浙江大学杭州国际科创中心

Amino acid non-centrosymmetric self-assemblies, possessing inherent polarization as well as biocompatibility, can be employed as bioinspired alternatives for the development of implantable piezoelectric bioelectronics. This could enable the harvesting of biomechanical energy for in situ in vivo monitoring and avoid the need for secondary surgeries, potentially overcoming the trade-off between high-efficiency sensing and the biosafety limitations of traditional inorganic or polymeric piezoelectric counterparts. In this regard, the electromechanical coupling behaviors of the minimalistic metabolite self-assemblies are reported. Experimental tests reveal that compared to other natural amino acid crystals, threonine (T) crystals exhibit a high Young’s modulus of up to approximately 80 GPa by forming a denser three-dimensional hydrogen-bonding network, with each molecule interacting with seven adjacent ones. Computational analysis reveals that side-chain entities dramatically affect crystal packing, with polar hydroxyl moieties accounting for the distinct piezoelectric features underlying the macroscopic performance. This highlights the potential of exploiting T crystals to develop biodegradable piezoelectric bioelectronics that exhibit highly sensitive linear responses for tactile sensing and post-implantation in vivo motion monitoring. This study demonstrates the feasibility of exploiting minimalistic metabolite self-assemblies for piezoelectric bioelectronics in bio-machine interface and biomedical engineering applications.

Quantum computers may outperform classical computers on machine learning tasks. Yet, quantum learning systems may suffer from catastrophic forgetting, which is widely believed to be an obstacle to achieving continual learning. Here, we report an experimental demonstration of quantum continual learning on a superconducting processor. In particular, we sequentially train a quantum classifier with three tasks, two about identifying real-life images and one on classifying quantum states, and demonstrate its catastrophic forgetting. To overcome this dilemma, we exploit the elastic weight consolidation strategy and show that the quantum classifier can incrementally retain knowledge across three tasks with an average accuracy exceeding 92.3%. Additionally, for sequential tasks involving quantum-engineered data, we demonstrate that the quantum classifier outperforms a classical classifier with a comparable number of parameters. Our results establish a viable strategy for empowering quantum learning systems with adaptability to sequential tasks.

The utilization of Kerr nonlinearity in lithium niobate has been extensively investigated over the years. Nevertheless, the practical implementation of Kerr nonlinearity in waveguides has been constrained by the material's inherently low third-order nonlinear coefficients. Here, we present a significant advancement by demonstrating Pockels effect-induced strong effective Kerr nonlinearity in a periodically poled thin-film lithium niobate waveguide. Both effective four-wave mixing (FWM) and cascaded effective FWM processes are experimentally observed. The induced FWM process achieves a maximum output power of -8.5 dBm, spanning a wavelength spectrum of over 116.8 nm. Analysis reveals that the induced effective Kerr nonlinearity exhibits a substantial effective nonlinear refractive index n(2,)(eff) more than 2.9 & times; 10(-15) m(2)/W, corresponding to an effective nonlinear refractive index enhancement factor of 1.6 & times; 10(4) relative to the intrinsic value. Moreover, a wavelength-converting experiment demonstrates a flat optic-to-optic response over a broadband radio frequency spectrum, confirming that signal integrity is well preserved after on-chip effective FWM conversion. Therefore, the demonstrated efficient and broadband Pockels effect-induced effective Kerr nonlinearity paves the way for novel applications in diverse fields, including spectroscopy, parametric amplification, quantum correlation studies, and wavelength conversion technologies.

Strawberries naturally grow in clusters, interwoven with leaves, stems, and other fruits, which frequently leads to occlusion. This inherent growth habit presents a significant challenge for robotic picking, as traditional percept-plan-control systems struggle to reach fruits amid the clutter. Effectively picking an occluded strawberry demands dexterous manipulation to carefully bypass or gently move the surrounding soft objects and precisely access the ideal picking point located at the stem just above the calyx. To address this challenge, we introduce a strawberry-picking robotic system that learns from human demonstrations. Our system features a 4-DoF SCARA arm paired with a human teleoperation interface for efficient data collection and leverages an End Pose Assisted Action Chunking Transformer (ACT) to develop a fine-grained visuomotor picking policy. Experiments under various occlusion scenarios demonstrate that our modified approach significantly outperforms the direct implementation of ACT, underscoring its potential for practical application in occluded strawberry picking.

The rapid growth of wearable electronics demands power sources that are not only flexible and durable but also inherently safe. Conventional lithium-ion batteries pose safety risks due to toxic and flammable electrolytes. Aqueous metal-ion batteries offer a promising alternative, yet their application remains limited by poor mechanical compliance, leading to interfacial instability and electrolyte leakage. Here, we report a bionic self-assembly strategy for aqueous zinc-ion batteries using a lipopeptide electrolyte additive named C16K, enabling bulk self-assembly into supramolecular nanohelices to accelerate ion transport and interfacial organization into a dynamic bilayer for interphase regulation. This dual-function synergistically suppresses the formation of Zn dendrites or side reactions, enabling stable Zn plating/stripping. This achieves an ultralong cycling stability and ultrahigh cumulative plating capacity along with a high coulombic efficiency. Therefore, the synergistic reinforcement endows the pouch cell to deliver a high initial capacity, allowing to power electronics in a safe manner. In a following manner, a scorpion tail-inspired bionic flexible battery structure is designed to deliver sustainable energy outputs across various mechanical states using the reinforced systems, effectively powering the wearable multimodal sensors. Our results present a self-assembly strategy using a lipopeptide additive to synergistically reinforce the ions transport and interfacial stability, coordination with a bionic structural design, potentially offering a bioinspired routine for high-performance flexible batteries for wearable electronics.