have some special features has not been studied before. This observational study examines the rainfall distribution in binary TCs over the western North Pacific during 2001–2020. The results show that the asymmetric component of TC rainfalls strengthened with the decreasing of the separation distance between the two TCs. If orient binary TCs in the west‐east direction, the rainfalls of the western TC were distinctly weakened in its Quadrant IV. The suppression effect cannot be neglected within 2,050‐km separation distance between binary TCs. Composite analyses indicate that the asymmetric rainfalls in one of the binary TCs were related to the deep‐layer vertical wind shear induced by its companion. The consistent pattern of rainfall asymmetry indicates a highly predictable component of rainfall distribution at vortex‐scale in binary TCs and can help improve the precipitation forecast of binary TCs.
Key Points
The rainfall asymmetry increases with the decreasing separation distance between binary tropical cyclones (TCs)
If the two TCs are oriented west‐east and within about 2,050 km, rainfall in the western TC is suppressed in its Quadrant IV
The asymmetric convective activity in one TC can be explained by the deep‐layer vertical wind shear from its companion
We present a strategy to design and construct self-assembling DNA nanostructures that define intricate curved surfaces in three-dimensional (3D) space using the DNA origami folding technique. ...Double-helical DNA is bent to follow the rounded contours of the target object, and potential strand crossovers are subsequently identified. Concentric rings of DNA are used to generate in-plane curvature, constrained to 2D by rationally designed geometries and crossover networks. Out-of-plane curvature is introduced by adjusting the particular position and pattern of crossovers between adjacent DNA double helices, whose conformation often deviates from the natural, B-form twist density. A series of DNA nanostructures with high curvature—such as 2D arrangements of concentric rings and 3D spherical shells, ellipsoidal shells, and a nanoflask—were assembled.
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