Materials Horizons最新文献

For various engineering equipment, design parameters such as the metamaterial band gap range, weight, and size are often variable. Previous design of metamaterials enables customized designs for specific operating frequency requirements, different space size constraints, and other requirements. However, due to the complexity of metamaterial configurations and the cumbersome process of band gap calculation, existing metamaterial design methods cannot accommodate the dynamic and complex design requirements in engineering applications. To this end, we propose an elastic metamaterial on-demand design method based on a semi-analytical band gap rapid extraction approach, implemented using the COMSOL-MATLAB co-simulation platform. This method can quickly identify the vibration-absorbing band gap range through modal displacement calculations at specific wave vector points, enabling semi-analytical band gap extraction for elastic metamaterials. Additionally, through iterative design and genetic algorithm optimization, we build and autonomously update a metamaterial performance database, and establish a metamaterial customized design software platform. Compared to current methods, the semi-analytical band gap extraction ensures high computational efficiency for intelligent algorithms, while the co-simulation design significantly reduces design complexity. The design results of the method proposed in this paper are accurate and reliable, providing a technical approach for the rapid optimization design of vibration-absorbing metamaterials and customized low-frequency vibration control in industrial applications.

Synergistic enhancement of luminescence and ferroelectricity (SELF) was explored in a (Z)-isomer of tetraphenylethene derivatives containing two clipping units in (Z)-configuration (TPC2-(Z)). TPC2-(Z) was synthesized utilizing a 'body core' precursor, which exclusively afforded (Z)-configuration. High-resolution transmission electron microscopy measurements indicated that TPC2-(Z) formed a layered morphology in film, with well-ordered crystalline structures, which was ascribed to the (Z)-clipped self-assembled structures. The film exhibited good photoluminescence performances with 45.6% quantum yield. Simultaneously, the film exhibited high ferroelectricity as inferred from high remnant polarization (P_r = 2.54 μC cm^-2) and saturated polarization (3.56 μC cm^-2) along with a longitudinal piezoelectric coefficient (d₃₃ = -23.8 pm V^-1), indicating that TPC2-(Z) exhibits excellent SELF. Owing to its fluorescence and thermal stability, we fabricated light-emitting electrochemical cells (LEC) that exhibited maximum 890 cd m^-2 at V_on of 3.9 V. This was more than 40% enhanced performance compared to that of the (E)/(Z) mixture. A new self-powered, stimuli-sensitive electroluminescent device was demonstrated with TPC2-(Z), where the piezoelectrically tunable LECs effectively 'switched on' luminescence, showing 120-fold increased brightness after 254 bending at 1 Hz, compared to the 'off' state without bending. These results underscore that Z-clipping is an effective method for enhancing SELF and could create new self-powered, stimuli-sensitive electroluminescent devices.

Structural color-based impact sensors output light or electrical signals through entropic elasticity storing and releasing of the polymer network, inspiring the design of armors for underwater equipment. Designing self-damping units at the molecular and nanostructural levels will contribute to capturing and analyzing relevant impact and mechanical signals by the naked eye. Herein, inspired by the octopus' sucker, we proposed self-damping photonic crystals (SDPCs) with differentiated reversible crosslinking domains, which can delayed-release entropic elasticity in water and visually perceive stress field evolution via structural color. These domains are generated by weak and strong hydrogen bonds (H-bonds) assigned by differentiated copolymerization, corresponding to weak and strong crosslinking domains, respectively. The compressed network stores entropic elasticity, showing size-effect-induced blueshift structural colors. During entropic elasticity release, the weak/strong crosslinking domains are disrupted successively, resulting in temporary macropore asymmetry and forming transient Laplacian pressure difference (ΔP). The self-damping effect based on the continuous recombination of domains and the equilibrium iteration of ΔP achieves a delayed visual perception of entropy elasticity release. Given this, impact stress sensing and structural color self-erasing techniques have been developed.

Traditional flexible electronic sensing materials have fallen short in meeting the diverse application needs and environments of modern times. Hence, we require a multi-functional elastomer material to improve the overall performance and expand the functionality of flexible electronic sensors. In this study, we fabricated a multi-block polyurethane (PU) elastomer based on semi-crystalline polycaprolactone (PCL) chain segments and highly flexible polydimethylsiloxane (PDMS) chain segments, which showcases outstanding mechanical properties, self-healing capabilities, and recyclability. By adjusting the ratio parameters of the chain segments, we were able to modulate the thermodynamic behavior, hydrophobicity, mechanical behavior, and self-healing properties of the designed PU elastomers. The optimized ratios exhibited good tensile strength (16.26 MPa), high elongation at break (3300.84%), good toughness (278.82 MJ m^-3, fracture energy ≈ 234.96 KJ m^-2), high self-repairing (≈100%, at room temperature for 12 h), efficient recyclability, and puncture resistance. Self-healing is accomplished through the interactions between dynamic disulfide bonds, dynamic boron-oxygen bonds, and hydrogen bonds. The conductive ink (PEDOT:PSS) was encapsulated within this elastomer to construct a flexible electronic sensor, attaining excellent sensing performance (stable output for 1000 cycles). This multi-functional polyurethane elastomer acts as an ideal matrix material for flexible electronic sensors, offering novel concepts and perspectives for the next generation of green electronic flexible materials, electronic flexible robots, and other stimulus-responsive materials.

The development of advanced environmentally friendly energy storage capacitors is critical to meet escalating demands of pulsed power systems. However, challenges persist in enhancing both the recoverable energy density (W_rec) and efficiency (η) simultaneously. In the present study, a strategy involving domain configuration modulation, achieved by simple single rare earth ion doping, was proposed to enhance the energy-storage performance of BaTiO₃. The designed Ba_1-1.5xLa_xTiO₃ (BLT-x) ceramics exhibited an ultrahigh W_rec of 9.2 J cm^-3 and η of 85.0% when x = 0.10. Furthermore, the origin of the superior performance was revealed through first-principles calculations and atomic-scale displacement analysis. The introduction of La generated intense structural fluctuations in the ordered ferroelectric domains, leading to relaxors with weakly coupled polar nanoregions and delayed saturation polarization. Such factors, combined with enhanced E_b, contributed to elongated P-E loops and ultimately ultrahigh W_rec and η. Meanwhile, the BLT-0.10 ceramic demonstrated exceptional temperature stability (-40-120 °C), frequency stability (10-250 Hz) and fatigue stability (10⁶ cycles), along with notable charging-discharging capabilities. The present research not only provides a potential candidate for advanced pulsed power systems, but also offers a novel strategy for achieving superior energy-storage performance in perovskite ferroelectrics through single rare earth ion-doping.

The rapid advancement of indoor perovskite solar cells (IPSCs) stems from the growing demand for sustainable energy solutions and the proliferation of internet of things (IoT) devices. With tunable bandgaps and superior light absorption properties, perovskites efficiently harvest energy from artificial light sources like LEDs and fluorescent lamps, positioning IPSCs as a promising solution for powering smart homes, sensor networks, and portable electronics. In this review, we introduce recent research that highlights advancements in material optimization under low-light conditions, such as tailoring wide-bandgap perovskites to match indoor light spectra and minimizing defects to enhance stability. Notably, our review explores the integration of artificial intelligence (AI) and machine learning (ML), which are transforming IPSC development by facilitating efficient material discovery, optimizing device architectures, and uncovering degradation mechanisms. These advancements are driving the realization of sustainable indoor energy solutions for interconnected smart technologies.

Polymers are ubiquitous in life; they are used across many length scales, from bulk engineering to precision nanomedicine. The broad applicability of polymers stems from the myriad of different architectures and chemistries that can be produced. In essence, a polymer is a large macromolecule consisting of many repeat units, a concept first proposed by Hermann Staudinger in 1920. However, the use of polymers predates this; natural polymers have been used throughout human history and synthetic polymers such as polyvinyl chloride and Bakelite were invented in the late 19th century. Throughout the 20th century, a plethora of synthetic polymers have been developed. This editorial will highlight the cutting-edge research recently reported across Materials Horizons and Nanoscale Horizons, covering four critical research areas: catalytic polymer materials, polymers in additive manufacturing, self-healing polymeric materials, and recyclable/sustainable polymers.

The advent of the Internet of Things (IoT) has led to exponential growth in data generated from sensors, requiring efficient methods to process complex and unstructured external information. Unlike conventional von Neumann sensory systems with separate data collection and processing units, biological sensory systems integrate sensing, memory, and computing to process environmental information in real time with high efficiency. Memristive neuromorphic sensory systems using memristors as their basic components have emerged as promising alternatives to CMOS-based systems. Memristors can closely replicate the key characteristics of biological receptors, neurons, and synapses by integrating the threshold and adaptation properties of receptors, the action potential firing in neurons, and the synaptic plasticity of synapses. Furthermore, through careful engineering of their switching dynamics, the electrical properties of memristors can be tailored to emulate specific functions, while benefiting from high operational speed, low power consumption, and exceptional scalability. Consequently, their integration with high-performance sensors offers a promising pathway toward realizing fully integrated artificial sensory systems that can efficiently process and respond to diverse environmental stimuli in real time. In this review, we first introduce the fundamental principles of memristive neuromorphic technologies for artificial sensory systems, explaining how each component is structured and what functions it performs. We then discuss how these principles can be applied to replicate the four traditional senses, highlighting the underlying mechanisms and recent advances in mimicking biological sensory functions. Finally, we address the remaining challenges and provide prospects for the continued development of memristor-based artificial sensory systems.

Noise affecting quantum processors still limits quantum simulations to a small number of units and operations. This is especially true for the simulation of open quantum systems, which involve additional units and operations to map environmental degrees of freedom. Hence, finding efficient approaches for the simulation of open quantum systems is an open issue. In this work, we demonstrate how using units with d > 2 levels (qudits) results in a reduction of up to two orders of magnitude in the number of operations (gates) required to implement state-of-the-art algorithms. We explore two conceptually distinct families of these algorithms that were initially designed for qubits and discuss the gate complexity scaling that different platforms (qubit-based vs. qudit-based) offer. Additionally, we present realistic simulations of an experimental platform based on molecular spin qudits coupled to superconducting resonators, where the main hardware error sources are included. We show that, in all cases considered, the use of qudits leads to a remarkable reduction in circuit complexity and that molecular nanomagnets are ideal qudit hosts.

Four-dimensional (4D) printing, a state-of-the-art additive manufacturing technology, enables the creation of objects capable of changing shape, properties, or functionality over time in response to external stimuli. However, the lack of effective remote control and reliance on a single actuation method pose significant challenges, limiting its applications in various fields. This study aims to address these limitations by developing a novel multi-responsive nanocomposite. By coating near-infrared light (NIR)-responsive polypyrrole (PPy) onto the surface of magnetic iron oxide (Fe₂O₃) nanoparticles (NPs), multi-responsive PPy@Fe₂O₃ NPs were synthesized. Doping PPy@Fe₂O₃ into a thermo-responsive shape memory polymer (SMP) matrix created a nanocomposite with excellent NIR and magnetic responsiveness, enabling dynamic, remote-controlled shape transformation of printed objects with precise timing and positioning using NIR and a magnetic field. Using the nanocomposite, a proof-of-concept semi-tubular construct was fabricated to evaluate its controllable transformation capability and assess the potential for modulating neural stem cell (NSC) behaviors. Furthermore, three proof-of-concept smart robots with distinct features were designed and fabricated for cargo delivery in diverse scenarios and different purposes. Importantly, all complex operations of these robots were remotely controlled using NIR illumination and an external magnetic field. This novel approach demonstrates significant progress in addressing the key challenges of remote control and actuation in 4D printing, highlighting its potential for enhanced versatility and functionality across various applications.