Cell Reports Physical Science最新文献

As commercial batteries and battery packs become larger and larger, one topic that is gaining interest is that of cell-to-cell variations and inhomogeneities. In this theoretical study, we use a degradation mode model along with a segmented cell approach to investigate the impact of different inhomogeneity modes on the performance of two typical Li-ion batteries. This unique approach shows that out of the nine considered modes (state of charge, rate, resistance, and capacity for each electrode as well as their offset), when at a mild level and randomly distributed, only three could affect performance, with two unlikely to happen in real cells because they would disappear during rest. Model results show that some of these inhomogeneities open the possibility of a snowball effect to induce local rate variations and lithiation inhomogeneities. Our study also shows that it is necessary to assess the level at which the paralleling occurs, electrode or full cell, as the model predicts an impact on how the current, and how much of it, is flowing within or in between the electrodes.

Pneumatic soft robotics are highly desirable for interacting with humans and navigating uncertain environments. However, it remains a great challenge to simultaneously achieve high actuation efficiency, programmable deformations, real-time feedback, and robustness. Herein, a textile engineering approach is harnessed to integrate multifunctionality into woven actuators by tailoring yarn groupings using all-in-one industrial weaving technologies. The unique nearly zero Poisson’s ratio inflatable deformation of the actuators contributes to a large bending strain (2,250° m⁻¹), a high output force (30 N MPa⁻¹), and robust mechanical performance. Bilateral bending actuators with negative, zero, and positive curvatures are realized by hierarchical shape transformations of the woven layers. The embedded sensing yarns provide facile and effective methods to proprioceptively sense actuation deformation without compromising actuation performance. Moreover, this manufacturing method is cost efficient and highly scalable, which expands practical applications of soft actuators in healthcare and offers a new perspective on the structure design of customized soft actuators.

The onboard acquisition of data from electrochemical impedance spectroscopy (EIS) is critically important to the state assessment and fault diagnosis of mobile batteries, but it is technically challenging due to the stringent test requirements, limited modeling data, and varying mechanisms among batteries with different chemistries. This paper, without requiring any additional sensors, extends the traditional EIS measurement to online generation and covers most battery-using scenarios, including different battery chemistries, aging degrees, remaining capacities, and temperatures. Virtual simulation and transfer techniques are employed to train a deep neural network with a significantly reduced dataset. Specifically, we train the network with no more than 24 groups of data and achieve an average relative error lower than 5%, outperforming most “big data”-involved algorithms of its kind. Our method lowers the threshold of using EIS onboard and unlocks new opportunities to monitor the battery’s performance in both time and frequency domain comprehensively in real time.

Impedance spectroscopy enables the electrical properties of samples to be probed and is commonly used to characterize solids. Extending this technique to analyze fluids within microfluidic channels could enable the rapid characterization of bodily fluids such as sweat. Here, we present a low-cost microfluidic platform with integrated aerosol-jet printed electrodes for the electrical characterization of fluids via impedance spectroscopy. A novel analysis method is presented to accurately determine the concentration of several aqueous ionic chloride solutions, namely NaCl, KCl, CaCl₂, and MgCl₂. Importantly, we identify a key parameter, the turning point frequency of the capacitance-frequency graph, which is found to have a highly linear correlation with the solution concentration for each species spanning at least three orders of magnitude. This linear dependence is highly reproducible across different cationic species, making it useful for accurate fluid characterization. Applying this technique to analyze bodily fluids in real time has implications for remote health monitoring.

Transition metal dichalcogenides (TMDs) have received considerable attention in recent years because of their intriguing chemical and physical properties. However, conventional synthesis methods, including chemical vapor deposition and wet-chemical synthesis, still face many challenges in mass production. Here, we develop a dynamic salt capsulation method to massively prepare TMDs (MoS₂, WS₂) at atmospheric pressure in air with a high yield of over 95%. With the help of binary salts (KCl, KBr), TMDs can be easily obtained for a short reaction time of 1 h at a relatively low temperature (400°C). The as-synthesized MoS₂ powders show flower-like nanospheres, which exhibit a desired catalytic performance in hydrogen evolution reactions and good electrochemical performance as anode materials in lithium-ion batteries. This work provides a simple method to synthesize high-quality and large quantities of TMDs with low cost and time consumption, which has a great potential to integrate into industrial production.

Active mechanical metamaterials are an attractive proposition for soft robotics, electronic devices, and biomedical devices. However, the utilization of their uncommon physical and mechanical behaviors remains underexplored because existing fabrication processes limit the decoupling of structural frameworks from the responsive mechanisms. Here, we propose a multi-step fluidic control programming strategy by fabricating three-dimensional (3D) magnetic soft materials (MSMs) with reconfigurable mechanical metamaterial behaviors, enabling magnetic-field-driven alteration between three different geometry modes in a single structure. The MSM lattices exhibit fast 3D transitions between positive (ν_max = 3.41) and negative (ν_max = −2.64) Poisson’s ratios. We then create MSMs with reconfigurable orthotropic behaviors, which demonstrate the positive and negative Poisson’s effect in perpendicular planes. In further demonstrations, the fast and wireless response is validated by manipulating falling loads and switching the states of electrical circuits. This research provides a controllable workflow for future magnetic soft metamaterials.

We explore the potential of clamp-G nucleobase-modified peptide nucleic acids (cGPNAs) as microRNA and messenger RNA inhibitors. For proof of concept, we target miR-155, which is upregulated in diffuse large B cell lymphoma. cGPNA shows significant downregulation of miR-155 and the upregulation of its downstream targets in multiple lymphoma cell lines. Also, cGPNA treatment in vivo reduced tumor growth and improved survival in the U2932 cell-derived xenograft mouse model. To assess the broad application of cGPNA as an antisense modality, we also target transthyretin (TTR) mRNA. We establish a dose-dependent effect of antisense cGPNA on TTR mRNA levels. For in vivo studies, we conjugated cGPNA-based TTR antisense with lactobionic acid-based targeting ligand for in vivo liver delivery. We establish that cGPNA exhibits significant TTR protein knockdown compared to unmodified peptide nucleic acid (PNA) in vivo. Overall, we confirm that clamp-G-modified PNA analogs are a robust antisense therapy platform.

Transition metal oxides with small grain sizes are promising candidates for capacitive charge storage. However, the overall performance of such oxide materials is still limited by low tap density and finite conductivity. Here, we present a type of densely packed titanium dioxide (TiO₂) composite that comprises three-dimensional aligned mesoporous TiO₂ microspheres and coated ultrathin mesoporous carbon shells. The fabricated mesoporous meso-TiO₂@meso-C complex possesses a highly accessible surface area (134 m² g⁻¹), dual mesopore channels (11.8 and 21.6 nm), and a much higher tap density (1.52 g cm⁻³). As expected, this designed mesoporous composite achieves superior electrochemical performance, including both a maximized specific capacity of 255 mAh g⁻¹ and a volumetric capacity of 390 mAh cm⁻³ at 0.025 A g⁻¹. Our mesoscopic composite electrode that enables fast redox reaction reveals the importance of incorporating conductive and dense mesostructures as an alternative pathway for high-volumetric pseudocapacitive materials.

Many control algorithms for formation of robot swarms are often inspired by animal swarms. However, these algorithms require robots having sensing and computational capabilities and are not applicable to robot swarms working in extreme environments, such as at micro/nanoscale and in space. Here, we directly apply the differential adhesion hypothesis (DAH) of cell biology to the formation of robot swarms. Like cell collectives, swarms of sensor-less robots aggregate and sort in a self-organized manner. We quantitatively investigate the DAH principle in both swarms of cells and robots. We find that the sorting time is nonlinearly related to the levels of adhesion differences. This sheds light on the mechanisms of timing control in morphogenesis. Based on these findings, we program robot swarms to form functional morphologies by tuning their adhesion. This work advances swarm robotics in forming functional morphologies in a self-organized manner and enables us to investigate morphogenesis in cell collectives using robot swarms.