3D (three-dimensional) printing of soft/tough hydrogels has been widely used in flexible electronics, regenerative medicine, and other fields. However, due to their loose crosslinking, strong hydration and plasticizing effect of solvent (typically water) and susceptibility to swelling, the printed hydrogels always suffer from bearing compressive stress and shear stress. Here we report a 3D photo-printable hard/soft switchable hydrogel composite which is enabled by the phase transition (liquid/solid transition) of supercooled hydrated salt solution (solvents) within hydrogel. In hard status, it achieved a hardness of 86.5 Shore D (comparable to hard plastics), a compression strength of 81.7 MPa, and Young’s modulus of 1.2 GPa. These mechanical property parameters far exceed those of any currently 3D printed hydrogels. The most interesting thing is that the soft/hard states are easily switchable and this process can be repeated for many times. In the supercooled state, the random arrangement of liquid solvent molecules within hydrogels makes it as soft as conventional hydrogels. Upon artificial seeding of the crystal nucleus, the solvent in hydrogel undergoes rapid crystallization, resulting in the in-situ formation of numerous rigids, ordered rod-like nanoscale crystals uniformly embedded within the hydrogel matrix. This hierarchical structure remarkably enhances the Young’s modulus from kPa to GPa. Furthermore, the softness of hydrogel can be restored by heating and then cooling down to recover the supercooled state of the solvent. Taking advantage of soft/hard status switching, the hydrogel can conform to complex surface morphologies in its soft state and subsequently freeze that shape through crystallization, enabling rapid mold fabrication. Moreover, a shape fixation and recyclable smart hydrogel medical plaster bandage was also developed, capable of conforming the limb shapes and providing adequate support for the bone fracture patients after 10 min of crystallization. Our work suggests a bright future for the direct use of hard hydrogel as a robust industrial material.

Fabricating damage tolerant porous ceramics with efficient energy absorption and impact-resistant capability has been a challenge because of the brittle nature of ceramic materials. In nature, mineralized tissues or organisms such as cuttlebones and diatoms have evolved with hierarchical porous structures to overcome this difficulty. A bioinspired design of ceramic lattice structure with pores at multiple length scales, ranging from few nanometers to hundreds of micrometers, is proposed in the present work. These ceramic lattices with hierarchical porous structures were successfully fabricated via 3D cryogenic printing. Under quasi-static compressions, the printed ceramic lattices showed unprecedented long plateau strain (～60%) and a specific energy absorption of ~10 kJ·kg−1 with a porosity of ~90%. The resulting energy absorption capability was comparable with most composites and metals, thus overcoming the brittle nature of traditional porous ceramics. This was attributed to the delayed destruction of the lattice structure, as well as the gradual collapse of pores at multiple length scales. Similar trends have also been observed under split Hopkinson pressure bar (SHPB) tests, indicating excellent energy absorption under high strain-rate impacts. The proposed 3D printing technique that produces hierarchical pores was also demonstrated to apply to other functional materials, such as silicon carbide, barium titanate, hydroxyapatite, and even titanium alloy, thus opening up new possibilities for fabricating bioinspired hierarchical porous structures.

Hetero-deformation induced (HDI) strengthening generally yields a weak effect on the mechanical property improvement of particle-reinforced metal matrix composites (MMCs). In the present work, a novel strategy was reported to induce remarkable HDI strengthening in MMCs by selecting a reinforcing material with excellent geometrically necessary dislocation (GND) storage ability. The viability of the proposed strategy was tested on additively manufactured nickel matrix composites consisting of Inconel 625 alloy (IN625) as the matrix and high-entropy alloy VNbMoTa as the reinforcing material. It was found that the average grain size and dislocation density of the additively manufactured MMCs gradually decreased with the increase in the additional amount of VNbMoTa. All the samples possessed a similar two-layer VNbMoTa-matrix interface structure containing a high-entropy alloy layer and a Laves phase layer; however, the interface width varied. This two-layer interface could hold GND pile-ups without breaking to ensure a good load transfer effect, and ductile VNbMoTa particles demonstrated excellent GND storage capacity to induce significant HDI stress. The HDI stress for the IN625-(10 wt%) VNbMoTa sample was approximately 200 MPa higher than that for the pure IN625 alloy, resulting in an excellent strength-ductility synergy. The yield strength and elongation of the IN625-(10 wt%) VNbMoTa sample reached (1 032.5 ± 18.8) MPa and (11.8 ± 1.2)%, respectively. In addition, the IN625-(10 wt%) VNbMoTa composite also demonstrated superior mechanical properties at 650 °C that were comparable to those at room temperature, implying that VNbMoTa addition remarkably limited strength reduction caused by temperature. Deformable VNbMoTa particles effectively alleviated the stress concentration, delayed the crack initiation, generated more dislocations and pile-ups, and, in turn, improved the overall high-temperature strength of composites.

Metal 3D printing holds great promise for future digitalized manufacturing. However, the intricate interplay between laser and metal powders poses a significant challenge for conventional trial-and-error optimization. Meanwhile, the “optimized” yet fixed parameters largely limit possible extensions to new designs and materials. Herein, we report a high throughput design coupled with machine learning (ML) guidance to eliminate the notorious cracks and porosities in metal 3D printing for improved corrosion resistance and overall performance. The high throughput methodologies are mostly on obtaining the printed samples and their structural and physical properties, while ML is used for data analysis by model building for prediction (optimization), and understanding. For 316L stainless steel, we concurrently printed 54 samples with different parameters and subjected them to parallel tests to generate an extensive dataset for ML analysis. An ensemble learning model outperformed the other five single learners while Bayesian active learning recommended optimal parameters that could reduce porosity from 0.57% to below 0.1%. Accordingly, the ML-recommended samples showed higher tensile strength (609.28 MPa) and elongation (50.67%), superior anti-corrosion (Icorr = 4.17 × 10−8 A·cm−2), and stable alkaline oxygen evolution for >100 hours (at 500 mA·cm−2). Remarkably, through the correlation analysis of printing parameters and targeted properties, we find that the influence of hardness on corrosion resistance is second only to porosity. We then expedited optimization in AlSi7Mg using the learned knowledge and feed hardness and relative density, thus demonstrating the method’s general extensibility and efficiency. Our strategy can significantly accelerate the optimization of metal 3D printing and facilitate adaptable design to accommodate diverse materials and requirements.

Transpiration cooling is crucial for the performance of aerospace engine components, relying heavily on the processing quality and accuracy of microchannels. Laser powder bed fusion (LPBF) offers the potential for integrated manufacturing of complex parts and precise microchannel fabrication, essential for engine cooling applications. However, optimizing LPBF’s extensive process parameters to control processing quality and microchannel accuracy effectively remains a significant challenge, especially given the time-consuming and labor-intensive nature of handling numerous variables and the need for thorough data analysis and correlation discovery. This study introduced a combined methodology of high-throughput experiments and Gaussian process algorithms to optimize the processing quality and accuracy of nickel-based high-temperature alloy with microchannel structures. 250 parameter combinations, including laser power, scanning speed, channel diameter, and spot compensation, were designed across ten high-throughput specimens. This setup allowed for rapid and efficient evaluation of processing quality and microchannel accuracy. Employing Bayesian optimization, the Gaussian process model accurately predicted processing outcomes over a broad parameter range. The correlation between various processing parameters, processing quality and accuracy was revealed, and various optimized process combinations were summarized. Verification through computed Tomography testing of the specimens confirmed the effectiveness and precision of this approach. The approach introduced in this research provides a way for quickly and efficiently optimizing the process parameters and establishing process-property relationships for LPBF, which has broad application value.

Piezoceramic is ubiquitously used in high-performance sensors and actuators. Three-dimensional (3D) printing of lead zirconate titanate (PZT) is attractive and highly desired for such device applications, but most of the existing methods are inherently limited to micron resolution, which makes them untenable for fabricating complex 3D architectures with high-definition features. Here, an electrohydrodynamic jet (E-Jet) nanoprinting strategy has been proposed to fabricate PZT 3D structures with the characteristics of flexibility and scalability. Different kinds of 3D PZT true nanostructures (resolution ~40 nm, aspect ratio ~400) were directly fabricated using a 100 μm-sized nozzle. And the PZT nanostructures exhibited well-developed perovskite crystal morphology, large elastic strain (elongation ≈ 13%), and high piezoelectric property (d31 ≈ (236.5 × 10−12) C·N−1). A bionic PZT air-flow sensor was printed to monitor air-flow detection, demonstrating well sensitivity with ultra-slow air-flow of 0.02 m·s−1. The discovery reveals an efficient pathway to 3D-printing PZT nanostructures for next-generation high-performance piezoelectric devices.

The position-dependent feature in current vat photopolymerization-based additive manufacturing leads to challenges in controlling the dimensional accuracy of printed components. To overcome this intrinsic limitation, we propose a time-dependent dynamic laser writing (DLW) approach for the precise volumetric printing of complex-shaped lenses. In the DLW-based volumetric printing, the formed surface is generated by accumulating the material growth functions (MGFs) on the scanning path, where the MGF is created by the laser direct irradiation with controlled energy doses. Benefiting from the stability of MGFs and the process homogenization, the DLW is less sensitive to process errors when compared to current vat photopolymerization-based additive manufacturing techniques. Furthermore, the continuous scanning leads to the naturally ultra-smooth feature of the printed surfaces. As a demonstration, a millimeter-scale spherical lens was printed in 5.67 min, achieving a three-dimensional (3D) form error of 0.135 μm (root mean square, RMS) and a surface roughness of 0.31 nm (RMS). The printing demonstrated comparable efficiency while achieving form errors an order of magnitude smaller than those of state-of-the-art continuous layer-wise and volumetric printing methods. In addition, polymer lens arrays, freeform polymer lenses, and fused silica lenses were successfully printed, demonstrating promise for advancing the state-of-the-art in 3D printing of precision lenses.

Additive manufacturing of aluminum (Al) alloys has attracted significant attention in the aerospace industry. However, achieving ultrahigh-strength (> 500 MPa) Al alloys remains challenging due to their intrinsic poor printability. Here, we report a novel hybrid additive manufacturing (HAM) approach to process ultrahigh-strength AlMgSc alloy, which combines laser powder bed fusion (LPBF) with interlayer ultrasonic shot peening (USP). The results show that the interlayer ultrasonic shot peening depth reached ~700 μm, leading to almost full density and residual stress convection from tension to compression. The HAM method promotes equiaxed grain formation and refines grain due to grain recrystallizations. Interestingly, the HAM followed by aging treatment tailors the hierarchically multi-gradient structures, inhibits Mg element intragranular segregation, and promotes the multi-nanoprecipitates (e.g. Al3 (Sc, Zr) and Al6Mn) precipitation. Remarkably, the HAM followed by aging treatment achieves yield strength of 609 MPa and breaks elongation of 7.5%, demonstrating ultrahigh strength and good ductility compared with other Al alloys manufactured by AM and forging as reported in the literature. The strength enhancement mechanisms in this AlMgSc alloy are discussed. The high-density Al3 (Sc, Zr) precipitates are the main strengthening contributor, and unique hetero-deformation induced (HDI) strengthening (originates from the heterogeneous microstructures) further enhances the strength of the material. This work highlights a novel approach for processing complex-structured ultrahigh strength Al alloy components by hybrid additive manufacturing.

Black wings of butterfly Ornithoptera goliath and infrared-band radiative cooling function of Rapala dioetas butterfly wings are associated with black pigment (e.g., melanin) and unique hierarchical micro/nanostructures, greatly stimulating biomimetic fabrication of functional photonic structures but mainly targeted to one prototype. Targeted at two-prototype integrated biomimetic fabrication from fully compositional/structural/functional aspects, femtosecond (fs) laser subtractive/additive-integrated hierarchical micro/nano-manufacturing technique is proposed in this work. This technique can one-step transfer refractory metals (e.g., W, Mo, Nb, Ta) into black non-stoichiometric oxide nanomaterials with abundant oxygen vacancies and simultaneously enable the realization of in situ quasi-controllable micro/nanoscale hierarchical aggregation and assembly, all displaying black color but with tunable infrared emission. Adjusting the scan interval for biomimetic manufacturing can tailor the structural oxidation degree, the emission in the long-wave infrared (LWIR) band while keeping the blackness of hierarchical aggregates, and the confined height between the covering quartz plate and the ablated sample. The blackening efficiency of this technique can reach ~11.25 cm2·min−1, opening opportunities for high-throughput optical/thermal applications. Selectively patterned Chinese characters, Arabic numbers, and English letters are easily fabricable, which are intrinsically invisible-infrared dual-band encrypted but decryptable via static/dynamic environment stimuli (e.g., sample heating/cooling, introducing external hot/cold sources including human hands). The self-evolution from ‘orderless’ structuring to ‘ordered’ functionalization is validated for the proposed fs laser subtractive/additive-integrated biomimetic manufacturing, specifically from the synthesis of diverse black nanomaterials and the seemingly disordered micro/nano-aggregates to the ordered optical/thermal regulation capacities for a delicate modulation of information encryption and decryption, unveiling a new concept for future exploration and extension.

Cellular spheroids, closely resembling native tissue microenvironments, have emerged as pivotal constructs in biomedicine as they can facilitate complex cell-cell and cell-matrix interactions. However, current methods for constructing spheroid assembloids with spatial arrangement or heterogeneous structures are limited, which has become a barrier for studying tissue engineering and in vitro disease modeling. Here, we demonstrate an acoustofluidic pick-and-place operation system capable of spatially assembling of spheroids into desired patterns in both two dimensional (2D) and three dimensional (3D) spaces. The underlying physical mechanism of the device is systematically studied to explain the interrelationship between trapping cell spheroids, acoustic streaming, and the acoustic radiation force (ARF) induced by the acoustically activated microneedle. We exploit these mechanisms to successfully transfer cellular spheroids into hydrogel solutions, enabling them to be precisely patterned and fused into assembloids of predefined shapes. Besides, we demonstrate arranging MC3T3-E1 cellular spheroids into a ring shape to fabricate the osteogenic tissues. Besides, a co-culture model involving tumor cells (MCF-7) and normal human dermal fibroblasts (NHDFs) is constructed to validate our method’s ability to reconstruct heterogeneous tumor model, revealing that the fibroblast spheroids promote tumor spheroid invasion. Our method holds significant potential prospects in regenerative medicine, disease model construction and drug screening.

Compared with traditional rain gauges and weather radars, hydrogel flexible electronic sensor capable of responding directly to rainfall events with promptness and authenticity, shows great prospects in real-time rainfall monitoring. Aluminum coordination hydrogel (Al-HG), one of the most qualified sensors suitable for rainfall monitoring, however, is currently impeded from widespread application by its weak mechanical properties due to the low binding strength between Al3+ and functional ligands. Herein, inspired by the antifreeze proteins (AFPs) that protect those Patagonian toothfishes by strongly binding to ice crystals at freezing temperatures, a low temperature-induced strategy is introduced to promote more and stronger ligand carboxyls firm combination with Al3+, thus forming a high-coordinated structure to deal with this challenge. Expectedly, the whole mechanical performance of the product Al-HGF1/F2 obtained by the low temperature-induced strategy is improved. For example, the tensile fracture toughness and the maximum compressive stress of Al-HGF1/F2 are 1.66 MJ·m−3 and 12.01 MPa, approximately twice those of the sample Al-HGF3/F0 obtained by traditional soaking method (0.86 MJ·m−3 and 7.38 MPa, respectively). Coupled with its good biocompatibility, ionic conductivity, and sensing ability, Al-HGF1/F2 demonstrates promising application for real-time rainfall monitoring in discrepant rainfall intensities, different zones, and even under extreme environments. This work aims to offer a stride toward mechanically robust aluminum coordination hydrogel sensors for real-time rainfall monitoring as well as provide insights into flood prevention and disaster mitigation.

Surgical electrodes are frequently associated with disadvantages such as high surface adhesion and severe thermal damage to adjacent normal tissues, which threaten operation quality and patient safety. In this study, by mimicking the micromorphology and bio-anti-adhesion of shark skin, we proposed a strategy that utilized nanoscale aluminium oxide (Al2O3) films deposited on bioinspired shark skin (BSS) microstructures to design a composite surface (Al2O3@BSS) and integrated it into both flat sides of the surgical electrodes. Micro/nano-manufacturing of the Al2O3@BSS surface was sequentially accomplished using nanosecond laser texturing, atomic layer deposition, and low-temperature annealing, endowing it with excellent blood-repellent properties. Visualisation experiments revealed that the tensile stress gradient of the blood coagulum with increasing thickness under a thermal field prompted it to separate from the Al2O3@BSS surface, resulting in anti-adhesion. Furthermore, it was observed for the first time that Al2O3 films could transiently excite discharge along a dielectric surface (DADS) to ablate tissues while suppressing Joule heat, thereby minimising thermal damage. A combination of ex vivo tissue and living mouse experiments demonstrated that the Al2O3@BSS electrodes exhibited optimal comprehensive performance in terms of anti-adhesion, damage minimisation, and drag reduction. In addition, the Al2O3@BSS electrodes possessed remarkable antibacterial efficacy against E. coli and S. aureus. The proposed strategy can meet the extreme application requirements of surgical electrodes to improve operation quality and offer valuable insights for future studies.

Sodium-ion hybrid capacitors (SICs), which combine the high energy density of batteries with the high power density and long cycle life of capacitors, are considered promising next-generation energy storage devices. Ensuring the performance of SICs in low-temperature environments is crucial for applications in high-altitude cold regions, where the desolvation process of Na+ and the transport process in the solid electrolyte interphase (SEI) are determinant. In this paper, we proposed a multi-ether modulation strategy to construct a solvation sheath with multi-ether participation by modulating the coordination of Na+ and solvents. This unique solvation sheath not only reduces the desolvation energy barrier of Na+, but more importantly forms a Na2O-rich inorganic SEI and enhances the ionic dynamics of Na+. Benefiting from the excellent solvation structure design, SICs prepared with this electrolyte can achieve energy density of up to 178 Wh·kg−1 and ultra-high power density of 42 390 W·kg−1 at room temperature. Notably, this SIC delivers record-high energy densities of 149 Wh·kg−1 and 119 Wh·kg−1 as well as power densities of up to 25 200 W·kg−1 and 24 591 W·kg−1 at −20 °C and −40 °C, respectively. This work provides new ideas for the development of high-performance SICs for low-temperature operating environments.

As an ultrathin wide-bandgap (WBG) material, CaNb2O6 exhibits excellent optical and electrical properties. Particularly, its highly asymmetric crystal structure provides new opportunities for designing novel nanodevices with directional functionality. However, due to the significant challenges in applying conventional techniques to nanoscale samples, the in-plane anisotropy of CaNb2O6 has still remained unexplored. Here, we leverage the resonant nanoelectromechanical systems (NEMS) platform to successfully quantify both the mechanical and thermal anisotropies in such an ultrathin WBG crystal. Specifically, by measuring the dynamic response in both spectral and spatial domains, we determine the anisotropic Young’s modulus of CaNb2O6 as EY (a) = 70.42 GPa and EY(b) = 116.2 GPa. By further expanding this technique to cryogenic temperatures, we unveil the anisotropy in thermal expansion coefficients as (a) = 13.4 ppm·K−1, (b) = 2.9 ppm·K−1. Interestingly, through thermal strain engineering, we successfully modulate the mode sequence and achieve a crossing of (1 × 2)-(2 × 1) modes with perfect degeneracy. Our study provides guidelines for future CaNb2O6 nanodevices with additional degrees of freedom and new device functions.

Prosthetic devices designed to assist individuals with damaged or missing body parts have made significant strides, particularly with advancements in machine intelligence and bioengineering. Initially focused on movement assistance, the field has shifted towards developing prosthetics that function as seamless extensions of the human body. During this progress, a key challenge remains the reduction of interface artifacts between prosthetic components and biological tissues. Soft electronics offer a promising solution due to their structural flexibility and enhanced tissue adaptability. However, achieving full integration of prosthetics with the human body requires both artificial perception and efficient transmission of physical signals. In this context, synaptic devices have garnered attention as next-generation neuromorphic computing elements because of their low power consumption, ability to enable hardware-based learning, and high compatibility with sensing units. These devices have the potential to create artificial pathways for sensory recognition and motor responses, forming a “sensory-neuromorphic system” that emulates synaptic junctions in biological neurons, thereby connecting with impaired biological tissues. Here, we discuss recent developments in prosthetic components and neuromorphic applications with a focus on sensory perception and sensorimotor actuation. Initially, we explore a prosthetic system with advanced sensory units, mechanical softness, and artificial intelligence, followed by the hardware implementation of memory devices that combine calculation and learning functions. We then highlight the importance and mechanisms of soft-form synaptic devices that are compatible with sensing units. Furthermore, we review an artificial sensory-neuromorphic perception system that replicates various biological senses and facilitates sensorimotor loops from sensory receptors, the spinal cord, and motor neurons. Finally, we propose insights into the future of closed-loop neuroprosthetics through the technical integration of soft electronics, including bio-integrated sensors and synaptic devices, into prosthetic systems.

Artificial sensory systems mimic the five human senses to facilitate data interaction between the real and virtual worlds. Accurate data analysis is crucial for converting external stimuli from each artificial sense into user-relevant information, yet conventional signal processing methods struggle with the massive scale, noise, and artificial sensory systems characteristics of data generated by artificial sensory devices. Integrating artificial intelligence (AI) is essential for addressing these challenges and enhancing the performance of artificial sensory systems, making it a rapidly growing area of research in recent years. However, no studies have systematically categorized the output functions of these systems or analyzed the associated AI algorithms and data processing methods. In this review, we present a systematic overview of the latest AI techniques aimed at enhancing the cognitive capabilities of artificial sensory systems replicating the five human senses: touch, taste, vision, smell, and hearing. We categorize the AI-enabled capabilities of artificial sensory systems into four key areas: cognitive simulation, perceptual enhancement, adaptive adjustment, and early warning. We introduce specialized AI algorithms and raw data processing methods for each function, designed to enhance and optimize sensing performance. Finally, we offer a perspective on the future of AI-integrated artificial sensory systems, highlighting technical challenges and potential real-world application scenarios for further innovation. Integration of AI with artificial sensory systems will enable advanced multimodal perception, real-time learning, and predictive capabilities. This will drive precise environmental adaptation and personalized feedback, ultimately positioning these systems as foundational technologies in smart healthcare, agriculture, and automation.

Real-time sensory signal monitoring systems are crucial for continuous health tracking and enhancing human-interface technologies in virtual reality/augmented reality applications. Recent advancements in micro/nanofabrication technologies have enabled wearable and implantable sensors to achieve sufficient sensitivity for measuring subtle sensory signals, while integration with wireless communication technologies allows for real-time monitoring and closed-loop user feedback. However, highly sensitive sensing materials face challenges, as their detection results can easily be altered by external factors such as bending, temperature, and humidity. This review discusses methods for decoupling various stimuli and their applications in human interfaces. We cover the latest advancements in decoupled systems, including the design of sensing materials using micro/nanostructured materials, 3-dimensional (3D) sensory system architectures, and Artificial intelligence (AI)-based signal decoupling processing techniques. Additionally, we highlight key applications in robotics, wearable, and implantable health monitoring made possible by these decoupled systems. Finally, we suggest future research directions to address the remaining challenges of developing decoupled artificial sensory systems that are resilient to external stimuli.

Wire-arc directed energy deposition (wire-arc DED) enables the fabrication of large-scale metal components with rapid manufacturing ability and diverse material selection, making it a compelling technology in industries and defenses. However, challenges in both macroscale and microscale defects still limit printed component widespread applications. Recent advances in automatic and intelligent technologies have brought a range of quality controllable strategies to the forefront. This review covers these new strategies for the printing component, including path planning, process monitoring, auxiliary processes, and post processing, while discussing the expectation for structure and quality improvement. In addition, the work brings new areas of intelligent wire-arc DED development, including advances in digital twin, visualization, and human-processing interaction to promote its performance. It is anticipated that a focus on intelligent system will be key to smart and high-quality manufacturing for future wire-arc DED.

Additive manufacturing (AM) offers the unique capability of directly creating three-dimensional complicated ceramic components with high process flexibility and outstanding geometry controllability. However, current ceramic AM technology is mainly limited to the creation of a single material, which falls short of meeting the multiple functional requirements under increasingly harsh service circumstances. Ceramic multi-material additive manufacturing (MMAM) technology has great potential for integrally producing multi-dimensional multi-functional components, allowing for point-by-point precision manufacturing of programmable performance/functions. However, there is a huge gap between the capabilities of the existing ceramic MMAM technology and the requirements for industrial application. In this review, we discuss and summarize the research status of ceramic MMAM technology from the perspectives of feedstock selection, printing process, post-processing, component performance, and application. Throughout the discussion, the challenges associated with ceramic MMAM such as heterogeneous material coupled printing, heterogeneous interfacial bonding, and co-sintering densification have been put forward. This review aims to bridge the gap between AM technologies and the requirements for multifunctional ceramic components by analyzing the existing limitations in ceramic MMAM and pointing out future needs.

With the rise of artificial intelligence (AI), neuromorphic sensory systems that emulate the five basic human sensations including tactility, audition, olfaction, gustation, and vision have attracted significant attention. In particular, research on integrating sensors with artificial synapses is being carried out extensively. These studies offer valuable opportunities for making another breakthrough in AI technology, including autonomous systems, real-time monitoring systems, and human-machine interactions. In this review, we introduce promising reports of neuromorphic sensory systems. Specifically, the core sensing material, device architecture, fabrication process, and applications of the proposed systems are presented in detail. Finally, the unsolved challenges and the prospects of neuromorphic sensory systems are discussed.

Neuromorphic computing extends beyond sequential processing modalities and outperforms traditional von Neumann architectures in implementing more complicated tasks, e.g., pattern processing, image recognition, and decision making. It features parallel interconnected neural networks, high fault tolerance, robustness, autonomous learning capability, and ultralow energy dissipation. The algorithms of artificial neural network (ANN) have also been widely used because of their facile self-organization and self-learning capabilities, which mimic those of the human brain. To some extent, ANN reflects several basic functions of the human brain and can be efficiently integrated into neuromorphic devices to perform neuromorphic computations. This review highlights recent advances in neuromorphic devices assisted by machine learning algorithms. First, the basic structure of simple neuron models inspired by biological neurons and the information processing in simple neural networks are particularly discussed. Second, the fabrication and research progress of neuromorphic devices are presented regarding to materials and structures. Furthermore, the fabrication of neuromorphic devices, including stand-alone neuromorphic devices, neuromorphic device arrays, and integrated neuromorphic systems, is discussed and demonstrated with reference to some respective studies. The applications of neuromorphic devices assisted by machine learning algorithms in different fields are categorized and investigated. Finally, perspectives, suggestions, and potential solutions to the current challenges of neuromorphic devices are provided.

The laser-assisted manufacturing technology has significant advantages in meeting various demands such as complex structures, functional integration, customized devices, and cost-effectiveness, which makes it a highly attractive option for fabricating sensors. In this review, the latest advancements and strategies in intelligent sensor development through laser processing were surveyed and outlined following the interaction of laser and materials. Laser-assisted manufacturing technologies have been extensively applied in materials science and device processing. Firstly, laser technology can be utilized in a wide range of materials, encompassing carbon-based materials, metals, and metallic oxides. In the field of device scale processing, laser manufacturing is widely used in micro/nano structures, planar device construction, and stereoscopic electronic devices such as cutting, engraving, and lithography. Additionally, laser technology provides robust support for sensor applications, covering fields such as pressure sensing, temperature sensing, gas sensing, and biosensors. Furthermore, laser considerably serves in real application areas such as multifunctional sensing systems, actuators, and robots. The widespread application of laser manufacturing technology in sensor platform fabrication offers effective solutions for realizing the miniaturization, multifunctionality, and integration of sensors.