Stabilizing Redox-Active Hexaazatriphenylene in a 2D Conductive Metal–Organic Framework for Improved Lithium Storage Performance

Organic redox-active materials are promising electrode candidates for lithium-ion batteries by virtue of their designable structure and cost-effectiveness. However, their poor electrical conductivity and high solubility in organic electrolytes limit the device's performance and practical applications. Herein, the π-conjugated nitrogen-containing heteroaromatic molecule hexaazatriphenylene (HATN) is strategically embedded with redox-active centers in the skeleton of a Cu-based 2D conductive metal–organic framework (2D c-MOF) to optimize the lithium (Li) storage performance of organic electrodes, which delivers improved specific capacity (763 mAh g⁻¹ at 300 mA g⁻¹), long-term cycling stability (≈90% capacity retention after 600 cycles at 300 mA g⁻¹), and excellent rate performance. The correlation of experimental and computational results confirms that this high Li storage performance derives from the maximum number of active sites (CN sites in the HATN unit and CO sites in the CuO₄ unit), favorable electrical conductivity, and efficient mass transfer channels. This strategy of integrating multiple redox-active moieties into the 2D c-MOF opens up a new avenue for the design of high-performance electrode materials.     

Surface Control and Electrical Tuning of MXene Electrode for Flexible Self-Powered Human–Machine Interaction

MXene materials emerge as promising candidates for energy harvesting and storage application. In this study, the effect of the surface chemistry on the work function of MXenes, which determines the performance of MXene-based triboelectric nanogenerator (TENG), is elucidated. First-principles calculations reveal that the surface functional group greatly influences MXene work function:  OH termination reduces the work function with respect to that of bare surface, while  F and  Cl increase it. Then, work functions are experimentally determined by Kelvin probe force microscopy. The MXene prepared by gentle etching at 40 °C for 48 h (GE_40/48) has the largest work function. Furthermore, an electron-cloud potential-well model is established to explain the mechanism of electron emission-dominated charge transfer and assemble a triboelectric device to verify experimentally its conclusions. It is found that GE_40/48 has the best performance with a 281 V open-circuit voltage, 9.7 µA short-current current, and storing 1.019 µC of charge, which is consistent with the model. Last, a patterned TENG is demonstrated for self-powered human–machine interaction application. This finding enhances the understanding of the inherent mechanism between the surface structure and the output performance of MXene-based TENG, which can be applied to other TENG based on 2D materials.     

Macrophage Membrane-Coated Nanoparticles Sensitize Glioblastoma to Radiation by Suppressing Proneural–Mesenchymal Transformation in Glioma Stem Cells

Radiotherapy is identified as a crucial treatment for patients with glioblastoma, but recurrence is inevitable. The efficacy of radiotherapy is severely hampered partially due to the tumor evolution. Growing evidence suggests that proneural glioma stem cells can acquire mesenchymal features coupled with increased radioresistance. Thus, a better understanding of mechanisms underlying tumor subclonal evolution may develop new strategies. Herein, data highlighting a positive correlation between the accumulation of macrophage in the glioblastoma microenvironment after irradiation and mesenchymal transdifferentiation in glioblastoma are presented. Mechanistically, elevated production of inflammatory cytokines released by macrophages promotes mesenchymal transition in an NF-κB-dependent manner. Hence, rationally designed macrophage membrane-coated porous mesoporous silica nanoparticles (MMNs) in which therapeutic anti-NF-κB peptides are loaded for enhancing radiotherapy of glioblastoma are constructed. The combination of MMNs and fractionated irradiation results in the blockage of tumor evolution and therapy resistance in glioblastoma-bearing mice. Intriguingly, the macrophage invasion across the blood-brain barrier is inhibited competitively by MMNs, suggesting that these nanoparticles can fundamentally halt the evolution of radioresistant clones. Taken together, the biomimetic MMNs represent a promising strategy that prevents mesenchymal transition and improves therapeutic response to irradiation as well as overall survival in patients with glioblastoma.     

Compact,ultra-low vibration,closed-cycle helium recycler for uninterrupted operation of MEG with SQUID magnetometers

A closed-cycle helium recycler was developed for continuous uninterrupted operation for magnetometer-based whole-head magnetoencephalography (MEG) systems. The recycler consists of a two stage 4 K pulse-tube cryocooler and is mounted on the roof of a magnetically shielded room (MSR). A flexible liquid helium (LHe) return line on the recycler is inserted into the fill port of the MEG system in the MSR through a slotted opening in the ceiling. The helium vapor is captured through a line that returns the gas to the top of the recycler assembly. A high-purity helium gas cylinder connected to the recycler assembly supplies the gas, which, after it is liquefied, increases the level of LHe in the MEG system during the start-up phase. No storage tank for evaporated helium gas nor a helium gas purifier is used. The recycler is capable of liquefying helium with a rate of ∼17 L/d after precooling the MEG system. It has provided a fully maintenance-free operation under computer control for 7 months without refill of helium. Although the recycler is used for single-orientation operation at this initial testing site, it is designed to operate at ±20° orientations, allowing the MEG system to be tilted for supine and reclining positions. Vibration of the recycler is dampened to an ultra-low level by using several vibration isolation methods, which enables uninterrupted operation during MEG measurements. Recyclers similar to this system may be quite useful even for MEG systems with 100% magnetometers.     

A modified teaching–learning-based optimisation algorithm for bi-objective re-entrant hybrid flowshop scheduling

In this paper, a modified teaching–learning-based optimisation (mTLBO) algorithm is proposed to solve the re-entrant hybrid flowshop scheduling problem (RHFSP) with the makespan and the total tardiness criteria. Based on the simple job-based representation, a novel decoding method named equivalent due date-based permutation schedule is proposed to transfer an individual to a feasible schedule. At each generation, a number of superior individuals are selected as the teachers by the Pareto-based ranking phase. To enhance the exploitation ability in the promising area, the insertion-based local search is embedded in the search framework as the training phase for the TLBO. Due to the characteristics of the permutation-based discrete optimisation, the linear order crossover operator and the swap operator are adopted to imitate the interactions among the individuals in both the teaching phase and the learning phase. To store the non-dominated solutions explored during the search process, an external archive is used and updated when necessary. The influence of the parameter setting on the mTLBO in solving the RHFSP is investigated, and numerical tests with some benchmarking instances are carried out. The comparative results show that the proposed mTLBO outperforms the existing algorithms significantly.     

Precise identification of moving vehicular parameters based on improved glowworm swarm optimization algorithm

This paper proposes an indirect method for the identification of moving vehicular parameters using the dynamic responses of the vehicle. The moving vehicle is modelled as 2-DOF system with 5 parameters and 4-DOF system with 12 parameters, respectively. Finite element method is used to establish the equation of the coupled bridge–vehicle system. The dynamic responses of the system are calculated by Newmark direct integration method. The parameter identification problem is transformed into an optimization problem by minimizing errors between the calculated dynamic responses of the moving vehicle and those of the simulated measured responses. Glowworm swarm optimization algorithm (GSO) is used to solve the objective function of the optimization problem. A local search method is introduced into the movement phase of GSO to enhance the accuracy and convergence rate of the algorithm. Several test cases are carried out to verify the efficiency of the proposed method and the results show that the vehicular parameters can be identified precisely with the present method and it is not sensitive to artificial measurement noise.     

Direct numerical simulations for assessment of gas-solid drag models in two-dimensional random arrays of particles

The momentum exchange between the phases plays a vital role in modelling of gas–solid flows and it is mathematically described by drag models. However, no consensus exists on which drag model gives the most accurate prediction of the drag force, and, despite the increase in available computing power, the same drag models are used in two-dimensional and three-dimensional simulations. In this study, direct numerical simulations of gas flow through multiple random configurations of static monodisperse particles are performed. The variations of solid volume fraction and particle Reynolds number are in the ranges of 0.05–0.4 and 13.7–136.9, respectively. The drag force exerted on particles is calculated and properly averaged. Based on the simulation results, thirteen drag models are compared and correction factors are introduced using the stochastic gradient descent algorithm. The correction factors provide a simple adjustment for the models to be used in 2D modelling.