Search Results (6,307)

Ni-Mo alloy coating has shown exciting potential as a candidate to replace chromium coating. In this paper, Ni-Mo alloy coatings were successfully electrodeposited from a citrate/ammonia bath, and the effect of the bath pH values over a wide range (4–10) on the characteristics and corrosion resistance of Ni-Mo alloy coating was studied in detail. Results show that all the deposited Ni-Mo alloy coatings consist of a crystalline solid-solution Ni(Mo) fcc structure. An increase in bath pH values could facilitate the deposition of Mo, thereby increasing the Mo content and decreasing the crystallite size of Ni-Mo alloy coatings. However, there are subtle gaps between the coarse grains on the surface of the Ni-Mo alloy coating deposited at pH 10. These subtle gaps tend to form between the coarse grains on the surface of the electrodeposited Ni-Mo alloy coating because of the relatively high Mo content, refined grains, and appropriate morphology. The Ni-Mo alloy coating deposited at pH 9 exhibits optimal corrosion resistance, attributed to its lowest corrosion current density i_corr (7.31 × 10⁻⁶ A cm⁻²), the strongest polarization resistance (11.13 kΩ·cm⁻²), and impedance value, which are mainly contributed to by the coating resistance and charge-transfer resistance. Full article

Electric vehicles (EVs) rely on a battery pack as their primary energy source, making it a critical component for their operation. To guarantee safe and correct functioning, a Battery Management System (BMS) is employed, which uses variables such as State of Charge (SOC) to set charge/discharge limits and to monitor pack health. In this article, we propose a Multilayer Perceptron (MLP) network to estimate the SOC of a 14.8 V battery pack installed in a robotic vacuum cleaner. Both offline and online (real-time) tests were conducted under continuous load and with rest intervals. The MLP’s output is compared against two commonly used approaches: NARX (Nonlinear Autoregressive Exogenous) and CNN (Convolutional Neural Network). Performance is evaluated via statistical metrics, Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), and we also assess computational cost using Operational Intensity. Finally, we map these results onto a Roofline Model to predict how the MLP would perform on an automotive-grade microcontroller unit (MCU). A generalization analysis is performed using Transfer Learning and optimization using MLP–Kalman. The best performers are the MLP–Kalman network, which achieved an RMSE of approximately 13% relative to the true SOC, and NARX, which achieved approximately 12%. The computational cost of both is very close, making it particularly suitable for use in BMS. Full article

The development of lithium-ion batteries necessitates cathode materials that possess excellent mechanical and thermal properties in addition to electrochemical performance. As a prominent functional ceramic, the properties of spinel LiMn₂O₄ are governed by its atomic-level structure. This study systematically investigates the impact of Ni doping concentration on the mechanical and thermal properties of spinel LiNi_xMn_2−xO₄ via first-principles calculations combined with the bond valence model. The results suggest that when x = 0.25, the LiNi_xMn_2−xO₄ shows excellent mechanical properties, including a high bulk modulus and hardness, due to the favorable ratio of bond valence to bonds length in octahedra. Furthermore, this optimized composition shows a lower thermal expansion coefficient. Additionally, Ni doping concentration has a very minimal influence on the maximum tolerable temperature of the cathode material during rapid heating. Therefore, from the perspective of mechanical and thermal properties, this composition could be beneficial for improving the cycling life of the battery, since comparatively inferior mechanical properties and a higher thermal expansion coefficient make it prone to microcrack formation during charge–discharge cycles. Full article

Designing and constructing heterojunctions has emerged as a pivotal strategy for improving the photocatalytic efficiency of semiconductors. In this study, we report the controlled synthesis of an Au/Zn₃In₂S₆ Schottky junction through a combination of hydrothermal and in situ photodeposition methods. The structural, morphological, and photoelectrochemical properties of the catalyst were meticulously characterized using a suite of techniques including X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), diffuse reflectance spectroscopy (DRS), X-ray photoelectron spectroscopy (XPS), photoelectrochemical (PEC) measurements, and electron spin resonance (ESR) spectroscopy. The optimized 3% Au/Zn₃In₂S₆ composite exhibited a remarkable enhancement in both photocatalytic activity and stability, achieving a 90.4% removal of bisphenol A (BPA) under UV–visible light irradiation within 100 min. The corresponding first-order reaction rate constant was approximately 1.366 h⁻¹, nearly 4.37 times greater than that of the pristine Zn₃In₂S₆. This substantial improvement can be attributed to several key factors, including increased BPA adsorption, enhanced light absorption, and the efficient charge separation facilitated by the Au/Zn₃In₂S₆ heterojunction. Photogenerated holes, superoxide radicals, and hydroxyl radicals were identified as the primary reactive species responsible for the BPA degradation. This work highlights the potential of metal-modified semiconductors for advanced photocatalytic applications, offering insights into the design of highly efficient materials for environmental remediation. Full article

To enhance the power supply resilience of distribution networks with high-penetration distributed photovoltaic (PV) integration during extreme disasters, deploying Mobile Energy Storage Systems (MESSs) proves to be an effective countermeasure. This paper proposes an optimized operational strategy for distribution networks, integrating coordinated clustering of distributed PV systems and MESS operation to ensure power supply during both pre-disaster prevention and post-disaster restoration phases. In the pre-disaster prevention phase, an improved Louvain algorithm is first applied for PV clustering to improve source-load matching efficiency within each cluster, thereby enhancing intra-cluster power supply security. Subsequently, under the worst-case scenarios of PV output fluctuations, a robust optimization algorithm is utilized to optimize the pre-deployment scheme of MESS. In the post-disaster restoration phase, cluster re-partitioning is performed with the goal of minimizing load shedding to ensure power supply, followed by reoptimizing the scheduling of MESS deployment and its charging/discharging power to maximize the improvement of load power supply security. Simulations on a modified IEEE 123-bus distribution network, which includes two MESS units and twenty-four PV systems, demonstrate that the proposed strategy improved the overall restoration rate from 68.98% to 86.89% and increased the PV utilization rate from 47.05% to 86.25% over the baseline case, confirming its significant effectiveness. Full article

7xxx series aluminum alloys are critical structural materials in aerospace applications, but their susceptibility to hydrogen embrittlement (HE) poses significant challenges to service safety and durability. The effects of Si, Er, and Zr microalloying, combined with optimized heat treatments on the HE resistance of Al–Zn–Mg–Cu alloys, were systematically investigated using transmission electron microscopy (TEM), scanning electron microscopy (SEM), and mechanical testing. Three alloys—1# (AlZnMgCuZr), 2# (AlZnMgCuErZr), and 3# (AlZnMgCuSiErZr)—were subjected to single-stage or two-stage homogenization, followed by solution treatments at 470 °C/2 h and 540 °C/1 h, and peak aging at 125 °C. The hydrogen charging experiment was conducted by first applying a modified acrylic resin coating to protect the gripping sections of the specimen, followed by a tensile test. Results demonstrate that alloy 3# with Si addition exhibited the lowest RA_loss, followed by the 2# alloy, which effectively improved the alloys’ hydrogen embrittlement behavior. Compared with the solution in 470 °C/2 h, the 540 °C/1 h solution treatment enabled complete dissolution of Mg₂Si phases, promoting homogeneous precipitation and peak hardness comparable to alloy 2#. Two-stage homogenization significantly enhanced the number density and refinement of L₁₂-structured Al₃(Er,Zr) nanoprecipitates. Silicon further accelerated the precipitation kinetics, leading to more Al₃(Er,Zr) nanoprecipitates, finely dispersed T′/η′ phases, and lath-shaped GPB-II zones. The GPB-II zones effectively trapped hydrogen, thereby improving HE resistance. This work provides a viable strategy for enhancing the reliability of high-strength aluminum alloys in hydrogen-containing environments. Full article

Rechargeable lithium-, sodium-, and potassium-ion batteries are utilized as essential energy storage devices for portable electronics, electric vehicles, and large-scale energy storage systems. In these systems, anode materials play a vital role in determining energy density, cycling stability, and safety of various batteries. However, the complex electrochemical reactions and dynamic changes that occur in anode materials during charge–discharge cycles generate major challenges for performance optimization and understanding failure mechanisms. In situ characterization techniques, capable of real-time tracking of microstructures, composition, and interface dynamics under operating conditions, provide critical insights that bridge macroscopic performance and microscopic mechanisms of anodes. This review systematically summarizes the applications of such techniques in studying anodes for lithium-, sodium-, and potassium-ion batteries, with a focus on their contributions across different anode types. It also indicates current challenges and future directions of these techniques, aiming to offer valuable references for relevant applications and the design of high-performance anodes. Full article

In this report, a thickness-driven, aggregation–structure–transport optimum in sonicated poly(3-hexylthiophene) (P3HT) FETs was investigated. Mobility peaks at ~10–20 nm, coincident with a minimum in the photoluminescence (PL) vibronic ratio I₀₋₀/I₀₋₁ (strong H-aggregate interchain coupling) and X-ray diffraction sharpening of the (100) lamellar peak with slightly reduced d-spacing, indicate tighter π–π stacking and larger crystalline coherence. Absorption analysis (Spano model) is consistent with this enhanced interchain order. The mobility maximum arises from an optimal balance: J-aggregate–like intrachain planarity supports along-chain transport, while H-aggregates provide interchain connectivity for efficient hopping. Below this thickness, insufficient interchain coupling limits transport; above it, over-aggregation and disorder introduce traps and weaken gate control. The sharp rise in threshold voltage beyond the critical thickness indicates more trap states or fixed charges forming within the film bulk. As a result, a larger gate bias is needed to deplete the channel (remove excess holes) and switch the device off. These results show that electrical gating can be tuned via solution processing (sonication) and film thickness—guiding the design of P3HT devices for photovoltaics and sensing. Full article

As Battery Management Systems (BMSs) continue to expand in both scale and capacity, conventional state-of-charge (SOC) estimation methods—such as Coulomb counting and model-based observers—face increasing challenges in meeting the requirements for cell-level precision, scalability, and adaptability under aging and operating variability. To address these limitations, this study integrates a Deep Neural Network (DNN)–based estimation framework into a node-level BMS architecture, enabling edge-side computation at each individual battery cell. The proposed architecture adopts a decentralized node-level structure with distributed parameter synchronization, in which each BMS node independently performs SOC estimation using shared model parameters. Global battery characteristics are learned through offline training and subsequently synchronized to all nodes, ensuring estimation consistency across large battery arrays while avoiding centralized online computation. This design enhances system scalability and deployment flexibility, particularly in high-voltage battery strings with isolated measurement requirements. The proposed DNN framework consists of two identical functional modules: an offline training module and a real-time estimation module. The training module operates on high-performance computing platforms—such as in-vehicle microcontrollers during idle periods or charging-station servers—using historical charge–discharge data to extract and update battery characteristic parameters. These parameters are then transferred to the real-time estimation chip for adaptive SOC inference. The decentralized BMS node chip integrates preprocessing circuits, a momentum-based optimizer, a first-derivative sigmoid unit, and a weight update module. The design is implemented using the TSMC 40 nm CMOS process and verified on a Xilinx Virtex-5 FPGA. Experimental results using real BMW i3 battery data demonstrate a Root Mean Square Error (RMSE) of 1.853%, with an estimation error range of [4.324%, −4.346%]. Full article

Background: While intravenous administration of nanoparticles (NPs) is effective for targeting the lungs and liver, directing them to other organs and tissues remains challenging. Methods: Here, we report alternative administration routes that improve organ-specific accumulation of poly (lactic-co-glycolic acid) (PLGA) NPs (100 nm, negatively charged) loaded with the near-infrared dye Cyanine 7 (Cy7). NP cytotoxicity was evaluated in HEK293, mMSCs, C2C12, L929, and RAW264.7 cells. Hemocompatibility was assessed using WBCs and RBCs. NPs were administered via the tail vein, carotid, renal, and femoral arteries in BALB/c mice. Administration safety was evaluated by laser speckle contrast imaging and histological analysis. NP biodistribution and accumulation were assessed using in vivo and ex vivo fluorescence tomography and confocal microscopy of cryosections. Results: PLGA-Cy7 NPs demonstrate low cytotoxicity even at high doses and exhibit good hemocompatibility. Administration of NPs through the mouse carotid, renal, and femoral arteries significantly increases accumulation in the target ipsilateral brain hemisphere (31.7-fold) and salivary glands (28.3-fold), kidney (13.7-fold), and hind paw (3.6-fold), respectively, compared to intravenous administration. Injection of NPs through arteries supplying the target organs and tissues does not result in significant changes in blood flow, morphological alterations, or irreversible embolization of vessels, provided the procedure is performed correctly and the optimal dosage is used. Conclusions: These results highlight the potential of intra-arterial delivery of NPs for organ-specific drug targeting, underscoring the synergistic impact of advances in materials science, minimally invasive endovascular surgery, and nanomedicine. Full article

Precise morphology engineering is essential for enhancing the charge-storage capabilities of cobalt molybdate (CoMoO₄). In this study, cobalt molybdate (CoMoO₄, abbreviated as CoMo), cobalt molybdate–cetyltrimethylammonium bromide (CoMo-CTAB), and cobalt molybdate–cetyltrimethylammonium bromide/polyethylene glycol (CoMo-CTAB/PEG) electrodes were synthesized through a cationic–nonionic surfactant-assisted hydrothermal route. he introduction of CTAB promoted the formation of well-defined nanoflake structures, whereas the synergistic CTAB/PEG system produced a highly porous and interconnected nanosheet architecture, enabling enhanced electrolyte diffusion and redox accessibility. As a result, the CoMo-CTAB/PEG electrode delivered a high areal capacitance of 10.321 F cm⁻² at 10 mA cm⁻², markedly outperforming CoMo-CTAB and pristine CoMo electrodes. It also exhibited good rate capability, maintaining 63.64% of its capacitance at 50 mA cm⁻². Long-term cycling tests revealed excellent durability, with over 83% capacitance retention after 12,000 cycles and high coulombic efficiency, indicating highly reversible Faradaic behavior. Moreover, an asymmetric pouch-type supercapacitor device (APSD) assembled using the optimized electrode demonstrated robust cycling stability. These findings underscore surfactant-directed morphology modulation as an effective and scalable strategy for developing high-performance CoMoO₄-based supercapacitor electrodes. Full article

Cancer immunotherapy increasingly relies on nucleic acid-based vaccines, yet achieving efficient and safe delivery remains a critical limitation. Polyplexes—electrostatic complexes of cationic polymers and nucleic acids—have emerged as versatile carriers offering greater chemical tunability and multivalent targeting capacity compared to lipid nanoparticles, with lower immunogenicity than viral vectors. This review summarizes key design principles governing polyplex performance, including polymer chemistry, architecture, and assembly route—emphasizing microfluidic fabrication for improved size control and reproducibility. Mechanistically, effective systems support stepwise delivery: tumor targeting, cellular uptake, endosomal escape (via proton-sponge, membrane fusion, or photochemical disruption), and compartment-specific cargo release. We discuss therapeutic applications spanning plasmid DNA, siRNA, miRNA, mRNA, and CRISPR-based editing, highlighting preclinical data across multiple tumor types and early clinical evidence of on-target knockdown in human cancers. Particular attention is given to physiological barriers and engineering strategies—including size-switching systems, charge-reversal polymers, and tumor-penetrating peptides—that improve intratumoral distribution. However, significant challenges persist, including cationic toxicity, protein corona formation, manufacturing variability, and limited clinical responses to date. Current evidence supports polyplexes as a modular platform complementary to lipid nanoparticles in selected oncology indications, though realizing this potential requires continued optimization alongside rigorous translational development. Full article

The rapid growth of electric vehicle (EV) adoption poses significant challenges for the rational planning of charging infrastructure, where economic efficiency and service quality are inherently conflicting. To support scientific decision-making in charging station siting, this study proposes an integrated multi-objective optimization and decision-support framework that combines an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) with an entropy-weighted TOPSIS method. A bi-objective siting model is developed to simultaneously minimize total operator costs and maximize user satisfaction. User satisfaction is explicitly characterized by a nonlinear charging distance perception function and a queuing-theoretic waiting time model, enabling a more realistic representation of user service experience. To enhance convergence performance and solution diversity, the NSGA-II algorithm is improved through variable-wise random chaotic initialization, opposition-based learning, and adaptive crossover and mutation operators. The resulting Pareto-optimal solutions are further evaluated using an improved entropy-weighted TOPSIS approach to objectively identify representative compromise solutions. Simulation results demonstrate that the proposed framework achieves superior performance compared with the standard NSGA-II algorithm in terms of operating cost reduction, user satisfaction improvement, and multi-objective indicators, including hypervolume, inverted generational distance, and solution diversity. The findings confirm that the proposed NSGA-II–TOPSIS framework provides an effective, robust, and interpretable decision-support tool for EV charging station planning under conflicting objectives. Full article

In complex energy storage operating scenarios, batteries seldom undergo complete charge–discharge cycles required for periodic capacity calibration. Methods based on accelerated aging experiments can indicate possible aging paths; however, due to uncertainties like changing operating conditions, environmental variations, and manufacturing inconsistencies, the degradation information obtained from such experiments may not be applicable to the entire lifecycle. To address this, we developed a stage-wise state-of-health (SOH) prediction approach that combined offline training with online updating. During the offline training phase, multiple single-cell experiments were conducted under various combinations of depth of discharge (DOD) and C-rate. Multi-dimensional health features (HFs) were extracted, and an accelerated aging probability $p_{AA}$ was defined. Based on the correlation statistics between HFs, $k_{HF}$, the SOH, and $p_{AA}$, all cells in the dataset were divided into general early, middle, and late aging stages. For each stage, cells were further classified by their longevity (long, medium, and short), and multiple models were trained offline for each category. The results show that models trained on cells following similar aging paths achieve significantly better performance than a model trained on all data combined. Meanwhile, HF optimization was performed via a three-step process: an initial screening based on expert knowledge, a second screening using Spearman correlation coefficients, and an automatic feature importance ranking using a random forest regression (RFR) model. The proposed method is innovative in the following ways: (1) The stage-wise multi-model strategy significantly improves the SOH prediction accuracy across the entire lifecycle, maintaining the mean absolute percentage error (MAPE) within 1%. (2) The improved model provides uncertainty quantification, issuing a warning signal at least 50 cycles before the onset of accelerated aging. (3) The analysis of feature importance from the model outputs allows the indirect identification of the primary aging mechanisms at different stages. (4) The model is robust against missing or low-quality HFs. If certain features cannot be obtained or are of poor quality, the prediction process does not fail. Full article

Metalloporphyrin-based covalent organic frameworks (MPor-COFs) are emerging porous crystalline materials that combine the optoelectronic properties of metalloporphyrins with the highly ordered structure of COFs. Such a combination not only extends the light absorption spectrum of COFs by incorporating porphyrins but also improves the separation and transport capabilities of photo-generated electrons and holes by leveraging the structural advantages of organic frameworks. At the same time, the metal ions embedded in the porphyrin ring provide abundant active sites and optimize charge transfer channels, showing particular advantages in photocatalysis. The molecular design, construction, and photocatalytic application of MPor-COFs were reviewed in this paper. The intrinsic relationship among the structure, optoelectronic properties, and specific photocatalytic application received special attention. First, the role of the metal center in regulating the electronic structure and photophysical property of porphyrin monomers was introduced, as well as the impact of bond type on framework stability and charge transport efficiency. Then, the synthesis strategies for MPor-COFs were summarized. Finally, the applications of these materials in photocatalysis were critically reviewed, and their prospects and challenges in energy conversion and environmental remediation were also discussed. Full article