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With the exponential growth in data density and user ends of wireless networks, fronthaul is tasked with supporting aggregate bandwidths exceeding thousands of gigahertz while accommodating high-order modulation formats. However, it must address the bandwidth and noise limitations imposed by optical links and devices in a cost-efficient manner. Here we demonstrate a high-fidelity fronthaul system enabled by self-homodyne digital-analog radio-over-fiber superchannels, using a broadband electro-optic comb and uncoupled multicore fiber. This self-homodyne superchannel architecture not only offers capacity boosting but also supports carrier-recovery-free reception. Our approach achieves a record-breaking 15,000 GHz aggregated wireless bandwidth, corresponding to a 0.879 Pb/s common public radio interface (CPRI) equivalent data rate. Higher-order formats up to 1,048,576 quadrature-amplitude-modulated (QAM) are showcased at a 100 Tb/s class data rate. Furthermore, we employ a packaged on-chip electro-optic comb as the sole optical source to reduce the cost, supporting a data rate of 100.5 Tb/s with the 1024-QAM format. These demonstrations propel fronthaul into the era of Pb/s-level capacity and exhibit the promising potential of integrated-photonics implementation, pushing the boundaries to new heights in terms of capacity, fidelity, and cost. Here the authors propose a self-homodyne fronthaul architecture, utilizing DA-RoF super channels and multicore fiber, paving the way for the Pb/s era in fronthaul transmission, enabling ultra-highspeed Internet access. The remarkable data speeds reaching 0.879 Pb/s and the 256-QAM format make it possible for 150,000 5G channels to be accessed simultaneously.

With the rapid development of information technology, the global data volume has been continuously expanding, placing unprecedented demands on communication networks to accommodate precipitously increasing throughput. Thin-film lithium niobate (TFLN) modulators, characterized by their large theoretical bandwidth, low half-wave voltage, and suitability for high-density integration, show great application potential in high-speed optical modules and optical interconnection networks. However, the persistent issue of velocity mismatch between radio frequency (RF) signals and optical carriers invariably hinders the utilization of higher-frequency bands, which restricts the modulation speed of the fabricated devices. In this paper, an electrode co-loaded with square serrations and T-shaped stubs was utilized to achieve precise velocity matching and excellent impedance matching. Leveraging this approach, a TFLN modulator chip with an electro-optic bandwidth far exceeding 67 GHz and a return loss of greater than 12 dB was successfully fabricated on a silicon substrate. The velocity of RF signals can be tuned by altering the lengths of the slow-wave structures, which provides guidance for the design and optimization of broadband modulators.

Recently, thin-film lithium niobate electro-optical modulators have developed rapidly and have become the core solution for the next generation of electro-optical problems. Compared with bulk lithium niobate modulators, these modulators not only retain the advantages of lithium niobate materials, such as low loss, high extinction ratio, high linear response and high optical power handling capabilities, but can also effectively improve some performance parameters, such as the voltage bandwidth performance of the modulator. Unfortunately, the extremely small electrode gap of thin-film lithium niobate EO (electro-optic) modulators causes metal absorption, resulting in higher microwave losses. The electro-optical performance of the modulator, thus, deteriorates at high frequencies. We designed traveling-wave electrodes with microstructures to overcome this limitation and achieve a 3 dB electro-optical bandwidth of 51.2 GHz. At the same time, we maintain low on-chip losses of <2 dB and a high extinction ratio of 15 dB. It is important to note that the devices we manufactured were metal-encapsulated and passed a series of reliability tests. The success of this modulator module marks a key step in the commercialization and application of thin-film lithium niobate modulation devices.

We propose a compact, high extinction ratio, and low-loss polarization beam splitter (PBS) on a lithium-niobate-on-insulator (LNOI) platform, based on an asymmetrical directional coupler and using a silicon nitride nanowire assisted waveguide (WG) and a grooved WG. By properly designing Si3N4 nanowires and grooved LN WGs, TE polarization meets the phase matching condition, while significant mismatching exists for TM polarization. Numerical simulations show that the PBS has an ultra-high extinction ratio (ER) of TE0 and TM0 (larger than 40 dB and 50 dB, respectively). The device extinction ratios are larger than 10 dB over 100 nm wavelength ranges. Moreover, the device has an ultra-low insertion loss (IL less than 0.05 dB) at the wavelength of 1550 nm and maintains ILs less than 0.4 dB over 100 nm wavelength ranges.

A simple photonics-based dual-channel system is proposed to simultaneously measure the Doppler frequency shift (DFS) and angle of arrival (AOA) of microwave signals. The system applies two parallel push–pull Mach–Zehnder modulators (MZMs) for carrier suppression dual-sideband (CS-DSB) modulation. The introduction of the reference signal results in a DFS measurement without direction ambiguity. The DFS can be determined by measuring the frequency of the down-converted intermediate frequency (IF) signal, and the AOA can be calculated by comparing the phase shift of the two channels. A proof-of-concept experiment shows that the DFS measurement error is less than 0.4 Hz during ±100 kHz, and the AOA measurement error is within 1.5° in a range of 0–70°.

Highly ordered mesoporous carbon-alumina nanocomposites （OMCA） have been synthesized for the first time by a multi-component co-assembly method followed by pyrolysis at high temperatures. In this synthesis, resol phenol-formaldehyde resin （PF resin） and alumina sol were respectively used as the carbon and alumina precursors and triblock copolymer Pluronic F127 as the template. N2-adsorption measurements, X-ray diffraction, and transmission electron microscopy revealed that, with an increase of the alumina content in the nanocomposite from 11 to 48 wt.%, the pore size increased from 2.9 to 5.0 nm while the ordered mesoporous structure was retained. Further increasing the alumina content to 53 wt.% resulted in wormhole-like structures, although the pore size distribution was still narrow. The nanocomposite walls are composed of continuous carbon and amorphous alumina, which allows the ordered mesostructure to be well preserved even after the removal of alumina by HF etching or the removal of carbon by calcination in air. The OMCA nanocomposites exhibited good thermostability below 1000℃; at higher temperatures the ordered mesostructure partially collapsed, associated with a phase transformation from amorphous alumina into γ-Al2O3. OMCA-supported Pt catalysts exhibited excellent performance in the one-pot transformation of cellulose into hexitols thanks to the unique surface properties of the nanocomposite.

The accurate placement of an ancient whole-genome duplication (WGD) in relation to the lineage divergence is important. Here, we re-investigated the Aquilegia coerulea WGD and found it is more likely lineage-specific rather than shared by all eudicots.

Ceramic capacitors with ultrahigh power density are crucial in modern electrical applications, especially under high-temperature conditions. However, the relatively low energy density limits their application scope and hinders device miniaturization and integration. In this work, we present a high-entropy BaTiO 3 -based relaxor ceramic with outstanding energy storage properties, achieving a substantial recoverable energy density of 10.9 J/cm 3 and a superior energy efficiency of 93% at applied electric field of 720 kV/cm. Of particular importance is that the studied high-entropy composition exhibits excellent energy storage performance across a wide temperature range of −50 to 260 °C, with variation below 9%, additionally, it demonstrates great cycling reliability at 450 kV/cm and 200 °C up to 10 6 cycles. Electrical and in-situ structural characterizations revealed that the high-entropy engineered local structures are highly stable under varying temperature and electric fields, leading to superior energy storage performance. This study provides a good paradigm of the efficacy of the high-entropy engineering for developing high-performance dielectric capacitors. The authors utilize a high-entropy design strategy to enhance the high-temperature energy storage capabilities of BaTiO 3 -based ceramic capacitors, realizing energy storage performance from −50 °C to 260 °C and maintaining functionality after one million charge-discharge cycles at 200 °C.

Artificial intelligence (AI) chatbots are poised to have a profound impact on medical education. Medical students, as early adopters of technology and future health care providers, play a crucial role in shaping the future of health care. However, little is known about the utilization of, perceptions on, and intention to use AI chatbots among medical students in China. This study aims to explore the utilization of, perceptions on, and intention to use generative AI chatbots among medical students in China, using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. By conducting a national cross-sectional survey, we sought to identify the key determinants that influence medical students' acceptance of AI chatbots, thereby providing a basis for enhancing their integration into medical education. Understanding these factors is crucial for educators, policy makers, and technology developers to design and implement effective AI-driven educational tools that align with the needs and expectations of future health care professionals. A web-based electronic survey questionnaire was developed and distributed via social media to medical students across the country. The UTAUT was used as a theoretical framework to design the questionnaire and analyze the data. The relationship between behavioral intention to use AI chatbots and UTAUT predictors was examined using multivariable regression. A total of 693 participants were from 57 universities covering 21 provinces or municipalities in China. Only a minority (199/693, 28.72%) reported using AI chatbots for studying, with ChatGPT (129/693, 18.61%) being the most commonly used. Most of the participants used AI chatbots for quickly obtaining medical information and knowledge (631/693, 91.05%) and increasing learning efficiency (594/693, 85.71%). Utilization behavior, social influence, facilitating conditions, perceived risk, and personal innovativeness showed significant positive associations with the behavioral intention to use AI chatbots (all P values were <.05). Chinese medical students hold positive perceptions toward and high intentions to use AI chatbots, but there are gaps between intention and actual adoption. This highlights the need for strategies to improve access, training, and support and provide peer usage examples to fully harness the potential benefits of chatbot technology.

Human-machine interaction has emerged as a revolutionary and transformative technology, bridging the gap between human and machine. Gesture recognition, capitalizing on the inherent dexterity of human hands, plays a crucial role in human-machine interaction. However, existing systems often struggle to meet user expectations in terms of comfort, wearability, and seamless daily integration. Here, we propose a handwriting recognition technology utilizing an intelligent hybrid-fabric wristband system. This system integrates spun-film sensors into textiles to form the smart fabric, enabling intelligent functionalities. A thermal encapsulation process is proposed to bond multiple spun-films without additional materials, ensuring the lightweight, breathability, and stretchability of the spun-film sensors. Furthermore, recognition algorithms facilitate precise accurate handwriting recognition of letters, with an accuracy of 96.63%. This system represents a significant step forward in the development of ergonomic and user-friendly wearable devices for enhanced human-machine interaction, particularly in the virtual world. Balancing comfort, wearability and integration for human-machine interaction systems is difficult. The authors develop a hybrid-fabric wristband system, where spun-film sensors are integrated into textiles, enabling a lightweight, breathable, and stretchable device.