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HighlightsThis Review encompasses the role, requirement, and development direction of water-based electrolytes for sustainable, safe, high-performance Li-ion batteries.Water-based electrolytes (aqueous liquid and gel electrolytes) and their mechanisms are comprehensively summarized to widen the electrolyte electrochemical stability window and battery operating voltage and to achieve long-term operation stability.Current lithium-ion batteries (LIBs) rely on organic liquid electrolytes that pose significant risks due to their flammability and toxicity. The potential for environmental pollution and explosions resulting from battery damage or fracture is a critical concern. Water-based (aqueous) electrolytes have been receiving attention as an alternative to organic electrolytes. However, a narrow electrochemical-stability window, water decomposition, and the consequent low battery operating voltage and energy density hinder the practical use of aqueous electrolytes. Therefore, developing novel aqueous electrolytes for sustainable, safe, high-performance LIBs remains challenging. This Review first commences by summarizing the roles and requirements of electrolytes–separators and then delineates the progression of aqueous electrolytes for LIBs, encompassing aqueous liquid and gel electrolyte development trends along with detailed principles of the electrolytes. These aqueous electrolytes are progressed based on strategies using superconcentrated salts, concentrated diluents, polymer additives, polymer networks, and artificial passivation layers, which are used for suppressing water decomposition and widening the electrochemical stability window of water of the electrolytes. In addition, this Review discusses potential strategies for the implementation of aqueous Li-metal batteries with improved electrolyte–electrode interfaces. A comprehensive understanding of each strategy in the aqueous system will assist in the design of an aqueous electrolyte and the development of sustainable and safe high-performance batteries.

For the practical use of synthetic hydrogels as artificial biological tissues, flexible electronics, and conductive membranes, achieving requirements for specific mechanical properties is one of the most prominent issues. Here, we demonstrate superstrong, superstiff, and conductive alginate hydrogels with densely interconnecting networks implemented via simple reconstructing processes, consisting of anisotropic densification of pre-gel and a subsequent ionic crosslinking with rehydration. The reconstructed hydrogel exhibits broad ranges of exceptional tensile strengths (8–57 MPa) and elastic moduli (94–1,290 MPa) depending on crosslinking ions. This hydrogel can hold sufficient cations (e.g., Li + ) within its gel matrix without compromising the mechanical performance and exhibits high ionic conductivity enough to be utilized as a gel electrolyte membrane. Further, this strategy can be applied to prepare mechanically outstanding, ionic-/electrical-conductive hydrogels by incorporating conducting polymer within the hydrogel matrix. Such hydrogels are easily laminated with strong interfacial adhesion by superficial de- and re-crosslinking processes, and the resulting layered hydrogel can act as a stable gel electrolyte membrane for an aqueous supercapacitor. Specific mechanical properties are one of the most important issues for application of synthetic hydrogels as biological tissue, flexible electronics or in conductive membranes. Here, the authors demonstrate that a reconstruction process consisting of anisotropic densification of pre-gel and subsequent ionic crosslinking and rehydration leads to strong, stiff, and conductive alginate hydrogels with densely interconnecting networks.

Hydrogels are major components of the human body. To replace a damaged hydrogel in the body or support/monitor its normal operation, artificial hydrogels similar to those found in nature are required. As the development of morphologically adaptable soft yet tough hydrogels such as double‐network (DN) and polyampholyte (PA) gels, they are applied to soft tissues such as the neural and epithelial tissues with elastic moduli ranging from a few pascals to several kilopascals. However, creating strong and stiff hydrogels emulating stiff load‐bearing connective tissues with elastic moduli in the MPa‐to‐GPa range remains challenging. Herein, recent strategies and potential methods for strengthening and stiffening tough DN and PA gels (such as the reinforcement addition, polymer chain alignment, and solvent exchange) as well as the reinforcing and fracture mechanisms of the resulting hydrogels are summarized. The objective is to provide some insights into the optimal strategy and method for fabricating hydrogels with a combination of strength, stiffness, and toughness, which can emulate natural load‐bearing tissues. Strengthening and stiffening tough double‐network (DN) or polyampholyte (PA) hydrogels are a promising approach to fabricate synthetic hydrogels emulating the natural load‐bearing tissues with elastic moduli in the MPa‐to‐GPa. Herein, several strategies for strengthening and stiffening the hydrogels are summarized, and their reinforcing and fracture mechanisms are outlined, providing future directions to develop hydrogels with desirable mechanical properties.

Current therapies for autoimmune diseases, such as multiple sclerosis (MS), induce broad suppression of the immune system, potentially promoting opportunistic infections. Here, we report an immunosuppressive biomaterial-based therapeutic vaccine carrying self-antigen and tolerance-inducing inorganic nanoparticles to treat experimental autoimmune encephalomyelitis (EAE), a mouse model mimicking human MS. Immunization with self-antigen-loaded mesoporous nanoparticles generates Foxp3 + regulatory T-cells in spleen and systemic immune tolerance in EAE mice, reducing central nervous system-infiltrating antigen-presenting cells (APCs) and autoreactive CD4 + T-cells. Introducing reactive oxygen species (ROS)-scavenging cerium oxide nanoparticles (CeNP) to self-antigen-loaded nanovaccine additionally suppresses activation of APCs and enhances antigen-specific immune tolerance, inducing recovery in mice from complete paralysis at the late, chronic stage of EAE, which shows similarity to chronic human MS. This study clearly shows that the ROS-scavenging capability of catalytic inorganic nanoparticles could be utilized to enhance tolerogenic features in APCs, leading to antigen-specific immune tolerance, which could be exploited in treating MS. Multiple sclerosis is a debilitating autoimmune disease, for which therapy is not curative, only slowing down progression at the expense of general immune suppression. Here authors show that in a mouse model of multiple sclerosis, disease progression could be halted or even reversed by a nanovaccine, composed of reactive oxygen species scavenging cerium oxide nanoparticles, which establishes immune tolerance against the relevant autoantigen.

Vaccine efficiency is enhanced by mesoporous silica rods that spontaneously form a 3D microenvironment for immune cells. Implanting materials in the body to program host immune cells is a promising alternative to transplantation of cells manipulated ex vivo to direct an immune response, but doing so requires a surgical procedure. Here we demonstrate that high-aspect-ratio, mesoporous silica rods (MSRs) injected with a needle spontaneously assemble in vivo to form macroporous structures that provide a 3D cellular microenvironment for host immune cells. In mice, substantial numbers of dendritic cells are recruited to the pores between the scaffold rods. The recruitment of dendritic cells and their subsequent homing to lymph nodes can be modulated by sustained release of inflammatory signals and adjuvants from the scaffold. Moreover, injection of an MSR-based vaccine formulation enhances systemic helper T cells T H 1 and T H 2 serum antibody and cytotoxic T-cell levels compared to bolus controls. These findings suggest that injectable MSRs may serve as a multifunctional vaccine platform to modulate host immune cell function and provoke adaptive immune responses.

Biological systems are exquisitely sensitive to the location and timing of physiologic cues and drugs. This spatiotemporal sensitivity presents opportunities for developing new therapeutic approaches. Polymer-based delivery systems are used extensively for attaining localized, sustained release of bioactive molecules. However, these devices typically are designed to achieve a constant rate of release. We hypothesized that it would be possible to create digital drug release, which could be accelerated and then switched back off, on demand, by applying ultrasound to disrupt ionically cross-linked hydrogels. We demonstrated that ultrasound does not permanently damage these materials but enables nearly digital release of small molecules, proteins, and condensed oligonucleotides. Parallel in vitro studies demonstrated that the concept of applying temporally short, high-dose \"bursts\" of drug exposure could be applied to enhance the toxicity of mitoxantrone toward breast cancer cells. We thus used the hydrogel system in vivo to treat xenograft tumors with mitoxantrone, and found that daily ultrasound-stimulated drug release substantially reduced tumor growth compared with sustained drug release alone. This approach of digital drug release likely will be applicable to a broad variety of polymers and bioactive molecules, and is a potentially useful tool for studying how the timing of factor delivery controls cell fate in vivo.

Porous biomaterials have been widely used as scaffolds in tissue engineering and cell-based therapies. The release of biological agents from conventional porous scaffolds is typically governed by molecular diffusion, material degradation, and cell migration, which do not allow for dynamic external regulation. We present a new active porous scaffold that can be remotely controlled by a magnetic field to deliver various biological agents on demand. The active porous scaffold, in the form of a macroporous ferrogel, gives a large deformation and volume change of over 70% under a moderate magnetic field. The deformation and volume variation allows a new mechanism to trigger and enhance the release of various drugs including mitoxantrone, plasmid DNA, and a chemokine from the scaffold. The porous scaffold can also act as a depot of various cells, whose release can be controlled by external magnetic fields.

Flexible electronic technology has developed rapidly in recent years, showing broad application prospects in motion monitoring, wearable devices, and personalized medicine. Consequently, the demand for high sensitivity and wide sensing range has gradually increased. Conductive hydrogels have high flexibility, excellent conductivity, and good biocompatibility, making them ideal candidates for fabricating flexible sensors. However, conductive hydrogels exhibit weak mechanical stability, which limits their applications. Therefore, sufficient mechanical properties and fatigue resistance are usually needed to fulfill their application requirements. Herein, the research frontiers of sensors based on mechanically robust conductive hydrogels are reviewed. While published papers always focus on the configuration design and application of sensors and the improvement of sensing performance, research on the network design of hydrogels and their effects on mechanical properties and sensing performance are limited. It is attempted in this review to fill this gap by focusing on the design principles of mechanically enhanced conductive hydrogels and their applications in flexible electronic devices. Herein, hydrogels’ structural designs, toughening mechanisms, mechanical properties, and sensing applications are discussed. The different working mechanisms of flexible sensors composed of tough conductive hydrogels and their applications are also reviewed. Finally, the future development directions and challenges in this field are highlighted. Hydrogels have gained significant attention in the field of flexible sensors because of their outstanding conductivity and flexibility. This review aims to provide an overview of the design and fabrication of different types of conductive hydrogels, while also investigating the intricate relationships between their structure and performance. Their working mechanisms, sensing capabilities, and applications to flexible sensors are explored.

In this study, we propose a personalized glucose prediction model using deep learning for hospitalized patients who experience Type-2 diabetes. We aim for our model to assist the medical personnel who check the blood glucose and control the amount of insulin doses. Herein, we employed a deep learning algorithm, especially a recurrent neural network (RNN), that consists of a sequence processing layer and a classification layer for the glucose prediction. We tested a simple RNN, gated recurrent unit (GRU), and long-short term memory (LSTM) and varied the architectures to determine the one with the best performance. For that, we collected data for a week using a continuous glucose monitoring device. Type-2 inpatients are usually experiencing bad health conditions and have a high variability of glucose level. However, there are few studies on the Type-2 glucose prediction model while many studies performed on Type-1 glucose prediction. This work has a contribution in that the proposed model exhibits a comparative performance to previous works on Type-1 patients. For 20 in-hospital patients, we achieved an average root mean squared error (RMSE) of 21.5 and an Mean absolute percentage error (MAPE) of 11.1%. The GRU with a single RNN layer and two dense layers was found to be sufficient to predict the glucose level. Moreover, to build a personalized model, at most, 50% of data are required for training.

Financial technology is crucial for the sustainable development of financial systems. Algorithmic trading, a key area in financial technology, involves automated trading based on predefined rules. However, investors cannot manually analyze all market patterns and establish rules, necessitating the development of supervised learning trading systems that can discover market patterns using machine or deep learning techniques. Many studies on supervised learning trading systems rely on up–down labeling based on price differences, which overlooks the issues of nonstationarity, complexity, and noise in stock data. Therefore, this study proposes an N‐period volatility trading system that addresses the limitations of up–down labeling systems. The N‐period volatility trading system measures price volatility to address uncertainty and enables the construction of a stable, long‐term trading system. Additionally, an instance‐selection technique is utilized to address the limitations of stock data, including noise, nonlinearity, and complexity, while effectively reducing the data size. The effectiveness of the proposed model is evaluated through trading simulations of stocks comprising the NASDAQ 100 index and compared with up–down labeling trading systems. The experimental results demonstrate that the proposed N‐period volatility trading system exhibits higher stability and profitability than other trading systems.