Advanced Materials Technologies最新文献

Hexagonal boron nitride (H-BN)-based nanomaterials have attracted much attention in the fields of environmental remediation and sustainable technologies due to their high specific surface area (SSA), excellent thermal stability, chemical inertness and biocompatibility and other physical, chemical and biological properties. This paper reviews the structures, properties, and synthesis methods of h-BN and its diverse applications in the environmental fields, including adsorption, photocatalysis, oil–water separation, seawater desalination, wastewater treatment, antibacterial application, environmental sensing, and energy conversion and storage. Among these applications, h-BN shows good recyclability. However, the wide bandgap of h-BN (≈5.9 eV) leads to its poor electrical conductivity, limiting its performance in electronic devices. To address this key limitation, current research mainly focuses on strategies such as element doping, functionalization, and composite modification with conductive materials (such as graphene, metals) to expand their applications in the fields of energy, catalysis, and sensing.

Wirelessly powered brain-implantable devices are emerging as promising approaches for treating neural disorders through the precise recording and stimulation of neuronal activity. Magnetoelectric (ME) antennas hold substantial potential for addressing the fundamental trade-off between size and resonant frequency in implantable antennas, as they transduce magnetic waves into acoustic resonance at their intrinsic structural frequency. This allows the ME antenna to achieve a microscale size at low operating frequencies while maintaining high power transfer efficiency, robust misalignment tolerance, and minimal tissue attenuation, thereby overcoming the limitations of conventional near-field and far-field wireless power transfer. Furthermore, ME-antenna-based energy harvesting systems enable seamless integration with complementary circuits, facilitate fully integrated designs at the system level with compact size and low power consumption. This review presents the primary building blocks, design principles, and performance parameters of ME-antenna-based power links for neural interfacing, outlining a vision toward minimally invasive and reliable next-generation self-powered wireless brain implantable devices.

Passive daytime radiative cooling (PDRC) is the sustainable cooling solution for climate change mitigation and heat island effects. Present work focuses on developing flexible fiber network to reflect the sunlight and emit infrared energy to reduce cooling loads on roofs. Here, inorganic high bandgap fillers namely, hexagonal boron nitride (hBN), zirconium oxide (ZrO₂), and calcium oxide (CaO) is mixed with dielectric polymer PVDF-HFP to fabricate single pigment radiative cooling film (SPRC) and multi pigment radiative cooling (MPRC) films by electrospinning method. Developed films with a unique micro/nano porous and cross-linked structure reflect ≈ 90% of sunlight in solar window (0.2–2.5 µm) and emits 98% of heat in thermal window of atmosphere (2.5–20 µm). During peak solar irradiance, MPRC3 maintains just 1 °C above the ambient temperature (35 °C) and produced maximum cooling power of ≈ 45 Wm⁻² than other developed films. Further, the ability of dye degradation is examined using piezocatalytic effect and hence, ZrO₂ mixed SPRC and MPRC films is investigated experimentally. The MPRC4 film has achieve a degradation of 83% owing to improve dielectric properties. Hence, these films are suggested, ideal candidates for building cooling and dye degradation.

This study presents a hybrid knitted fabric engineered for high-performance electromagnetic interference shielding effectiveness (EMI SE), achieving a maximum shielding effectiveness of 59.4 dB. The functionalized fabric is made from copper-cotton core-spun yarn with alternating bi-layer of silver nanoparticles (AgNPs), MXene to enhance both conductivity and reflection/absorption. By employing a reactive in situ synthesis process, AgNPs are successfully synthesized directly onto the fabric, achieving particle sizes that range from 15 to 38 nm. Notably, smaller nanoparticles showed improved shielding performance by 7 dB, highlighting the important role of particle size in enhancing EMI shielding effectiveness. In addition, increasing the number of coating layers enhances the shielding effectiveness. In order to improve the durability of the fabric, (3-Aminopropyl)triethoxysilane (APTES) treatment is performed, which contributed to the preservation of stable EMI shielding properties, even after 90 min of washing. Beyond EMI shielding performance, the APTES-treated fabrics demonstrate good air permeability and moisture vapor transmission rates, ensuring they are breathable and comfortable, making them potential candidates for wearable applications where EMI exposure is a concern.

Acoustic signals are vital as they contain key information in characteristic parameters, including phase, amplitude, frequency, harmonics, signal-to-noise ratio, waveform, spectral density, reverberation, and modulation patterns. Recent advancements in signal processing and machine learning have significantly transformed the application of acoustic signals, from early detection of vocal cord diseases to enhancing biometric technology through voiceprint biometrics. Self-powered acoustic sensors enable non-invasive continuous monitoring, which aids in the recognition of various pulmonary and cardiovascular diseases such as asthma, bronchitis, aortic stenosis, and congestive heart failure. Beyond healthcare, it invaluably contributed to environmental monitoring, industrial diagnostics, and structural health monitoring, detecting and analyzing sounds from natural events, machinery, and infrastructure for early anomaly detection and proactive intervention. This article provides a comprehensive overview of acoustic sensing and energy harvesting methodologies, key parameters, and various machine learning algorithms. Use cases are discussed to demonstrate the potential of AI-integrated acoustic sensors across different domains, along with strategies to mitigate errors in developing these systems.

Physiological temperature sensors are essential for accurately tracking body temperature, yet human body temperature sensors with both high precision and linearity have not been widely reported. In this work, a solution-processed, low-voltage TFT-based temperature sensor is developed using LiInSnO₄ ionic gate dielectric with ZnO (or SnO₂) as semiconductors. Alongside excellent TFT performance, the devices exhibit high precision and sensitivity of 0.7 µA °C⁻¹ during heating and 0.6 µA °C⁻¹ during cooling for ZnO-based TFT, while SnO₂-based TFT shows slightly higher sensitivity with 1.1 µA °C⁻¹ in heating and 1.2 µA °C⁻¹ in cooling. Additionally, the voltage sensitivity remains consistent at 0.01 V °C⁻¹ for both TFTs under all conditions. Furthermore, the devices demonstrate high linearity in temperature coefficient of current (TCC) and temperature coefficient of voltage (TCV) curves, confirming their capability for accurate temperature measurement.

The geometric appearance of weld is very important for the evaluations of welded joint performance and weld quality during oscillating laser welding (OLW). To represent the characteristics of weld appearance more accurately, the surface reconstruction method is often adopted to obtain the complete geometric appearance of weld. The efficiency of surface reconstruction method is crucial for practical application, especially for processing large-scale point cloud. An improved octree-based surface reconstruction method is proposed to enhance the computational efficiency for complex weld appearance based on the point cloud. The large-scale point cloud of weld which is transformed from numerical simulation of OLW is preprocessed by octree structure to reduce the number of points and the Poisson surface reconstruction (PSR) method is utilized to reconstruct the three-dimensional profile of weld appearance. The reconstruction accuracy is quantified by contrasting the cloud-to-mesh distances between the raw point cloud and the reconstructed mesh model. Furthermore, the reconstruction efficiency and accuracy of the proposed method are compared with those of PSR method and the reconstruction quality of proposed method with octree structure is compared with those of methods with other sampling strategies. The results demonstrate that the reconstruction efficiency of the proposed method is significantly increased with excellent reconstruction accuracy. The improved octree-based surface reconstruction method is of great importance for analyzing weld appearance characteristics and evaluating weld quality.

Physiological temperature sensors are essential for accurately tracking body temperature, yet human body temperature sensors with both high precision and linearity have not been widely reported. In this work, a solution-processed, low-voltage TFT-based temperature sensor is developed using LiInSnO₄ ionic gate dielectric with ZnO (or SnO₂) as semiconductors. Alongside excellent TFT performance, the devices exhibit high precision and sensitivity of 0.7 µA °C⁻¹ during heating and 0.6 µA °C⁻¹ during cooling for ZnO-based TFT, while SnO₂-based TFT shows slightly higher sensitivity with 1.1 µA °C⁻¹ in heating and 1.2 µA °C⁻¹ in cooling. Additionally, the voltage sensitivity remains consistent at 0.01 V °C⁻¹ for both TFTs under all conditions. Furthermore, the devices demonstrate high linearity in temperature coefficient of current (TCC) and temperature coefficient of voltage (TCV) curves, confirming their capability for accurate temperature measurement.

Inorganic oxide-based perovskites are recognized as efficient switching materials due to their oxygen anionic conduction, high dielectric constant, and ambient stability. However, their rigid crystal structure limits use in flexible electronics. This study presents a fully flexible crossbar memristor array using hybrid ink of inorganic BaTiO₃ (BTO) nanoparticles and organic polymer polyvinyl alcohol (PVA) in a 1:15 wt% ratio. The 6 × 6 memristor array was fabricated on indium tin oxide (ITO)-coated polyethylene terephthalate substrate, where the ITO bottom electrode was patterned by photolithography, followed by screen printing of the BTO–PVA layer and nickel top electrode deposition via sputtering. The devices showed stable bipolar resistive switching with low SET/RESET voltages (1.70 V/–1.75 V), high ON/OFF ratio (~10⁴), excellent endurance (〉10⁶ s), and minimal variability. Mechanical robustness was tested by bending to radii of 19.1 mm, 9.5 mm, and 6.4 mm; ON/OFF ratio stayed ~10⁴, SET/RESET voltages shifted ≤26%, with slight current-noise increase (maximum standard deviation of 1.29 at 〈0.3 V). Switching was trap-controlled: BTO provided traps, PVA enabled hopping transport, flexibility, and high resistance states. Conductance-based color mapping of a bent 6 × 6 array confirmed reliable switching, supporting BTO–PVA hybrid ink as a robust, flexible memory platform.

The geometric appearance of weld is very important for the evaluations of welded joint performance and weld quality during oscillating laser welding (OLW). To represent the characteristics of weld appearance more accurately, the surface reconstruction method is often adopted to obtain the complete geometric appearance of weld. The efficiency of surface reconstruction method is crucial for practical application, especially for processing large-scale point cloud. An improved octree-based surface reconstruction method is proposed to enhance the computational efficiency for complex weld appearance based on the point cloud. The large-scale point cloud of weld which is transformed from numerical simulation of OLW is preprocessed by octree structure to reduce the number of points and the Poisson surface reconstruction (PSR) method is utilized to reconstruct the three-dimensional profile of weld appearance. The reconstruction accuracy is quantified by contrasting the cloud-to-mesh distances between the raw point cloud and the reconstructed mesh model. Furthermore, the reconstruction efficiency and accuracy of the proposed method are compared with those of PSR method and the reconstruction quality of proposed method with octree structure is compared with those of methods with other sampling strategies. The results demonstrate that the reconstruction efficiency of the proposed method is significantly increased with excellent reconstruction accuracy. The improved octree-based surface reconstruction method is of great importance for analyzing weld appearance characteristics and evaluating weld quality.