ABSTRACT Large‐scale wireless sensor networks increasingly rely on radio frequency fingerprinting to authenticate devices at the physical layer. However, practical deployments face two persistent challenges: Detecting previously unseen devices at runtime and onboarding them with minimal labeled data. We present an episodic meta‐learning pipeline that addresses both challenges. A compact convolutional encoder is trained with episodes to produce class‐discriminative embeddings. Class prototypes represent known devices with per‐class inverse covariances. Extreme value theory models the tail of Mahalanobis distances. On the test set, the closed‐set identification achieved an accuracy of 98.69%. The open‐set detector attains an area under the ROC curve of 1.0000. Few‐shot onboarding with five examples per new device yielded binary accuracy 0.956 ± 0.018, precision 0.999 ± 0.004, recall 0.913 ± 0.035, and F1‐score 0.954 ± 0.019, while multiclass onboarding achieved accuracy 0.944 ± 0.020, precision 0.926 ± 0.034, recall 0.960 ± 0.033, and F1‐score 0.931 ± 0.035. Sequential onboarding showed that accuracy on previously known devices remained between 0.969 ± 0.011 and 0.974 ± 0.010, while recognition among onboarded devices remained strong with accuracy between 0.911 ± 0.061 and 0.924 ± 0.060. These results indicate that a meta‐learned RF representation combined with lightweight statistical decision rules enables practical open‐set authentication and rapid zero‐day onboarding for wireless sensor networks.
ABSTRACT This study addresses the challenges of high communication latency, significant packet loss, and insufficient adaptability in self‐recovering multi‐interface encrypted communication terminals under complex environments. To tackle these issues, an OMAPL138 heterogeneous dual‐core processor is adopted to construct a dual‐core cooperative architecture, in which the ARM core runs an embedded Linux system for scheduling, while the DSP core handles real‐time signal processing and encryption. An SSX0912 encryption chip is extended via SPI, and a multilink communication model with ping‐pong buffer switching is implemented to enhance the communication rate. Experimental results show that under strong electromagnetic interference, the communication latency remains below 55 μs, the packet loss rate does not exceed 0.06%, and the maximum communication rate reaches 1550 MB/s. It is concluded that this approach achieves an organic integration at the system architecture level and markedly improves communication integrity, security, and self‐recovery capability in complex scenarios.
ABSTRACT The prediction of time‐dependent pile settlement remains challenging due to the nonlinear behavior of saturated clay soils, consolidation, and pile setup effects. The complexity of this phenomenon is not fully captured by classical elasticity theories. This study develops a hybrid model for time‐dependent pile settlement based on the mobilized stiffness of an instrumented field pile. A Finite Element Method (FEM) is combined with an Artificial Neural Network (ANN) to predict load‐settlement curves at different times. Advanced performance metrics, such as the A 20 , Scatter, and Agreement indices, confirm the robustness of the model. A regression‐based methodology is subsequently used to derive the mobilized stiffness of the pile‐soil system from the initial portion of the curves, revealing a logarithmic trend. A formulation is proposed by extending the elastic shaft stiffness of Randolph and Wroth's solution with a time‐dependent amplification factor accounting for pile installation, geometric and scale effects. The rate of stiffness evolution is related to soil compressibility parameters, including void ratio, modified swelling and compression indices. Validation through laboratory model pile tests and comparison with existing analytical frameworks shows that the proposed model can reliably capture the time‐dependent settlement and stiffness evolution within the working‐load range.
ABSTRACT This study employs machine learning methods, utilizing social media reviews as a data source, to explore the perceptual characteristics and dynamic changes of urban residents towards community park landscapes. Focusing on 25 community parks in Shanghai, 32,733 review texts from the Dianping platform spanning 2016–2025 were collected. After cleaning and word segmentation, a landscape perception corpus was constructed. The Latent Dirichlet Allocation (LDA) model was used for topic modeling of the unstructured text, identifying four core topics of residents' landscape perception: Comfort (40.49%), Landscape Attractiveness (24.04%), Recreational Experience (20.45%), and Cultural Aesthetics (15.01%). The study found that residents' perception of community parks exhibits a hierarchical structure dominated by environmental comfort, supplemented by landscape attractiveness and recreational experience, with cultural aesthetics being relatively weaker. Temporal analysis indicates that the comfort topic remained stable long‐term, while landscape attractiveness and recreational experience showed periodic fluctuations influenced by seasonal landscapes, facility updates, and social events. From the perspective of resident perception, the study proposes that community parks should prioritize environmental comfort and accessibility, enhance distinctive landscapes and multifunctional activity spaces, and promote the integration of cultural connotations with daily usage scenarios. This paper validates the feasibility of using social media data and machine learning methods in landscape perception research, providing empirical evidence for the refined design and human‐centered renewal of community parks.
ABSTRACT This study aims to solve the problems that the demand for personalized customization in the field of luxury jewelry design is difficult to coordinate with strict structural esthetic norms efficiently, and the existing generation algorithms are not precise enough in the control of complex topological structures. It proposes a personalized design generation algorithm based on Multi‐Constraint Shape Grammar (MCSG) enhanced Diffusion Model (DM). The technological innovation of MCSG lies in the integration of explicit rule constraints and implicit deep generative features, establishing the first computable MCSG system. At the level of algorithm architecture, based on Latent Diffusion Model (LDM), a grammar guide module is designed. It maps the rule codes of shape grammar to the denoising network through cross‐attention mechanism, and realizes the physical level constraint on the generated contour closure and internal pattern details. With the Low‐Rank Adaptation (LoRA) technology, fine‐tune the luxury style to ensure that the generated results accurately match the simple texture of modern luxury esthetics and the personalized input of users while retaining the delicate charm of Asian traditional crafts. Ten independent experiments conducted on the OrnAsia dataset show that the Fréchet Inception Distance (FID) of the proposed model is 12.45 ± 0.21. The Kernel Inception Distance (KID) is 3.15 ± 0.09. The Contrastive Language–Image Pre‐training score (CLIP score) is 32.68 ± 0.18. The Inception Score (IS) is 18.95 ± 0.15. In the geometric structure evaluation, the closed‐loop rate reaches 96.80% ± 0.39%, the non‐manifold error rate drops to 1.20% ± 0.11%, and the manufacturability index reaches 0.910 ± 0.007. Compared with the optimal comparison model in each evaluation dimension, the differences of related indexes all reach statistical significance level after correction by Holm–Bonferroni, indicating that the performance improvement of the model remained stable under different random seeds and sampling batches.
ABSTRACT The Casson‐micropolar fluid model herein represents the microstructure (yield stress) and micro‐rotational dynamics of engineered nanoparticles in blood‐analog suspensions. We present the mathematical model for a Casson (blood)‐gold nanoparticle suspension and perform gradient‐based objective optimization to (i) maximize the convective heat‐transfer coefficient and (ii) minimize total entropy generation. The computational framework of non‐Newtonian exothermic Casson‐micropolar fluid with variable viscosity and thermal conductivity is implemented via the spectral method, while the optimization scheme employs the multi‐start Nelder–Mead methodology to simulate the decision variables by random restarts with local convergence and a derivative‐free simplex method. Fluid velocity decreases with higher nanoparticle volume fractions but increases with the Frank‐Kamenetskii parameter, particularly near the convective slippery wall, and the thermodynamic irreversibility escalates with the Frank‐Kamenetskii parameter near the thermally insulated wall, whereas increasing the nanoparticle volume fraction and thermal conductivity effectively minimizes entropy generation. The least contribution of the variable viscosity, maximum values of thermal conductivity, Kamenetskii parameter, higher convective heating, continuous injection of nanoparticle volume fraction, and the avoidance of thermal radiation have the greatest impact on minimizing the disorderliness in the considered geometry. Maximizing the system heat transfer, the dominant contribution of nanoparticle fraction, thermal radiation, convective heat transfer, and variability of the thermal conductivity is required.
ABSTRACT In this paper, a 4‐13GHz low power low‐noise amplifier (LNA) for UWB based on two‐stage topology is presented. Wideband matching, low noise figure (NF), and low power are achieved by using a gate inductor‐assisted impedance matching and a current reuse feed‐forward noise cancellation techniques, respectively. The low power consumption UWB LNA is designed using standard 65 nm CMOS technology. This LNA achieves a 16.9 dB gain, a 4.3 dB minimum NF. The consumes 9.4 mW from a 1‐V supply.
ABSTRACT Medium‐term electricity demand forecasts (days–weeks ahead) are essential for scheduling, maintenance, and tariff or hedging decisions, yet remain challenging due to multi‐scale seasonality and weather‐driven non‐stationarity. We propose a Multi‐Scale Transformer (MSTr) that fuses diurnal, weekly, and seasonal context via scale‐specific pooling and learned softmax gating. The model is trained in a leak‐safe, direct‐H protocol for horizons H∈{24,168,336,672} using only information available at decision time. On hourly household data from Northeast Mexico, MSTr consistently outperforms strong baselines (LightGBM, LSTM, and Seasonal/Naive) across RMSE, MAE, WAPE, sMAPE, and R2, with the largest gains at 168–336 h where weekly and seasonal signals dominate. Explainability analyses (SHAP, partial dependence, temperature‐load sensitivity, and multi‐scale gating diagnostics) indicate that MSTr captures meteorological and calendar effects more faithfully than baselines. An ablation study confirms the utility of positional encodings, multi‐scale pooling, and learned gates, while a block‐bootstrap evaluation and Diebold–Mariano tests show that MSTr's improvements are statistically significant at key horizons. The approach remains computationally practical, is straightforward to integrate into existing forecasting pipelines, and supports transparent reporting through gate weights, seasonal slices, and feature‐level explanations.
ABSTRACT Current enterprise capital structure optimization faces the challenge of conflicting multiple objectives and the difficulty of obtaining an equilibrium solution efficiently. This paper introduces the Nondominated Sorting Genetic Algorithm III to construct a novel multiobjective optimization model for corporate capital structure, uniquely integrating enterprise value maximization, financial risk minimization, and weighted average cost of capital minimization within a single framework. This model also incorporates debt repayment constraints and capital limits. By using a reference point set within the algorithm to guide population search and combining iterative evolution with simulated binary crossover and polynomial mutation, this method achieves a systematic approximation of the Pareto front in high‐dimensional target spaces. Using financial data from selected Chinese listed companies as a sample, this experiment yielded Pareto solutions with mean uniformity of 0.040 (Spacing, SP) and 0.120 (Diversity ), a convergence index (GD) of 0.0037, and a coverage ratio η of 0.95. Results show that NSGA‐III effectively solves the multiobjective optimization problem of corporate capital structure and provides a comprehensive and balanced allocation solution for decision‐making.
ABSTRACT In recent years, the integration of transfer learning (TL) and self‐supervised learning (SSL) has gained considerable attention as an effective strategy for improving model performance across a wide range of machine learning (ML) and deep learning (DL) tasks. TL enables the reuse of knowledge acquired from pretrained models to accelerate learning in new tasks, while SSL exploits large volumes of unlabeled data to learn meaningful representations without manual annotation. This article presents a systematic and comprehensive survey of existing approaches that harmonize SSL with TL. The survey reviews key mechanisms such as self‐supervised pre‐training, feature extraction, fine‐tuning strategies, and domain adaptation across both ML and DL paradigms, including convolutional neural networks, recurrent neural networks, transformer‐based architectures, and classical ML models. By synthesizing findings reported in the literature, this study highlights how the combination of self‐supervised representation learning and TL enhances generalization, robustness, and data efficiency. Furthermore, the survey discusses representative application domains, emerging trends, and comparative insights across benchmark tasks. Finally, open challenges and future research directions are outlined, emphasizing scalability, domain shift handling, and the role of foundation models. This survey aims to provide researchers and practitioners with a structured understanding of current progress and opportunities in harmonizing TL and SSL.
ABSTRACT Over the past decade, the development of accurate turbulence models to describe blood flow in the aorta for cardiovascular applications has increased substantially, leading to growing efforts to quantify turbulence‐related hemodynamic parameters and improved model fidelity. At the same time, a unified computational standard, including for clinical use, remains absent. Moreover, there is still no unified standard for modeling turbulent flows in the aorta, and the choice of turbulence model, boundary conditions, and validation. In this paper, a systematic review of the simulation studies of turbulent blood flow in the aorta. Scopus, PubMed, and ScienceDirect databases were used in the literature search, which was completed on the 9th of July 2026. 38 articles were included after the selection process. The data from the research works were structured in different categories (Geometry, Viscosity model, Model turbulence, Parameters, Validation). The aim of this systematic review is to systematize the methodological approaches used in the state‐of‐the‐art literature, including the choice of turbulence models, validation methods, as well as the identification of the main hemodynamic parameters characterizing turbulent flow.
ABSTRACT To address safety hazards caused by valve‐closing water hammer in long‐distance gravity flow water conveyance projects and ensure the safe and stable operation of water transmission networks. This study focuses on the Ruyang Water Supply Project of the Northern Trunk Pipeline of Qianping Reservoir in Henan Province (the water conveyance pipeline has a total length of 87.4 km and a water level difference of 102 m). Using Bentley Hammer software to construct a hydraulic calculation model, the water hammer effects of different valve closure schemes were simulated and analyzed based on the characteristic line method. Combined with NSGA‐II, this study investigates valve closure scheme optimization. The research first analyzes the influence patterns of three one‐stage valve closure durations—10, 20, and 60 s—on water hammer effects. Subsequently, optimization calculations were performed for the stage‐two and stage‐three valve‐closing curve schemes, with the system comparing the water hammer protection effectiveness of different valve‐closing modes; the multistage valve‐closing scheme was further optimized using the NSGA‐II algorithm, validating the reliability and superiority of the optimized solution. Research findings indicate that valve closure time is negatively correlated with the intensity of water hammer effects. The shorter the valve closure time, the more pronounced the water hammer effect. The multistage valve closure optimization scheme demonstrates significantly superior protective performance compared to the single‐stage closure scheme. The three‐stage valve‐closing scheme optimized using the NSGA‐II algorithm not only achieves a higher optimization success rate but also provides more effective protection against water hammer. The optimization results are highly dependent on the selected hydraulic model, boundary conditions, valve closure duration constraints, objective function, and parameter ranges; in practical applications, they must be re‐validated based on specific engineering conditions.
ABSTRACT Black phosphorus quantum dots (BPQDs) have emerged as promising nanoscale building blocks for advanced energy‐storage and energy‐conversion technologies owing to their tunable electronic structures, abundant active sites, quantum‐confinement effects, and versatile surface chemistry. The integration of BPQDs with complementary materials has enabled the development of hybrid systems with enhanced charge‐transfer behavior, structural robustness, and multifunctional energy capabilities. Despite rapid progress, existing studies remain largely application‐oriented, while the fundamental relationships among interface construction, hybrid architecture, stability, and functional performance are often examined independently. This review provides a critical and design‐oriented perspective on BPQD hybrid systems with particular emphasis on interfacial engineering. Major interface‐construction strategies, including van der Waals assembly, surface functionalization, covalent coupling, and in situ growth, are comparatively analyzed in terms of their structural features, advantages, and limitations. The interplay among component selection, dimensional architecture, interface configuration, and stability is further discussed to establish a unified framework for rational hybrid design. Representative advances in alkali‐ion storage, electrocatalytic water splitting, nitrogen reduction, and photocatalytic hydrogen production are evaluated from the viewpoint of interfacial functionality. Finally, emerging directions involving stability‐by‐design concepts, scalable manufacturing, data‐driven materials discovery, and integrated energy systems are highlighted. By bridging interface engineering, materials design, and energy functionality, this review provides a comprehensive framework for the development of next‐generation BPQD hybrid platforms for sustainable energy applications.
ABSTRACT This study examines the thermal and flow behavior of a radiative Darcy–Forchheimer nanofluid in a porous medium, incorporating internal heat source and sink effects. The combined influence of thermal radiation, porous resistance, and nonlinear inertial drag on momentum, heat, and mass transfer is systematically analyzed. The Darcy–Forchheimer model captures both viscous and inertial resistance, while thermal radiation and volumetric heat generation/absorption are included to reflect realistic high‐temperature porous systems. The coupled nonlinear momentum and energy equations are solved numerically using the MATLAB bvp4c solver, and the effects of key parameters—Forchheimer number, radiation parameter, and heat source/sink strength on velocity, temperature, and Nusselt number are evaluated. Results show that increasing radiation enhances the thermal boundary layer thickness and Nusselt number, whereas higher Forchheimer resistance suppresses velocity, with a range of 0.2 ≥ f′(ξ,n) ≥ 0.17 (mean value). Heat sources raise the temperature profiles to 0.19 ≤ θ (ξ,n) ≤ 0.3 (mean), while sinks stabilize the thermal field. These findings provide quantitative insights into controlling flow and heat transfer in porous media, with direct relevance to advanced cooling systems, energy storage devices, and industrial thermal management applications.
ABSTRACT Jet impingement cooling offers high convective heat transfer coefficients, making it effective for high‐heat‐flux applications. However, conventional circular impinging jets (CIJs) typically produce non‐uniform heat transfer distributions, with high Nusselt numbers (Nu) at the stagnation point that rapidly decline in the wall‐jet region, causing thermal stresses. This review critically analyzes the flow field and heat transfer performance of jet impingement from special‐shaped holes, including swirling (threaded and twisted‐tape), converging/diverging, lobed, chevron, cross‐shaped, and racetrack nozzles. It synthesizes experimental and numerical studies examining the effects of nozzle geometry on jet structure, vortex dynamics, entrainment, turbulence generation, and local/average Nu distributions. Swirling impinging jets (SIJs) introduce tangential velocity through helical inserts or twisted tapes, generating additional streamwise and azimuthal vortices. These enhance radial momentum redistribution and significantly improve thermal uniformity. Non‐circular orifices (lobed, chevron, and cross‐shaped) promote stronger entrainment and axis‐switching, yielding Nu enhancements of 25%–110% relative to circular jets, especially at low H/D ratios. The integration of nanofluids (Al2O3, CuO, TiO2, and hybrids) with both CIJs and SIJs is also examined. Nanofluids further augment heat transfer (typically 20%–75%) via increased thermal conductivity and microconvection, although they increase viscosity and pressure drop (Δp). Key parameters, including Reynolds number (Re), H/D, swirl number, and nanoparticle concentration, are systematically evaluated using compiled data and correlations. The review concludes that special‐shaped holes shift cooling from localized high‐heat‐transfer zones toward more uniform temperature distributions, making them suitable for gas turbine blades and electronics. Critical gaps remain in combined SIJ‐nanofluid systems on non‐planar surfaces and long‐term nanofluid stability under high‐shear conditions.
ABSTRACT The post‐fracture mechanical performance of laminated glass (LG) is a critical factor for structural safety, primarily governed by fragment size and interlayer coupling. In this article, attention is given to the experimental analysis of 2‐ply small‐scale LG elements in the partially and fully cracked stages. Using a three‐point‐bending (3PB) setup, annealed (AN) glass specimens are subjected to the fracture of the residual glass layer, followed by a cyclic protocol with large imposed displacements. The study evaluates the impact of several geometrical and mechanical parameters on the observed bending response, including variations of glass thickness (4 mm or 10 mm), interlayer type (EVA or SG), interlayer thickness (0.76 mm or 1.52 mm), and fracture layout. Experimental results are discussed in relation to reference performance limits, such as the intact (SI) and monolithic (SM) schemes. The experimental findings emphasize that a significant residual mechanical capacity can be maintained even after prolonged cyclic loading, providing key insights for the design of LG components.
ABSTRACT The selection of sustainable suppliers is a critical multicriteria decision‐making (MCDM) problem. In the era of Industry 5.0, supplier selection is no longer limited to economic criteria such as cost, quality, and delivery performance. In addition to conventional economic factors, it must also prioritize environmental, social, ethical, and human‐centric dimensions to ensure a resilient and sustainable supply chain. As a result, supplier selection is becoming a complex and heterogeneous aspect of manufacturing and supply chain management that includes a number of interdependent and often conflicting criteria. To effectively address these challenges, this research adopts an integrated fuzzy multicriteria decision‐making (MCDM) framework that combines the Fuzzy Best‐Worst Method (BWM) with the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). A total of twenty‐three evaluation criteria were identified, encompassing economic, social, environmental, ethical, and human‐centric dimensions, which were further organized into four overarching categories. The Fuzzy BWM is applied to systematically determine the relative importance of the criteria, while the Fuzzy TOPSIS is employed to prioritize the suppliers by measuring their relative proximity to the ideal alternative, thereby accounting for the inherent uncertainty in expert evaluations. Our findings indicate that resilience, sustainability, and Industry 5.0 together represent nearly half of the overall weight, underscoring their substantial impact on supplier ranking. The approach is verified by expert input and tested for robustness with sensitivity analysis. The study enhances supplier selection using fuzzy MCDM methods and incorporates Industry 5.0‐based criteria. The study offers actionable insights for practitioners seeking to align supplier evaluation with sustainability, resilience, and human‐centric innovation, and lays a foundation for future empirical studies with larger expert panels.
ABSTRACT This study proposes a block‐wise data synthesis approach for operational high‐resolution rainfall prediction in resource‐constrained environments. To address the infeasibility of conventional full‐resolution LSTM training under limited computational capacity, the proposed method decomposes raw data into multiple subblocks for independent prediction, followed by reassembly and reconstruction to achieve high‐resolution output. Monthly precipitation data from the National Tibetan Plateau Data Center were employed alongside Long Short‐Term Memory (LSTM) networks for model development. During the data preprocessing stage, average pooling was applied to reduce the spatial resolution of the training data to one‐fifth of the original to accommodate available hardware constraints. The core model training was conducted on the compressed block‐wise data, with spatial inversion subsequently applied to restore high‐resolution outputs, thereby establishing a complete workflow encompassing “data decomposition–block‐wise training–reassembly and reconstruction.” Experimental results demonstrate that the proposed method achieves acceptable prediction accuracy for monthly rainfall forecasting tasks while significantly reducing per‐GPU memory consumption and training time. Compared with conventional full‐resolution training approaches, the method enables high‐resolution modeling over larger spatial extents under equivalent hardware conditions. The experimental results validate the engineering feasibility and operational deployment value of the block‐wise data synthesis approach in resource‐constrained scenarios. Future research will explore the incorporation of additional meteorological variables and model coupling strategies to further enhance the capability of this methodology. Furthermore, monthly rainfall forecasting is directly relevant to engineering practices such as water resources management, flood prevention and disaster mitigation, and agricultural production. The forecasting workflow presented in this study can support reservoir system operation at the basin scale, urban flood control infrastructure design, and optimization of disaster preparedness plans, underscoring its practical value within operational systems.
ABSTRACT Ferroelectric materials are pivotal for advancing room‐temperature flexible portable electrocaloric refrigeration. Ferroelectric ceramic–polymer nanocomposites, which integrate the advantages of both material types, have emerged as a promising solution. The dielectric properties (e.g., dielectric constant and dielectric breakdown strength) of these nanocomposites are key determinants of their electrocaloric performance, and they are highly dependent on microstructural features of the composites. In this study, finite element models of two typical ferroelectric composites, namely Ethylene Vinyl Acetate (EVA)‐Barium Titanate (BaTiO3, BTO) and Poly(vinylidene fluoride‐trifluoroethylene) P(VDF‐TrFE)‐BTO, are established using the electrostatic analysis model. The effects of BTO filler parameters on the dielectric constant and dielectric breakdown strength of the composites are systematically investigated. The results indicate that: (1) provided that the filler loading ratio is kept constant, nanoscale variations in BTO particle size (50–500 nm) have no significant impact on the dielectric properties of the composites; (2) increasing BTO volume fraction, adopting chain‐like particle distribution, or increasing BTO fiber aspect ratio can effectively enhance the dielectric constant of the composites; (3) a trade‐off exists between dielectric constant and dielectric breakdown strength. A 40% BTO volume fraction is experimentally identified as the flexibility‐maintaining upper limit. The improvements in dielectric constant are accompanied by a decrease in dielectric breakdown strength. This research provides a theoretical basis for the microstructural design of high‐performance ferroelectric electrocaloric composites, laying the groundwork for the development of efficient room‐temperature flexible electrocaloric refrigeration devices.
ABSTRACT Graphene quantum dots (GQDs) are promising photocatalytic nanomaterials for sustainable water purification because of their tunable electronic structure, abundant active sites, and efficient interfacial charge‐transfer capability. This review critically examines sustainable synthesis and interface‐engineering strategies for GQD‐based photocatalysts, with emphasis on the structure–property relationships governing their performance. Biomass‐derived, hydrothermal, microwave‐assisted, and ultrasound‐mediated routes are evaluated in terms of structural control, environmental impact, reproducibility, and scalability. The roles of quantum confinement, defects, edge chemistry, and surface interactions in charge separation, reactive oxygen species generation, and pollutant degradation are also discussed. Unlike previous reviews that primarily catalogue synthesis methods or photocatalytic efficiencies, this work establishes an integrated synthesis–structure–interface–performance framework linking precursor selection, electronic structure, interfacial charge utilization, catalyst stability, and realistic water‐treatment deployment. Evidence from dye degradation, volatile organic compound removal, and complex aqueous remediation indicates that performance depends on the combined effects of adsorption, surface reactivity, and interfacial electron dynamics. Finally, life‐cycle sustainability, catalyst recovery, fouling, scalable production, and circular treatment integration are critically assessed to guide the practical advancement of GQD photocatalysts.
