Wuhan Textile University

applications, the IONPs must have combined properties of high magnetic saturation, stability, biocompatibility, and interactive functions at the surface. Moreover, the surface of IONPs could be modified by organic materials or inorganic materials, such as polymers, biomolecules, silica, metals, etc. The new functionalized strategies, problems and major challenges, along with the current directions for the synthesis, surface functionalization and bioapplication of IONPs, are considered. Finally, some future trends and the prospects in these research areas are also discussed.

Incorporating passive radiative cooling structures into personal thermal management technologies could effectively defend humans against intensifying global climate change. We show that large-scale woven metafabrics can provide high emissivity (94.5%) in the atmospheric window and high reflectivity (92.4%) in the solar spectrum because of the hierarchical-morphology design of the randomly dispersed scatterers throughout the metafabric. Through scalable industrial textile manufacturing routes, our metafabrics exhibit desirable mechanical strength, waterproofness, and breathability for commercial clothing while maintaining efficient radiative cooling ability. Practical application tests demonstrated that a human body covered by our metafabric could be cooled ~4.8°C lower than one covered by commercial cotton fabric. The cost-effectiveness and high performance of our metafabrics present substantial advantages for intelligent garments, smart textiles, and passive radiative cooling applications.

Abstract High conductivity, large mechanical strength, and elongation are important parameters for soft electronic applications. However, it is difficult to find a material with balanced electronic and mechanical performance. Here, a simple method is developed to introduce ion‐rich pores into strong hydrogel matrix and fabricate a novel ionic conductive hydrogel with a high level of electronic and mechanical properties. The proposed ionic conductive hydrogel is achieved by physically cross‐linking the tough biocompatible polyvinyl alcohol (PVA) gel as the matrix and embedding hydroxypropyl cellulose (HPC) biopolymer fibers inside matrix followed by salt solution soaking. The wrinkle and dense structure induced by salting in PVA matrix provides large stress (1.3 MPa) and strain (975%). The well‐distributed porous structure as well as ion migration–facilitated ion‐rich environment generated by embedded HPC fibers dramatically enhances ionic conductivity (up to 3.4 S m −1 , at f = 1 MHz). The conductive hybrid hydrogel can work as an artificial nerve in a 3D printed robotic hand, allowing passing of stable and tunable electrical signals and full recovery under robotic hand finger movements. This natural rubber‐like ionic conductive hydrogel has a promising application in artificial flexible electronics.

Finding a needle in a haystack: A new technology is demonstrated to enrich circulating tumor cells (CTCs) with high efficiency by integrating an antibody-coated silicon nanopillar (SiNP, see picture; gray) substrate with an overlaid polydimethylsiloxane (PDMS) microfluidic chaotic mixer (turquoise). It shows significantly improved sensitivity in detecting rare CTCs from whole blood, thus providing an alternative for monitoring cancer progression. Detailed facts of importance to specialist readers are published as ”Supporting Information”. Such documents are peer-reviewed, but not copy-edited or typeset. They are made available as submitted by the authors. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Abstract In this decade, the demands of energy saving and diverse personal thermoregulation requirements along with the emergence of wearable electronics and smart textiles give rise to the resurgence of personal thermal management (PTM) technologies. PTM, including personal cooling, heating, insulation, and thermoregulation, are far more flexible and extensive than the traditional air/liquid cooling garments for the human body. Concomitantly, many new advanced materials and strategies have emerged in this decade, promoting the thermoregulation performance and the wearing comfort of PTM simultaneously. In this review, an overview is presented of the state‐of‐the‐art and the prospects in this burgeoning field. The emerging materials and strategies of PTM are introduced, and classed by their thermal functions. The concept of infrared‐transparent visible‐opaque fabric (ITVOF) is first highlighted, as it triggers the work on advanced PTM by combining it with radiative cooling, and the corresponding implementations and realizations are subsequently introduced, followed by wearable heaters, flexible thermoelectric devices, and sweat‐management Janus textiles. Finally, critical considerations on the challenges and opportunities of PTM are presented and future directions are identified, including thermally conductive polymers and fibers, physiological/psychological statistical analysis, and smart PTM strategies.

Photocatalytic degradation of toxic organic pollutants is a challenging tasks in ecological and environmental protection. Recent research shows that the magnetic iron oxide-semiconductor composite photocatalytic system can effectively break through the bottleneck of single-component semiconductor oxides with low activity under visible light and the challenging recycling of the photocatalyst from the final products. With high reactivity in visible light, magnetic iron oxide-semiconductors can be exploited as an important magnetic recovery photocatalyst (MRP) with a bright future. On this regard, various composite structures, the charge-transfer mechanism and outstanding properties of magnetic iron oxide-semiconductor composite nanomaterials are sketched. The latest synthesis methods and recent progress in the photocatalytic applications of magnetic iron oxide-semiconductor composite nanomaterials are reviewed. The problems and challenges still need to be resolved and development strategies are discussed.

Owing to their capability of bypassing conventional high-priced and inflexible silicon based electronics to manufacture a variety of devices on flexible substrates by using large-scale and high-volume printing techniques, printed electronics (PE) have attracted increasing attention in the field of manufacturing industry for electronic devices. This simple and cost-effective approach could enhance current methods of constructing a patterned surface for nanomaterials and offer opportunities for developing fully-printed functional devices, especially offering the possibility of ubiquitous low-cost and flexible devices. This review presents a summary of work to date on the inorganic nanomaterials involved in PE applications, focused on the utilization of inorganic nanomaterials-based inks in the successful preparation of printed conductive patterns, electrodes, sensors, thin film transistors (TFTs) and other micro-/nanoscale devices. The printing techniques, sintering methods and printability of functional inks with their associated challenges are discussed, and we look forward so you can glimpse the future of PE applications.

Inorganic materials with controllable shapes have been an intensely studied subject in nanoscience over the past decades. Control over novel and anisotropic shapes of inorganic nanomaterials differing from those of bulk materials leads to unique and tunable properties for widespread applications such as biomedicine, catalysis, fuels or solar cells and magnetic data storage. This review presents a comprehensive overview of shape-controlled inorganic nanomaterials via nucleation and growth theory and the control of experimental conditions (including supersaturation, temperature, surfactants and secondary nucleation), providing a brief account of the shape control of inorganic nanoparticles during wet-chemistry synthetic processes. Subsequently, typical mechanisms for shape-controlled inorganic nanoparticles and the general shape of the nanoparticles formed by each mechanism are also expounded. Furthermore, the differences between similar mechanisms for the shape control of inorganic nanoparticles are also clearly described. The authors envision that this review will provide valuable guidance on experimental conditions and process control for the synthesis of inorganic nanoparticles with tunable shapes in the solution state.

Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable data-driven scientific discovery, and many advanced ML and DL technologies developed for big data have inadvertently provided solutions for small data problems. As a result, significant progress has been made in ML and DL for small data challenges in the past decade. In this review, we summarize and analyze several emerging potential solutions to small data challenges in molecular science, including chemical and biological sciences. We review both basic machine learning algorithms, such as linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), and gradient boosting trees (GBT), and more advanced techniques, including artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer, transfer learning, active learning, graph-based semi-supervised learning, combining deep learning with traditional machine learning, and physical model-based data augmentation. We also briefly discuss the latest advances in these methods. Finally, we conclude the survey with a discussion of promising trends in small data challenges in molecular science.

Iron oxide nanoparticles (NPs) hold great promise for future biomedical applications because of their magnetic properties as well as other intrinsic properties such as low toxicity, colloidal stability, and surface engineering capability. Numerous related studies on iron oxide NPs have been conducted. Recent progress in nanochemistry has enabled fine control over the size, crystallinity, uniformity, and surface properties of iron oxide NPs. This review examines various synthetic approaches and surface engineering strategies for preparing naked and functional iron oxide NPs with different physicochemical properties. Growing interest in designed and surface-engineered iron oxide NPs with multifunctionalities was explored in in vitro/in vivo biomedical applications, focusing on their combined roles in bioseparation, as a biosensor, targeted-drug delivery, MR contrast agents, and magnetic fluid hyperthermia. This review outlines the limitations of extant surface engineering strategies and several developing strategies that may overcome these limitations. This study also details the promising future directions of this active research field.

See also the editorial by Bae in this issue.

Multivariable time series prediction has been widely studied in power energy, aerology, meteorology, finance, transportation, etc. Traditional modeling methods have complex patterns and are inefficient to capture long-term multivariate dependencies of data for desired forecasting accuracy. To address such concerns, various deep learning models based on Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) methods are proposed. To improve the prediction accuracy and minimize the multivariate time series data dependence for aperiodic data, in this article, Beijing PM2.5 and ISO-NE Dataset are analyzed by a novel Multivariate Temporal Convolution Network (M-TCN) model. In this model, multi-variable time series prediction is constructed as a sequence-to-sequence scenario for non-periodic datasets. The multichannel residual blocks in parallel with asymmetric structure based on deep convolution neural network is proposed. The results are compared with rich competitive algorithms of long short term memory (LSTM), convolutional LSTM (ConvLSTM), Temporal Convolution Network (TCN) and Multivariate Attention LSTM-FCN (MALSTM-FCN), which indicate significant improvement of prediction accuracy, robust and generalization of our model.

This review systematically summarizes the host-design strategies for Zn anodes regarding substrate and interface fabrication, aiming to provide a prospective guideline for developing high-performance Zn anodes.

Although flexible and multifunctional textile-based electronics are promising for wearable devices, it is still a challenge to seamlessly integrate excellent conductivity into textiles without sacrificing their intrinsic flexibility and breathability. Herein, the vertically interconnected conductive networks are constructed based on a meshy template of weave cotton fabrics with interwoven warp and weft yarns. The two-dimensional early transition metal carbides/nitrides (MXenes), with unique metallic conductivity and hydrophilic surfaces, are uniformly and intimately attached to the preformed fabric via a spray-drying coating approach. Through adjusting the spray-drying cycles, the degree of conductive interconnectivity for the fabrics is precisely tuned, thereby affording highly conductive and breathable fabrics with integrated Joule heating, electromagnetic interference (EMI) shielding and strain sensing performances. Interestingly, triggered by the interwoven conductive architecture, the MXene-decorated fabrics with a low loading of 6 wt % (0.78 mg cm–2) offer an outstanding electrical conductivity of 5 Ω sq–1. The promising electrical conductivity further endows the fabrics with superior Joule heating performance with a heating temperature up to 150 °C at a supply voltage of 6 V, excellent EMI shielding performance, and highly sensitive strain responses to human motion. Consequently, this work offers a novel strategy for the versatile design of multifunctional textile-based wearable devices.

The primary developing trends in flexible and stretchable electronics involve the innovation of material synthesis, mechanical design, and fabrication strategies that employ soft substrates. The biggest challenge is that the entire electronic system must allow not only bending but also stretching. Therefore, stretchable conductors become a crucial construction unit for the connection of working circuits of various stretchable devices. Owing to the success of stretchable conductors, various stretchable electronic devices are fabricated with the help of multiple manufacturing strategies, including stretchable heaters, stretchable energy conversion and storage devices, stretchable transistors, sensors and artificial skin. The continuous development of stretchable electronics has led to the new functionality of transparency, and the fabrication of transparent stretchable electronic devices has gained a lot of interest due to the potential of wearable electronic systems. This review presents technology developments in the preparation of related materials, fabrication strategies and various applications of stretchable electronics. It focuses on the fundamental structural design, mechanisms, and tactics, as well as on challenges and opportunities in the manufacture of stretchable electronic devices and their various applications.

Although anti-PD-1 immunotherapy is widely used to treat melanoma, its efficacy still has to be improved. In this work, we present a therapeutic method that combines immunotherapy and starvation therapy to achieve better antitumor efficacy. We designed the CMSN-GOx method, in which mesoporous silica nanoparticles (MSN) are loaded with glucose oxidase (GOx) and then encapsulate the surfaces of cancer cell membranes to realize starvation therapy. By functionalizing the MSN's biomimetic surfaces, we can synthesize nanoparticles that can escape the host immune system and homologous target. These attributes enable the nanoparticles to have improved cancer targeting ability and enrichment in tumor tissues. Our synthetic CMSN-GOx complex can ablate tumors and induce dendritic cell maturity to stimulate an antitumor immune response. We performed an in vivo analysis of these nanoparticles and determined that our combined therapy CMSN-GOx plus PD-1 exhibits a better antitumor therapeutic effect than therapies using CMSN-GOx or PD-1 alone. Additionally, we used the positron emission tomography imaging to measuring the level of glucose metabolism in tumor tissues, for which we investigate the effect with the cancer therapy in vivo.

A nanostructured platform that combines electrospun TiO2 nanofibers (TiNFs)-deposited substrate and cell-capture agent realizes significant capture of circulating tumor cells (CTCs). The enhanced local topographic interactions between the horizontally packed TiNFs deposited substrates and extracellular matrix scaffolds, in addition to anti-EpCAM/EpCAM biological recognition, contributes to the significantly enhanced capture efficiency compared to flat surfaces. Detailed facts of importance to specialist readers are published as ”Supporting Information”. Such documents are peer-reviewed, but not copy-edited or typeset. They are made available as submitted by the authors. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Abstract Hybrid metamaterials that exhibit reconfigurable responses under external stimulus, such as electric fields and light radiation, have only recently been demonstrated by combining active media with patterned metallic structures. Nevertheless, hybrid terahertz (THz) metamaterials whose spectral performance can be dynamically tuned over a large scale remain rare. Compared with most active media (for instance, silicon) that provide limited activity, vanadium dioxide (VO 2 ), which exhibits an insulator-to-metal transition, has been recently explored to facilitate dynamically tunable metamaterials. More importantly, the phase transition yields a three orders of magnitude increase in THz electrical conductivity, which suggests the potential for creating VO 2 based hybrid resonators that operate at THz frequencies. Here, we show that an integration of VO 2 structures and conventional metallic resonating components can enable a class of highly tunable THz metamaterials. Considering the widely studied phase-transition dynamics in VO 2 , the proposed hybrid metamaterials are capable of offering ultrafast modulation of THz radiation.

Commercial or clinical tissue adhesives are currently limited due to their weak bonding strength on wet biological tissue surface, low biological compatibility, and slow adhesion formation. Although catechol-modified hyaluronic acid (HA) adhesives are developed, they suffer from limitations: insufficient adhesiveness and overfast degradation, attributed to low substitution of catechol groups. In this study, we demonstrate a simple and efficient strategy to prepare mussel-inspired HA hydrogel adhesives with improved degree of substitution of catechol groups. Because of the significantly increased grafting ratio of catechol groups, dopamine-conjugated dialdehyde–HA (DAHA) hydrogels exhibit excellent tissue adhesion performance (i.e., adhesive strength of 90.0 ± 6.7 kPa), which are significantly higher than those found in dopamine-conjugated HA hydrogels (∼10 kPa), photo-cross-linkable HA hydrogels (∼13 kPa), or commercially available fibrin glues (2–40 kPa). At the same time, their maximum adhesion energy is 384.6 ± 26.0 J m–2, which also is 40–400-fold, 2–40-fold, and ∼8-fold higher than those of the mussel-based adhesive, cyanoacrylate, and fibrin glues, respectively. Moreover, the hydrogels can gel rapidly within 60 s and have a tunable degradation suitable for tissue regeneration. Together with their cytocompatibility and good cell adhesion, they are promising materials as new biological adhesives.

Graphitic carbon nitride nanosheets (g-C3N4 NSs) hybridized nitrogen doped titanium dioxide (N-TiO2) nanofibers (GCN/NT NFs) have been synthesized in situ via a simple electrospinning process combined with a modified heat-etching method. The prepared GCN/NT NFs were characterized by a variety of methods and their photocatalytic activities were evaluated by hydrogen (H2) production from water splitting and degradation of rhodamine B in aqueous solution. It was found that the GCN/NT NFs have a mesoporous structure, composed of g-C3N4 NSs and N-doped TiO2 crystallites. The g-C3N4 NSs synthesized after heat-etching were found to be embedded in, and covered, the hybrid NFs to form stable interfaces. The partial decomposition of g-C3N4 releases its nitrogen content which eventually gets doped into the nearby TiO2 skeleton. The GCN/NT NFs give a high photocatalytic H2 production rate of 8,931.3 μmol·h−1·g−1 in aqueous methanol solution under simulated solar light. Such a highly efficient photocatalytic performance can be ascribed to the combined effects of g-C3N4 NSs and N-doped TiO2 with enhanced light absorption intensity and improved electron transport ability. Also, the large surface area of the mesoporous NFs minimizes light reflection on the surface and provides more surface-active sites. This work highlights the potential of quasi-one dimensional hybrid materials in the field of solar energy conversion.