Showing papers by "Hefei University of Technology published in 2021"

Generalized Incomplete Multiview Clustering With Flexible Locality Structure Diffusion

Abstract: An important underlying assumption that guides the success of the existing multiview learning algorithms is the full observation of the multiview data. However, such rigorous precondition clearly violates the common-sense knowledge in practical applications, where in most cases, only incomplete fractions of the multiview data are given. The presence of the incomplete settings generally disables the conventional multiview clustering methods. In this article, we propose a simple but effective incomplete multiview clustering (IMC) framework, which simultaneously considers the local geometric information and the unbalanced discriminating powers of these incomplete multiview observations. Specifically, a novel graph-regularized matrix factorization model, on the one hand, is developed to preserve the local geometric similarities of the learned common representations from different views. On the other hand, the semantic consistency constraint is introduced to stimulate these view-specific representations toward a unified discriminative representation. Moreover, the importance of different views is adaptively determined to reduce the negative influence of the unbalanced incomplete views. Furthermore, an efficient learning algorithm is proposed to solve the resulting optimization problem. Extensive experimental results performed on several incomplete multiview datasets demonstrate that the proposed method can achieve superior clustering performance in comparison with some state-of-the-art multiview learning methods.

NaNbO3-(Bi0.5Li0.5)TiO3 Lead-Free Relaxor Ferroelectric Capacitors with Superior Energy-Storage Performances via Multiple Synergistic Design

Abstract: Relaxor ferroelectric (FE) ceramic capacitors have attracted increasing attention for their excellent energy-storage performance. However, it is extremely difficult to achieve desirable comprehensive energy-storage features required for industrial applications. In this work, very high recoverable energy density W-rec approximate to 8.73 J cm(-3), high efficiency eta approximate to 80.1%, ultrafast discharge rate of

AttentionFGAN: Infrared and Visible Image Fusion Using Attention-Based Generative Adversarial Networks

Abstract: Infrared and visible image fusion aims to describe the same scene from different aspects by combining complementary information of multi-modality images. The existing Generative adversarial networks (GAN) based infrared and visible image fusion methods cannot perceive the most discriminative regions, and hence fail to highlight the typical parts existing in infrared and visible images. To this end, we integrate multi-scale attention mechanism into both generator and discriminator of GAN to fuse infrared and visible images (AttentionFGAN). The multi-scale attention mechanism aims to not only capture comprehensive spatial information to help generator focus on the foreground target information of infrared image and background detail information of visible image, but also constrain the discriminators focus more on the attention regions rather than the whole input image. The generator of AttentionFGAN consists of two multi-scale attention networks and an image fusion network. Two multi-scale attention networks capture the attention maps of infrared and visible images respectively, so that the fusion network can reconstruct the fused image by paying more attention to the typical regions of source images. Besides, two discriminators are adopted to force the fused result keep more intensity and texture information from infrared and visible image respectively. Moreover, to keep more information of attention region from source images, an attention loss function is designed. Finally, the ablation experiments illustrate the effectiveness of the key parts of our method, and extensive qualitative and quantitative experiments on three public datasets demonstrate the advantages and effectiveness of AttentionFGAN compared with the other state-of-the-art methods.

Exploiting Subspace Relation in Semantic Labels for Cross-Modal Hashing

Abstract: Hashing methods have been extensively applied to efficient multimedia data indexing and retrieval on account of the explosion of multimedia data. Cross-modal hashing usually learns binary codes by mapping multi-modal data into a common Hamming space. Most supervised methods utilize relation information like class labels as pairwise similarities of cross-modal data pair to narrow intra-modal and inter-modal gap. In this paper, we propose a novel supervised cross-modal hashing method dubbed Subspace Relation Learning for Cross-modal Hashing (SRLCH), which exploits relation information of labels in semantic space to make similar data from different modalities closer in the low-dimension Hamming subspace. SRLCH preserves the modality relationships, the discrete constraints and nonlinear structures, while admitting a closed-form binary codes solution, which effectively enhances the training efficiency. An iterative alternative optimization algorithm is developed to simultaneously learn both hash functions and unified binary codes. With these binary codes and hash functions, we can index multimedia data and search them in an efficient way. Evaluations in two cross-modal retrieval tasks on several widely-used datasets show that the proposed SRLCH outperforms most cross-modal hashing methods. Theoretical analysis also illustrates reasons for our method’s promotion in subspace relation learning.

Emotion Recognition From Multi-Channel EEG via Deep Forest

Abstract: Recently, deep neural networks (DNNs) have been applied to emotion recognition tasks based on electroencephalography (EEG), and have achieved better performance than traditional algorithms. However, DNNs still have the disadvantages of too many hyperparameters and lots of training data. To overcome these shortcomings, in this article, we propose a method for multi-channel EEG-based emotion recognition using deep forest. First, we consider the effect of baseline signal to preprocess the raw artifact-eliminated EEG signal with baseline removal. Secondly, we construct 2 $D$ frame sequences by taking the spatial position relationship across channels into account. Finally, 2 $D$ frame sequences are input into the classification model constructed by deep forest that can mine the spatial and temporal information of EEG signals to classify EEG emotions. The proposed method can eliminate the need for feature extraction in traditional methods and the classification model is insensitive to hyperparameter settings, which greatly reduce the complexity of emotion recognition. To verify the feasibility of the proposed model, experiments were conducted on two public DEAP and DREAMER databases. On the DEAP database, the average accuracies reach to 97.69% and 97.53% for valence and arousal, respectively; on the DREAMER database, the average accuracies reach to 89.03%, 90.41%, and 89.89% for valence, arousal and dominance, respectively. These results show that the proposed method exhibits higher accuracy than the state-of-art methods.

Synergy between dynamic covalent boronic ester and boron–nitrogen coordination: strategy for self-healing polyurethane elastomers at room temperature with unprecedented mechanical properties

Abstract: Achieving mechanical robustness and highly efficient self-healing simultaneously at room temperature is always a formidable challenge for polymeric materials. Herein, a series of novel supramolecular polyurethane elastomers (SPUEs) are developed by incorporating dynamic covalent boronic ester and boron–nitrogen (B–N) coordination. The SPUEs demonstrate the highest tensile toughness (∼182.2 MJ m−3) to date for room-temperature self-healable polymers, as well as an excellent ultimate tensile strength (∼10.5 MPa) and ultra-high fracture energy (∼72 100 J m−2), respectively, owing to a synergetic quadruple dynamic mechanism. It is revealed that the B–N coordination not only facilitates the formation and dissociation of boronic ester at room temperature but also dramatically enhances the mechanical properties by the intermolecular coordinated chain crosslinking and intramolecular coordinated chain folding. Meanwhile, the B–N coordination and urethane hydrogen interaction also serve as sacrificial bonds, which rupture during stretching to dissipate energy and recover after release, leading to superior notch insensitiveness and recoverability. The SPUEs restore their mechanical robustness after self-healing at room temperature and the self-healing efficiency can be dramatically accelerated by surface wetting.

A Comparative Study of Metaheuristic Algorithms for Reliability-Based Design Optimization Problems

Abstract: The ever-increasing demands for resource-saving, engineering technology progress, and environmental protection stimulate the progress of the progressive design method. As an excellent promising design method for dealing with the inevitable uncertainty factors, reliability-based design optimization (RBDO) is capable of offering reliable and robust results and minimizing the cost under the prescribed uncertainty level, which can provide a trade-off between economy and safety. However, the primary challenges, including global convergence capacity and complicated mixed design variable type, hinder the wider application of RBDO. This study presents a comprehensive work on the application of ten popular and recent metaheuristic algorithms of five engineering problems. Furthermore, we focus on the RBDO equip with metaheuristic algorithms about its global convergence, robustness, accuracy, and computational speed. This paper also presents the major difference of convergence property between metaheuristic algorithms and gradient algorithms. The detailed statement of this study presents the state-of-the-art in RBDO to demonstrate its crucial technologies and great challenges, as well as the beneficial future development direction.

Dual Encoding for Video Retrieval by Text.

Abstract: This paper attacks the challenging problem of video retrieval by text. In such a retrieval paradigm, an end user searches for unlabeled videos by ad-hoc queries described exclusively in the form of a natural-language sentence, with no visual example provided. Given videos as sequences of frames and queries as sequences of words, an effective sequence-to-sequence cross-modal matching is crucial. To that end, the two modalities need to be first encoded into real-valued vectors and then projected into a common space. In this paper we achieve this by proposing a dual deep encoding network that encodes videos and queries into powerful dense representations of their own. Our novelty is two-fold. First, different from prior art that resorts to a specific single-level encoder, the proposed network performs multi-level encoding that represents the rich content of both modalities in a coarse-to-fine fashion. Second, different from a conventional common space learning algorithm which is either concept based or latent space based, we introduce hybrid space learning which combines the high performance of the latent space and the good interpretability of the concept space. Dual encoding is conceptually simple, practically effective and end-to-end trained with hybrid space learning. Extensive experiments on four challenging video datasets show the viability of the new method.

Recent updates on the chemistry, bioactivities, mode of action, and industrial applications of plant essential oils

Abstract: Background Since time immemorial, natural active compounds including essential oils (EOs) and their components have been used due to their flavor and fragrance. Out of 3000 known varieties, 300 are commercially utilized for the food and pharmaceutical industries. Scope and approach In recent years, studies on EOs have enormously increased owing to their remarkable biological activities and health benefits. As a result, their pharmacological attributes have played an immense role to identify natural and safe alternative therapeutics to extend their industrial applications. Key findings and conclusions: This review covers the sources and composition of EOs, recent progress in their extraction methods, factors affecting their quality and yield, their most important activities, such as antioxidant and antimicrobial activities as well as their mechanisms of action. Besides, the importance of EOs in food, biomedicine, and agricultural industries is also highlighted. For the food industrial applications, we mainly aimed at the incorporation of EOs as such or as nanoemulsions into active or smart packaging with a particular emphasis on the food preservation and shelf-life extension of food products.

A multi-responsive healable supercapacitor

Abstract: Self-healability is essential for supercapacitors to improve their reliability and lifespan when powering the electronics. However, the lack of a universal healing mechanism leads to low capacitive performance and unsatisfactory intelligence. Here, we demonstrate a multi-responsive healable supercapacitor with integrated configuration assembled from magnetic Fe3O4@Au/polyacrylamide (MFP) hydrogel-based electrodes and electrolyte and Ag nanowire films as current collectors. Beside a high mechanical strength, MFP hydrogel exhibits fast optical and magnetic healing properties arising from distinct photothermal and magneto-thermal triggered interfacial reconstructions. By growing electroactive polypyrrole nanoparticles into MFP framework as electrodes, the assembled supercapacitor exhibits triply-responsive healing performance under optical, electrical and magnetic stimuli. Notably, the device delivers a highest areal capacitance of 1264 mF cm-2 among the reported healable supercapacitors and restores ~ 90% of initial capacitances over ten healing cycles. These prominent performance advantages along with the facile device-assembly method make this emerging supercapacitor highly potential in the next-generation electronics.

DerainCycleGAN: Rain Attentive CycleGAN for Single Image Deraining and Rainmaking

Abstract: Single Image Deraining (SID) is a relatively new and still challenging topic in emerging vision applications, and most of the recently emerged deraining methods use the supervised manner depending on the ground-truth (i.e., using paired data). However, in practice it is rather common to encounter unpaired images in real deraining task. In such cases, how to remove the rain streaks in an unsupervised way will be a challenging task due to lack of constraints between images and hence suffering from low-quality restoration results. In this paper, we therefore explore the unsupervised SID issue using unpaired data, and propose a new unsupervised framework termed DerainCycleGAN for single image rain removal and generation, which can fully utilize the constrained transfer learning ability and circulatory structures of CycleGAN. In addition, we design an unsupervised rain attentive detector (UARD) for enhancing the rain information detection by paying attention to both rainy and rain-free images. Besides, we also contribute a new synthetic way of generating the rain streak information, which is different from the previous ones. Specifically, since the generated rain streaks have diverse shapes and directions, existing derianing methods trained on the generated rainy image by this way can perform much better for processing real rainy images. Extensive experimental results on synthetic and real datasets show that our DerainCycleGAN is superior to current unsupervised and semi-supervised methods, and is also highly competitive to the fully-supervised ones.

Deconfounded Video Moment Retrieval with Causal Intervention

Abstract: We tackle the task of video moment retrieval (VMR), which aims to localize a specific moment in a video according to a textual query. Existing methods primarily model the matching relationship between query and moment by complex cross-modal interactions. Despite their effectiveness, current models mostly exploit dataset biases while ignoring the video content, thus leading to poor generalizability. We argue that the issue is caused by the hidden confounder in VMR, i.e., temporal location of moments, that spuriously correlates the model input and prediction. How to design robust matching models against the temporal location biases is crucial but, as far as we know, has not been studied yet for VMR. To fill the research gap, we propose a causality-inspired VMR framework that builds structural causal model to capture the true effect of query and video content on the prediction. Specifically, we develop a Deconfounded Cross-modal Matching (DCM) method to remove the confounding effects of moment location. It first disentangles moment representation to infer the core feature of visual content, and then applies causal intervention on the disentangled multimodal input based on backdoor adjustment, which forces the model to fairly incorporate each possible location of the target into consideration. Extensive experiments clearly show that our approach can achieve significant improvement over the state-of-the-art methods in terms of both accuracy and generalization.

Collaborative Neural Social Recommendation

Abstract: Collaborative filtering (CF) is one of the most popular techniques for building recommender systems. To overcome the data sparsity in CF, social recommender systems have emerged to boost recommendation performance by utilizing social correlation among users’ interests. Recently, inspired by the immense success of deep learning for embedding learning, neural network-based recommender systems have shown promising recommendation performance. Nevertheless, few researchers have attempted to tackle the social recommendation problem with neural models. To this end, in this paper, we design a neural architecture that organically combines the intrinsic relationship between social network structure and user–item interaction behavior for social recommendation. Two key challenges arise in this process: first, how to incorporate the social correlation of users’ interests in this neural model, and second, how to design a neural architecture to capture the unique characteristics of user–item interaction behavior for recommendation. To tackle these two challenges, we develop a model named collaborative neural social recommendation (CNSR) with two parts: 1) a social embedding part and 2) a collaborative neural recommendation (CNR) part. In CNSR, the user embedding leverages each user’s social embedding learned from an unsupervised deep learning technique with social correlation regularization. The user and item embeddings are then fed into a unique neural network with a newly designed collaboration layer to model both the shallow collaborative and deep complex interaction relationships between users and items. We further propose a joint learning framework to allow the social embedding part and the CNR part to mutually enhance each other. Finally, extensive experimental results on two real-world datasets clearly demonstrate the effectiveness of our proposed model.

Interface Engineered Microcellular Magnetic Conductive Polyurethane Nanocomposite Foams for Electromagnetic Interference Shielding.

Abstract: Lightweight microcellular polyurethane (TPU)/carbon nanotubes (CNTs)/ nickel-coated CNTs (Ni@CNTs)/polymerizable ionic liquid copolymer (PIL) composite foams are prepared by non-solvent induced phase separation (NIPS). CNTs and Ni@CNTs modified by PIL provide more heterogeneous nucleation sites and inhibit the aggregation and combination of microcellular structure. Compared with TPU/CNTs, the TPU/CNTs/PIL and TPU/CNTs/Ni@CNTs/PIL composite foams with smaller microcellular structures have a high electromagnetic interference shielding effectiveness (EMI SE). The evaporate time regulates the microcellular structure, improves the conductive network of composite foams and reduces the microcellular size, which strengthens the multiple reflections of electromagnetic wave. The TPU/10CNTs/10Ni@CNTs/PIL foam exhibits slightly higher SE values (69.9 dB) compared with TPU/20CNTs/PIL foam (53.3 dB). The highest specific EMI SE of TPU/20CNTs/PIL and TPU/10CNTs/10Ni@CNTs/PIL reaches up to 187.2 and 211.5 dB/(g cm−3), respectively. The polarization losses caused by interfacial polarization between TPU substrates and conductive fillers, conduction loss caused by conductive network of fillers and magnetic loss caused by Ni@CNT synergistically attenuate the microwave energy.

Self-Doping Surface Oxygen Vacancy-Induced Lattice Strains for Enhancing Visible Light-Driven Photocatalytic H2 Evolution over Black TiO2.

Abstract: The synergistic effect of surface oxygen vacancy with induced lattice strains on visible light-driven photocatalytic H2 evolution over black TiO2 was investigated. Experimental measurements and theoretical calculations on the lattice parameters of black TiO2 show that surface oxygen vacancies induce internal lattice strain during two-step aluminothermic reduction, which regulates the band structure and optimizes the photoinduced charge behavior of black TiO2. The hydrogen evolution rate of black TiO2 with strain modification shows a 12-fold increase to 1.882 mmol/g· h (equal to 4.705 μmol/cm2·h) under visible light illumination. The metastable state caused by the surface oxygen vacancies leads to the formation of a high-energy surface, which enhances visible light absorption and improves the photoinduced charge separation efficiency. Furthermore, the internal lattice strain provides the driving force and channel for the directional movement of photoinduced electrons from the bulk to the high-energy surface for photocatalytic H2 evolution. This strategy provides a new method for designing a high-performance photocatalyst for H2 production.

Collaborative Cloud-Edge-End Task Offloading in Mobile-Edge Computing Networks With Limited Communication Capability

Abstract: Mobile edge computing (MEC) is an emerging computing paradigm for enabling low-latency, high-bandwidth and agile mobile services by deploying computing platform at the edge of network. In order to improve the cloud-edge-end processing efficiency of the tasks within the limited computation and communication capabilities, in this article, we investigate the collaborative computation offloading, computation and communication resource allocation scheme, and develop a collaborative computing framework that the tasks of mobile devices (MDs) can be partially processed at the terminals, edge nodes (EN) and cloud center (CC). Then, we propose the pipeline-based offloading scheme, where both MDs and ENs can offload computation-intensive tasks to a particular EN and CC, according to their computation and communication capacities, respectively. Based on the proposed pipeline offloading strategy, a sum latency of all MDs minimization problem is formulated with the consideration of the offloading strategy, computation resource, delivery rate and power allocation, which is a non-convex problem and difficult to deal with. To solve the optimization problem, by using the classic successive convex approximation (SCA) approach, we transform the non-convex optimization problem into the convex one. Finally, simulation results indicate that the proposed collaboration offloading scheme with the pipeline strategy is efficient and outperforms other offloading schemes.

Toward Realistic Face Photo–Sketch Synthesis via Composition-Aided GANs

Abstract: Face photo–sketch synthesis aims at generating a facial sketch/photo conditioned on a given photo/sketch. It covers wide applications including digital entertainment and law enforcement. Precisely depicting face photos/sketches remains challenging due to the restrictions on structural realism and textural consistency. While existing methods achieve compelling results, they mostly yield blurred effects and great deformation over various facial components, leading to the unrealistic feeling of synthesized images. To tackle this challenge, in this article, we propose using facial composition information to help the synthesis of face sketch/photo. Especially, we propose a novel composition-aided generative adversarial network (CA-GAN) for face photo–sketch synthesis. In CA-GAN, we utilize paired inputs, including a face photo/sketch and the corresponding pixelwise face labels for generating a sketch/photo. Next, to focus training on hard-generated components and delicate facial structures, we propose a compositional reconstruction loss. In addition, we employ a perceptual loss function to encourage the synthesized image and real image to be perceptually similar. Finally, we use stacked CA-GANs (SCA-GANs) to further rectify defects and add compelling details. The experimental results show that our method is capable of generating both visually comfortable and identity-preserving face sketches/photos over a wide range of challenging data. In addition, our method significantly decreases the best previous Frechet inception distance (FID) from 36.2 to 26.2 for sketch synthesis, and from 60.9 to 30.5 for photo synthesis. Besides, we demonstrate that the proposed method is of considerable generalization ability.

Alkyne-Palladium(II)-Catalyzed Living Polymerization of Isocyanides: An Exploration of Diverse Structures and Functions.

Abstract: ConspectusInspired by the perfect helical structures and the resulting exquisite functions of biomacromolecules, helical polymers have attracted increasing attention in recent years. Polyisocyanide is well known for its distinctive rodlike helical structure and various applications in chiral recognition, enantiomer separation, circularly polarized luminescence, liquid crystallization, and other fields. Although various methods and catalysts for isocyanide polymerization have been reported, the precise synthesis of helical polyisocyanides with controlled molecular weight, low dispersity, and high tacticity remains a formidable challenge. Owing to a limited synthesis strategy, the controlled synthesis of topological polyisocyanides has barely been realized. This Accounts highlights our recent endeavors to explore novel catalysts for the living polymerization of isocyanides. Fortunately, we discovered that alkyne-Pd(II) catalysts could initiate the living polymerization of isocyanides, resulting in helical polyisocyanides with controlled structures, high tacticity, and tunable compositions. These catalysts are applicable to various isocyanide monomers, including alkyl isocyanides, aryl isocyanides, and diisocyanobenzene derivatives. Incorporating chiral bidentate phosphine ligands onto alkyne-Pd(II) complexes formed chiral Pd(II) catalysts, which promoted the asymmetric living polymerization of achiral isocyanide, yielding single left- and right-handed helices with highly optical activities.Using alkyne-Pd(II) catalysts, various topological polyisocyanides have been facilely prepared, including hybrid block copolymers, bottlebrush polymers, core cross-linked star polymers, and organic/inorganic nanoparticles. For instance, various hybrid block polyisocyanides were easily produced by coupling alkyne-Pd(II)-catalyzed living isocyanide polymerization with controlled radical polymerization and ring-opening polymerization (ROP). Combining the ring-opening metathesis polymerization (ROMP) of norbornene with Pd(II)-catalyzed isocyanide polymerization, bottlebrush polyisocyanides and core cross-linked star polymers were easily prepared. Pd(II)-catalyzed living polymerization of poly(lactic acid)s with isocyanide termini resulted in densely grafted bottlebrush polyisocyanides with closely packed side chains. Moreover, the surface-initiated living polymerization of isocyanides produced a family of polyisocyanide-grafted organic/inorganic hybrid nanoparticles using nanoparticles with alkyne-Pd(II) catalysts anchored on the surfaces. Surprisingly, the nanoparticles and star polymers with helical polyisocyanide arms performed exceptionally well in terms of chiral recognition and resolution. Incorporated organocatalysts such as proline and prolinol units onto the pendants of optically active helical polyisocyanides, a family of polymer-based chiral organocatalysts, were generated, which showed significantly improved stereoselectivity for the asymmetric Aldol reaction and Michael addition and can be easily recycled.Using a chiral alkyne-Pd(II) catalyst, single-handed helical polyisocyanides bearing naphthalene and pyrene probes were produced from achiral isocyanide monomers. These polymers showed excellent self-sorting properties as revealed using a fluorescence resonance energy transfer (FRET) investigation and were self-assembled into two-dimensional (2D) smectic nanostructures driven by both helicity and chain length. Incorporating helical poly(phenyl isocyanide) (PPI) onto semiconducting poly(3-hexylthiophene) (P3HT) induced the asymmetric assembly of the resulting P3HT-b-PPI copolymers into single-handed cylindrical micelles with controlled dimensions and tunable photoluminescence.

Kernelized Multiview Subspace Analysis By Self-Weighted Learning

Abstract: With the popularity of multimedia technology, information is always represented from multiple views. Even though multiview data can reflect the same sample from different perspectives, multiple views are consistent to some extent because they are representations of the same sample. Most of the existing algorithms are graph-based ones to learn the complex structures within multiview data but overlook the information within data representations. Furthermore, many existing works treat multiple views discriminatively by introducing some hyperparameters, which is undesirable in practice. To this end, abundant multiview-based methods have been proposed for dimension reduction. However, there is still no research that leverages the existing work into a unified framework. In this paper, we propose a general framework for multiview data dimension reduction, named kernelized multiview subspace analysis (KMSA) to handle multiview feature representation in the kernel space, providing a feasible channel for multiview data with different dimensions. Compared with the graph-based methods, KMSA can fully exploit information from multiview data with nothing to lose. Since different views have different influences on KMSA, we propose a self-weighted strategy to treat different views discriminatively. A co-regularized term is proposed to promote the mutual learning from multiviews. KMSA combines self-weighted learning with the co-regularized term to learn the appropriate weights for all views. We evaluate our proposed framework on 6 multiview datasets for classification and image retrieval. The experimental results validate the advantages of our proposed method.

Interfacially Engineered Zn x Mn 1- x S@Polydopamine Hollow Nanospheres for Glutathione Depleting Photothermally Enhanced Chemodynamic Therapy

Abstract: Fenton-like reactions driven by manganese-based nanostructures have been widely applied in cancer treatment owing to the intrinsic physiochemical properties of these nanostructures and their improved sensitivity to the tumor microenvironment. In this work, ZnxMn1-xS@polydopamine composites incorporating alloyed ZnxMn1-xS and polydopamine (PDA) were constructed, in which the Fenton-like reactions driven by Mn ions can be tuned by a controllable release of Mn ions in vitro and in vivo. As a result, the ZnxMn1-xS@PDA exhibited good biocompatibility with normal cells but was specifically toxic to cancer cells. In addition, the shell thickness of PDA was carefully investigated to obtain excellent specific toxicity to cancer cells and promote synergistic chemodynamic and photothermal therapies. Overall, this work highlights an alternative strategy for fabricating high-performance, multifunctional composite nanostructures for a combined cancer treatment.

2D Heterostructured Nanofluidic Channels for Enhanced Desalination Performance of Graphene Oxide Membranes.

Abstract: The two-dimensional (2D) lamellar membrane assembly technique shows substantial potential for sustainable desalination applications. However, the relatively wide and size-variable channels of 2D me...