Infomat最新文献

Dynamic detection is crucial for intelligent vision systems, enabling applications like autonomous vehicles and advanced surveillance. Event-based sensors, which convert illumination variations into sparse event spikes, are highly effective for dynamic detection with low data redundancy. However, current event-based vision sensors with simplified photosensitive capacitor structures face limitations, particularly in their spectral response, which hinders effective information acquisition in multispectral scenes. Here, we introduce a two-terminal thin-film event-based vision sensor that innovatively integrates an inorganic oxide p–n junction with the pyro-phototronic effect, synergistically combining the photovoltaic and pyroelectric mechanisms. This innovation enables spiking signals with a tenfold increase in responsivity, a dynamic range of 110 dB, and an extended spectral response from ultraviolet (UV) to near-infrared (NIR). With a thin-film sensor array, these spiking signals accurately extract fingerprint edge features even under low-light conditions, benefiting from high sensitivity to minor luminance variations. Additionally, the sensors' broadband spiking response captures richer information, achieving 99.25% accuracy in multispectral dynamic gesture recognition while reducing data processing by over 65%. This approach effectively eliminates redundant data while minimizing information loss, offering a promising alternative to current dynamic perception technologies.

Continuously printable electronics have the significant advantage of being efficient for fabricating conductive polymer composites; however, the precise tailoring of the 3D hierarchical morphology of conductive nanocomposites in a simple dripping step remains challenging. Here, we introduce a one-step direct printing technique to construct diverse microdome morphologies influenced by the interfacial Marangoni effect and nanoparticle interactions. Using a jet dispenser for continuous processing, we effectively fabricated a soft epidermis-like e-skin containing 64 densely arrayed pressure sensing pixels with a hierarchical dome array for enhanced linearity and ultrasensitivity. The e-skin has 36 temperature-sensing pixels in the outer layer, with a shield-shaped dome that is insensitive to pressure stimuli. Our prosthetic finger inserted with the printed sensor arrays was capable of ultragentle detection and manipulation, such as stably holding a fragile biscuit, using a soft dropper to elaborately produce water droplets and harvesting soft fruits; these activities are challenging for existing high-sensitivity tactile sensors.

Triboelectric nanogenerators (TENGs) as a clean energy-harvesting technology are experiencing significant growth in the pursuit of carbon neutrality, accompanied by the increasing use of environmentally friendly biomaterials. However, biomaterials exhibit inferior triboelectric properties compared with petro-materials, hindering the development of bio-based TENGs. Here, leveraging the crystal boundary-tuning strategy, we develop a chitosan aerogel-based TENG (CS-TENG) that is capable of delivering power density over 116 W m^–2, beyond that of the previous reports for CS-TENG by an order of magnitude. With a high output voltage of 3200 V, the CS-TENG directly illuminated 1000 LEDs in series. Notably, the CS aerogel exhibits self-healing, waste recycling and gas-sensitive properties, ensuring the long-term durability, environmental benignity and sensing characteristics of the CS-TENG. Furthermore, a breath-activated mask-integrated CS-TENG ammonia monitoring system is engineered, which accurately detects changes in ammonia concentration within the range of 10–160 ppm, enabling real-time monitoring of ammonia in the environment. Our results set a record for the ultrahigh power density of CS-TENG, representing a significant advancement in the practical application of TENGs.

Since its emergence in 2009, perovskite photovoltaic technology has achieved remarkable progress, with efficiencies soaring from 3.8% to over 26%. Despite these advancements, challenges such as long-term material and device stability remain. Addressing these challenges requires reproducible, user-independent laboratory processes and intelligent experimental preselection. Traditional trial-and-error methods and manual analysis are inefficient and urgently need advanced strategies. Automated acceleration platforms have transformed this field by improving efficiency, minimizing errors, and ensuring consistency. This review summarizes recent developments in machine learning-driven automation for perovskite photovoltaics, with a focus on its application in new transport material discovery, composition screening, and device preparation optimization. Furthermore, the review introduces the concept of the self-driven Autonomous Material and Device Acceleration Platforms (AMADAP) laboratory and discusses potential challenges it may face. This approach streamlines the entire process, from material discovery to device performance improvement, ultimately accelerating the development of emerging photovoltaic technologies.

Chalcogenides, despite their versatile functionality, share a notably similar local structure in their amorphous states. Particularly in electronic phase-change memory applications, distinguishing these glasses from neighboring compositions that do not possess memory capabilities is inherently difficult when employing traditional analytical methods. This has led to a dilemma in materials design since an atomistic view of the arrangement in the amorphous state is the key to understanding and optimizing the functionality of these glasses. To tackle this challenge, we present a machine learning (ML) approach to separate electronic phase-change materials (ePCMs) from other chalcogenides, based upon subtle differences in the short-range order inside the glassy phase. Leveraging the established structure–property relations in chalcogenide glasses, we select suitable features to train accurate machine learning models, even with a modestly sized dataset. The trained model accurately discerns the critical transition point between glass compositions suitable for use as ePCMs and those that are not, particularly for both GeTe–GeSe and Sb₂Te₃–Sb₂Se₃ materials, in line with experiments. Furthermore, by extracting the physical knowledge that the ML model has offered, we pinpoint three pivotal structural features of amorphous chalcogenides, that is, the bond angle, packing efficiency, and the length of the fourth bond, which provide a map for materials design with the ability to “predict” and “explain”. All three of the above features point to the smaller Peierls-like distortion and more well-defined octahedral clusters in amorphous ePCMs than non-ePCMs. Our study delves into the mechanisms shaping these structural attributes in amorphous ePCMs, yielding valuable insights for the AI-powered discovery of novel materials.

Wavelength selective imaging has a wide range of applications in image recognition and other application scenarios, which can effectively improve the recognition rate of objects. However, in the existing technical scenarios, it is usually necessary to use complex optical devices such as filters or gratings to achieve wavelength extraction. These methods inevitably bring about the problems of complex structure and low integration. Therefore, it is necessary to realize the wavelength extraction function at the device level. Here, we realize the wavelength extraction function and wide-spectrum imaging function in the visible to infrared band based on a visible light absorber/floating gate storage layer/near-infrared (NIR) photogating layer configuration. Under infrared irradiation, the device exhibits negative photoresponse through the absorption of infrared light by the Ge substrate and the photogating effect, and realizes visible positive light response through the absorption of visible light by MoS₂. Utilizing the memory function of the device, by cleverly changing the gate voltage pulse, the photoresponse state of the output voltage is effectively adjusted to achieve three imaging states: visible light response only, response to both visible and infrared light, and infrared light response only. Active selective imaging of the word “XDU” was achieved at 532 and 1550 nm wavelength. By using the photoresponse data of the device, the passive imaging of the topography of Xi'an, Shaanxi Province was obtained, which effectively improves the recognition rate of mountains and rivers. The proposed reconfigurable visible–infrared wavelength-selective imaging photodetector can effectively extract image information and improve the image recognition rate while ensuring a simple structure. The single-chip-based spectral separation imaging solution lays a good foundation for the further development of visible–infrared vision applications.

The cover art, prepared by Ong's group at Xiamen University Malaysia, showcases the advancement and application of layered double hydroxides (LDHs) and other cutting-edge electrocatalysts, driving the transition to a net-zero future. The train symbolizes the momentum towards renewable fuels powered by next-generation electrochemical energy conversion and storage technologies. This captivating journey highlights the development of robust, advanced electrocatalysts that tackle environmental challenges while generating value-added energy products.

Ternary MAX phases, characterized by the chemical formula M₂AX, represent a group of layered materials with hexagonal lattices. These MAX phases have been the subject of extensive experimental and theoretical studies. Formation energy and thermodynamic calculations indicate that MAX phases containing late transition metals, such as Rh, Ru, Pt, Pd, Co, and Ni, are unlikely to form. Here, we introduce an alternative family of orthorhombic and monoclinic materials, the LAX phases, which exhibit similarities to MAX phases in terms of their layered structure and A and X elements. However, LAX materials incorporate late transition metals in place of the early transition metals. Advanced techniques for predicting the crystal structure of materials, coupled with data-driven materials research and machine learning algorithms, were employed to investigate the stable structures containing transition metals from the last groups of the d-block elements. The analyses revealed 207 ternary LAX systems that demonstrate robust stability against decomposition, with 100 of these systems showing dynamic stability. An in-depth examination of the top 10 structures revealed five LAX systems that are phase stable and exhibit superior mechanical properties, outperforming MAX phase counterparts in Young's modulus, stiffness, and hardness. These findings indicate that many LAX phase structures are viable candidates for future synthesis, highlighting the potential of heuristic-based structure searches in material discovery.

Hydrogel-based sensors are recognized as key players in revolutionizing robotic applications, healthcare monitoring, and the development of artificial skins. However, the primary challenge hindering the commercial adoption of hydrogel-based sensors is their lack of high stability, which arises from the high water content within the hydrogel structure, leading to freezing at subzero temperatures and drying issues if the protective layer is compromised. These factors result in a significant decline in the benefits offered by aqueous gel electrolytes, particularly in terms of mechanical properties and conductivity, which are crucial for flexible wearable electronics. Previous reports have highlighted several disadvantages associated with using cryoprotectant co-solvents and lower mechanical properties for ion-doped anti-freezing hydrogel sensors. In this study, the design and optimization of a photocrosslinkable ionic hydrogel utilizing silk methacrylate as a novel natural crosslinker are presented. This innovative hydrogel demonstrates significantly enhanced mechanical properties, including stretchability (>1825%), tensile strength (2.49 MPa), toughness (9.85 MJ m^–3), and resilience (4% hysteresis), compared to its non-ion-doped counterpart. Additionally, this hydrogel exhibits exceptional nonfreezing behavior down to −85°C, anti-drying properties with functional stability up to 2.5 years, and a signal drift of only 5.35% over 2450 cycles, whereas the control variant, resembling commonly reported hydrogels, exhibits a signal drift of 149.8%. The successful application of the developed hydrogel in advanced robotics, combined with the pioneering demonstration of combinatorial commanding using a single sensor, could potentially revolutionize sensor design, elevating it to the next level and benefiting various fields.

Reconfigurable memristors featuring neural and synaptic functions hold great potential for neuromorphic circuits by simplifying system architecture, cutting power consumption, and boosting computational efficiency. Building upon these attributes, their additive manufacturing on sustainable substrates further offers unique advantages for future electronics, including low environmental impact. Here, exploiting the structure–property relationship of inkjet-printed MoS₂ nanoflake-based resistive layer, we present paper-based reconfigurable memristors. We demonstrate a sustainable process covering material exfoliation, device fabrication, and device recycling. With >90% yield from a 16 × 65 device array, our memristors demonstrate robust resistive switching, with >10⁵ ON–OFF ratio and <0.5 V operation in non-volatile state. Through modulation of compliance current, the devices transition into a volatile state, with only 50 pW switching power consumption. These performances rival state-of-the-art metal oxide-based counterparts. We show device recyclability and stable, reconfigurable operation following disassembly, material collection and re-fabrication. We further demonstrate synaptic plasticity and neuronal leaky integrate-and-fire functionality, with disposable applications in smart packaging and simulated medical image diagnostics. Our work shows a sustainable pathway toward printable, reconfigurable neuromorphic devices, with minimal environmental footprints.