ABSTRACT High‐crossing mechanically interlocked molecules are attractive because their dense entanglement can encode unusual stereochemistry, but predictable handedness remains difficult to achieve. Here we show that multiple interlocking in coordination‐driven self‐assembly gives access to a 12‐crossing vertex‐fused double‐triangle [5]catenane ( Rh‐2 R ). X‐ray crystallography reveals a composite T(3,3)#T(3,3) topology that can be viewed as two Hopf‐link‐derived T(3,3) units sharing a central tetranuclear ring. The topological handedness of Rh‐2 R tracks the coconformational mechanical helicity of the Hopf‐link motifs, and the opposite ligand enantiomer gives the mirror‐image product both in solution and in the solid state. The solvent composition or concentration shifts the balance between Rh‐2 R and a less entangled Hopf‐linked state ( Rh‑2′ R ), whereas increased ligand steric demand suppresses multiple interlocking. These results reveal that Hopf links can serve as modular stereochemical directors for building and modulating complex topologically chiral links.

Biomass-derived carbon aerogels are promising sustainable multifunctional materials, yet broadband microwave absorption with fully integrated performance remains challenging. Herein, inspired by the chitin-protein architecture of insect cuticles, we designed a "skeleton-matrix" FeCo@ANC-2 aerogel via bidirectional icetemplating to construct a hierarchical architecture with ordered pores and cross-linked lamellar channels, thereby enabling multifunctional integration. The aerogels were fabricated using chitosan as the framework and FeCo-loaded Aspergillus niger as bridging support through bidirectional freeze-casting followed by thermal treatment. By tuning the interlayer spacing and carbonization temperature to optimize dielectric/magnetic loss and impedance matching, the optimized FeCo@ANC-2 achieves outstanding microwave absorption at only 3.17 wt% loading, delivering an effective absorption bandwidth (EAB) of 8.48 GHz. Furthermore, a gradient multi-layer architecture was introduced to broaden the EAB to 13.1 GHz (4.9-18.0 GHz). The aerogel also shows radar stealth capability, with a radar cross-section (RCS) reduction of 23.8 dB at normal incidence. Besides EM functionality, it also possesses ultralight weight, mechanical strength, and excellent thermal insulation with a surface temperature of 53.2 degrees C on a 100 degrees C hot stage. Therefore, with sustainable biomass feedstocks, FeCo@ANC-2 aerogels hold potential for lightweight EM attenuation and thermal management in aerospace and electronic protection applications.

Frozen seafood plays an important role in the global food supply, but maintaining its quality during frozen storage and cold-chain distribution remains a significant challenge. Although freezing effectively slows microbial growth and enzymatic activity, it cannot completely prevent quality deterioration. During frozen storage, seafood undergoes a series of interconnected physicochemical changes, including ice crystal growth, protein denaturation and oxidation, lipid oxidation, water redistribution, and texture deterioration. These changes gradually reduce sensory quality, nutritional value, and overall commercial acceptability. Conventional quality assessment methods, including destructive laboratory analyses and sensory evaluation, are still widely used. However, they are often labor-intensive, time-consuming, and unsuitable for rapid or real-time monitoring in modern cold-chain systems. As a result, increasing attention has been given to non-destructive sensing technologies that can evaluate seafood quality quickly and objectively. This review summarizes the major mechanisms responsible for quality deterioration in frozen seafood, together with recent advances in sensing technologies used to monitor these changes. The sensing approaches discussed include near-infrared (NIR) and Raman spectroscopy, hyperspectral and fluorescence imaging, low-field nuclear magnetic resonance (LF-NMR), electronic nose (E-nose), electronic tongue (E-tongue), colorimetric sensor arrays (CSAs), and biosensors. This review also discusses the growing role of artificial intelligence in frozen seafood quality assessment, including chemometrics, machine learning, deep learning, and multi-sensor data fusion. Particular attention is given to their applications in quality prediction, industrial implementation, and decision support. Finally, current challenges and future research needs are highlighted, with emphasis on the development of interpretable, transferable, and real-time monitoring systems that can support more reliable quality assurance throughout the frozen seafood supply chain.

In this work, a synchronous detection method for chloramphenicol (CAP) and oxytetracycline (OTC) was developed by integrating electromagnetically driven fluorescence visual sensing platform with a smartphone. Typically, red-AuNCs and green-AuNCs were used as fluorescence signal probes to respectively bind CAP and OTC aptamers, and strong- and weak- magnetic nanoparticles (S-MNPs and W-MNPs) combining with the corresponding complementary chain were functioned as capture probes. Based on the characteristics of dualemission AuNCs, synchronous and sensitive detection of CAP and OTC can be achieved by fluorescence. Further, due to the different magnetic response of MNPs, red-AuNCs and green-AuNCs were separated in the electromagnetically driven zonal chip platform, fluorescent visual detection of CAP and OTC was achieved by coupling with a smartphone. The fluorescence detection limits of the constructed method for CAP and OTC were 0.008 & micro;mol/L and 0.005 & micro;mol/L respectively, and the fluorescence visualization detection limits were 0.075 & micro;mol/L and 0.037 & micro;mol/L.

Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is a critical task in battery management systems. To address the nonlinearity and nonstationarity of raw capacity-fading data, as well as the fluctuation disturbances induced by localized capacity regeneration, this paper proposes a hybrid prediction method that combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a bidirectional long short-term memory neural network (Bi-LSTM). The proposed method first decomposes the battery capacity sequence using CEEMDAN and then separates the resulting modal components into high-frequency and low-frequency parts according to the zero-crossing rate (ZCR) criterion. Pearson and Spearman correlation analyses are further performed to verify that the low-frequency (LF) component is representative of the overall degradation trend. Subsequently, under the offline prediction setting, baseline models including LSTM, Bi-RNN, and Bi-LSTM are compared to evaluate the effectiveness of the CEEMDAN–Bi-LSTM strategy. For the online prediction setting, a multi-input multi-output (MIMO) rolling multi-step ahead forecasting strategy is adopted, where the low-frequency component (Bi-LSTM-1) is used as the primary prediction target to enhance rolling stability and improve cycle estimation near the failure threshold. Finally, extended experiments are conducted on the NASA 18650 dataset (B05/B06) to validate the applicability of the proposed method across different battery samples. Experimental results show that the CEEMDAN–Bi-LSTM approach can track the capacity-fading trend more stably under different prediction starting points in online prediction, and achieves favorable performance in both offline and online tasks.

Quality assessment of crude palm oil remains a critical challenge globally, particularly in resource-poor areas where traditional methods are time-consuming and destructive. This study explores machine learning-assisted Raman spectroscopy for non-destructive assessment of peroxide value (PV) and iodine value (IV) in palm oil. Raman spectra were collected from 200 samples from five Ghanaian markets, with second derivative preprocessing significantly enhancing feature resolution. Twelve predictive models were developed by combining three variable selection algorithms (CARS, GA, UVE) with three regression methods (PLS, SVM, RF). The genetic algorithm-random forest (GA-RF) model demonstrated exceptional prediction accuracy for both PV (Rp = 0.9831, RPD = 7.7397) and IV (Rp = 0.9752, RPD = 6.3927). Key spectral regions associated with unsaturation (1287-1657 cm⁻¹) and oxidation (1748-1840 cm⁻¹) were identified as crucial predictors. This approach enables rapid, non-destructive quality assessment with potential applications throughout the palm oil value chain.

The surimi processing industry faces persistent challenges in maintaining gel quality, particularly in terms of gel strength, water-holding capacity (WHC), and whiteness, key attributes that directly influence consumer acceptance and marketability. This study investigates the use of atmospheric cold plasma (ACP) treatment to enhance surimi gel quality and employs hyperspectral imaging (HSI) combined with deep learning for rapid, nondestructive quality assessment. Surimi prepared from Lateolabrax japonicus was treated with ACP at six different durations (0-90 s), followed by standardized thermal gelation, with optimal results observed at 45 s, significantly improving gel strength (4166.36 g mm), WHC (83 %), and whiteness (80.05). To comprehensively assess the gel quality parameters, a hybrid Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) model was developed. Spectral-spatial data from 616 wavelengths were acquired for 90 samples, and regions of interest (ROIs) were extracted using image binarization and morphological filtering. To improve model robustness and generalizability, data augmentation techniques-including Gaussian noise addition, time warping, and Fourier-based reconstruction-were applied to the extracted spectra. The CNN-GRU model outperformed single-model approaches, achieving high predictive accuracy for gel strength, WHC, and whiteness (Rp2 = 0.9224, 0.8945, and 0.9189; RPD = 3.59, 3.09, and 3.53, respectively). Additionally, pixel-level predictions were visualized using pseudo-color maps, providing spatial insights into gel quality distribution across treatments. This study demonstrates that ACP is effective in enhancing surimi gel properties, and that HSI coupled with deep learning provides a powerful, non-destructive solution for real-time quality monitoring and process optimization in surimi manufacturing.

Ensuring the safety and shelf life of chilled foods remains a critical challenge in modern supply chains. Conventional packaging provides passive protection but cannot monitor real-time freshness or microbial activity. Smart packaging systems integrating gas sensing with artificial intelligence (AI) offer a promising solution by enabling continuous detection of spoilage-related volatile compounds such as carbon dioxide, ammonia, hydrogen sulphide, and ethylene. This review summarizes recent advances in gas-sensing technologies including electrochemical, metal-oxide semiconductor, optical, and nanomaterial-based sensors and their application in chilled food monitoring. The integration of AI improves pattern recognition, spoilage prediction, and shelf-life estimation, particularly when combined with multi-sensor arrays. Current challenges include sensor stability under cold and humid conditions, device miniaturization, data standardization, and secure digital infrastructure. Future developments are expected to focus on low-cost biodegradable sensing platforms, edge-AI-enabled decision-making, and Internet of Things (IoT) connectivity for real-time monitoring. Overall, AI-assisted gas-sensing smart packaging represents a promising approach to enhance chilled food safety, reduce waste, and support sustainable supply chains.

The proton conduction mechanism of imidazole and its homologues within confined spaces has attracted much attention from researchers, which is highly beneficial for the development of novel proton exchange membranes. Traditionally, the hydrogen at the 1-position (H-1) on the nitrogen (N-1) of imidazole is seen as the exclusive source of mobile protons. However, we suggest that the 3-position nitrogen atom (N-3) can also generate mobile protons under hydrous conditions. This is because N-3 can form hydrogen bonds with water, which are particularly robust in confined spaces, thereby enhancing proton ionization from water and facilitating proton transfer. Based on this concept, 1-methylimidazole was introduced into a covalent organic framework (COF), resulting in a remarkable proton conductivity of 2.40 & times; 10-3 S/cm at 70 degrees C and 100 % relative humidity. This performance is on par with that of COFs doped with imidazole, demonstrating the key role of N-3 & centerdot;& centerdot;& centerdot;H2 O interactions within the framework in producing mobile protons and facilitating proton diffusion. Furthermore, this challenges the conventional viewpoint that H-1 of imidazole is the sole contributor to proton concentration, offering a new strategy for the preparation of high-performance proton conductors. (c) 2026 Published by Elsevier B.V. on behalf of Chinese Chemical Society and Institute of Materia Medica, Chinese Academy of Medical Sciences.

An expanding presence of aflatoxin B1 (AFB1) contamination across the global food supply represents a serious threat to public health. This work developed a portable sensor using Zn2+-doped upconversion nanoparticles (ZUCNPs) that Cu2+-induced fluorescence quenching with an injectable polyvinyl alcohol/gelatin/borax (PGB) hydrogel. First, ZUCNPs exhibiting enhanced fluorescence were modified with branched polyethylenimine (BPEI) to form signal probes known as BZUCNPs. Additionally, mesoporous silica nanoparticles (MSNs) were fabricated via sol-gel methods, loaded with Cu2+ and coated with aptamer (Apt) and polydopamine (PDA) to create dual-gated capture probes (PACMSNs). Afterwards, BZUCNPs and PACMSNs were mixed to form sensing probes and then integrated into the PGB hydrogel, forming the final sensor (BZUCNPs-PACMSNs/PGB). In the presence of AFB1 at pH 5.5, the AFB1 diffused into the PGB hydrogel, disrupting the PDA and Apt layer due to acid environment and changing the structure of Apt as AFB1 specifically bound to it. Then, Cu2+ released and facilitated the formation of BPEI@Cu2+ complex on the surface of ZUCNPs, which produced inner filter effect (IFE) that quenched fluorescence. When experimental conditions were optimized, a pronounced linear calibration curve (R2 = 0.994) was observed, linking fluorescence to the logarithm of AFB1 levels across a 0.1-100 mu g/ kg AFB1 range. And its sensitivity was characterized with a detection limit of 0.03 mu g/kg. Furthermore, the sensor's applicability was confirmed through analysis of food matrices, including wheat, corn, peanuts, and water, yielding high analytical accuracy with relative standard deviations below 6.08% and recovery rates ranging from 93.77% to 107.67%.

Improving gel quality in minced pork remains a key challenge in the meat processing industry, affecting both product integrity and consumer satisfaction. This study explores the use of ultrasonic treatment (UT) to enhance the gel strength and water-holding capacity (WHC) of minced pork, and develops a multimodal, non-destructive prediction model based on fused spectral and image data. UT was applied at varying durations, with a 20-min treatment yielding optimal gel strength (338.2 g x cm) and WHC (77.87 %). To understand the physicochemical mechanisms behind these improvements, Raman spectroscopy and image-based texture analysis were employed. Raman results showed significant alterations in protein secondary structure, including unfolding and reorganization, while Gray Level Co-occurrence Matrix (GLCM) analysis of gel surfaces indicated increased structural uniformity and reduced randomness. These complementary features were integrated using a low-level data fusion strategy and modeled using Extreme Learning Machine (ELM), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). The CNN model trained on augmented fused dataset achieved the highest prediction accuracy (Rp = 0.8954 for gel strength; Rp = 0.8887 for WHC), demonstrating the potential of combining chemical and spatial descriptors for real-time quality monitoring. This study not only confirms the effectiveness of ultrasound in improving pork gel quality but also introduces a robust and interpretable framework for intelligent meat processing and non-invasive quality assessment.

Sustainable food packaging is gaining importance as the world increasingly addresses environmental concerns, food waste, and safety. Although traditional plastic packaging has long been effective, it significantly contributes to pollution and relies on nonrenewable resources. In response, this review highlights recent advancements in sustainable packaging, focusing on biodegradable, compostable, and recyclable materials. Moreover, it explores the integration of active and smart packaging systems, as well as the emerging role of artificial intelligence (AI) in improving packaging efficiency. Material innovations, such as biopolymers, cellulose-based films, algae-derived coatings, and nanomaterials, offer eco-friendly alternatives while still maintaining essential functions like barrier protection, antimicrobial activity, and shelf-life extension. In addition, active and smart packaging technologies enhance preservation through antimicrobial and antioxidant agents and also enable real-time monitoring of food freshness via embedded sensors. Furthermore, AI technologies, particularly machine learning, support the optimization of material selection, shelf-life prediction, and quality control through data analytics. Ultimately, the synergy between sustainable materials and digital technologies is transforming the food packaging landscape. This integrated approach, therefore, offers a promising route to reduce environmental impact, enhance food safety, and improve operational efficiency throughout the supply chain.

Ionic polysaccharides improve gelation and hydration in meat systems, yet their structure-function relationships remain poorly defined. This study compared κ-carrageenan (CAR, anionic), agar (weakly anionic), curdlan (CUR, neutral), and chitosan (CHI, cationic) using a dual-system strategy integrating minced chicken gels with purified myofibrillar proteins. Concentration screening (1-2%) identified 1.75% as optimal for CAR, CUR, and CHI, and 1.25% for agar. At these levels, CAR and agar produced high gel strength (∼4.0 × 103 g × mm) and storage modulus above 10 kPa, while CUR maximized water-holding capacity (∼68%). In contrast, CHI showed limited improvement due to charge incompatibility. To elucidate these differential outcomes, mechanistic analyses revealed charge-dependent effects across treatments: CAR maintained controlled protein aggregation (ζ-potential -29.45 mV) enabling organized networks, whereas agar preserved protein structure with highest β-sheet content (∼52%). Conversely, CUR induced spontaneous aggregation (particle size 2.3 μm), and CHI formed stable complexes preventing gelation (solubility ∼83%). Notably, molecular force analysis further demonstrated multi-modal interactions involving electrostatic, hydrophobic, and potentially hydrogen-bonding contributions varying by polysaccharide type. Overall, anionic polysaccharides, especially CAR, provided the most effective network formation. Collectively, these findings establish that charge compatibility is critical for protein gelation, thereby offering practical guidance for formulating structured meat products.

A rapid, precise, and cost-efficient sorting technology for retired power batteries is essential for large-scale echelon utilization. However, existing methods face a core dilemma: the inability to ensure consistent degradation trajectories among regrouped cells stems from ineffective decoupling of different aging mechanisms’ contributions to overall battery degradation. Meanwhile, their reliance on extra testing or massive training data hinders large-scale industrial deployment. To address this challenge, we propose a novel method based on rapid extraction of multiple degradation features and subsequent optimized clustering. This method synchronizes aging feature extraction with the charge alignment operation, requiring only minutes of post-charge relaxation voltage data without additional instrumentation. Improved feature engineering characterizing instantaneous, short-term, and long-term relaxation behavior enables precise separation of loss of lithium inventory (LLI) and loss of active material (LAM). Finally, based on the new features, reliable battery sorting is achieved through implementation of an enhanced unsupervised clustering algorithm. Experimental validation demonstrates that the proposed method correctly sorts over 99% of samples using merely 10-minute post-charge relaxation data, overcoming efficiency-accuracy trade-offs and providing an industrially viable solution for reliable retired battery sorting.

Sodium-ion batteries (SIBs), owing to their abundant resources, low cost, and superior low-temperature performance, show great potential for applications in energy storage systems and electric vehicles. Accurate state of charge (SOC) estimation, a critical function of battery management systems (BMS), is essential for ensuring operational safety and optimizing efficiency. However, existing SOC estimation methods for SIBs often face challenges including limited accuracy, poor wide-temperature adaptability, high computational demands, or insufficient mechanistic clarity. To overcome these issues, this study proposes a hybrid SOC estimation method integrating an Improved Gas-Liquid Dynamic (IGLD) model with the Cubature Kalman Filter (CKF). The IGLD model refines the original framework by introducing a temperature-dependent correction mechanism that dynamically maps battery capacity and internal resistance to temperature variations, thereby enhancing characterization across wide temperature ranges. Comprehensive validation was conducted under multiple temperatures (-20 degrees C to 45 degrees C) and dynamic driving cycles (DST, FUDS, UDDS, CLTC). Results indicate that the IGLD model reduces root mean square error (RMSE) and mean absolute error (MAE) to within 1.8 %, outperforming the original model. Further integration with CKF improves robustness: the IGLD-CKF method achieves RMSE and MAE below 1 %, converges within 35 iterations even with 100 % initial SOC error, and maintains errors under 1 % under strong noise interference (+10 mV voltage, +100 mA current). These results confirm the method's high accuracy, strong robustness, and excellent temperature adaptability, offering a reliable technical solution for the safe and efficient deployment of SIBs in real-world dynamic scenarios.

Carbon-based materials attract attention for electromagnetic wave (EMW) absorption due to low density and excellent dielectric loss. However, conventional powdered absorbers usually suffer from unsustainability and/or limited functionality. While, fungal hyphae, as biological templates with controllable growth and network-forming ability, offer ideal skeletons for 3D porous composites. To address these issues, we synthesized hyphae-derived Co/C/rGO aerogels with a lamellar porous architecture through hydrothermal reduction and carbonization. The material consists of hollow tubular Co/C wrapped by reduced graphene oxide. Its electromagnetic parameters and absorption performance are tunable by adjusting precursor Co/C content. The optimized aerogel demonstrates dual loss mechanisms, achieving an impressive effective absorption bandwidth (EAB) of 7.92 GHz, enhanced by heterojunctions and magnetic coupling. Furthermore, a multilayer gradient design can extend the EAB to an ultra-broadband 13.10 GHz. Besides, the aerogels also display an ultralow density of 3.18 mg/cm3, great thermal insulation property generating a temperature gradient of over 58.4 degrees C at 100.0 degrees C and hydrophobicity behavior with water contact angle of 138.6 degrees. The radar cross-section reduction value can reach 22.3 dB m2, which can effectively protect targets from being detected by radar. Therefore, this hyphae-derived multifunctional aerogel has promising applications in electromagnetic wave absorption, radar stealth and thermal insulation.

As biosafety requirements continue to increase, there is a growing interest in foodborne-pathogen detection methods that can be operated safely. In this work, a dual-functional "sterilization-detection" photoelectrochemical (PEC) biosensing platform is developed based on a Fe3O4/copper-benzene-1,3,5-tricarboxylic acid (Cu-BTC), which integrates efficient photothermal sterilization with excellent photoelectric performance. The aptamer-modified magnetic substrate is employed to specifically capture and magnetically separate Escherichia coli O157:H7, inducing a steric hindrance effect that decreases the photocurrent. Upon near-infrared irradiation, the composite exhibits a strong photothermal response, enabling efficient in situ sterilization of the captured bacteria. Subsequently, the lipopolysaccharides (LPS) released during bacterial lysis are adsorbed onto the Fe3O4/Cu-BTC surface via Cu-O-P bonds. The phosphate groups in LPS coordinate with Cu2+, passivating surface defects and suppressing nonradiative recombination, thereby prolonging carrier lifetime and boosting the photocurrent. The resulting ratiometric PEC sensing platform shows a linear range of 2.2 × 102-2.2 × 106 CFU/mL with detection limit as low as 41 CFU/mL. Thus, this work not only achieves the enhancement of PEC signals through in situ adsorption of LPS but also proposes an integrated strategy for the simultaneous inactivation and accurate quantification of foodborne pathogens. It demonstrates promising potential for on-site food safety monitoring and proactive biosafety control.

Polyanion-type phosphate cathodes with three-dimensional (3D) frameworks and open ion channels show promise for sodium-ion batteries (SIBs). Na2VTi(PO4)3 (NVTP) exhibits excellent structural stability and high theoretical capacity. However, its sluggish electron transfer kinetics causes severe polarization, limiting its practical energy density. Density functional theory (DFT) calculations reveal that electron transfer in NVTP predominantly occurs on transition metals (TM) and ligand oxygens, with negligible contribution from the phosphate framework. Guided by this fundamental mechanistic insight, we rationally designed an anion-doped Na2.08VTi(PO4)2.92(SiO4)0.08 (NVTP-Si). Lower-electronegativity Si dopants displace adjacent oxygen atoms toward TM centers, promoting ligand oxygen electron delocalization and accelerating the kinetics of both Ti3+/Ti4+ and V2+/V3+/V4+ redox couples. NVTP-Si delivers extended voltage plateaus, excellent rate capability, and a high energy density of 440.1 Wh kg−1 (active material basis). Reinforced TM–O bonds mitigate lattice strain during Na+ (de)intercalation, enabling 94.6% capacity retention after 2000 cycles at 500 mA g−1. This work clarifies the electron transfer mechanism and volume strain origin in NVTP, providing an effective anion doping strategy to boost NASICON cathode performance for SIBs.

Atmospheric cold plasma (ACP) is a promising non-thermal processing technology capable of modifying protein structures and enhancing functional performance in food systems. In this study, the effect of atmospheric cold plasma (ACP) treatment on the gel quality of minced beef was investigated, with emphasis on the underlying gel quality change mechanisms involving myofibrillar protein (MP) aggregation and conformational modifications. Firstly, minced beef was exposed to ACP for 30-180 s, then divided into two portions: one was subjected to stepwise heating (40 degrees C/30 min + 80 degrees C/30 min) for gel quality assessment, while the other was used to extract MPs for structural characterization. The results showed that 60-90 s treatment resulted in the most significant improvement in gel characteristics, with gel strength more than doubling (5800 g center dot mm vs. 2200 g center dot mm) and textural attributes such as springiness and cohesiveness significantly improving. To uncover the mechanism of gel quality improvement, the study analyzed MP solubility, sulfhydryl modifications, surface hydrophobicity, secondary structure, and aggregation behavior. The findings showed that ACP induced controlled protein unfolding and beta-sheet enrichment, which facilitated efficient cross-linking during heating and enhanced gel network formation. Furthermore, rheological evaluation confirmed higher storage modulus (G ') and favorable tan delta, consistent with strong yet elastic gel networks. Notably, WHC remained relatively stable (66.2-67.5 %) across treatments, indicating that gel strengthening occurred through improved protein organization without compromising water retention. These insights provide a mechanistic understanding of how ACP influences gel quality, thus supporting ACP as a promising clean-label strategy to improve the texture and functionality of meat products.

Accurate estimation of the state of charge (SOC) is a critical technological component for ensuring the performance and safe operation of power batteries. In practical applications, individual cells are configured into parallel battery module (PBM), series battery module (SBM), and series-parallel battery module (SPBM) through various topological arrangements such as parallel, series, and series-parallel connections. Owing to the inconsistencies among individual cells, conventional single-cell SOC estimation methods exhibit limited applicability when extended to battery modules. This paper introduces a gated recurrent unit based on physical loss integrated with unscented Kalman filter (PLGRU-UKF), aiming to achieve robust and accurate estimation of the extremal SOC values within battery modules. To validate the effectiveness of the proposed method, a comprehensive experimental framework is established, comprising both single-cell and battery module testing platforms. These platforms are utilized to select appropriate cells for module assembly and to construct multi-condition datasets for PBM, SBM and SPBM. Subsequent analyses evaluate cell inconsistency and the estimation accuracy under different connection topologies and varying numbers of cells. The results show that the proposed method maintains RMSE within 1.3 % across all datasets. In addition, the method demonstrates strong robustness to measurement noise, confirming its effectiveness and reliability in practical applications.