Cell Reports Physical Science最新文献

Enzymatic fuel cells (EFCs) have emerged in recent years as a promising power source for wearable and implantable electronic devices. Here, successful in vivo implantation of a glucose/O₂ EFC beyond 70 days is reported that exploits an innovative “cavity electrode” concept for biocatalyst entrapment to address lifetime and biocompatibility issues. The hollow bioanode shows long-term in vitro bioelectrocatalytic storage stability of >25 days. The hollow buckypaper-based EFC exhibits attractive maximum voltage and power outputs of 0.62 V and 0.79 mW cm⁻², respectively, and high storage stability of ∼80% after 19 days. The maximum in vivo performance outputs are 0.34 ± 0.05 V and 38.7 ± 4.7 μW. After 74 days in Sprague-Dawley rats, the hollow EFC continues to present a stable 0.59 V. Postmortem analysis confirms high-level robustness and operational performance. Autopsy findings reveal no signs of rejection and demonstrate effective biocompatibility.

Viral translocation is considered a common way for respiratory viruses to spread and contaminate the surrounding environment. Thus, the discovery of non-eluting polymers that immobilize severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) upon contact provides an opportunity to develop new coating materials for better infection control. Here, virion-binding polymers are discovered from an existing monomer library via experimental high-throughput screening. Among them, poly([2-diethylamino] ethyl acrylate) (pDEAEA) demonstrates dual functions: binding virions strongly and its speed to inactivate adsorbed SARS-CoV-2. Computational models are built based on the experimental screening data. Polymers that are predicted to be pro-adsorption by the virtual screening are poly(1-{4-[5-(4-methoxyphenyl)-1H-pyrazol-3-yl]piperidin-1-yl}prop-2-en-1-one) (pMPPPP), poly(1-(6-isobutyloctahydropyrrolo[3,4-d]azepin-2[1H]-yl)-2-methylprop-2-en-1-one) (piBOHPAMP), and poly(N-(3-((1-benzylpiperidin-4-yl)oxy)propyl)acrylamide) (pBPOPAm), and these are found to adsorb virions. However, due to limitations in the diversity of structures in the training set, the computational models are unable to predict the adsorption of virions for all polymer structures. Summarily, these findings indicate the utility of the methodology to identify coating polymers that effectively immobilize SARS-CoV-2, with potential practical applications (e.g., water and air filtration).

Effective surface passivation is pivotal for achieving high performance in crystalline silicon (c-Si) solar cells. However, many passivation techniques in solar cells involve high temperatures and cost. Here, we report a low-cost and easy-to-implement sulfurization treatment as a surface passivation strategy. By treating p-type c-Si (p-Si) wafers with (NH₄)₂S solution, sulfur can be introduced onto the surface and passivate the dangling bonds by forming an Si–S bond. Sulfurization also contributes to a higher negative fixed charge at the p-Si/Al₂O₃ interface and, thus, better field-effect passivation. Due to the improved passivation, sulfurization effectively enhances hole selectivity, evidenced by the substantially improved open-circuit voltage and efficiency of solar cells. Eventually, by employing sulfurization in hole-selective contacts, remarkable efficiencies of 19.85% and 22.01% are attained for NiO_x- and MoO_x-based passivating contact c-Si solar cells, respectively. Our work highlights a promising sulfurization strategy to enhance surface passivation and hole selectivity for dopant-free c-Si solar cells.

The phenomenon of using traveling waves is widely observed in organisms like centipedes, stingrays, and snails. Energy is uniformly distributed through wave propagation, reducing energy loss and enhancing motion efficiency. This offers valuable guidance for designing robots. Here, we report a miniature robot emulating the traveling-wave behavior of snails. A single-frame robot is designed with a rigid square-frame structure and four piezoelectric ceramics to generate traveling waves. The robot achieves a linear speed of 12 body lengths per second (BL/s), with a volume of 27.5 × 26 × 4 mm³ and a weight of 7.9 g. Two-dimensional planar motion is realized by connecting two single-frame robots to form a double-frame robot, achieving a linear speed of 12 BL/s, a rotational speed of 690°/s, and a load capacity of 200 g. An integrated robot, combining a customized power supply and an image acquisition system, achieves untethered motion and image perception. This work provides a valuable design reference for miniature robots.

The emerging fluidic memristor, capable of emulating ion transport and signaling in brains, has shown promising features in neuromorphic computing but is still in its nascent stage of development. We introduce a droplet memristor in which applied voltage drives a non-conductive liquid crystal droplet to penetrate into a microwell, blocking the ionic conduction path and increasing the resistance. Our system exhibits switchable excitatory and inhibitory features, modulated by altering the polarity of the ionic surfactants at the liquid-liquid interface. We find that memory effects are proportional to the voltage amplitude and inversely proportional to the scanning frequency, consistent with predictions by Newton’s dynamic theory. We emulate adaptive learning akin to biological synapses and demonstrate that low-temperature-induced phase changes in droplets reduce the handwriting recognition accuracy in droplet artificial neuron networks, promising in-sensing computing capabilities. The droplet memristor can benefit from the diverse liquid properties to extend the functionalities and applications in future neuromorphic computing.

Efficient long-distance energy transport is a cornerstone of photosynthetic light harvesting, enabling excitation energy to traverse multiple antenna proteins to reach the reaction center (RC), where it drives photochemistry. While extensive studies on energy transfer dynamics within individual light-harvesting complexes (LHCs) have been conducted, the inter-protein transfers crucial for understanding the overall efficiency of these systems have remained experimentally elusive. This arises mainly because the spectral signatures of the subunits are often remarkably similar, complicating the identification of energy transfer pathways among them. This study bridges this gap by utilizing ultrafast transient absorption spectroscopy, under conditions with and without singlet-singlet annihilation, on the photosystem II (PSII) LHCII-CP24-CP29 subcomplex and on its constituents. Our findings reveal rapid equilibration within monomeric complexes, contrasted by six-times slower equilibration in the LHCII trimer and eight-times slower equilibration in the LHCII-CP24-CP29 subcomplex, highlighting the inter-complex energy transfer as the rate-limiting step in excitation delivery to the RC.

Mass transfer in electrolyte films on electrodes is crucial to the performance of electrochemical energy devices, which is difficult or impossible to observe experimentally. Here, we develop a framework utilizing deep learning to analyze vast molecular dynamics (MD) data to reveal the molecular-level transport properties in electrolyte films. This framework contains physical feature analysis and selection based on MD simulations, surrogate model training, structure-transport relationship analysis, and structure discovery. This framework is then applied to explore oxygen transport in fuel cells, which allows the transport properties and their relationships to the structural characteristics of electrolyte films to be revealed, and thus, the critical features limiting oxygen transport are identified. Accordingly, increasing the catalyst surface hydrophilicity and suppressing the electrolyte film density fluctuation are favorable for oxygen transport. Moreover, this framework is transferable to revealing similar molecular-level transport phenomena in electrolyte films that widely exist in other electrochemical energy devices.

The actomyosin network, consisting of actin filaments and myosin motors, is essential for cell dynamic behaviors. The sliding motion of actin filaments propelled by myosin motors is converted into contraction of the cytoskeleton network, leading to cell deformation. Here, we demonstrated that active gels of actomyosin networks exhibited varied contraction geometries such as local radial patterns and global network contraction depending on the motor mobility condition at the boundary. Under two motor conditions (immobile and mobile), both experimental and computational methods were utilized to characterize the contraction dynamics at varied network connectivities. We revealed that the effect of network connectivity on the contraction dynamics depends on the motor mobility condition. Our computational models simulate the cellular functions such as cell division and muscle contraction, providing insights into disease development related to motor mobility conditions. Our study helps to explain the dynamics of active materials under varied mechanical environments.

Contact lens (CL)-associated bacterial keratitis (BK), a prevalent and underestimated disorder caused by unhygienic CL wear, poses a risk to permanent loss of visual acuity. Clinically, low drug-delivery efficiency, frequent administration, hormone complications, and antibiotic resistance remain the major unsolved challenges. Here, we introduce a chlorogenic acid (CGA)-conjugated CL material based on gelatin methacrylate via cryogelation(cGelMA/CGA-CL) to strengthen the prevention and treatment of BK. The cGelMA/CGA-CL features a highly moist, macroporous, adjustable structure for sustained release of CGA and is favorably biocompatible to cells, providing antimicrobial protection against opportunistic pathogens and inhibiting excessive ocular inflammatory responses through the JAK2-STAT1/STAT2 signaling pathway. Furthermore, the cGelMA/CGA-CL effectively alleviates the symptoms of BK with immunoregulation of macrophage recruitment and anti-inflammatory factor release in a mouse model of BK. The cGelMA/CGA-CL offers a promising candidate for the prevention and treatment of BK, which may significantly reduce the risk of infection for CL wearers.

The rapid advancement of highly flexible and reliable artificial intelligence (AI) holds the promise of unlocking transformative capabilities in response to imminent energy and environmental challenges. Toward future energy, we propose this perspective and introduce a groundbreaking paradigm for a versatile energy AI, termed artificial general intelligence for energy (AGIE). AGIE is designed to address a spectrum of energy-related issues with flexibility, drawing upon information such as energy parameters, equipment images, and expert voice feedback. The applications of AGIE are diverse, ranging from energy diagnostics and operational optimization to offering advice on energy policies. By incorporating human-in-the-loop interactions and leveraging domain knowledge, AGIE has the capacity to assimilate the habits of energy users. Through continuous reinforcement learning, it aspires to establish a new paradigm of explainable reasoning, paving the way for the development of credible energy robots with attributes similar to human understanding. We anticipate that AGIE-enabled applications will lead to new approaches in energy usage and the consideration of serious technical and societal challenges ranging from data integration to privacy and security concerns, environmental impacts, and constraints in hardware and software. Addressing these issues is crucial for realizing the full potential of generalist energy intelligence, leading to enhanced energy efficiency and contributing to the resolution of global energy problems.