Coupling the hydrazine oxidation reaction (HzOR) with the hydrogen evolution reaction (HER) offers an energy-saving strategy for hydrogen production, addressing both energy scarcity and environmental concerns. However, efficient bifunctional electrocatalysts remain a key bottleneck. Herein, by taking advantage of lysozyme with massive reserves in nature, abundant heteroatoms and low cost, the ultrafine Ru nanoparticles loaded on N,S-codoped porous carbon (Ru-PNSC) with honeycomb structures and large surface area is synthesized, which exhibits excellent HER/HzOR bifunctional activity and stability across alkaline and neutral media. Specifically, at 10 mA cm-2, Ru-PNSC achieves remarkably low HER operating potentials (vs. RHE) of -2.8 mV (alkaline) and -21.6 mV (neutral), and HzOR potentials of -59 mV and 190 mV, respectively. Notably, the assembled hydrazine-assisted system operates at voltages of 10.3 mV (alkaline) and 231.5 mV (neutral) at 10 mA cm- 2. Furthermore, this system can be efficiently driven by a direct hydrazine-H2O2 fuel cell or a commercial solar cell, delivering substantial H2 production rates of 1.05 and 1.56 mmol h- 1, respectively.
Fiber-optic temperature sensors based on the Vernier effect possess ultra-high detection sensitivity, yet their measurement range is inherently limited by the periodic repetition characteristics of the Vernier envelope signal. To address this problem, this paper proposes a high-sensitivity temperature sensor based on free spectral range identification of the Vernier-effect spectrum and experimentally verifies its sensing performance. The sensor adopts 1.8 m and 2.0 m polarization-maintaining fibers to construct the reference and sensing Sagnac interferometers, respectively. The slight difference in the free spectral range (FSR) between the two interferometers generates the Vernier effect, thereby amplifying the sensing sensitivity. On the other hand, the FSR of the Vernier-effect envelope increases with rising temperature. By measuring the specific value of the envelope FSR, the envelope period can be calibrated to achieve accurate temperature determination. Experimental results show that, within the temperature range of 40 degrees C to 47 degrees C, the sensor's temperature sensitivity is approximately -16.0 nm/degrees C. Repeatability experiments involving temperature cycling and long-term stability tests further verified that the sensor exhibits excellent response consistency and measurement reliability. While retaining the high-sensitivity amplification advantage of the Vernier effect, the proposed method extends the effective temperature measurement range from a single envelope period (2.2 degrees C) to three consecutive periods (7.0 degrees C), giving a quantitative range extension factor of about 3.18. This provides an effective technical solution to the trade-off between sensitivity and measurement range in Vernier-effect fiber-optic sensors.
Following natural disasters, emergency rescue route planning becomes substantially more challenging due to damaged transportation networks, urgent rescue demands, and the operational complexity of heterogeneous rescue resources. Efficient rescue decision-making under constraints including time windows, regional accessibility, and casualty survival risk is therefore of considerable practical significance. However, existing studies often suffer from inadequate modeling of casualty survival dynamics, excessive reliance on manually designed operator control strategies, and limited search robustness in highly constrained environments. To address these challenges, this paper proposes a reinforcement learning-assisted iterated greedy algorithm for heterogeneous rescue and route planning, termed Dueling Double Deep Q-Network Iterated Greedy (D3QNIG). First, a disaster rescue routing optimization model integrating casualty evacuation and relief material distribution is developed, where an injury-adaptive survival-risk mechanism is incorporated alongside capacity, time-window, and accessibility constraints. Second, a reinforcement learning-assisted iterated greedy framework is designed to adaptively control operator selection and destruction intensity during the destruction, reconstruction, and local search stages. Third, a state representation approach based on set attention pooling is constructed for variable-length and heterogeneous routing solutions, together with a hierarchical Dueling Double DQN decision architecture to enhance policy-learning stability and search effectiveness. Finally, a two-stage reward-acceptance decoupling strategy is introduced to alleviate the credit assignment issue and improve training stability. Experimental results demonstrate that the proposed D3QNIG achieves superior solution quality, faster convergence, and stronger robustness than benchmark methods, indicating its effectiveness for heterogeneous rescue route optimization in complex disaster scenarios.
Optical biosensors require high sensitivity, stability and measurement accuracy for trace-level biomolecule detection. Tacrolimus (TAC) is a first-line immunosuppressant with a narrow therapeutic window (5–20 ng/mL), and its blood concentration varies greatly among patients, making real-time and accurate monitoring essential for clinical medication safety after organ transplantation. In this work, a fiber-optic biosensing system integrated with a laser ring cavity is proposed for sensitive, rapid and accurate detection of TAC. A nested dual-tapered multimode fiber-multi-core fiber-multimode fiber (MMF-MCF-MMF) probe was fabricated to strengthen the evanescent field, boost light-matter interaction and improve sensor sensitivity. The nested dual-taper structure was analyzed using RSoft software, and the optimized probe was verified by experimental characterization. An erbium-doped fiber ring laser with a full width at half-maximum (FWHM) of 0.116 nm acts as the narrow-linewidth near-infrared excitation source. ZnCdS quantum dots are immobilized on the probe surface to improve sensing responses, while Apt122 aptamers functionalized on the fiber enable specific molecular recognition of small-molecule TAC. The developed biosensor achieved a sensitivity of 0.447 nm/lg(ng/mL) and a limit of detection (LoD) of 193.8 pg/mL, about 15.85-fold lower than that of the conventional amplified-spontaneous-emission-based sensing system. Selectivity, stability and pH-response tests further confirmed the reliable quantitative detection performance of the sensor. This sensor provides reliable technical support for real-time and precise monitoring of blood TAC concentrations after organ transplantation.
In-wheel motor driven distributed drive electric vehicles (DDEVs) extend vehicle dynamic performance boundaries with independent torque control capabilities. However, the deep control coupling between active front steering (AFS) and direct yaw control (DYC) induces functional redundancy and execution conflicts, significantly increasing instability risks, which are a critical precursor to loss-of-control accidents in driver-system shared control scenarios. To address this, this paper constructs a handling stability-oriented cooperative integrated control framework for AFS and DYC. The system parameter uncertainties are handled based on the finite-vertex polytope method, and a compact polytopic robust model is formulated through convex optimization theory. Building upon this modeling, a distributed model predictive control (DMPC)-driven multi-agent dynamic interaction architecture is designed to realize real-time state prediction and cooperative decision optimization. Furthermore, a parallel distributed control mechanism is established by incorporating cooperative game theory to coordinate dynamic weight distribution of actuators, thereby satisfying the modularity and scalability requirements of chassis integrated control. To guarantee system asymptotic stability, terminal input constraints and cost functions are incorporated into a game-theoretic formulation, which is formalized within a linear matrix inequality (LMI) solution framework. An H-infinity robust compensation mechanism is further integrated to suppress external disturbances, which plays a key role in preventing accident-prone instability under system uncertainties. Finally, validation through hardware-in-the-loop (HIL) tests and real vehicle experiments demonstrates the significant effectiveness of the proposed scheme in enhancing lateral stability, reducing accident risk, and improving active safety performance in complex driving scenarios.
