Fibrin fibers form the fundamental load-bearing skeleton of blood clots and determine their stability and routes to failure. Understanding the rupture behavior of fibrin fiber networks is therefore essential for clarifying the mechanics in the process of thrombus formation, persistence, and removal. Here, we develop a coarse-grained fibrin-fiber model and a workflow to generate biomimetic random fibrin fiber networks that capture the microstructural variation of fibrin network during clot deformations. Using this framework, we systematically investigate how junction density, specimen width-to-length ratio, initial crack length, and fiber tortuosity govern network deformation and rupture. Our results reveal that rupture dynamics are largely controlled by the deformational freedom of fiber segments (fibers spanning between two junctions), which dictates the rupture sequence and the redistribution of load upon local failure. Increasing junction density in networks yields smoother stress-strain responses, whereas sparse networks fail through discrete, geometry-specific rupture events. In contrast to continuum fracture, rupture propagation in fibrin fiber networks is dominated by segment-to-segment strain heterogeneity induced by necking, which drives failure across crack and connector zones. The width-to-length ratio modulates rupture by changing the number of load-bearing fibers, whereas fiber tortuosity increases segment length, enhances deformational freedom, and improves fracture toughness. Together, these findings identify deformational freedom as a unifying principle that links geometry, mechanics, and rupture in fibrin fiber networks. By clarifying how structural parameters dictate fracture toughness and failure pathways, this work advances a mechanistic understanding of thrombus rupture and may help inform strategies for assessing rupture risks and guiding surgical thrombectomy.

Quantum Artificial Intelligence (QAI) has emerged at the nexus of quantum computing and AI, promising to redefine computational frontiers. This survey critically synthesizes the state-of-the-art through 2024, elucidating the profound bidirectional synergy between these fields. We analyze how classical machine learning is accelerating quantum hardware control, circuit optimization, and error correction. Conversely, we assess the potential quantum advantage of algorithms, including variational and kernel-based methods, across domains such as drug discovery, financial modeling, and cybersecurity. Our analysis reveals a critical trade-of between the utility of near-term Noisy Intermediate-Scale Quantum (NISQ) devices and the long-term promise of fault-tolerant architectures. We identify fundamental obstacles to QAI's advancement, including hardware decoherence, algorithmic barren plateaus, and data-encoding bottlenecks. While QAI's potential is transformative, achieving practical quantum advantage requires a concerted effort to overcome these core challenges at the hardware-software interface. This work provides a roadmap for navigating the current landscape and prioritizing future research in this rapidly evolving discipline.

Cell stiffness is a key determinant of how cells deform, migrate, and adapt to mechanically restrictive environments, yet existing single-cell stiffness assays remain difficult to combine with molecular analysis and downstream functional studies. To address these limitations, we introduce a microfluidic platform, stiffness-based ferrohydrodynamic cell sorting (Stiff-FCS), designed for high-throughput quantification of single-cell stiffness, on-chip molecular analysis, and post-assay cell recovery. Stiff-FCS combines ferrofluid-driven actuation with graded confinement channels to control cell movement, induce deformation, and spatially separate cells based on stiffness. An inverse computational model converts cell position and morphology into quantitative Young's modulus values. We demonstrate stiffness profiling of hundreds to thousands of cells per chip within minutes, same-cell fluorescence-based protein analysis, and recovery of stiffness-defined cells for downstream assays. Across diverse human and mouse cell lines, Lamin A/C showed the most consistent association with stiffness, whereas softer cells exhibited greater migratory capacity than stiffer cells. In a series of human head and neck cancer cell models, Stiff-FCS further resolved a stiff, less migratory subpopulation enriched in a higher-molecular-weight Vimentin state, offering a workflow for linking single-cell stiffness to molecular heterogeneity and cell behavior.

The development of neural connections in the brain results from a complex interplay between biological processes and mechanical forces. A key question in neuroscience is how physical forces and the mechanical properties of brain tissue influence the formation of structural connections. Here, we demonstrate that mechanical forces play an essential role in shaping the emergence of short-range connections, particularly U-shaped fibers that link neighboring regions of the cortex. Using a computational model that incorporates our stress-dependent axon reorientation hypothesis, we simulate how growing axons respond to the mechanical stress field generated by cortical folding. Our results suggest that axonal growth and reorientation may be strongly influenced by local mechanical cues, helping establish the organization of these short-range pathways. Supported by in vivo diffusion tensor imaging and histological observations, our findings provide a physical explanation for why these fibers predominantly adopt U-shaped trajectories, and why connections between gyri (ridges) are more prevalent than those between sulci (valleys) or spanning gyri and sulci. These results suggest that understanding the mechanics of brain folding is critical for fully explaining the formation of brain connectivity and its variations in health and disorder.

Carbon fiber-reinforced polymer (CFRP) composites’ macroscopic performance is dictated by complex microstructural interactions, making the prediction of mechanical behavior a critical challenge. This study proposes a data-driven machine learning framework that predicts the full pre-failure stress evolution in CFRP microstructures under complex loading conditions. We generate a large dataset of random microstructures and associated stress histories using finite-element simulations, and train a U-Net model to map microstructural images directly to time-resolved stress fields. The network achieves coefficients of determination of R2 = 0.91, 0.86, and 0.85 under biaxial tension and the two combined tension-shear loading conditions respectively, demonstrating accurate reproduction of stress evolution and strong generalization across unseen microstructural configurations with different fiber volume fractions. To address the common situation in which microstructural images are unavailable, we further develop an auxiliary plugin that reconstructs an equivalent fiber distribution from early-stage stress fields under simple loading, achieving R2 = 0.92 on the validation set, which is then used to infer the complete stress evolution under complex loads. In conclusion, this workflow enables the prediction of CFRP stress evolution based on material distribution, establishes the correlation between early-stage stress and material distribution, and further allows for the prediction of stress under complex loading conditions using early-stage data obtained from simple loading conditions.

ABSTRACT Polymeric porous media are fundamental to sensing, thermal management, and filtration, yet traditional stochastic fabrication often forces trade‐offs between permeability and mechanical integrity. This review examines the paradigm shift from intrinsic chemical synthesis to extrinsic architectural programming, where the topology of “negative space” is treated as a precise design variable via additive manufacturing. We analyze this transition through four pivotal patterning strategies: (1) surface patterning, transforming passive boundaries into active electromechanical zones; (2) directional anisotropy, replacing random dispersion with vector‐dependent properties; (3) topological periodicity, utilizing lattice geometries to program mechanical metamaterials; and (4) hierarchical integration, decoupling conflicting functionalities across length scales. By synthesizing advances in vat photopolymerization and extrusion‐based printing, we demonstrate how these deterministic architectures enhance performance in multimodal sensing, soft actuation, thermal insulation, and selective filtration. Ultimately, bridging monolithic chemistry with biomimetic design offers a scalable pathway to adaptive, multifunctional material systems essential for addressing next‐generation structural and energy challenges.

Thrombosis drives myocardial infarction, acute ischemic stroke and pulmonary embolism, yet more than half of patients undergoing mechanical thrombectomy fail to achieve functional independence, and residual thrombus is detected in up to 91% of cases after catheter-directed thrombolysis. These persistent clinical failures reflect a fundamental gap: thrombus removal is not merely dissolution or extraction, but a multiphysics process governed by the fracture, deformation and fragmentation of a highly anisotropic composite material whose mechanical behavior remains poorly understood and insufficiently predicted. In this review, we examine physics-based and data-driven numerical models of thrombus mechanics, fracture, and fragmentation from a failure-oriented perspective and assess their potential clinical impact. A thrombus is a hierarchically organized composite in which a fibrin network scaffold, functionally differentiated platelet subpopulations and red blood cells interact across scales spanning six orders of magnitude, thus residual thrombus and fragment migration emerge as the fundamental challenges shared by all recanalization strategies from pharmacological thrombolysis to mechanical thrombectomy. We survey six major computational paradigms, including continuum-based approaches, lattice Boltzmann method, particle-based mesoscopic simulation, discrete fiber network models, multiscale coupling frameworks and artificial intelligence/machine learning-assisted methods, and trace the field's evolution over four decades from reaction kinetics to multiphysics fracture coupling. Finally, we delineate critical gaps in current simulation frameworks and argue that AI-driven surrogate models, physics-informed digital twins and generative virtual patient cohorts could transform thrombus simulation from a predominantly academic endeavor into a clinically actionable platform for real-time decision support. STATEMENT OF SIGNIFICANCE: Blood clots cause heart attacks, strokes, and pulmonary embolism, yet current treatments fail to fully remove clots in over half of cases. A key reason is that we still cannot predict how clots break apart during treatment. This review is the first to systematically examine thrombus simulation from a mechanical failure perspective, synthesizing six computational paradigms-from continuum mechanics to artificial intelligence-and tracing their four-decade evolution from modeling how clots form to predicting how they fail. By establishing residual thrombus and fragment migration as the shared unresolved challenges across all treatment strategies, we identify critical modeling gaps and propose AI-driven translational pathways, including digital twins and virtual patient cohorts, that could transform clot simulation into a real-time clinical decision-support tool.

Cortical folds encode the architecture of human cognition, yet the mechanisms that transform the smooth fetal cortex into its convoluted geometry remain elusive. Biophysical modeling enables mechanistic insight into cortical morphogenesis, but existing models often lack anatomical realism and fail to capture key hallmarks and morphometrics of dynamic cortical folding in the developing human brain. Here, we introduce a novel whole-brain developmental framework that integrates region-specific, data-driven growth laws with anatomically accurate cortical geometry to enable realistic and biologically interpretable modeling of cortical morphogenesis during gestation. Growth fields derived from large-scale prenatal magnetic resonance imaging data capture spatiotemporal variations in cortical expansion and thickness across parcellated regions. Incorporating this heterogeneous growth yields anatomically faithful folding patterns that closely match qualitative landmarks and quantitative morphometrics from human imaging. Systematic perturbations of geometry and growth attributes delineate control parameters that produce realistic morphological variability and replicate clinically atypical brain phenotypes consistent with lissencephaly, pachygyria, and polymicrogyria. This framework provides a quantitative foundation for elucidating the mechanisms of typical and atypical fetal brain development and can serve as a promising generative engine for high-fidelity, longitudinal synthetic brain datasets to advance AI-driven developmental neuroscience and clinical translation.

Natural irregularity offers an attractive route to lightweight, multifunctional, and damage tolerant mechanical metamaterials. However, tailoring nonlinear tensile responses in such systems remains challenging because of the complex relationships between structure and properties and the intrinsic trade-off between specific energy absorption and specific strength. Here, a spiderweb inspired inverse design framework for disordered metamaterials with programmable tensile behavior is reported. A virtual growth algorithm constructs disordered architectures from global prior frequency fields mapped from spiderweb patterns, while a residual SwiGLU deep neural network predicts their complex nonlinear mechanical responses. Pearson correlation and SHAP analyses further uncover the structure-property relationships and physical mechanisms underlying performance. To achieve inverse design, a surrogate assisted conditional generative adversarial network is integrated with Monte Carlo dropout, genetic algorithms and Bayesian optimization, enabling diverse and physically meaningful solutions to the one-to-many mapping problem. Following FEA validation, several generated disordered architectures remain beyond the pre-optimization Pareto front. The fabricability of three representative scaled architectures is further confirmed by tensile tests, and qualitative agreement is observed between the simulated and experimental crack paths. These results provide a systematic strategy for tailoring nonlinear tensile responses and developing disordered metamaterials with balanced SEA and SS.

The pursuit of novel mechanical metamaterials faces a core dilemma. While introducing architectural disorder unlocks unprecedented design space, it jeopardizes the structural connectivity essential for mechanical integrity. We introduce a data-driven framework to resolve this critical trade-off in programming nonlinear responses. Our approach pairs a generative autoregressive model for inverse design with a surrogate model for rapid forward prediction. The autoregressive model learns implicit connectivity rules from data to sequentially build valid structures targeting specific performances. We demonstrate that this method successfully designs disordered metamaterials with target nonlinear properties on demand. Analysis of the model’s internal mechanism reveals its learned strategies for maintaining connectivity, highlighting a critical yet controllable trade-off between performance and connectivity. The framework exhibits strong interpolation and extrapolation capabilities, generating novel designs that outperform those in the training set. By providing a powerful and generalizable design tool, this work establishes a pathway to reliably meet complex functional requirements with disordered metamaterials, moving them from conceptual appeal toward practical viability.

Balancing static stiffness and dynamic vibrational properties in biphasic composites remains a challenge. We present an inverse design framework integrating a Convolutional Neural Network (CNN) and a Generative Adversarial Network (GAN). Diverse biphasic microstructures are generated via a corrosion algorithm and evaluated by finite element analysis. A CNN is trained to accurately predict their elastic modulus and frequencies. Unsupervised clustering reveals the inherent stiffness-vibration trade-off governed by the hard phase arrangement. To resolve this, we identify Pareto-optimal candidates satisfying dual property thresholds. These microstructures are then used as conditional input to a CGAN, enabling the on-demand generation of novel architectures with balanced performance. This integrated pipeline (combining prediction, analysis, and generation) provides an efficient computational tool for designing high-performance composites where both load-bearing capacity and vibration resistance are critical.

Fatigue crack growth assessment commonly extrapolates the correlation curve between crack advancement per cycle and cyclic stress intensity factor (da/dN-AK curve) from standard specimens to structural components. However, extensive evidence shows strong geometry dependence, not only calling into question AK as a universal fatigue crack driving force (FCDF) but also raising the principal question of what a proper parameter for FCDF is. In this work, a dual-scale framework was developed which reconstructs the near-tip field with K and non-singular T-stress and resolves crack-tip micro-mechanics via crystal plasticity, thereby linking in-plane geometry to slip. Across grain orientations and crack tip micro-structures, T-stress displays a robust humpback-shaped correlation with local slip intensity, reconciling in-plane geometry-induced shifts in da/dN-AK and furnishing a mechanistic criterion for FCDF. An ideal FCDF should inherently capture the influence of in-plane geometry, and thus reflect the evolution of crack-tip slip intensity. Applying this criterion, four widely used FCDF parameters: crack-tip accumulative equivalent plastic strain (AP), dissipated energy density (AW), crack-tip opening displacement (CTOD), and cyclic J-integral (AJ) were evaluated. The results show that AP and AW faithfully track slip intensity, whereas single AJ alone does not, requiring additional parameter for fully characterizing crack tip field. Crack-closure analyses further show that, although closure level modulates the magnitude of AJ, crack opening does not trigger abrupt changes in the slip field, which challenges the crack closure as a simple on-off mechanism. Consistent with this, the CTOD correlates with slip but remains sensitive to closure and thus requires further validation. By exposing the T-stress-slip link and providing a practical dual-scale assessment tool, this work establishes a mechanistic framework for evaluating FCDF parameters, which facilities transferring laboratory measurements to structural components beyond the limitations of AK.

Pelvic floor disorders (PFD) are common among women, causing dysfunction, incontinence, and discomfort. Surgeries to repair the descended tissues can result in complications due to implant material design, particularly from the hardness and mechanical mismatch to native tissue. A more flexible implant could reduce complications, such as exposure and tissue erosion. This work seeks to characterize a 3D-printed double-crosslinked hydrogel tissue scaffold consisting primarily of polyvinyl alcohol (PVA). It also compares its static/dynamic/thermal/biological properties to existing commercial products used in PFD therapies, showing our pelvic mesh's biodegradability/robustness advantages over the commercial ones. Tensile tests revealed that the hydrogel scaffold was more compliant than the commercial alternatives. Dynamic mechanical testing has shown that these polymers are durable enough to support organs with specific weight above the pelvic floor. In vivo mouse studies demonstrated low inflammation and good biocompatibility over a 28-day period. The development of this scaffold offers a promising alternative for more effective, long-lasting PFD treatments with fewer post-operative complications, advancing personalized medicine.

Hierarchical porous materials enable next‐generation protective, thermal, and biomedical devices by leveraging multiscale architectures with tunable mechanical and thermal properties. Current 3D printing techniques mainly yield periodic lattices and support limited polymer types, restricting patterning possibilities and scalability. Stochastic foam architectures, with heterogeneous and interconnected pores, mimic biological structures for improved energy dissipation and functional adaptability. However, scalable additive manufacturing of such foams remains scarcely explored. Here, a direct ink writing (DIW) strategy couples static mixer‐enabled reactive extrusion and in situ polymerization to manufacture stochastic polyurethane (PU) foams at ambient conditions, eliminating post‐processing. Precise control over pore size (0.2 µm to 1.2 mm), porosity (65–95%), and open‐cell architecture delivers thermal conductivities down to 0.067 W m −1 K −1 and elastic recovery exceeding 90% after 5000 cycles. A multi‐agent artificial intelligence framework enables the patterning of spatially organized, bioinspired motifs into print‐ready CAD geometries, resulting in architecturally structured foams with tailored anisotropy and spatial thermal management. Flow‐rate modulation further tunes morphology, optimizing the balance between stiffness and damping. This manufacturing platform integrates stochastic pore formation, AI‐guided patterning, and mechanical–thermal optimization to realize scalable, customizable materials for impact protection, wearable thermotherapy, and adaptive healthcare, advancing digital manufacturing, smart materials, and personalized function.

Traditional computational methods, such as the finite element analysis, have provided valuable insights into uncovering the underlying mechanisms of brain physical behaviors. However, precise predictions of brain physics require effective constitutive models to represent the intricate mechanical properties of brain tissue. In this study, we aimed to identify the most favorable constitutive material model for human brain tissue. To achieve this, we applied artificial neural network and multiple regression methods to a generalization of widely accepted classic models, and compared the results obtained from these two approaches. To evaluate the applicability and efficacy of the model, all setups were kept consistent across both methods, except for the approach to prevent potential overfitting. Our results demonstrate that artificial neural networks are capable of automatically identifying accurate constitutive models from given admissible estimators. Nonetheless, the five-term and two-term neural network models trained under single-mode and multi-mode loading scenarios, were found to be suboptimal and could be further simplified into two-term and single-term, respectively, with higher accuracy using multiple regression. Our findings highlight the importance of hyperparameters for the artificial neural network and emphasize the necessity for detailed cross-validations of regularization parameters to ensure optimal selection at a global level in the development of material constitutive models. This study validates the applicability and accuracy of artificial neural network to automatically discover constitutive material models with proper regularization as well as the benefits in model simplification without compromising accuracy for traditional multivariable regression.

Understanding the mechanical behavior of brain tissue is crucial for advancing both fundamental neuroscience and clinical applications. Yet, accurately measuring these properties remains challenging due to the brain's unique mechanical attributes and complex anatomical structures. This review provides a comprehensive overview of commonly used techniques for characterizing brain tissue mechanical properties, covering both invasive methods-such as atomic force microscopy, indentation, axial mechanical testing, and oscillatory shear testing-and noninvasive approaches like magnetic resonance elastography and ultrasound elastography. Each technique is evaluated in terms of working principles, applicability, representative studies, and experimental limitations. We further summarize existing publications that have used these techniques to measure human brain tissue mechanical properties. With a primary focus on invasive studies, we systematically compare their sample preparation, testing conditions, reported mechanical parameters, and modeling strategies. Key sensitivity factors influencing testing outcomes (e.g., sample size, anatomical location, strain rate, temperature, conditioning, and post-mortem interval) are also discussed. Additionally, selected noninvasive studies are reviewed to assess their potential for in vivo characterization. A comparative discussion between invasive and noninvasive methods, as well as in vivo versus ex vivo testing, is included. This review aims to offer practical guidance for researchers and clinicians in selecting appropriate mechanical testing approaches and contributes a curated dataset to support constitutive modeling of human brain tissue. STATEMENT OF SIGNIFICANCE: Accurate characterization of brain tissue mechanics is essential for both neurological research and the development of predictive biomechanical models. This review synthesizes current experimental approaches used in brain mechanical testing-spanning both invasive and noninvasive methods-with a focus on their principles, applications, and limitations. We further systematically compile and analyze a comprehensive set of invasive studies-supplemented by representative noninvasive reports-on human brain tissue mechanical properties. The collected dataset offers valuable support for constitutive modeling. Additionally, we discuss key factors affecting testing outcomes, offering practical insights to guide the design and interpretation of future brain mechanical research.

The precise segmentation of multi-feature martensite-austenite (M-A) islands in bainitic steels is essential for understanding the microstructure-property relationships. However, current methods face challenges due to the complex morphology and interdependencies of features. This study proposes a classification-guided two-stage framework to address these issues, where the first stage employs classification to identify and categorize M-A islands, and the second stage performs segmentation using customized CBAM-enhanced U-Net models to accurately delineate their boundaries and capture detailed morphological features. Initially, a classification model categorizes M-A islands into distinct morphological types-elongated, blocky, and irregularly aggregated, providing essential prior knowledge for subsequent segmentation. Based on these classifications, customized segmentation models are developed for each type, optimized to enhance boundary accuracy and handle class imbalances. The results demonstrate superior segmentation performance across the different M-A island types, with average IoU values of 65.5 %, 88.0 %, and 81.5 % for elongated, blocky, and irregularly aggregated M-A islands, respectively. The proposed framework outperforms hybrid-model approaches while reducing reliance on large labeled datasets. In addition, a systematic evaluation of data increments demonstrates that the classification-guided strategy can achieve high accuracy with fewer annotations. Cross-material validation further confirms the strong generalization capability of the framework, underscoring its potential for broader applications in microstructural analysis. Overall, this study establishes a morphology-aware approach that enables precise and efficient microstructure classification and segmentation, while elucidating critical structure-property relationships governing fatigue-fracture resistance optimization in advanced steel systems.

With the rapid advancements in large language model technology and the emergence of bioinformatics-specific language models (BioLMs), there is a growing need for a comprehensive analysis of the current landscape, computational characteristics, and diverse applications. This survey aims to address this need by providing a thorough review of BioLMs, focusing on their evolution, classification, and distinguishing features, alongside a detailed examination of training methodologies, datasets, and evaluation frameworks. We explore the wide-ranging applications of BioLMs in critical areas such as disease diagnosis, drug discovery, and vaccine development, highlighting their impact and transformative potential in bioinformatics. We identify key challenges and limitations inherent in BioLMs, including data privacy and security concerns, interpretability issues, biases in training data and model outputs, and domain adaptation complexities. Finally, we highlight emerging trends and future directions, offering valuable insights to guide researchers and clinicians toward advancing BioLMs for increasingly sophisticated biological and clinical applications.

Regarding the design of mechanical metamaterials, both periodic unit cells and irregular structures with specific continuity have demonstrated promising application potential. More importantly, material distribution-based design methods also provide a representative perspective. However, existing studies rarely associate mature unit cells with irregular structures while simultaneously considering the influence of material distribution. This hybrid design problem warrants further investigation and holds significant potential for expanding the design space of mechanical metamaterials. This study proposes an inverse design strategy capable of accounting for diverse unit cells and multiple materials in a sole metamaterial design with targeted macroscopic mechanical stiffness. An ensembled deep learning model with variational autoencoders and artificial neural networks is constructed to decouple structural and material contributions to overall mechanical properties, which facilitates the independent design of unit cell and material distribution for targeted properties. Integrating the virtual growth algorithm, the proposed method addresses critical challenges in geometric continuity among various types of unit cells. Accurate reconstruction and prediction of hybrid distributions are realized, with SHAP analysis confirming effective decoupling of structural and material influences on the targeted metamaterial design. Final design targets show excellent accuracy of homogenized properties, indicating the efficacy of our approach. The proposed workflow pioneers a novel decoupled approach for designing mechanical metamaterial with hybrid unit cells and multiple materials, setting a foundation for applications in complex mechanical systems and complicated inverse design problems.

The disposal of wind turbine blade (WTB) waste poses a significant environmental challenge due to its high volume and complex composition. This study introduces an innovative approach to address this issue by repurposing WTB-derived glass fibers (GF) into high-performance polyacrylonitrile (PAN)-GF composite fibers through a scalable dry-jet wet spinning and forced assembly process. By integrating alternating layers of PAN and PAN-GF, layer thickness was precisely controlled to the micrometer scale, ensuring enhanced GF dispersion and improved orientation through shear stress at layer interfaces. The individual layer thickness in the multilayered PAN-GF fibers decreased progressively with an increasing number of layers, with 32-layered fibers exhibiting comparatively thicker layers, while 256-layered fibers demonstrated significantly thinner layers. The effects of WTB-GF incorporation on the thermal and mechanical properties of PAN fibers were examined using tensile testing and thermogravimetric analysis (TGA). Using GF loadings of 1-4 wt %, the 256-layered composite fibers demonstrated remarkable mechanical improvements, with stiffness (modulus) increasing by 54.7% from 15.10 to 23.37 GPa and tensile strength rising by 27.2% from 521.71 to 663.66 MPa compared to pure PAN fibers. TGA results indicate that increasing the GF content leads to higher residual weight at 900 °C, reflecting enhanced thermal stability and greater char yield. The 256-layered 10PAN-4GF fibers showed the highest residual mass (41.23 wt %), highlighting the significant contribution of GF reinforcement to thermal stabilization. Heat treatment further transformed these precursor fibers into carbonized fibers (CF) with exceptional thermal stability and performance under extreme conditions. This process highlights a sustainable pathway for reusing WTB waste and producing advanced composite fibers, making them ideal candidates for demanding applications such as aerospace and space exploration.