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Flexible and stretchable biosensors that can monitor and quantify the electrical or chemical signals generated by specific microenvironments have attracted a great deal of attention. Wearable biosensors that can be intimately attached to skin or tissue provide a new opportunity for medical diagnostics and therapy. In recent years, there has been enormous progress in device integration and the design of materials and manufacturing processes for flexible and stretchable systems. Here, we describe the most recent developments in nanomaterials employed in flexible and stretchable biosensors. We review successful examples of such biosensors used for the detection of vital physiological and biological markers such as gas released from organisms. Furthermore, we provide a detailed overview of recent achievements regarding integrated platforms that include multifunctional nanomaterials. The issues and challenges related to the effective integration of multifunctional nanomaterials in bio-electronics are also discussed.

Nanoporous two-dimensional materials are attractive for ionic and molecular nanofiltration but limited by insufficient mechanical strength over large areas.We report a large-area graphene-nanomesh/single-walled carbon nanotube (GNM/SWNT) hybrid membrane with excellent mechanical strength while fully capturing the merit of atomically thin membranes. The monolayer GNM features high-density, subnanometer pores for efficient transport of water molecules while blocking solute ions or molecules to enable size-selective separation.The SWNT network physically separates the GNM into microsized islands and acts as the microscopic framework to support the GNM, thus ensuring the structural integrity of the atomically thin GNM. The resulting GNM/SWNT membranes show high water permeance and a high rejection ratio for salt ions or organic molecules, and they retain stable separation performance in tubular modules.

Two-dimensional (2D) semiconductors such as molybdenum disulfide (MoS 2 ) have attracted tremendous interest for transistor applications. However, the fabrication of 2D transistors using traditional lithography or deposition processes often causes undesired damage and contamination to the atomically thin lattices, partially degrading the device performance and leading to large variation between devices. Here we demonstrate a highly reproducible van der Waals integration process for wafer-scale fabrication of high-performance transistors and logic circuits from monolayer MoS 2 grown by chemical vapour deposition. By designing a quartz/polydimethylsiloxane semirigid stamp and adapting a standard photolithography mask-aligner for the van der Waals integration process, our strategy ensures a uniform mechanical force and a bubble-free wrinkle-free interface during the pickup/release process, which is crucial for robust van der Waals integration over a large area. Our scalable van der Waals integration process allows damage-free integration of high-quality contacts on monolayer MoS 2 at the wafer scale and enables high-performance 2D transistors. The van-der-Waals-contacted devices display an atomically clean interface with much smaller threshold variation, higher on-current, smaller off-current, larger on/off ratio and smaller subthreshold swing than those fabricated with conventional lithography. The approach is further used to create various logic gates and circuits, including inverters with a voltage gain of up to 585, and logic OR gates, NAND gates, AND gates and half-adder circuits. This scalable van der Waals integration method may be useful for reliable integration of 2D semiconductors with mature industry technology, facilitating the technological transition of 2D semiconductor electronics. A semirigid stamp and a standard photolithography mask-aligner enable a reliable and scalable pickup and release process for van der Waals materials integration at the wafer scale.

Two-dimensional (2D) materials 1 , 2 and the associated van der Waals (vdW) heterostructures 3 – 7 have provided great flexibility for integrating distinct atomic layers beyond the traditional limits of lattice-matching requirements, through layer-by-layer mechanical restacking or sequential synthesis. However, the 2D vdW heterostructures explored so far have been usually limited to relatively simple heterostructures with a small number of blocks 8 – 18 . The preparation of high-order vdW superlattices with larger number of alternating units is exponentially more difficult, owing to the limited yield and material damage associated with each sequential restacking or synthesis step 8 – 29 . Here we report a straightforward approach to realizing high-order vdW superlattices by rolling up vdW heterostructures. We show that a capillary-force-driven rolling-up process can be used to delaminate synthetic SnS 2 /WSe 2 vdW heterostructures from the growth substrate and produce SnS 2 /WSe 2 roll-ups with alternating monolayers of WSe 2 and SnS 2 , thus forming high-order SnS 2 /WSe 2 vdW superlattices. The formation of these superlattices modulates the electronic band structure and the dimensionality, resulting in a transition of the transport characteristics from semiconducting to metallic, from 2D to one-dimensional (1D), with an angle-dependent linear magnetoresistance. This strategy can be extended to create diverse 2D/2D vdW superlattices, more complex 2D/2D/2D vdW superlattices, and beyond-2D materials, including three-dimensional (3D) thin-film materials and 1D nanowires, to generate mixed-dimensional vdW superlattices, such as 3D/2D, 3D/2D/2D, 1D/2D and 1D/3D/2D vdW superlattices. This study demonstrates a general approach to producing high-order vdW superlattices with widely variable material compositions, dimensions, chirality and topology, and defines a rich material platform for both fundamental studies and technological applications. A simple but flexible technique based on a capillary-force-driven rolling-up process produces high-order van der Waals superlattices that are hard to produce with existing fabrication techniques.

The controllable growth of two-dimensional (2D) heterostructure arrays is critical for exploring exotic physics and developing novel devices, yet it remains a substantial synthetic challenge. Here we report a rational synthetic strategy to fabricate mosaic heterostructure arrays in monolayer 2D atomic crystals. By using a laser-patterning and an anisotropic thermal etching process, we create periodic triangular hole arrays in 2D crystals with precisely controlled size and atomically clean edges, which function as robust templates for endoepitaxial growth of another 2D crystal, to obtain monolayer mosaic heterostructures with atomically sharp heterojunction interfaces. Systematic microstructure and spectroscopic characterizations reveal periodic modulation of chemical compositions, lattice strains and electronic band gaps throughout the mosaic heterostructures. The robust growth of the monolayer mosaic heterostructures with a high level of synthetic control opens a pathway for band structure engineering and spatially modulating the potential landscapes in the atomically thin 2D crystals, establishing a designable material platform for fundamental studies and development of complex devices and integrated circuits from 2D heterostructures. An endoepitaxy approach enables the realization of two-dimensional mosaic heterostructures with atomically sharp heterojunction interfaces.

Two-dimensional (2D) semiconductors hold great promises for ultra-scaled transistors. In particular, the gate length of MoS 2 transistor has been scaled to 1 nm and 0.3 nm using single wall carbon nanotube and graphene, respectively. However, simultaneously scaling the channel length of these short-gate transistor is still challenging, and could be largely attributed to the processing difficulties to precisely align source-drain contact with gate electrode. Here, we report a self-alignment process for realizing ultra-scaled 2D transistors. By mechanically folding a graphene/BN/MoS 2 heterostructure, source-drain metals could be precisely aligned around the folded edge, and the channel length is only dictated by heterostructure thickness. Together, we could realize sub-1 nm gate length and sub-50 nm channel length for vertical MoS 2 transistor simultaneously. The self-aligned device exhibits on-off ratio over 10 5 and on-state current of 250 μA/μm at 4 V bias, which is over 40 times higher compared to control sample without self-alignment process. The simultaneous scaling down of the channel length and gate length of 2D transistors remains challenging. Here, the authors report a self-alignment process to fabricate vertical MoS 2 transistors with sub-1 nm gate length and sub−50 nm channel length, exhibiting on-off ratios over 10 5 and on-state currents of 250 μA/μm at 4 V bias.

Device-to-device (D2D) is a pivotal technology in the next generation of communication, allowing for direct task offloading between mobile devices (MDs) to improve the efficient utilization of idle resources. This paper proposes a novel algorithm for dynamic task offloading between the active MDs and the idle MDs in a D2D–MEC (mobile edge computing) system by deploying multi-agent deep reinforcement learning (DRL) to minimize the long-term average delay of delay-sensitive tasks under deadline constraints. Our core innovation is a dynamic partitioning scheme for idle and active devices in the D2D–MEC system, accounting for stochastic task arrivals and multi-time-slot task execution, which has been insufficiently explored in the existing literature. We adopt a queue-based system to formulate a dynamic task offloading optimization problem. To address the challenges of large action space and the coupling of actions across time slots, we model the problem as a Markov decision process (MDP) and perform multi-agent DRL through multi-agent proximal policy optimization (MAPPO). We employ a centralized training with decentralized execution (CTDE) framework to enable each MD to make offloading decisions solely based on its local system state. Extensive simulations demonstrate the efficiency and fast convergence of our algorithm. In comparison to the existing sub-optimal results deploying single-agent DRL, our algorithm reduces the average task completion delay by 11.0% and the ratio of dropped tasks by 17.0%. Our proposed algorithm is particularly pertinent to sensor networks, where mobile devices equipped with sensors generate a substantial volume of data that requires timely processing to ensure quality of experience (QoE) and meet the service-level agreements (SLAs) of delay-sensitive applications.

Unmanned aerial vehicles (UAVs) have increasingly become integral to multi-access edge computing (MEC) due to their flexibility and cost-effectiveness, especially in the B5G and 6G eras. This paper aims to enhance the quality of experience (QoE) in large-scale UAV-MEC networks by minimizing the shrinkage ratio through optimal decision-making in computation mode selection for each user device (UD), UAV flight trajectory, bandwidth allocation, and computing resource allocation at edge servers. However, the interdependencies among UAV trajectory, binary task offloading mode, and computing/network resource allocation across numerous IoT nodes pose significant challenges. To address these challenges, we formulate the shrinkage ratio minimization problem as a mixed-integer nonlinear programming (MINLP) problem and propose a two-tier optimization strategy. To reduce the scale of the optimization problem, we first design a low-complexity UAV partition coverage algorithm based on the Welzl method and determine the UAV flight trajectory by solving a traveling salesman problem (TSP). Subsequently, we develop a coordinate descent (CD)-based method and an alternating direction method of multipliers (ADMM)-based method for network bandwidth and computing resource allocation in the MEC system. Extensive simulations demonstrate that the CD-based method is simple to implement and highly efficient in large-scale UAV-MEC networks, reducing the time complexity by three orders of magnitude compared to convex optimization methods. Meanwhile, the ADMM-based joint optimization method achieves approximately an 8% reduction in shrinkage ratio optimization compared to baseline methods.

Emerging evidence shows that signal transducer and activator of transcription 6 (STAT6) plays critical roles in tumor development. We previously found high-level expression of STAT6 in human lung adenocarcinoma and squamous cell carcinoma, specifically in infiltrated immune cells located in the lung interstitium. Nevertheless, the role of STAT6 signaling in lung carcinogenesis and lung cancer proliferation and its underlying mechanisms remain unclear. This study aimed to investigate the role of STAT6 and the interaction between STAT6 and the tumor microenvironment in pulmonary tumorigenesis. We established a murine model of primary lung carcinogenesis in STAT6-deficient (STAT6 ) and STAT6 wild-type (WT) BALB/c mice using the carcinogen urethane. Two-month-old male mice were intraperitoneally injected with urethane (1 g/kg) dissolved in phosphate buffered saline (PBS). Primary tumors were monitored by positron emission tomography scanning. At 4, 6, and 9 months after urethane injection, lung tumors were harvested from the STAT6 and WT mice for analysis. Small interfering RNA was used to downregulate the expression of in tumor cells. Fluorescence activated cell sorting analysis was used to analyze fluorescence-conjugated cell markers. Transwell assays were used in coculturing experiments. STAT6 protein expression was detected by Western blotting, immunohistochemistry, and immunofluorescence. STAT6 mRNA expression was detected by quantitative real time-polymerase chain reaction. Cell Counting Kit-8 and colony formation assays were performed to evaluate cell proliferation. We detected high expression of STAT6 in CD11b cells of lung carcinoma. Our results indicate that STAT6 deficiency inhibits carcinogen-induced tumor growth and improves prognosis. STAT6 deficiency also decreased the mobilization and differentiation of CD11b cells. STAT6 deficiency in CD11b cells but not tumor cells decreased interleukin (IL)-4 secretion and the differentiation of CD11b cells into M2 macrophage cells. In conclusion, our findings indicate that IL-4/STAT6 signaling in CD11b cells promotes lung cancer progression by triggering an IL-4 positive feedback loop and increasing M2 myeloid cells. STAT6 may be a new therapeutic target for the prevention and treatment of lung cancer.

Critical transitions in experimental and theoretical systems can be anticipated on the basis of specific warning signs, with ‘critical slowing down’ being the best studied; long-term data from a real system, a Chinese lake, now show that a flickering phenomenon can be observed up to 20 years before the critical transition to a eutrophic state. Flicker of recognition is fair warning Critical transitions in experimental and theoretical systems can be anticipated on the basis of specific warning signs, raising the prospect that it might also be possible to predict future real-world events on the scale of the 2007 global financial crisis and Arab spring. But what to measure? Recent work has focused on critical slowing down, in which a system's recovery from perturbation is reduced as the transition is approached. Another possibility is flickering, in which increasing shifts between alternative stable states are seen in the run-up to the transition. This study uses long-term data from a real system, a Chinese lake, to show that flickering can be observed and that it occurs up to 20 years before a critical transition — in this case the deterioration of a lake towards a dead 'eutrophic' state as algal growth consumes the last available oxygen. There is a recognized need to anticipate tipping points, or critical transitions, in social–ecological systems 1 , 2 . Studies of mathematical 3 , 4 , 5 and experimental 6 , 7 , 8 , 9 systems have shown that systems may ‘wobble’ before a critical transition. Such early warning signals 10 may be due to the phenomenon of critical slowing down, which causes a system to recover slowly from small impacts, or to a flickering phenomenon, which causes a system to switch back and forth between alternative states in response to relatively large impacts. Such signals for transitions in social–ecological systems have rarely been observed 11 , not the least because high-resolution time series are normally required. Here we combine empirical data from a lake-catchment system with a mathematical model and show that flickering can be detected from sparse data. We show how rising variance coupled to decreasing autocorrelation and skewness started 10–30 years before the transition to eutrophic lake conditions in both the empirical records and the model output, a finding that is consistent with flickering rather than critical slowing down 4 , 12 . Our results suggest that if environmental regimes are sufficiently affected by large external impacts that flickering is induced, then early warning signals of transitions in modern social–ecological systems may be stronger, and hence easier to identify, than previously thought.