Neutral aqueous organic redox flow batteries(AORFBs)represent a compelling candidate for scalable energy storage integrated with renewable power generation,largely due to the earth-abundant,tunable,and sustainable nature of organic electroactive compounds.However,current redox-active species still face critical limitations,including insufficient solubility,inadequate voltage window compatibility,and poor cycling stability,hindering their practical deployment in neutral-pH aqueous electrolytes.In this work,a new class of pyridinium derivatives of thiadiazole intermediates,namely 4,4'-(1,3,4-thiadiazole-2,5-diyl)bis(1-(3-trimethylammonio)propyl)pyridin-1-ium(BnSn)and 4,4'-(1,3,4-thiadiazole-2,5-diyl)bis(1-methylpyridin-1-ium)(BnSm)were designed and synthesized.By introducing a 1,3,4-thiadiazole bridging unit into the molecular framework,the conjugation system was systematically extended,and an intermolecular hydrogen-bonding network based on nitrogen lone pair electrons was synergistically constructed.The resulting molecule achieves exceptional aqueous solubility(0.89 mol·L-1),a well-positioned low potential(-0.98 V),and outstanding electrochemical reversibility under neutral conditions.When employed as the negative electrolyte,it delivers remarkable long-term stability.After 500 constant-current charge-discharge cycles at 0.05 mol·L-1 and a current density of 600 A·m⁻²,the capacity retention rate remained above 70%,corresponding to an average cycle decay rate of only 0.016%and a daily decay rate of 0.16%.Combined density functional theory calculations and experimental mechanistic analysis indicate that the 1,3,4-thiadiazole bridge not only enhances the intramolecular electron delocalization to improve the structural rigidity and thermodynamic stability of the redox center,but also effectively inhibits the irreversible dimerization,hydrolysis and free radical-mediated degradation pathways of active species by virtue of its strong polarity and hydrogen bond donor-acceptor dual functional properties.Thus,it simultaneously optimizes the dissolution kinetics,charge transfer efficiency and cycle durability.
Aiming at the problems of traditional Discrete Fourier Transform channel estimation algorithms in orthogonal frequency division multiplexing systems,such as fixed use of cyclic prefix length,severe energy leakage under non-integer delay channels,and insufficient noise suppression,an improved adaptive threshold DFT algorithm based on joint delay and signal-to-noise ratio estimation was proposed.Based on the initial least square estimation,the algorithm introduced the multiple signal classification super-resolution technology to achieve accurate estimation of channel multipath delays,and dynamically deter-mined the optimal time-domain denoising window according to the actual maximum delay,thus getting rid of the dependence on fixed CP length.Based on the dynamic window,the pure signal interval and noise interval were re-divided,the signal power and noise power were accurately calculated to realize reliable SNR estimation,and the estimated SNR value was used as the core basis to construct an adaptive denois-ing threshold,and noise suppression and signal protection were completed through two-stage time-domain filtering.Finally,the final channel response was obtained through frequency-domain transformation and window function compensation.Simulation results show that under Rayleigh multipath fading channels,compared with the traditional DFT algorithm and fixed threshold algorithm,the mean square error and bit error rate are significantly reduced,and the performance improvement is particularly obvious in medium and high SNR and non-integer delay scenarios.The algorithm realizes the joint adaptive optimization of delay estimation,window length and denoising threshold,which effectively improves the channel estima-tion performance.However,affected by the characteristics of the MUSIC algorithm,the overall computa-tional complexity is higher than that of traditional methods,which has certain limitations in delay-sensitive scenarios,and further research will focus on complexity optimization in the future.
Traditional table tennis rubbers are prone to friction performance drift under prolonged ball-striking friction and thermal-oxidative ageing conditions,and often present the challenge of balancing grip capability(friction)with rebound(durability).To enhance the friction stability and ageing resistance of rubber surfaces,this study utilized the metal-organic framework material HKUST-1 as a precursor.Through high-temperature carbonisation,a Cu/C composite porous carbon material(MOFs-C)was pre-pared.This material was then incorporated into a styrene-butadiene rubber(SBR)matrix at varying ratios to construct composite rubber films.Characterisation via SEM,XRD and other methods indicates that MOFs-C retains its octahedral-like particle morphology and crystalline features after carbonisation,while developing a porous,rough surface structure conducive to enhancing specific surface area and interfacial interaction sites.In accelerated ageing and friction tests simulating actual usage conditions,the coefficient of friction for pure SBR membranes increased from 0.75 to 1.32,whereas the SBR/MOFs-C composite membranes maintained a coefficient of friction within the range of 0.77 to 0.82.The results indicate that the synergistic interaction between the porous structure of MOFs-C and the filler-rubber interface reduces the effective diffusion of oxygen within the rubber and inhibits thermal-oxidative degradation of molecular chains.This enables the rubber to maintain more stable surface tribological behaviour under repeated fric-tion conditions.This study provides a reference for the application of MOF-derived porous carbon materi-als in regulating the durable friction properties of sports equipment rubber.
To address the challenge of balancing high transfection efficiency with low cytotoxicity and poor serum stability in polyethylenimine(PEI)-based gene carriers,low molecular weight polyethyleneimine(PEI,600 u)was employed as the cationic core and grafted onto biodegradable PEG-like polyester back-bones with varying chain lengths(diethylene glycol,triethylene glycol,tetraethylene glycol and pentaethyl-ene glycol),successfully constructing four novel polymers designated as L1 to L4.The chemical struc-tures of the obtained polymers were characterized by proton nuclear magnetic resonance(1H NMR),while their molecular weight distributions were determined by gel permeation chromatography(GPC).Further-more,the DNA binding affinity of these polymers was evaluated by agarose gel electrophoresis.In addi-tion,three cell lines—cervical carcinoma(HeLa),human osteosarcoma(U2OS)and hepatocellular carci-noma(HepG2)served as in vitro models for evaluating the transfection efficiency of the polymer/DNA complexes under serum-free conditions using green fluorescent protein expression from the pEGFP-N1 reporter gene,with PEI 25 ku serving as a positive control.Both 1H NMR and GPC analyses confirmed the successful synthesis of the target polymers.Gel electrophoresis indicated that all polymers exhibited good DNA-binding ability.In vitro transfection experiments demonstrated that polymer L3 achieved the highest transfection efficiency across all three cell types.Notably,in U2OS cells,the entire series of poly-mers demonstrated significantly higher transfection efficiency than PEI 25 ku.This study provides a new material design strategy for developing non-viral gene carriers with high efficiency,low toxicity,and enhanced stability.
Precise segmentation of colon polyps is crucial for early screening and clinical diagnosis of colorectal cancer.However,large variations in polyp size,shape,and boundary characteristics,along with low contrast and blurred edges,make it challenging to balance segmentation accuracy and computational efficiency.To address these issues,a lightweight dynamic multi-scale segmentation network(LDMS-Net)was proposed.The model adopted a U-shaped encoder-decoder architecture and introduced a dynamic multi-scale module(DMM)in the encoder.Specifically,global average pooling and a two-layer fully connected network were used to generate adaptive weights,enabling dynamic fusion of multiple convolution branches with different dilation rates(1,2,3)and a standard convolution branch for adaptive multi-scale feature representation.A structural re-parameterization strategy was further applied to merge the multi-branch structure into a single 3×3 convolution during inference to reduce computational cost.In addition,a regional detail aggregator(RDA)and a comprehensive context integrator(CCI)were designed to capture local texture details and global semantic dependencies,respectively,and were integrated in the decoder via a hierarchical late fusion strategy.Experiments on Kvasir-SEG and CVC-ClinicDB datasets show that,using only 800 training images from Kvasir-SEG,the proposed method achieves a mean Dice coefficient of 93.65%and a mean IoU of 89.39%,with 6.54×106 parameters and 7.23×109 floating-point operations.The results demonstrate that LDMS-Net achieves high accuracy and robustness while maintaining a lightweight design.
