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Graph Neural Networks (GNNs) have attracted increasing attentions for multimodal Emotion Recognition in Conversation (ERC) due to their good performance in contextual understanding. However, most existing GNN-based methods suffer from two challenges: 1) How to explore and propagate appropriate information in a conversational graph. Typical GNNs in ERC neglect to mine the emotion commonality and discrepancy in the local neighborhood, leading to learn similar embbedings for connected nodes. However, the embeddings of these connected nodes are supposed to be distinguishable as they belong to different speakers with different emotions. 2) Most existing works apply simple concatenation or co-occurrence prior for modality combination, failing to fully capture the emotional information of multiple modalities in relationship modeling. In this paper, we propose a multimodal Decoupled Distillation Graph Neural Network (D2GNN) to address the above challenges. Specifically, D2GNN decouples the input features into emotion-aware and emotion-agnostic ones on the emotion category-level, aiming to capture emotion commonality and implicit emotion information, respectively. Moreover, we design a new message passing mechanism to separately propagate emotion-aware and -agnostic knowledge between nodes according to speaker dependency in two GNN-based modules, exploring the correlations of utterances and alleviating the similarities of embeddings. Furthermore, a multimodal distillation unit is performed to obtain the distinguishable embeddings by aggregating unimodal decoupled features. Experimental results on two ERC benchmarks demonstrate the superiority of the proposed model. Code is available at https://github.com/gityider/D2GNN.

Domain adaptation person re-identification (Re-ID) is a challenging task, which aims to transfer the knowledge learned from the labeled source domain to the unlabeled target domain. Recently, some clustering-based domain adaptation Re-ID methods have achieved great success. However, these methods ignore the inferior influence on pseudo-label prediction due to the different camera styles. The reliability of the pseudo-label plays a key role in domain adaptation Re-ID, while the different camera styles bring great challenges for pseudo-label prediction. To this end, a novel method is proposed, which bridges the gap of different cameras and extracts more discriminative features from an image. Specifically, an intra-to-intermechanism is introduced, in which samples from their own cameras are first grouped and then aligned at the class level across different cameras followed by our logical relation inference (LRI). Thanks to these strategies, the logical relationship between simple classes and hard classes is justified, preventing sample loss caused by discarding the hard samples. Furthermore, we also present a multiview information interaction (MvII) module that takes features of different images from the same pedestrian as patch tokens, obtaining the global consistency of a pedestrian that contributes to the discriminative feature extraction. Unlike the existing clustering-based methods, our method employs a two-stage framework that generates reliable pseudo-labels from the views of the intracamera and intercamera, respectively, to differentiate the camera styles, subsequently increasing its robustness. Extensive experiments on several benchmark datasets show that the proposed method outperforms a wide range of state-of-the-art methods. The source code has been released at https://github.com/lhf12278/LRIMV.

Most current domain adaptation methods for fault diagnosis focus on single target domain. However, test data often comes from multiple target domains, as machines work under different operating conditions, subsequently generating a more complex and extensive distribution of target data. Unfortunately, single target domain adaptation methods are not adaptive for multiple target domains adaptation (MTDA), which results in transfer performance degradation. To this end, a novel disentanglement learning with adaptive centroid alignment is proposed for MTDA. Specifically for disentanglement learning, two encoders and two classifiers are constructed independently for fault-related and domain-related feature extractions and classifications. Followed by the dual-adversarial strategy, only fault-related but domain-irrelevant features are extracted. Furthermore, to achieve the category alignment, we also propose an adaptive centroid alignment strategy, so that the feature centroids of the same fault category in different domains are enforced to be close to each other. Extensive experiments demonstrate the superiority of our proposed method compared with other popular approaches.

With the wide application of portable wearable devices, a variety of electronic energy storage devices, including micro-supercapacitors (MSCs), have attracted wide attention. Laser-induced graphene (LIG) is widely used as electrode material for MSCs because of its large porosity and specific surface area. To further improve the performance of MSCs, it is an effective way to increase the specific surface area and the number of internal active sites of laser-induced graphene electrode materials. In this paper, N-doped polyimide/polyvinyl alcohol (PVA) as precursor was used to achieve in situ doping of nitrogen atoms in laser-induced graphene by laser irradiation. Through the addition of N atoms, nitrogen-doped laser-induced three-dimensional porous graphene (N-LIG) exhibits large specific surface area, many active sites, and good wettability all of which are favorable conditions for enhancing the capacitive properties of laser-induced graphene. After assembly with PVA/H2SO4as gel electrolyte, the high surface capacitance of the MSC device with N-LIG as electrode material is 16.57 mF cm−2at the scanning rate of 5 mV s−1, which is much higher than the 2.89 mF cm−2of the MSC device with LIG as electrode material. In addition, MSC devices with N-LIG as electrode materials have shown excellent cyclic stability and flexibility in practical tests, so they have a high application prospect in the field of flexible wearable microelectronics.

Over recent years, deep learning has significantly boosted scene text detection performance, and current segmentation-based scene text detectors can achieve compact bounding boxes for irregular texts. However, it is also challenging to tackle crowded or overlapping texts for these existing methods due to conglutination between adjacent text instances in segmentation results. To address these issues, we propose a more accurate scene text detector, Text Position-Aware Pixel Aggregation Network, termed TPPAN. Specifically, a Gaussian threshold representation is adaptively learned instead of a constant setting in Adaptively Text Kernel Thresholding (ATKT) module to obtain more accurate text kernels. Then Text Position-Aware Region Pixel Aggregation (TPAR-PA) module predicts the text regions in relative positions and generates more accurate text contours. Adequate experiments have demonstrated that the resulting detector has achieved state-of-the-art performance on multi-oriented and curved scene text benchmarks.

While Transformer has achieved remarkable performance in various high-level vision tasks, it is still challenging to exploit the full potential of Transformer in image restoration. The crux lies in the limited depth of applying Transformer in the typical encoder-decoder framework for image restoration, resulting from heavy self-attention computation load and inefficient communications across different depth (scales) of layers. In this paper, we present a deep and effective Transformer-based network for image restoration, termed as U2-Former, which is able to employ self-attention of Transformer as the core operation for feature learning to perform image restoration in a deep encoding and decoding space. Specifically, it leverages the nested U-shaped structure to facilitate the interactions across different layers with different scales of feature maps. Furthermore, we optimize the computational efficiency for the basic Transformer block by introducing a simple yet effective feature-filtering mechanism to compress the token representation. Apart from the typical supervision ways for image restoration, our U2-Former also performs multi-view contrastive learning, which constructs positive pairs in various aspects, to learn noise-sensitive but content-irrelevant features and further decouple the noise component from the background image. Extensive experiments on various image restoration tasks, including reflection removal, rain streak removal and dehazing respectively, demonstrate the effectiveness of the proposed U2-Former.

A bi-isotropic Huygens’ metasurface (BIHMS) is proposed for polarization-insensitive cross-polarization conversion and wavefront manipulation, which can transmit and twist a linearly polarized (LP) incident wave with arbitrary azimuth to its cross-polarized component, meanwhile, the phase shift of the transmitted wave can be manipulated within $0\pi $ – $2\pi $ . The BIHMS unit cell contains four patch-via-patch (PVP) structures that form a fourfold rotational symmetry; each PVP structure consists of two orthogonally placed patches connected by a via, only with two layers of metal. As a proof of concept, a polarization-insensitive cross-polarization converter (PICPC) and a polarization-insensitive phase gradient metasurface (PIPGM) are proposed based on the unit cell. The simulated and measured results show that the PICPC exhibits a −1 dB cross-polarization transmission bandwidth within 8.4–11.2 GHz (27.7%) and the corresponding polarization conversion ratio (PCR) is over 92% under transverse electric, 45° LP and transverse magnetic incident waves, and the PIPGM deflects the different LP incident waves to the same direction within 8.6–10.8 GHz (22.7%) with efficiency over 60%. The proposed broadband BIHMS exploits a new option for polarization-insensitive electromagnetic (EM) manipulation and can find application in various wireless systems.

Magnetic and electric dipoles have similar symmetry and are therefore expected to exhibit many common structures. However, despite frequent reports of various spin textures composed of magnetic dipoles, investigations on long-range ordered electric dipoles have been scarce, until recently. Here we discover spontaneous toroidal polar topology in an emerging ferroelectric liquid state with polarization helices, dubbed ‘helielectric nematic’. The interplay between liquid-crystal elasticity and polar interactions results in a continuous in-plane rotation of the polarization, producing a periodic toroidal assembly. In partial analogy with magnetism, the neighbouring toroidal domains are separated by circularly shaped domain walls. The local polarization switching enables unique shrinkage and expansion dynamics of the toroidal domains. The discovery provides opportunities for developing designable and switchable ferroelectric-liquid-matter opto-electronics.

Severe capacity/voltage fading still poses substantial obstacles in the commercial applications of Li-rich layered oxides, which stems from the aggregation of Li2MnO3-like domains and unstable surface structure. Here, we report highly stabilized Co-free Li1.2Ni0.2Mn0.6O2with uniformly dispersed Li2MnO3-like domains and a protective rock-salt structure shell by reducing the oxygen partial pressure during high-temperature calcination. Experimental characterizations and DFT calculations reveal that the uniformly dispersed and small-sized Li2MnO3-like domains suppress the peroxidation of lattice oxygen, enabling highly reversible oxygen redox and excellent structural stability. Moreover, the induced rock-salt structure shell significantly restrains lattice oxygen release, TM dissolution, and interfacial side reactions, thereby improving the interfacial stability and facilitating Li+diffusion. Consequently, the obtained Li1.2Ni0.2Mn0.6O2which was calcinated under an oxygen partial pressure of 0.1% (LNMO-0.1) delivers a high reversible capacity of 276.5 mAh g–1at 0.1 C with superior cycling performance (a capacity retention rate of 85.4% after 300 cycles with a small voltage fading rate of 0.76 mV cycle–1) and excellent thermal stability. This work links the synthesis conditions with the domain structure and electrochemical performance of Li-rich cathode materials, providing some insights for designing high-performance Li-rich cathodes.

Superoscillation is an extraordinary phenomenon capable of generating small-size focusing spots, which can be used for super-resolution focusing and imaging. Herein, we elaborate on a reconfigurable near-field focusing reflector based on the superoscillation principle, where the focal position and operation frequencies are modulated by the dynamic phase-compensation mechanism of an engineered metamirror. A varactor diode is embedded into each resonant meta-atom, enabling to achieve nearly 340° phase coverage. To validate the proposed reflector, the fabricated metamirror platform is experimentally measured in an anechoic chamber. The results show that the near-field focusing maintains subdiffraction property when the focal distances and frequencies are flexibly driven by the metamirror. Such a focusing reflector holds great integration potential to imaging devices for its tunability, high resolution, and nonnarrowband features.

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.

Conventional multiview clustering seeks to partition data into respective groups based on the assumption that all views are fully observed. However, in practical applications, such as disease diagnosis, multimedia analysis, and recommendation system, it is common to observe that not all views of samples are available in many cases, which leads to the failure of the conventional multiview clustering methods. Clustering on such incomplete multiview data is referred to as incomplete multiview clustering (IMC). In view of the promising application prospects, the research of IMC has noticeable advances in recent years. However, there is no survey to summarize the current progresses and point out the future research directions. To this end, we review the recent studies of IMC. Importantly, we provide some frameworks to unify the corresponding IMC methods and make an in-depth comparative analysis for some representative methods from theoretical and experimental perspectives. Finally, some open problems in the IMC field are offered for researchers. The related codes are released at https://github.com/DarrenZZhang/Survey_IMC.

Multichannel wide-angle staring synthetic aperture radar ground moving target indication (WasSAR-GMTI) has attracted a lot of interest for its ability of moving target detection and trajectory reconstruction. However, the parameter estimation of moving target is easily affected by clutter and noise, which deteriorates the performance of the following geolocation. In this article, a novel approach for trajectory reconstruction of moving target based on road line is proposed. First, after performing multichannel calibration, a novel Doppler centroid estimation and correction method is proposed. This method is not dependent on attitude information of platform nor does it require scene uniformity as a prior condition, which has high robustness in complex environments. Then, the weighted adaptive matched filtering (WAMF) and the modified Gaussian mixture probability hypothesis density (GMPHD) are performed for velocity estimation and tracking. Finally, the error items affecting geolocation accuracy were analyzed in detail. Also, a novel two-step geolocation method based on road lines is proposed to correct geolocation errors, which can be applied in complex road networks. The proposed method only needs 1-D parameter searching, which has high efficiency. The real-measured WasSAR data processing results demonstrated the effectiveness and practicability of the proposed algorithm.

The entropy of the codes usually serves as the rate loss in the recent learned lossy image compression methods. Precise estimation of the probabilistic distribution of the codes plays a vital role in reducing the entropy and boosting the joint rate-distortion performance. However, existing deep learning based entropy models generally assume the latent codes are statistically independent or depend on some side information or local context, which fails to take the global similarity within the context into account and thus hinders the accurate entropy estimation. To address this issue, we propose a special nonlocal operation for context modeling by employing the global similarity within the context. Specifically, due to the constraint of context, nonlocal operation is incalculable in context modeling. We exploit the relationship between the code maps produced by deep neural networks and introduce the proxy similarity functions as a workaround. Then, we combine the local and the global context via a nonlocal attention block and employ it in masked convolutional networks for entropy modeling. Taking the consideration that the width of the transforms is essential in training low distortion models, we finally produce a U-net block in the transforms to increase the width with manageable memory consumption and time complexity. Experiments on Kodak and Tecnick datasets demonstrate the priority of the proposed context-based nonlocal attention block in entropy modeling and the U-net block in low distortion situations. On the whole, our model performs favorably against the existing image compression standards and recent deep image compression models.

The present study compares a single‐band chorus wave against a banded chorus wave observed by Van Allen Probes at adjacent times, and demonstrates that the single‐band chorus wave is associated with an anisotropic electron population over a broad energy range, while the banded chorus wave is accompanied by an electron phase space density plateau and an electron anisotropy reduction around Landau resonant energies. We further compare banded chorus waves with different spectral gap widths, and show that a wider spectral gap is associated with electron isotropization extending to higher energies with respect to the equatorial Landau resonant energy. We suggest that early generated chorus waves isotropize electrons via Landau resonant acceleration, and the waves that propagate to higher latitudes isotropize electrons at higher energies. The isotropization extending to higher energies leads to a larger spectral gap of new chorus waves after electrons bounce back to the equator. Naturally occurring chorus waves in the Earth's magnetosphere typically consist of two frequency bands. The present study aims to explain what controls the bandwidth of the chorus frequency gap that separates chorus waves into two bands. We first compare a single‐band chorus wave against a banded chorus wave observed by a Van Allen Probe satellite at adjacent times. The banded chorus wave is accompanied by an electron phase space density plateau and an electron anisotropy reduction due to Landau resonance, while this phenomenon is not clearly seen in association with the single‐band chorus wave. We further compare banded chorus waves with different gap widths. Satellite observations indicate that a wider frequency gap is associated with electron isotropization extending to higher energies. We suggest that Landau resonant acceleration extending to high latitudes isotropizes electron distribution at high energies, leading to new chorus waves with a large frequency gap. In contrast, Landau resonance that stops at a relatively lower latitude (due to waves being damped) leads to new chorus waves with a smaller frequency gap. Freshly injected anisotropic electron population without a PSD plateau generates single band chorus wavesBanded chorus waves are more common because electrons usually have already undergone isotropization at Landau resonant energies along the drift pathLandau acceleration extending to higher energies occurring at higher latitudes leads to more pronounced electron isotropization and larger chorus spectral gaps Freshly injected anisotropic electron population without a PSD plateau generates single band chorus waves Banded chorus waves are more common because electrons usually have already undergone isotropization at Landau resonant energies along the drift path Landau acceleration extending to higher energies occurring at higher latitudes leads to more pronounced electron isotropization and larger chorus spectral gaps

Neurotransmitters play essential roles in regulating neural circuit dynamics both in the central nervous system as well as at the peripheral, including the gastrointestinal tract1–3. Their real-time monitoring will offer critical information for understanding neural function and diagnosing disease1–3. However, bioelectronic tools to monitor the dynamics of neurotransmitters in vivo, especially in the enteric nervous systems, are underdeveloped. This is mainly owing to the limited availability of biosensing tools that are capable of examining soft, complex and actively moving organs. Here we introduce a tissue-mimicking, stretchable, neurochemical biological interface termed NeuroString, which is prepared by laser patterning of a metal-complexed polyimide into an interconnected graphene/nanoparticle network embedded in an elastomer. NeuroString sensors allow chronic in vivo real-time, multichannel and multiplexed monoamine sensing in the brain of behaving mouse, as well as measuring serotonin dynamics in the gut without undesired stimulations and perturbing peristaltic movements. The described elastic and conformable biosensing interface has broad potential for studying the impact of neurotransmitters on gut microbes, brain–gut communication and may ultimately be extended to biomolecular sensing in other soft organs across the body.

Background Lung cancer is the most common cancer in the world and has become one of the malignancies with the highest incidence and mortality; more than half of patients die within one year of being diagnosed with lung cancer. In recent years, the concept of “patient-centered” service has gained popularity, and patients’ subjective feelings have gradually been used in clinical decision-making. Therefore, this study determined the application of visual patient report outcomes in the field of lung cancer, in order to provide reference for specific clinical practice.Methods Using the Web of Science core collection as the main analysis content, Citespace and VOSviewer were used to conduct this scientometric study.Results A total of 499 literatures that met the inclusion criteria were retrieved. The most prolific institution was The University of Texas MD Anderson Cancer Center, and the United States dominates this field.Conclusion The measurement of patient-reported outcomes is considered the gold standard for effectively evaluating patients' perceptions of clinical interventions or diseases. It is recommended that patient-reported outcomes be integrated into routine care for cancer patients in order to enhance communication between patients and healthcare providers.Patient or public contribution Not applicable. All data in this paper are obtained from the web of science database.

Elevated carbon dioxide (CO2) (eCO2) has been shown to affect the nitrous oxide (N2O) emission from terrestrial ecosystems by altering the interaction of plants, soils, and microorganisms. However, the impact of eCO2on the N2O emission from agricultural soils remains poorly understood. This meta-analysis summarizes the effect of eCO2on N2O emission in agricultural ecosystems and soil physiochemical and biological characteristics using 50 publications selected. The eCO2effect values, which equal to the percentage changes of N2O emission under eCO2, were calculated based on the natural logarithm of the response ratio to eCO2. We found that eCO2significantly increased N2O emission (by 44%), which varied depending on experimental conditions, agricultural practices, and soil properties. In addition, eCO2significantly increased soil water-filled pore space (by 6%), dissolved organic carbon content (by 11%), and nitrate nitrogen content (by 13%), but significantly reduced soil pH (by 1%). Moreover, eCO2significantly increased soil microbial biomass carbon (by 28%) and soil microbial biomass nitrogen (by 7%) contents. Additionally, eCO2significantly increased the abundances of ammonia-oxidizing bacteria (AOB) amoA(by 21%), nirK(by 15%), and nirS(by 15%), but did not affect the abundances of ammonia-oxidizing archaea (AOA) amoAand nosZ. Our findings indicate that eCO2substantially stimulates N2O emission in agroecosystems and highlight that optimization of nitrogen management and agronomic options might suppress this stimulation and aid in reducing greenhouse effect.

Fast-response artificial phototropic materials are a promising tool for solar energy utilisation, yet their preparation remains challenging. Herein, we report the so-called photothermal domino strategy for constructing fast-response artificial phototropic materials. In this strategy, photothermal generation, heat conduction and thermal actuation are sequentially optimised by a coordination effect. For the first time, lignin-based organic radicals boosted by this coordination effect are used to significantly enhance photothermal conversion. Interfacial coordination bonds between lignin and an elastomer matrix promote interfacial heat conduction. Light-stimulated thermal actuation is significantly improved by coordination-assisted mechanical training. The prepared biomimetic phototropic material exhibits excellent phototropic ability, with a 2.5 s light-tracking process, showing great application potential for efficient solar energy utilisation. This strategy shows great significance for fabricating high-performance intelligent phototropic materials using widely available, green raw materials.

Here we highlight 35 researchers approximately under the age of 35. Age, of course, is just a number—our target was emerging early-career academics. Contributors were recruited in a self-propagating “pay-it-forward” manner, with each invitee being suggested by a peer who had already contributed. The final collection is an inspiring look at the challenges the current generation of materials researchers are tackling.

Multiview learning has shown its superiority in visual classification compared with the single-view-based methods. Especially, due to the powerful representation capacity, the Gaussian process latent variable model (GPLVM)-based multiview approaches have achieved outstanding performances. However, most of them only follow the assumption that the shared latent variables can be generated from or projected to the multiple observations but fail to exploit the harmonization in the back constraint and adaptively learn a classifier according to these learned variables, which would result in performance degradation. To tackle these two issues, in this article, we propose a novel harmonization shared autoencoder GPLVM with a relaxed Hamming distance (HSAGP-RHD). Particularly, an autoencoder structure with the Gaussian process (GP) prior is first constructed to learn the shared latent variable for multiple views. To enforce the agreement among various views in the encoder, a harmonization constraint is embedded into the model by making consistency for the view-specific similarity. Furthermore, we also propose a novel discriminative prior, which is directly imposed on the latent variable to simultaneously learn the fused features and adaptive classifier in a unit model. In detail, the centroid matrix corresponding to the centroids of different categories is first obtained. A relaxed Hamming distance (RHD)-based measurement is subsequently presented to measure the similarity and dissimilarity between the latent variable and centroids, not only allowing us to get the closed-form solutions but also encouraging the points belonging to the same class to be close, while those belonging to different classes to be far. Due to this novel prior, the category of the out-of-sample is also allowed to be simply assigned in the testing phase. Experimental results conducted on three real-world data sets demonstrate the effectiveness of the proposed method compared with state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

The emerging ferroelectric nematic (NF) liquid crystal is a novel 3D-ordered liquid exhibiting macroscopic electric polarization. The combination of the ultrahigh dielectric constant, strong nonlinear optical signal, and high sensitivity to the electric field makes NFmaterials promising for the development of advanced liquid crystal electroopic devices. Previously, all studies focused on the rod-shaped small molecules with limited length (l) range and dipole moment (μ) values. Here, through the precision synthesis, we extend the aromatic rod-shaped mesogen to oligomer/polymer (repeat unit up to 12 with monodisperse molecular-weight dispersion) and increase the μ value over 30 Debye (D). The NFphase has a widespread existence far beyond our expectation and could be observed in all the oligomer/polymer length range. Notably, the NFphase experiences a nontrivial evolution pathway with the traditional apolar nematic phase completely suppressed, i.e., the NFphase nucleates directly from the isotropic liquid phase. The discovery of thte ferroelectric packing of oligomer/polymer rods not only offers the concept of extending the NFstate to oligomers/polymers but also provides some previously overlooked insights in oxybenzoate-based liquid crystal polymer materials.

Recently, we have demonstrated a general molecular design for emerging ferroelectric nematic liquid crystals by introducing large dipole moment and local bulkiness into rod-shaped mesogenic molecules. The giant dielectricity and strong nonlinear optic properties in the ferroelectric nematic liquid crystal materials could bring the vast technological potential for flexible supercapacitors, electro-optic devices, and nonlinear optical devices. Here, we extend the polar liquid crystal material from small molecules to polymer systems by elaborately manipulating the interplay between the side-chain joint position and dipole–dipole interaction. For this purpose, we developed three series of side-chain liquid crystalline polymers. We clarify that the polymers with side-jointed mesogens exhibit a polar nematic liquid crystalline phase with strong polarity confirmed by second harmonic generation, while the polymers with end-jointed mesogens self-assemble into a smectic A phase with no polarity. The magnitude of dipole moment was also critical for producing the macroscopic polarity in these side-chain liquid crystalline polymers.

Nickel-rich layered oxides (NCM) are a promising contender material for the cathode electrode of high-energy lithium-ion batteries (LIBs) due to their large reversible capacity and high operating voltage. However, the poor surface/interfacial stability and the dissolution of transition metal ions hinder the commercial application of NCM. To create an artificial cathode electrolyte interphase (CEI) with LiF-rich inorganic phase on the NCM surface, a practical and efficient way of quenching the NCM powder from high temperature in 1,1,2,2-Tetrafluoroethyl 2,2,2-trifluoroethyl ether (HFE) was devised. With this artificial CEI film, the side reactions between NCM and electrolytes are inhibited, and the dissolution of TM ions is retarded. The quenched-NCM achieved fantastic cycling performance and suppressed voltage decay. Our research offers an efficient and worthy approach for improving the surface/interfacial stabilization of nickel-rich cathode materials for high-energy-density LIBs.

The present study uncovers the fine structures of magnetosonic waves by investigating the EFW waveforms measured by Van Allen Probes. We show that each harmonic of the magnetosonic wave may consist of a series of elementary rising‐tone emissions, implying a nonlinear mechanism for the wave generation. By investigating an elementary rising‐tone magnetosonic wave that spans a wide frequency range, we show that the frequency sweep rate is likely proportional to the wave frequency. We studied compound rising‐tone magnetosonic waves, and found that they typically consist of multiple harmonics in the source region, and may gradually become continuous in frequency as they propagate away from source. Both elementary and compound rising‐tone magnetosonic waves last for ∼1 min which is close to the bounce period of the ring proton distribution, but their relation is not fully understood. Naturally occurring magnetosonic waves are a kind of electromagnetic wave in the Earth's magnetized space. Their frequency spectra typically exhibit a harmonic structure. Sometimes they exhibit a rising‐tone feature, that is, the wave starts at a low pitch and gradually increases its frequency to a high pitch. Using the high‐resolution data measured by Van Allen Probe satellites, the present study uncovers that each harmonic of a magnetosonic wave may have a fine structure: they consist of a series of elementary rising‐tone emissions. The traditionally known rising‐tone magnetosonic waves consist of multiple harmonics, and we name them “compound rising‐tone magnetosonic waves”. The compound rising‐tone waves may gradually become continuous in spectrum as they propagate away from source. These findings give new insights to the generation of magnetosonic waves and their impact on space environment. Each harmonic of a magnetosonic wave may consist of a series of elementary rising‐tone emissionsThe frequency sweep rate of elementary magnetosonic waves is likely proportional to the frequencyCompound rising‐tone magnetosonic waves consist of harmonics at the source, and gradually develop a continuous spectrum during propagation Each harmonic of a magnetosonic wave may consist of a series of elementary rising‐tone emissions The frequency sweep rate of elementary magnetosonic waves is likely proportional to the frequency Compound rising‐tone magnetosonic waves consist of harmonics at the source, and gradually develop a continuous spectrum during propagation

Due to the powerful capability of the data representation, deep learning has achieved a remarkable performance in supervised hash function learning. However, most of the existing hashing methods focus on point-to-point matching that is too strict and unnecessary. In this article, we propose a novel deep supervised hashing method by relaxing the matching between each pair of instances to a point-to-angle way. Specifically, an inner product is introduced to asymmetrically measure the similarity and dissimilarity between the real-valued output and the binary code. Different from existing methods that strictly enforce each element in the real-valued output to be either +1 or −1, we only encourage the output to be close to its corresponding semantic-related binary code under the cross-angle. This asymmetric product not only projects both the real-valued output and the binary code into the same Hamming space but also relaxes the output with wider choices. To further exploit the semantic affinity, we propose a novel Hamming-distance-based triplet loss, efficiently making a ranking for the positive and negative pairs. An algorithm is then designed to alternatively achieve optimal deep features and binary codes. Experiments on four real-world data sets demonstrate the effectiveness and superiority of our approach to the state of the art. [ABSTRACT FROM AUTHOR]

• A novel unsupervised method is proposed for cross-modal retrieval. • Both similarity and diversity are considered to powerfully extract the correlation. • Hilbert Schmidt Independence Criterion (HSIC) is introduced to enforce the diversity. • An efficient algorithm is proposed to optimize the proposed method alternatively. • Experimental results on real-world databases show the superiority of the method. The heterogeneous gap among cross modalities is a critical problem in many applications (e.g., retrieval). Considering that the main purpose of cross-modal learning is to learn a common representation while there also exist specific components across different modalities, a similarity and diversity induced paired projection (SDPP) method is proposed in this paper. SDPP not only extracts the correlation in a common subspace, but also removes the view-specific information which does not contribute to our task. In order to model the specific components, the Hilbert Schmidt Independence Criterion (HSIC) is introduced as a co-regularization to explicitly enforce the diversity. Additionally, different from some existing subspace learning methods which are time consuming in the testing phase, a paired projection strategy is exploited, being capable of obtaining the similar information in a simple but effective way. To optimize the presented approach, an efficient algorithm is designed to update different variables alternatively. Finally, we apply our strategy to the cross-modal retrieval, and experimental results on several real-world datasets substantiate the effectiveness and superiority of our model compared with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

RNA is emerging as a valuable target for the development of novel therapeutic agents. The rational design of RNA-targeting small molecules, however, has been hampered by the relative lack of methods for the analysis of small molecule–RNA interactions. Here, we present our efforts to develop such a platform using photoaffinity labeling. This technique, termed Photoaffinity Evaluation of RNA Ligation-Sequencing (PEARL-seq), enables the rapid identification of small molecule binding locations within their RNA targets and can provide information on ligand selectivity across multiple different RNAs. These data, when supplemented with small molecule SAR data and RNA probing data enable the construction of a computational model of the RNA–ligand structure, thereby enabling the rational design of novel RNA-targeted ligands.

Group convolution is widely used in many mobile networks to remove the filter’s redundancy from the channel extent. In order to further reduce the redundancy of group convolution, this article proposes a novel repeated group convolutional (RGC) kernel, which has $M$ primary groups, and each primary group includes $N$ tiny groups. In every primary group, the same convolutional kernel is repeated in all the tiny groups. The RGC filter is the first kernel to remove the redundancy from group extent. Based on RGC, a sparse RGC (SRGC) kernel is also introduced in this article, and its corresponding network is called SRGC neural networks (SRGC-Net). The SRGC kernel is the summation of RGC kernel and pointwise group convolutional (PGC) kernel. The number of PGC’s groups is $M$ . Accordingly, in each primary group, besides the center locations in all channels, the values of parameters located in other $N-1$ tiny groups are all zero. Therefore, SRGC can significantly reduce the parameters. Moreover, it can also effectively retrieve spatial and channel-difference features by utilizing RGC and PGC to preserve the richness of produced features. Comparative experiments were performed on the benchmark classification data sets. Compared with the traditional popular networks, SRGC-Nets can perform better with timely reducing the model size and computational complexity. Furthermore, it can also achieve better performances than other latest state-of-the-art mobile networks on most of the databases and effectively decrease the test and training runtime.

The scarcity and weak durability of metal, especially precious metal catalysts are big obstacles for their large-scale application in many reactions. The state-of-the-art of the catalytic science prefers such type of catalysts, which can replace metal-based catalysts to alleviate energy and environmental crises and exhibit catalytic performance comparable to or even exceeding these metal catalysts. Herein, we report that N-doped porous carbon (NKC) derived from cheap and abundant radish can be employed as versatile and efficient bifunctional catalysts in both the catalytic reduction of 4-nitrophenol (NRR) and oxidation of styrene (SOR). The series of NKC catalysts were prepared with a simple and facile one-pot strategy by coupling the N-doping, carbonization and KOH activation processes. These catalysts show hierarchical porosity, with the specific surface area, total pore volume and N-doping content ranging from 918.9–3062.7 m2g−1, 1.01–2.04 cm3g−1and 1.29–15.3 at%, respectively. Interestingly, our finding suggests that the catalytic performance is not directly related to these parameters but correlates positively with the content of graphitic N dopants, which is the dominant contributor for impelling both the NRR and SOR. Another intriguing finding is that for both reactions, the optimal catalyst was found to be the NKC-3-800 which possesses the highest graphitic N content of 3.13 at%. In addition, to gain insight into the catalytic behavior, analyses of kinetics and thermodynamics were performed, and the catalytic mechanisms were postulated. This work paves the way for the construction of biomass-derived N-doped carbon catalysts for bi- or even multi-functional applications in various organic reactions.

In many repeat diseases, such as Huntington’s disease (HD), ongoing repeat expansions in affected tissues contribute to disease onset, progression and severity. Inducing contractions of expanded repeats by exogenous agents is not yet possible. Traditional approaches would target proteins driving repeat mutations. Here we report a compound, naphthyridine-azaquinolone (NA), that specifically binds slipped-CAG DNA intermediates of expansion mutations, a previously unsuspected target. NA efficiently induces repeat contractions in HD patient cells as well as en masse contractions in medium spiny neurons of HD mouse striatum. Contractions are specific for the expanded allele, independently of DNA replication, require transcription across the coding CTG strand and arise by blocking repair of CAG slip-outs. NA-induced contractions depend on active expansions driven by MutSβ. NA injections in HD mouse striatum reduce mutant HTT protein aggregates, a biomarker of HD pathogenesis and severity. Repeat-structure-specific DNA ligands are a novel avenue to contract expanded repeats.

Transient self-destroyed micromotors that autonomously disappear in biological media at controlled rates upon completing their task, without leaving a toxic residue, are presented. The propulsion and degradation characteristics of the self-destroyed Mg/ZnO, Mg/Si, and Zn/Fe Janus micromotors and single-component Zn micromotors are described. The degradation of the Janus micromotors relies on the different corrosion rates of their core–shell components. Inductively coupled plasma optical emission spectrometry measurements are used to probe the time-dependent degradation of the different constituents of the micromotors. The toxicity of the transient micromotors is discussed toward their potential use in biomedical applications. This concept of transient micromotors offers considerable potential for diverse practical applications in the near future.

Studies reported an association between rheumatoid arthritis and Diabetes mellitus. We sought to assess the causal association between rheumatoid arthritis and Diabetes mellitus risk using a two-way two-sample Mendelian randomization (MR) analysis.

Precise and reproducible manipulation of synthetic and biological microscale objects in complex environments is essential for many practical biochip and microfluidic applications. Here, we present an attractive acoustic topographical manipulation (ATM) method to achieve efficient and reproducible manipulation of diverse microscale objects. This new guidance method relies on the acoustically induced localized microstreaming forces generated around microstructures, which are capable of trapping nearby microobjects and manipulating them along a determined trajectory based on local topographic features. This unique phenomenon is investigated by numerical simulations examining the local microstreaming in the presence of microscale boundaries under the standing acoustic wave. This method can be used to manipulate a single microobject around a complex structure as well as collectively manipulate multiple objects moving synchronously along complicated shapes. Furthermore, the ATM can serve for automated maze solving by autonomously manipulating microparticles with diverse geometries and densities, including live cells, through complex maze-like topographical features without external feedback, particle modification, or adjustment of operational parameters.

The low ionic conductivities of aprotic electrolytes hinder the development of extreme fast charging technologies and applications at low temperatures for lithium-ion batteries (LIBs). Herein, we present an electrolyte with LiFSI in acetone (DMK). In DMK electrolytes, the solvation number is three, and solvent-separated ion pairs (SSIPs) are the dominant structure, which is largely different from other linear aprotic electrolytes where salts primarily exist as contact ion pairs (CIPs). With incompact solvation structures due to the weak solvation ability of DMK with Li+, the ionic conductivity reaches 45 mS/cm at room temperature. The percentage of SSIPs increases as temperatures decrease in DMK electrolytes, which is totally different from the carbonate-based electrolytes but greatly beneficial to low-temperature ionic conductivity. With the appropriate addition of VC and FEC, DMK-based electrolytes still exhibit a superhigh ionic conductivity. Even at -40 °C, the ionic conductivity is greater than 10 mS/cm. With DMK-based electrolytes, LIBs with thick LiFePO4electrodes can be cycled at high rates and at low temperatures.

Suprathermal electrons (~0.1–10 keV) in the inner magnetosphere are usually observed in a 90°‐peaked pitch angle distribution, formed due to the conservation of the first and second adiabatic invariants as they are transported from the plasma sheet. We report a peculiar field‐aligned suprathermal electron (FASE) distribution measured by Van Allen Probes, where parallel fluxes are 1 order of magnitude higher than perpendicular fluxes. Those FASEs are found to be closely correlated with large‐amplitude hiss waves and are observed around the Landau resonant energies. We demonstrate, using quasilinear diffusion simulations, that hiss waves can rapidly accelerate suprathermal electrons through Landau resonance and create the observed FASE population. The proposed mechanism potentially has broad implications for suprathermal electron dynamics as well as whistler mode waves in the Earth's magnetosphere and has been demonstrated in the Jovian magnetosphere. Hiss waves are structureless and incoherent “hissy” emissions found in the magnetized near‐Earth space, typically in a frequency range from 0.1 to 2 kHz. Hiss waves have traditionally been treated as an energetic electron removal mechanism, because they can precipitate energetic electrons through resonant interactions and cause electron loss into the atmosphere. Here we show, by presenting observations from NASA's Van Allen Probes, that intense hiss waves are accompanied by enhancements of field‐aligned suprathermal electrons. We propose that hiss waves can accelerate suprathermal electrons as they travel at the same speed in the direction along the magnetic field. Computer simulations successfully reproduce the rapid enhancement of field‐aligned suprathermal electrons under the impact of hiss waves, with detailed features similar to observations. Pronounced field‐aligned suprathermal electron enhancements are observed in correlation with intense hiss wavesNumerical simulations reproduce field‐aligned electron distributions similar to observationsIntense hiss waves can accelerate field‐aligned suprathermal electrons via Landau resonance on a timescale of several minutes

The self-assembly of thienophenanthrene derivative with V-shape conjugated core and four tetradecanoxyl chains (TPTD-Nap) at different solution concentrations has been investigated at the liquid–solid interface by scanning tunneling microscopy. n-Tetradecane was chosen as the solvent because its alkyl chain is commensurate with the length of the molecular side chains. At saturated concentration, TPTD-Nap forms a linear pattern with long-range disorder and short-range order. As the concentration decreases, a highly ordered linear structure is observed. At low concentrations, TPTD-Nap molecules self-assemble into three types of nanostructures with reduced packing density because n-tetradecane molecules participate in the self-assembled process by the coadsorption at different levels. The most remarkable feature of the TPTD-Nap arrangements is the self-adaption of symmetric V-shape cores as well as the alkyl chain-length of the molecule and solvent. The former contributes to fabricate the backbone of the nanostructures, and the latter determines the self-assembled polymorphism by the coadsorption.

A systematic investigation on the bromine substituent position effects on the self-assembly of fluorenone derivatives 1-bromo-7-(pentadecyloxy)-fluorenone (1-BFC15), 2-(pentadecyloxy)-7-bromo-fluorenone (2-BFC15), and 3-bromo-7-(pentadecyloxy)-fluorenone (3-BFC15) was performed by scanning tunneling microscopy at the 1-octanoic acid/HOPG interface. The charge distribution of the conjugated cores and the directionality of halogen bonds, as well as the principle of closest packing, account for the self-assembled characteristics. Density functional theory calculations and topology analysis of the electron density were employed to elucidate that the enlargement of the positive σ-hole on bromine atoms due to the varying substituent position enhances the electrostatic attraction between bromine and oxygen atoms, reduces the electrostatic attraction between hydrogen and bromine atoms, and further gives rise to the distinct alignments. This study is expected to provide significant insights into the formation mechanism of halogen/hydrogen bonds and the construction of the supramolecular halogen/hydrogen-bonded self-assembly nanostructures on solid surfaces.

Micro/nanorobotic systems capable of targeted transporting and releasing hold considerable promise for drug delivery, cellular surgery, biosensing, nano assembling, etc. However, on-demand precise control of the micro/nanorobot movement remains a major challenge. In particular, a practical interface to realize instant and customized interactions between human and micro/nanorobots, which is quite essential for developing next generation intelligent micro/nanorobots, has seldom been explored. Here, we present a human–microrobot user interface to perform direct and agile recognition of user commands and signal conversion for driving the microrobot. The microrobot platform is built based on locally enhanced acoustic streaming which could precisely transport microparticles and cells along a given pathway, while the interface is enabled by tuning the actuation frequency and time with different instructions and inputs. Our numerical simulations and experimental demonstrations illustrate that microparticles can be readily transported along the path by the acoustic robotic system, due to the vibration-induced locally enhanced acoustic streaming and resultant propulsion force. The acoustic robotic platform allows large-scale parallel transportation for microparticles and cells along given paths. The human microrobot interface enables the micromanipulator to response promptly to the users’ commands input by typing or music playing for accurate transport. For example, the music tone of a playing melody is used for manipulating a cancer cell to a targeted position. The interface offers several attractive capabilities, including tunable speed and orientation, quick response, considerable delivery capacities, high precision and favorable controllability. We expect that such interface will work as a compelling and versatile platform for myriad potential scenarios in transportation units of microrobots, single cell analysis instruments, lab-on-chip systems, microfactories, etc.

Lithium metal anodes offer high theoretical capacities (3,860 milliampere-hours per gram)1, but rechargeable batteries built with such anodes suffer from dendrite growth and low Coulombic efficiency (the ratio of charge output to charge input), preventing their commercial adoption2,3. The formation of inactive (‘dead’) lithium— which consists of both (electro)chemically formed Li+compounds in the solid electrolyte interphase and electrically isolated unreacted metallic Li0(refs 4,5)—causes capacity loss and safety hazards. Quantitatively distinguishing between Li+in components of the solid electrolyte interphase and unreacted metallic Li0has not been possible, owing to the lack of effective diagnostic tools. Optical microscopy6, in situ environmental transmission electron microscopy7,8, X-ray microtomography9and magnetic resonance imaging10provide a morphological perspective with little chemical information. Nuclear magnetic resonance11, X-ray photoelectron spectroscopy12and cryogenic transmission electron microscopy13,14can distinguish between Li+in the solid electrolyte interphase and metallic Li0, but their detection ranges are limited to surfaces or local regions. Here we establish the analytical method of titration gas chromatography to quantify the contribution of unreacted metallic Li0to the total amount of inactive lithium. We identify the unreacted metallic Li0, not the (electro)chemically formed Li+in the solid electrolyte interphase, as the dominant source of inactive lithium and capacity loss. By coupling the unreacted metallic Li0content to observations of its local microstructure and nanostructure by cryogenic electron microscopy (both scanning and transmission), we also establish the formation mechanism of inactive lithium in different types of electrolytes and determine the underlying cause of low Coulombic efficiency in plating and stripping (the charge and discharge processes, respectively, in a full cell) of lithium metal anodes. We propose strategies for making lithium plating and stripping more efficient so that lithium metal anodes can be used for next-generation high-energy batteries. Titration gas chromatography is developed as an analytical method of distinguishing between lithium metal and lithium compounds within a cycled battery and assessing the amount of unreacted metallic lithium available.

Micropump systems show great potential on the micropatterning process as a result of remarkable performance and functionality. However, existing micropumps cannot be employed as direct writing tools to perform the complex micropatterning process because of their lacking motility and controllability. Here, we propose a motile micropump system based on the combination of a water-driven ZnO/Ni/polystyrene Janus micromotor with a traditional immobilized micropump. This novel motile micropump system can translate the trajectory of Janus micromotors into predefined micropatterns by pumping away passive silica particles around the micromotor under the effect of diffusiophoresis. The resolution and efficiency of the micropatterning process can be regulated by controlling the diameters of Janus micromotors. Diverse surface micropatterns can be fabricated though remote magnetic control of the motile micropump system. Such ability to transform the versatile motile micropump into predetermined surface micropatterns creates new opportunities for mask-free micropatterning.

The transmission mode of shoot-associated endophytes in hyperaccumulators and their roles in root microbiome assembly and heavy metal accumulation remain unclear. Using 16S rRNA gene profiling, we investigated the vertical transmission of shoot-associated endophytes in relation to growth and Cd/Zn accumulation of Sedum alfredii(Crassulaceae). Endophytes were transmitted from shoot cuttings to the rhizocompartment of new plants in both sterilized (γ-irradiated) and native soils. Vertical transmission was far more efficient in the sterile soil, and the transmitted endophytes have become a dominant component of the newly established root-associated microbiome. Based on 16S rRNA genes, the vertically transmitted taxa were identified as the families of Streptomycetaceae, Nocardioidaceae, Pseudonocardiaceae, and Rhizobiaceae. Abundances of Streptomycetaceae, Nocardioidaceae, and Pseudonocardiaceae were strongly correlated with increased shoot biomass and total Cd/Zn accumulation. Inoculation of S. alfrediiwith the synthetic bacterial community sharing the same phylogenetic relatedness with the vertically transmitted endophytes resulted in significant improvements in plant biomass, root morphology, and Cd/Zn accumulation. Our results demonstrate that successful vertical transmission of endophytes from shoots of S. alfrediito its rhizocompartments is possible, particularly in soils with attenuated microbiomes. Furthermore, the endophyte-derived microbiome plays an important role in metal hyperaccumulation.

With compelling virtues of a large specific surface area, abundant active sites, and fast interfacial transport, nanomaterials have been demonstrated to be indispensable tools for water remediation applications. Accordingly, micro/nanomotors made by nanomaterials would also benefit from these properties. Though tuning the surface architecture on demand becomes a hot topic in the field of nanomaterials, there are still limited reports on the design of active surface architectures in chemically driven tubular micro/nanomachines. Here, a unique architecture composed of a fish-scale-like intercalated (FSI) surface structure and an active layer with 5 nm nanoparticles is constructed, which composes of Fe2O3and ramsdellite MnO2, Mn2O3, in the tubular micromotor using a versatile electrodeposition protocol. Tailoring the electrodeposition parameters enables us to modulate the active MnO2surface structure on demand, giving rise to a pronounced propulsion performance and catalytic activity. Upon exposure to the azo-dye waste solution, the degradation efficacy greatly raises by around 22.5% with FSI micromotor treatment when compared to the normal compact motors, owing to the synergistic effect between the Fe-related Fenton reaction and a large catalytic area offered by the hierarchically rough inner surface. Such unique micromachines with a large active surface area have great potential for environmental and biomedical applications.

For many medical treatments, particularly cancer, it is necessary to develop a biocompatible microscale device that can carry a sufficient amount of a drug and deliver it to target sites. While chemically powered micromotors have been applied in live animal therapy, many of them are difficult to biodegrade in vivo, which might cause toxicity and side effects. Here, we report on a microdevice that consists of a poly(aspartic acid) (PASP) microtube, a thin Fe intermediate layer, and a core of Zn. This device can be propelled using gastric acid as a fuel. After adsorption of doxorubicin onto a PASP surface, the microrocket can carry drugs, magnetically locate targets, permeate the gastric mucus gel layer, and increase drug retention in the stomach without inducing an obvious toxic reaction. All materials in the microrockets are biocompatible and biodegradable and can be readily decomposed by the gastric acid or by proteases in the digestive tract. Such microrockets, made with poly(amino acid)s, will extend the practical biomedical applications of micro- and nanomotors.

Highlights • A body surface feature-based method is proposed for Diabetes Mellitus detection. • Five modalities including tongue, face, sublingual vein, pulse and odor are exploited. • A novel multi-modal method with gaussian process prior is proposed. • The correlation across various modalities is extracted. • Experimental results show the superiority of the method. Abstract In recent year, the number of people who are suffering from the Diabetes Mellitus (DM) has increased remarkably and the detection of DM disease has attracted much attention. Different from some existing methods which are invasive, Traditional Chinese Medicine (TCM) provides a non-invasive strategy for DM diagnosis by exploiting some features in the body surface, including the tongue, face, sublingual vein, pulse and odor. Since a combination of these modalities would contribute to improving detection performance, a novel multi-modal learning method is proposed to learn a shared latent variable among the tongue, face, sublingual, pulse and odor information, which efficiently exploits the correlation. In detail, the raw images or signals of five modalities are first captured through our non-invasive devices. Their corresponding features are then extracted, respectively. Finally, a shared auto-encoder gaussian process latent variable model (SAGP) is introduced to learn a latent variable for various modalities in a non-linear and generative way. An efficient algorithm is designed to optimize the proposed model. The DM detection experiments are conducted on a dataset composed of 548 Healthy and 356 DM samples collected by us and the results substantiate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Self-assembled behaviors of three fluorenone derivatives substituted by different halogen atoms, 2-(pentadecyloxy)-6-bromo-fluorenone (Br-FC15), 2-(pentadecyloxy)-6-chloro-fluorenone (Cl-FC15), and 2-(pentadecyloxy)-6-fluoro-fluorenone (F-FC15), were investigated at the 1-phenyloctane/highly oriented pyrolytic graphite interface by scanning tunneling microscopy combined with density functional theory calculations in comparison with the self-assembly of 2-pentadecyloxy-fluorenone (H-FC15). It is found that different charge distributions on halogen substituents lead to a subtle change of the molecular packing nanostructures. By varying the solution concentrations, X-FC15 (X = Br, Cl, H) can self-assemble into polymorphic nanostructures, whereas only one pattern can be observed for the F-FC15 adlayer due to the stronger continuous C–H···F bonds. The intermolecular C–H···O═C hydrogen bonds are the main driving forces for all the self-assembled patterns. Particularly, the halogen-based hydrogen bonds and the type-I X···X (X = Br, Cl) bonds act as the collaborative forces to stabilize the alternate adlayers. Furthermore, the halogen bonds (C–Br···O═C and C–Cl···O═C) make the crucial contribution to the distinct lamellar and the dumbbell-like patterns. The investigation suggests that the engineering of organic nanoarchitectures can be effectively tailored by the introduction of different halogen atoms.

Two-dimensional molecular assemblies of two structural isomers p-DBPy and d-DBPy containing a diketopyrrolopyrrole (DPP) unit relevant to the field of organic electronics are investigated on a graphite surface in different solvents (phenyloctane and octanoic acid) using scanning tunneling microscopy. These two molecules only differ in the position of pyridyl N-atoms. The solution concentration and solvent effects on the morphology of the adlayer are observed. Both kinds of molecules could exhibit obviously different molecular conformations and packing patterns, which demonstrate that the variation of the N-atom position in the pyridyl leads to different intermolecular interactions. Positioning the pyridyl N-atoms proximal to the central conjugated core could change the molecular conformation into a banana shape by bending the molecule; however, the d-DBPy molecule in which pyridyl N-atoms are distal to the central conjugated cores keep a linear shape in the monolayers. The slight difference in the molecular chemical structure significantly influences the final morphology of the films. This systematic research work on the self-assembly of regioisomeric DPP-based semiconductors on surfaces could provide guidance for the rational design of optoelectronic materials and the control of the morphology of the films.

In the Earth's dayside reconnection boundary layer, whistler mode waves coincide with magnetic field openings and the formation of the resultant anisotropic electrons. Depending on the energy range of anisotropic electrons, whistlers can grow at frequencies in the upper and/or lower band. Observations show that whistler mode waves modulate Langmuir wave amplitude as they propagate toward the X line. Observations of whistler mode wave phase and Langmuir waves packets, as well as coincident electron measurements, reveal that whistler mode waves can accelerate electrons via Landau resonance at locations where E||is antiparallel to the wave propagation direction. The accelerated electrons produce localized beams, which subsequently drive the periodically modulated Langmuir waves. The close association of those two wave modes reveals the microscale electron dynamics in the exhaust region, and the proposed mechanism could potentially be applied to explain the modulation events observed in planetary magnetospheres and in the solar wind. The Sun's and Earth's magnetic field can merge and reconnect on dayside magnetopause. Using measurements from NASA's MMS spacecraft, we report that a class of electromagnetic wave, named whistler mode wave, coincides with the reconnected magnetic field lines. Besides, those whistlers are observed to modulate the electric field oscillations, known as Langmuir waves. Using high‐resolution wave and particle measurements, we explain that the whistlers are locally excited when electrons from both sides of the magnetopause mix and form an unstable distribution. The modulated Langmuir waves are generated due to localized electron acceleration, which occurs when the velocity of electrons matches that of whistlers in the direction along the magnetic field. The whistler mode waves and associated Langmuir waves can be used as an additional tool to remotely sense the occurrence of magnetic reconnections. Whistler mode waves are excited at open field lines where magnetospheric and magnetosheath electrons mix and form an anisotropic distributionWhistler mode waves drive and modulate Langmuir waves as they propagate toward the X lineLangmuir waves are excited by localized electron beams that are accelerated by whistlers via Landau resonance