Advanced Materials Technologies最新文献

Monitoring of mechanical forces on the rotator cuff in real time after surgical repair may allow the development of personalized rehabilitation programs for patients undergoing recovery. However, there are no currently available modalities for continuous monitoring of the healing process during rehabilitation; previously reported implantable sensors are either too bulky or stiff without sufficient sensitivity. In this study, a strategy employing a stretchable and implantable sensor is proposed for biomechanical monitoring of the rotator cuff. The sensor is made of a patterned boomerang on thin piezoelectric composite sheets, optimized for biosafety, suitability, and functionality. It allows strain and force sensitivities measurements of up to 755 V ε⁻¹ and 80 V N⁻¹, respectively. To the best of the knowledge, the sensor achieves the highest sensitivity among reported flexible piezoelectric strain sensors, although it has the smallest size. In a cadaveric study, the sensor is used to monitor tension changes in the rotator cuff during shoulder movements. In an in vivo study, the sensor exhibits excellent biocompatibility and functionality in a rabbit model. High-accuracy motion poses recognition is realized using machine learning, illustrating the potential applicability of the device for real-time monitoring of tendon healing.

Fluorinated graphene holds significant promise for optoelectronic applications, yet the instability of carbon–fluorine (C-F) bonds has limited its practical use. This study investigates the long-term operational and thermal stability of a photodetector containing graphene/fluorinated graphene heterostructure (Gr/F-Gr HS). In this design, the top graphene serves as a capping layer that stabilizes fluorine atoms in the bottom fluorinated graphene (F-Gr) via interlayer interactions and suppresses fluorine diffusion. This fluorine-trapping configuration effectively passivates the defects in the F-Gr layer, enhancing both the drain current and optical absorption without altering the device structure. The photodetector achieves > 10⁴ A/W responsivity, ≈10¹² Jones detectivity, and sub-200 µs response, enabling frequency resolution up to 1 kHz across a broad spectral wavelength range (300–1150 nm). The device retains ≈86% of its initial photocurrent after one year in ambient conditions and remains stable up to 200 °C. All-atom molecular dynamics simulations support the experimental results, revealing that the capping layer stabilizes C-F bonds by suppressing fluorine migration and enabling interlayer fluorine redistribution. Together, these findings highlight the superior thermal stability of Gr/F-Gr, reinforcing its promise for advanced optoelectronic applications.

This work presents a broadly applicable strategy for integrating highly conductive 2D conjugated metal-organic frameworks (2D c-MOFs) into electronic biosensors, demonstrated through the ultrasensitive detection of cortisol. For the first time, we report the solid-phase conversion of a pre-patterned Cu(OH)₂ nanoarray into vertically aligned CuHITP (HITP = 2,3,6,7,10,11-hexaiminotriphenylene). The Cu(OH)₂ nanoarray with tubular architecture provides uniform nucleation sites, resulting in a continuous, well-adhered, and 3D c-MOF film directly grown on the working electrodes. Long-standing challenges in the fabrication of conductive MOFs (c-MOFs) are overcome by enhancing the mass transport within the MOF layer. The resulting CuHITP film is integrated into an extended-gate field-effect transistor (EG-FET) biosensing platform functionalized via amine groups inherent to the c-MOF structure, enabling the optimal covalent attachment of cortisol-specific aptamers. The sensors achieve an impressive limit of detection down to 0.1 fM, with a broad dynamic range spanning 11 orders of magnitude. Additionally, the devices exhibit a high pH sensitivity (23 mV/pH), indicating the potential of a multi-purpose assay. These findings not only demonstrate the successful preservation of c-MOF structural integrity during fabrication but also establish 2D c-MOFs as a promising material for high-performance, ultrasensitive biosensors targeting small biomolecules.

Magnetotactic bacteria (MTB), inherently motile and self-powered, are promising biorobotic candidates for targeted anti-cancer drug delivery since they can actively deliver the therapeutic agent to the tumor, decreasing adverse side effects. However, the directed navigation of these bacteria through intricate microenvironments mimicking the natural microvasculature has not been investigated. Here, the directed navigation of MTB is demonstrated within a vasculature-on-a-chip platform. A perfusable vascular network is developed to investigate MTB at the single-microorganism level. MTB is demonstrated to successfully align and navigate along the magnetic field inside the microvessels. Surface interaction with the microvessel walls, hydrodynamic forces, and counterdirectional flows in the order of 10 µm∙s⁻¹ are examined as potential factors that may interfere with the MTB alignment and magnetotaxis. The average swimming speed of the studied bacteria within the vasculature-on-a-chip device is 13.9 µm s⁻¹. Finite Element Analysis reveals that under these conditions, MTB experience shear stresses of up to 30 Pa, and drag forces between 10 and 40 pN, depending on their relative orientation to the flow field. Altogether, this work provides a first demonstration of effective directed navigation of MTB in a vasculature-on-a-chip platform, and the influence of external factors on their field alignment and magnetotactic behavior.

In the field of health monitoring, there is an increased requirement for humidity sensing that can achieve a balance among high-performance detection, wearability, and environmental sustainability. Current humidity sensing often does not perform well enough due to the insufficient available adsorption sites for water molecules within the sensing layer and the lack of effective transmission paths. Herein, this study leverages the Ti─O─C bond at the MXene (Ti₃C₂T_x)-graphene oxide (GO) interface to drive the spontaneous formation of a 3D configuration with an enhanced specific surface area. The 3D MXene@GO configuration exhibits an overall morphology characterized by surface with micro-wrinkles. The MXene@GO composite sensing layer is successfully synthesized on rice paper-based interdigital electrode substrate through a facile dip-coating process. This 3D architecture is expected to increase water molecule adsorption sites, improve transmission pathways, thereby obtaining enhanced moisture-sensitive properties. By optimizing the process parameters, the sensor exhibits outstanding mechanical flexibility and superior response/recovery characteristics (36.306 s/21.376 s, from 22 to 98 %RH). The proof-of-concept demonstrations for continuous human respiratory pattern analysis and wound exudate level assessment further validates its potential in wearable biomedical scenarios.

The current study is based on lattice metamaterials that can achieve a programmable mechanical response. A novel approach to decomposing a surface of a lattice into a re-entrant structure is used to design Decomposed Surface Lattice Metamaterials (DSLMs), which are manufactured using 3D printing of short carbon fiber/polylactic acid (sCF/PLA). The DSLMs are characterized from both directions under static loading. The correlation between mechanical response and the displacement rate along the wall thickness effect, expressed as a relative volume fraction, is determined. DSLM successfully tunes the mechanical response. The Longitudinal direction of DSLM exhibits a dual modulus nature, while the Transverse direction is highly elastic. The crashworthiness index is successfully programmed to achieve the highest energy absorption of 2201 kJ m⁻³ and a plateau stress of 4 MPa in the Transverse direction. A nonlinear average Poisson's ratio of 0.02 is achieved from the same sCF/PLA due to the metamaterial's topology. A positive strain rate sensitivity is observed for both directions. The stretch-dominated deformation behavior is predicted by fitting the Gibson-Ashby model. Overall, the Transverse direction of sCF/PLA DSLM can be applied to supporting structures and frames, while the Longitudinal direction is recommended for applications in structural sensors.

The convergence of neuromorphic computing and 2D materials presents a transformative approach to overcoming the von Neumann bottleneck by unifying sensing, memory, and computation. Owing to their atomic thickness, diverse physical effects (e.g., ferroelectricity, quantum tunneling, and phase transitions), and van der Waals (vdWs) heterostructure compatibility, 2D materials have demonstrated remarkable potential in emulating synaptic plasticity and enabling multifunctional, low-power neuromorphic systems. This review comprehensively summarizes the recent progress from 2010 to 2025 in 2D materials-based neuromorphic devices, spanning memristors, electrolyte-gated and ferroelectric transistors, and floating-gate memory. Breakthroughs include sub-100 mV switching voltages, femtojoule-level energy consumption, and multimodal perception capabilities. Special attention is given to bioinspired interactive systems integrating tactile, visual, and auditory functions for real-time processing. Furthermore, the key material innovations that address non-idealities, such as device variability and instability, via interface engineering, defect control, and heterostructure design are analyzed. These developments underscore the critical role of 2D materials in enabling highly integrated, energy-efficient, and intelligent neuromorphic hardware for next-generation AI and edge computing applications.

The Quantum Diamond Microscope (QDM) is an emerging magnetic imaging tool enabling noninvasive characterization of electronic circuits through spatially mapping current densities. In this work, wafer-level current sensing is demonstrated for a current mirror circuit composed of 16 amorphous- indium-gallium-zinc oxide (a-IGZO) thin-film transistors (TFTs). a-IGZO TFTs are promising for flexible electronics due to their high performance. Using QDM, 2D magnetic field images produced by DC currents were obtained, from which accurate current density maps are extracted. Notably, QDM measurements agree well with conventional electrical probe station measurements, and enable current sensing in internal circuit paths inaccessible via conventional electrical probing techniques. The results highlight QDM's capability as a noninvasive diagnostic tool for the characterization of emerging semiconductor technologies, especially oxide-based TFTs. This approach provides essential insights to fabrication engineers, with potential to improve yield and reliability in flexible electronics manufacturing.

Bayesian neural networks (BNNs) provide inference with uncertainty prediction, which is critical for safety-sensitive applications such as autonomous driving and medical diagnosis. However, conventional implementations relying on Gaussian random number generators incur significant area and power overheads. In this work, the inherent random noise of phase change memory (PCM) devices are exploited to realize a compact, energy-efficient BNN hardware architecture. The fabricated 4-Mb indium-doped Ge₂Sb₂Te₅ (In - GST) PCM chip achieves a resistance ratio of over 190 times and can be programmed to 32 different conductance states. To mitigate the impact of conductance drift on inference accuracy, two compensation methods are proposed, with the measured minus fit method demonstrating superior reduction of epistemic uncertainty. The proposed PCM-based BNN architecture achieves a MNIST recognition accuracy of 98.08% on LeNet-5 and demonstrates an energy efficiency of 33.3 TOPS/W. This work establishes a unified memory-computation-randomness framework for probabilistic neural network hardware, enabling low-power and reliable inference with uncertainty quantification.

Nonadherence to prescribed medication regimens continues to pose a significant challenge in healthcare, highlighting the need for precise, user-friendly, and personalized drug-delivery systems. Transdermal drug delivery (TDD) using microneedle arrays (MNs) is a minimally invasive and patient-compliant alternative to conventional methods. However, traditional designs are hindered by their limited drug loading capacity, passive release mechanisms, and complex fabrication processes. In this study, we present a wearable TDD platform that integrates hollow-groove microneedles (HGMNs) with a wirelessly controlled, electronically programmable micropump for precise, on-demand liquid-phase drug administration. HGMNs, produced via digital light processing (DLP) 3D printing, feature engineered grooves to enhance drug transport and prevent tissue blockage. The system includes a refillable spiral microfluidic reservoir and a compact micropump capable of delivering customized dosing regimens, such as single-bolus, pulsatile, and sustained-release profiles. Utilizing ketamine hydrochloride as a model drug for post-traumatic stress disorder (PTSD), the platform demonstrated robust skin penetration, high delivery precision, and effective diffusion through the ex vivo porcine skin. Mechanical testing confirmed the structural integrity and force threshold required for skin insertion. This versatile platform facilitates programmable, noninvasive, and accurate drug administration, offering the potential to enhance treatment outcomes, improve patient adherence, and support personalized medicine.