Tsinghua University

The recycling of lithium-ion batteries (LIBs) has been dogged by air pollutants containing fluoride (e.g. HF, PF5, POF3). Pyrolysis is a technique that can eliminate polyvinylidene fluoride (PVDF) from the cathode electrode sheets of spent LIBs, effectively separating the cathode material from the aluminum (Al) foil. Nonetheless, the HF gas generated during pyrolysis not only corrodes equipment but also presents serious environmental risks. To address this, a novel, eco-friendly strategy is introduced for the direct upgrading of cathode active materials (CAM). The strategy's cornerstone involves incorporating a minor amount of calcium into the original cathode material's coating, and it leverages mechanical stirring during the waste battery material separation process to ensure the electrode is fully detached from the current collector at a reduced temperature. The pyrolysis mechanism elucidates that fluorine-containing organic pollutants are converted into metal fluorides and deposited on the surface of cathode particles during aerobic pyrolysis, thereby improving the interfacial stability of lithium nickel cobalt manganese oxide (NCM) materials, reducing transition metal dissolution. This strategy not only eliminates the release of fluorine-containing organic pollutants during pyrolysis but also achieves direct regeneration of CAM. This work underscores the importance of the cathode materials' manufacturing process in facilitating the recycling of spent LIBs and provides an environmentally friendly and economically viable solution for the battery recycling industry.

Entropy theories play a significant role in rotating machinery fault detection. However, key parameters of these methods are often selected subjectively based on trial-and-error methods or engineering experience. Unsuitable parameters would result in an inconsistency between the extracted entropy results and the realistic case. To address this issue, a complexity measurement method called swarm intelligence optimization entropy (SIOE) is proposed, which adaptively estimates optimal parameters using skewness metrics, logistic chaos theory, and African vulture optimization. By considering the variability and dynamic changes of various signals, SIOE enables the extraction of robust and discriminative dynamic features. Additionally, a collaborative intelligent fault detection method for rotating machinery fault detection is developed, based on SIOE and extreme gradient boosting. This method aims to accurately identify single faults, compound faults, and varying fault degrees within the rotating machinery. Simulation and fault detection experiments on rotating machines demonstrate that SIOE improves recognition accuracy by up to 21.25% compared to existing entropy methods. The proposed intelligent fault detection method improves recognition accuracy by up to 15.71% compared to advanced fault detection methods. These results highlight the advantages of SIOE in complexity measurement and feature extraction, as well as the effectiveness and accuracy of the proposed intelligent fault detection method in identifying rotating machinery faults.

In complex navigational environments, effective unmanned surface vehicle (USV) scheduling is critical. However, the obstacle avoidance problems are often ignored in the literature. This study addresses the multi-objective heterogeneous USV scheduling problems with obstacle avoidance. The objective is to minimize the maximum completion time and total carbon emissions. First, a mathematical model is developed to describe the concerned problems. Second, an A* algorithm is employed to obtain a path between task points with avoiding obstacles. Third, to obtain a high-quality scheduling scheme, an improved artificial bee colony (ABC) algorithm with deep Q-network (DQN) is designed. According to the problem nature, six novel rules are designed for finding high-quality solutions in initialized population. Five local search strategies are designed based on the structure of solution space. The scales of the instances and different objectives are utilized to designed a DQN, which recommends suitable local search strategies during iterations for improving convergence speed. Then, the Gurobi solver is employed to verify the proposed model. The effectiveness of the proposed strategies is verified by 13 instances with different scales. Finally, the experimental results and discussions show that the designed ABC with DQN has the strongest competitiveness among all compared algorithms.

Distributed manufacturing and fine-manufacturing are two typical scenarios of modern manufacturing industries in the context of globalization and customization. The distributed differentiation flowshop integrated scheduling problem (DDFISP) is a novel model that deals with the integrated scheduling problem of these two manufacturing scenarios. In the DDFISP, jobs have multiple customized types and are manufactured in a number of distributed factories. Each factory includes three fine-processing stages: parallel machine fabrication, single machine assembly, and dedicated machine differentiation. In the paper, a new distributed memetic evolutionary architecture is first built, which consists of four modules with distinct functions, including global exploration, local exploitation, knowledge transfer, and search restart. The exploration and exploitation are coevolved in the distributed way and communicated by knowledge transfer. This architecture can be used as a universal model to construct evolutionary algorithms. Following this architecture and devising each module innovatively, a novel knowledge transfer-driven distributed memetic algorithm (KTDMA) is constructed to solve the DDFISP. Specifically, global exploration is performed on multiple populations by dynamically selecting global exploration optimizers from predefined external repository. Local exploitation is executed on an independent elite archive by a destruction-construction local search and a key block local search. Knowledge transfer is conducted to communicate the superior information between exploration and exploitation based on a point-ring topology. Search restart is adaptively carried out to alleviate the search homogeneity. Computational results show the effectiveness of the proposed evolutionary architecture and special designs, and demonstrate that the KTDMA performs more competitive than the compared state-of-the-art algorithms.

This article attempts to solve the engineering optimization problem for control parameter configuration of underwater gliders with parameter uncertainty. The optimization objectives are maximizing the glider energy utilization rate and average voyage velocity; design variables include the net buoyancy adjustment amount and movable mass block translation amount; and uncertain parameters include the glider control parameter errors, manufacturing errors and sensor measurement errors. First, a dynamic simulation-based performance evaluation model of the glider is established, and interval number order theory is used to quantify the uncertainty. Then, surrogate models of the performance evaluation model are established, and the Sobol’ method-based sensitivity analysis is executed to identify the key uncertain parameters. Thus, a hierarchical optimization framework is introduced, and its system level and sub-level are used for determining optimal control parameter values and performance evaluation parameter intervals, respectively. Finally, optimization calculation is executed under different parameter configurations, and some rules are summarized.

We report an 850 nm surface-illuminated InGaAs modified uni-travelling-carrier photodiode (MUTC-PD) with distributed Bragg reflector (DBR) bottom mirror. By adopting InGaAs as the absorber, which exhibits a higher absorption coefficient than GaAs, the absorption layer thickness can be reduced to 960 nm, so as to minimize the transit time of the photogenerated carriers. The fabricated MUTC-PD demonstrates a 3-dB bandwidth of 27.5 GHz and a responsivity of 0.51 A/W under a bias of -2 V at the wavelength of 850 nm.

Industrial demand response is the main component of demand response, but it is limited by various aspects such as production operation and management, making it difficult to be optimized and utilized. The uncertainty of industrial demand response is analyzed in this paper, and a multi scenario generation method for the uncertainty of industrial resource group demand response is proposed. Then, this paper constructs corresponding optimization models for the main problems of industrial load aggregators participating in the power system demand response in three stages, including the load aggregator day-ahead bidding optimization model, the system side dayahead demand response scheduling plan optimization model, and the load aggregator intraday operation optimization model. Lastly, this paper proposes a dispatch model of optimizing the response and operation of resource groups for multi-scenario two-stage stochastic demand response. The effectiveness of the proposed model is verified through simulation.

We present a two-segment compound-cavity directly-modulated 1577 nm distributed feedback (DFB) laser based on identical epitaxial layer (IEL) scheme and uniform gratings. The laser exhibits a small-signal 3-dB bandwidth exceeding 50 GHz, and a modulation rate of 144 Gbps is implemented via four-level pulse-amplitude modulation (PAM-4) format. The eye diagrams remain open after transmission through 2.5 km single-mode fiber (SMF), demonstrating a great potential for 800 GbE applications or even higher data rates.

Any-hop (k-hop) reachability query is one fundamental operation in graph data analysis and its performance affects the efficiency of various tasks in social Internet of Things. As graph data scale increases, data is often outsourced to cloud servers. To protect the privacy of graph data, it is necessary to encrypt the data before outsourcing. Existing schemes can only support privacy-preserving 2-hop reachability queries. Only one scheme can support privacy-preserving k-hop reachability queries, but it discloses topological information and the query results is not verifiable. Most serious deficiency is that its efficiency is not practical. To address these issues, we propose a verifiable strong privacy-preserving k-hop reachability query on encrypted data. This scheme not only supports efficient privacy-preserving k-hop reachability queries without leaking any network topological information but also uses blockchain to achieve verifiability of query results. The security analysis shows that our scheme is secure. Compared to existing k-hop reachability query scheme, our scheme greatly improves query efficiency (at least 6.4∙105× faster when the number of nodes n≥100).

The efficient and secure management of resources within Flying Ad-Hoc Networks (FANETs) poses formidable challenges. FANETs constitute a pivotal element of the Space-Air-Ground-Integrated Network (SAGIN), employing Network Virtualization (NV) technology in tandem with Service Function Chain (SFC) to facilitate end-to-end network services, akin to terrestrial networks. Nonetheless, the transient, dynamic nature of FANETs coupled with their susceptibility to network attacks engenders considerable complexity in the placement of SFCs within these networks. To address the rationality and security of resource allocation for SFC placement, this paper proposes a reinforcement learning algorithm that sets strict security level restrictions on the placement process and fully extracts the key features in FANETs. Additionally, a multi-layer policy network is devised to dynamically perceive alterations in the FANET environment and compute an optimal SFC placement strategy. The proposed algorithm exhibits real-time adaptability to the dynamic environment, quantifies influential factors during placement, and achieves dynamic SFC placement. To assess the efficacy of the algorithm, three evaluation metrics—namely, SFC placement success rate, long-term average revenue, and long-term revenue cost ratio—are formulated and extensively evaluated through a plethora of experiments. Comparative analysis against alternative algorithms demonstrates enhancements of 20.6%, 15.3% and 12.1% in the aforementioned metrics, respectively. The experimental findings substantiate both the convergence and efficiency of the proposed algorithm.

Human activity recognition (HAR) is crucial in smart homes, security, and healthcare. Existing systems are limited by insufficient spatial diversity due to the constrained number of antennas. Additionally, challenges in noise reduction and feature extraction from sensing data, particularly channel state information (CSI), affect recognition performance. This study introduces a reconfigurable intelligent surface (RIS)-assisted passive human activity recognition (RISAR) method compatible with commercial Wi-Fi devices. RISAR leverages RIS to enhance the spatial diversity of Wi-Fi signals, capturing a broader range of spatial information. A novel high-dimensional factor model based on random matrix theory is proposed to improve noise reduction and feature extraction in the temporal domain. Furthermore, a dual-stream spatiotemporal attention network model is developed to assign variable weights to different characteristics and sequences, mimicking human cognitive processes in prioritizing essential information. Experimental results demonstrate that RISAR significantly outperforms existing HAR methods in both accuracy and efficiency, achieving an average accuracy of 97.26%. These findings highlight RISAR’s adaptability and potential as a robust activity recognition solution in real-world environments.

Achieving active and stable heterogeneous catalysts by encapsulating noble metal species within zeolites is highly promising for high utilization and cost efficiency in thermal and environmental catalytic reactions. Ru, considered an economical noble metal alternative with comparable performance, faces great challenges within MFI‐type microporous zeolites due to its high cohesive energy and mobility. Herein, an innovative strategy was explored that couples hydrothermal in situ ligand protection with stepwise calcination in a flowing atmosphere to embed ultrasmall Ru clusters anchored at K⁺‐healed silanol sites (≡Si−Ruδ+−O−K complexes) within 10‐membered ring sinusoidal channels of MFI. Comprehensive experiments and theoretical calculations unveiled that the interplay between confined Ru clusters and MFI induces local strain in MFI, creating a unique catalytic microenvironment around the Ru clusters. This synergy interaction enhances alkane deep oxidation as the confined Ru clusters and the MFI microenvironment collectively pre‐activate C3H8 and O2, facilitate the cleavage of C−H and C−C bonds at low temperatures. Notably, the stable geometric and electronic properties of the confined Ru show exceptional thermal stability up to 1000 °C, rivaling fresh catalysts. These findings shed vital methodological and mechanistic insights for developing efficacious heterogeneous catalysts for thermal catalysis.

Efficient CO2 electroreduction (CO2RR) to ethanol holds promise to generate value‐added chemicals and harness renewable energy simultaneously. Yet, it remains an ongoing challenge due to the competition with thermodynamically more preferred ethylene production. Herein, we presented a CO2 reduction predilection switch from ethylene to ethanol (ethanol‐to‐ethylene ratio of ~5.4) by inherently implanting Cu sites with perfluorooctane to create interfacial noncovalent interactions. The 1.83 %F‐Cu2O organic–inorganic hybrids (OIHs) exhibited an extraordinary ethanol faradaic efficiency (FEethanol) of ∼55.2 %, with an impressive ethanol partial current density of 166 mA cm⁻² and excellent robustness over 60 hours of continuous operation. This exceptional performance ranks our 1.83 %F‐Cu2O OIHs among the best‐performing ethanol‐oriented CO2RR electrocatalysts. Our findings identified that C8F18 could strengthen the interfacial hydrogen bonding connectivity, which consequently promotes the generation of active hydrogen species and preferentially favors the hydrogenation of *CHCOH to *CHCHOH, thus switching the reaction from ethylene‐preferred to ethanol‐oriented. The presented investigations highlight opportunities for using noncovalent interactions to tune the selectivity of CO2 electroreduction to ethanol, bringing it closer to practical implementation requirements.

Comorbid anxiety in chronic pain is clinically common, with a comorbidity rate of over 50%. The main treatments are based on pharmacological, interventional, and implantable approaches, which have limited efficacy and carry a risk of side effects. Here, we report a terahertz (THz, 10¹² Hz) wave stimulation (THS) technique, which exerts nonthermal, long-term modulatory effects on neuronal activity by reducing the binding between nano-sized glutamate molecules and GluA2, leading to the relief of pain and comorbid anxiety-like behaviors in mice. In mice with co-occurring anxiety and chronic pain induced by complete Freund’s adjuvant (CFA) injection, hyperactivity was observed in glutamatergic neurons in the anterior cingulate cortex (ACCGlu). Using whole-cell recording in ACC slices, we demonstrated that THS (34 THz) effectively inhibited the excitability of ACCGlu. Moreover, molecular dynamics simulations showed that THS reduced the number of hydrogen bonds bound between glutamate molecules and GluA2. Furthermore, THS target to the ACC in CFA-treatment mice suppressed ACCGlu hyperactivity and, as a result, alleviated pain and anxiety-like behaviors. Consistently, inhibition of ACCGlu hyperactivity by chemogenetics mimics THS-induced antinociceptive and antianxiety behavior. Together, our study provides evidence for THS as an intervention technique for modulating neuronal activity and a viable clinical treatment strategy for pain and comorbid anxiety.

Thermally activated delayed fluorescence (TADF) materials have emerged as a promising avenue for developing efficient and stable blue organic light‐emitting diodes (OLEDs) without incorporating heavy metals. Among the numerous donor‐acceptor architectures explored, the carbazole (Cz) and benzonitrile (BN) combination has demonstrated exceptional potential for blue OLED applications. Herein, we delve into the recent advances in TADF materials based on the Cz‐BN framework, with a particular focus on design strategies to accelerate exciton dynamics and improve material stability through kinetic and thermodynamic optimization. Eventually, challenges associated with the Cz‐BN systems in blue OLEDs, as well as prospective research directions that could unlock the full potential of this intriguing category of materials, are also discussed.

A distributed cooperative guidance law without numerical singularities is proposed for the simultaneous attack a stationary target by multiple vehicles with field-of-view constraints. Firstly, the vehicle engagement motion model is transformed into a multi-agent model. Then, based on the state-constrained consensus protocol, a coordination control law with field-of-view (FOV) constraints is proposed. Finally, the cooperative guidance law has been improved to make it more suitable for practical application. Numerical simulations verified the effectiveness and robustness of the proposed guidance law in the presence of acceleration saturation, communication delays and measurement noise.

Based on academic research and industrial applications over more than 20 years, the Reactor Monte Carlo code (RMC) developed by the REAL (Reactor Engineering Analysis Laboratory) team at Tsinghua University since 2000 has become a powerful, innovative, and versatile simulation platform for nuclear reactor analysis, shielding simulations, criticality safety calculations, fusion neutronics analysis and beyond. Utilizing collaborative and agile development technology, advanced methods and the most cutting-edge algorithms can be tested and implemented in RMC quickly and efficiently. RMC has been deployed on many world-class supercomputers in China and played an irreplaceable role in the design and analysis of commercial nuclear power plants and newly designed types of advanced nuclear reactors. This paper reviews the state-of-the-art technologies developed in RMC in recent years, such as stochastic and continuous-varying media modeling, advanced transient simulation capability, more accurate energy deposition model, etc. Parallel acceleration on heterogeneous architecture supercomputers and machine learning algorithms would be incorporated in ongoing research and future development plans.

Due to the accelerating effect of chloride and sulfate on the hydration of clinker, seawater-mixed cement (SC) paste is more prone to brittle cracking. Herein, this research systematically investigates the early crack resistance of seawater-mixed sintered sludge cement (SSSC) paste based on splitting tensile test and restrained squared eccentric ring test. The microstructure characteristics characterized by mercury intrusion porosimetry and scanning electron microscopy are combined to reveal the enhancement mechanism of sintered sludge ash (SSA) on SC paste, and a life cycle assessment is conducted around its carbon footprint. The results indicate that the SSA incorporation causes a continuous increase of 24.4% in the splitting tensile strength of SSSC paste. Meanwhile, the digital image correlation technology accurately captures the strain and displacement fields composed of crack propagation, which tends to be symmetrically distributed and reduces the crack width by 30%. During the inhomogeneous restrained shrinkage process, as the SSA increases, the crack deviation of the SSSC paste first magnifies and then reduces, the crack width decreases, and the cracking time extends. The pozzolanic activity of SSA is more active in SC paste, which significantly promotes the secondary accumulation of C-S-H and the increase of gel pore volume. This effectively reduces the risk of brittle cracking caused by uneven distribution of paste stiffness due to hydration acceleration during seawater mixing. In addition, replacing 50% cement with SSA only results in a total of 4.1% carbon emissions in SSSC paste and a 47.8% reduction in global warming potential from sludge transportation to activation treatment, demonstrating significant environmental advantages.