Search Results (273)

Achieving high-precision overlay target center localization is critical for image-based overlay (IBO) metrology in advanced semiconductor manufacturing. This paper proposes a novel IBO target localization algorithm based on symmetry center matching. Leveraging the symmetry design of the IBO optical system as a physical prior, the algorithm reformulates center localization as a global correlation optimization problem. The grayscale projection profile of a single-sided edge is extracted, spatially mirrored, and used as a reference template for sliding correlation matching against the opposite edge. The symmetry center is then determined from the peak of the Pearson correlation coefficient curve. Simulation results demonstrate a center localization accuracy better than 0.00013 pixels (3$\sigma$), with repeatability precision remaining within 0.012 pixels (3$\sigma$) under stringent noise and blur conditions. Experimental validation yields object-space repeatability precision of 0.129 nm (3$\sigma$) and 0.144 nm (3$\sigma$) in the X and Y directions, respectively, surpassing the 0.32 nm measurement uncertainty requirement for advanced process nodes. The average single-frame processing time is approximately 0.07 s, demonstrating that the proposed algorithm simultaneously satisfies the demands of high precision and high throughput. Full article

Background and Objectives: Systemic inflammation is a key driver in the progression and complexity of coronary artery disease (CAD). Serum calprotectin and the C-reactive protein–triglyceride–glucose index (CTI) have emerged as potential inflammatory and metabolic biomarkers; however, their association with angiographic disease severity has not been clearly defined. This study aimed to evaluate the relationship between serum calprotectin, CTI, and the SYNTAX score (SS) in patients with stable CAD. Materials and Methods: A total of 134 patients undergoing coronary angiography were enrolled. The SS was calculated to quantify coronary lesion complexity. Patients were classified into two groups based on the results of the coronary angiogram: low SS (n = 73, SS < 23), and intermediate–high SS (n = 61, SS ≥ 23). Serum calprotectin, and CTI were obtained at baseline. Correlation analyses were performed to evaluate associations between biomarkers and SS. Receiver operating characteristic (ROC) curve analysis assessed the ability of these biomarkers to predict intermediate–high SS. Univariable and multivariable logistic regression analyses were performed to determine independent associations. Results: Patients with intermediate–high SS had significantly higher levels of serum calprotectin (1009.5 vs. 505.7 ng/mL), and CTI (9.9 vs. 9.5) compared with those with low SS (all p < 0.001). Spearman correlation analysis demonstrated significant positive correlations between SS and, serum calprotectin (ρ = 0.488), and CTI (ρ = 0.453) (all p < 0.001). ROC analysis showed moderate association in respect to intermediate–high SS (0.739 for serum calprotectin, and 0.722 for CTI). In multivariable models, CTI showed the strongest independent association with intermediate–high SS (OR: 4.66, 95% CI: 2.00–10.84, p < 0.001). Conclusions: Serum calprotectin and CTI were significantly associated with coronary lesion complexity, as measured by the SS. These biomarkers may serve as valuable tools for identifying patients with greater CAD severity and anatomical complexity. Full article

Cervical cancer is associated with persistent human papillomavirus (HPV) infections. The early detection of HPV is one of the key strategies for the effective treatment of cervical cancer. Current HPV molecular detection methods use enzyme-based nucleic acid amplification strategies that, although specific and sensitive, involve extensive workflows. Enzyme-free isothermal amplification detection strategies with the potential to adapt to low-resource settings for HPV oncogenic transcripts remain limited. This study aimed to validate a fluorescence-based branched hybridization chain reaction (bHCR) assay for the targeted detection of HPV 16/35 E6 oncogenic transcripts. Analytical performance was evaluated using a synthetic target and a negative clinical matrix, whereas the diagnostic performance of the bHCR assay was evaluated using clinically characterized samples (n = 67). The study demonstrated assay linearity over an analyte concentration range of 0.625–40 µM, with a statistically significant correlation between the fluorescence signal and target concentration (r² = 0.928, p < 0.0001). Analytical accuracy was assessed by pre-extraction spike recovery; achieved recoveries ranged from 70% to 86%, indicating potential RNA loss during the assay workflow. Analytical sensitivity determined the background signal threshold limit of blank (LoB) as 16,251.6 RFU, with detection and quantification at concentrations of 0.0625 µM (≈2.6 × 10¹¹ copies per reaction, limit of detection (LoD) and 0.125 µM (≈5.3 × 10¹¹ copies per reaction, limit of quantification (LoQ). The assay exhibited high diagnostic performance, with a diagnostic cut-off of 16,481 RFU and an area under the curve (AUC) of 0.9194. Specificity and sensitivity of the assay were 94% and 86%, respectively, with a Negative Predictive Value (NPV) of 85% and a Positive Predictive Value (PPV) of 94%. These findings demonstrate a reliable analytical assay with excellent diagnostic discrimination and warrant further optimization and expanded clinical validation. Full article

Coalbed methane (CBM) development is strongly controlled by pore structure evolution in deformed coals and its influence on hydraulic fracturing behavior. To clarify the multifractal characteristics of cross-scale pores and their control on fracturing effectiveness, this study investigated eight different deformation coals from the Ordos Basin using low-temperature CO₂/N₂ adsorption (LT-CO₂A/LT-N₂A) and high-pressure mercury intrusion porosimetry (HMIP). Micropores (<2 nm), mesopores (2–50 nm), and macropores (>50 nm) were systematically characterized, and their pore size distributions (PSDs) were quantitatively analyzed using the Coal Structure Index (CSI) and multifractal theory. The results indicate that the multifractal parameters of macropores are significantly distinct from those of mesopores and micropores, exhibiting lower H (0.824–0.893) and D₁ (0.766–0.853), and higher α₀ (1.422–1.541), ΔD (1.230–1.408), and Δα (1.459–1.642). Macropores controlled by tectonic deformation exhibit stronger heterogeneity compared to mesopores and micropores in local parts of the coal mass; PSD varies significantly with deformation rising, derived from the differential pore structure evolution during brittle–ductile transition and the multi-scale synergistic effects including maturity and composition. Combined with field fracturing curves, the results further indicate that the α₀, ΔD, and Δα of macropores are negatively correlated with breakdown pressure, with correlation coefficients of 0.51, 0.61, and 0.59, respectively, and that strong local heterogeneity of macropores favors fracture initiation and propagation and reduces breakdown pressure. Cataclastic coal is the most favorable for hydraulic fracturing, followed by undeformed coal, whereas granulated coal shows the poorest fracturing performance. Full article

Dynamic characterization of calcareous (carbonate) sands is essential for performance-based design of offshore foundations, coastal reclamation, and marine infrastructure in tropical and subtropical regions. In contrast to silica sands, carbonate sediments are biogenic and typically comprise angular, irregular grains with intra-particle voids and fragile skeletal microstructure. These traits promote grain crushing and fabric evolution at relatively low-to-moderate confinement, leading to pronounced stress dependency, strong nonlinearity with strain amplitude, and substantial scatter in laboratory stiffness and damping measurements. Consequently, empirical correlations calibrated primarily on quartz sands may yield biased estimates when transferred to carbonate environments. This study presents an ML-driven, leakage-aware benchmarking framework for predicting two key dynamic parameters of biogenic calcareous sands, damping ratio $D$ and shear modulus $G$, using standard tabular descriptors commonly available in geotechnical practice. Two consolidated experimental databases were curated from resonant column and cyclic triaxial measurements ($D$: $n=890$; $G$: $n=966$), spanning mean effective confining stress $25 \le {\sigma }_m^{\prime }\le 1600 \mathrm{k}$Pa and a wide range of density and gradation conditions. To emphasize transferability, explicit deposit/site labels were excluded, and missingness arising from heterogeneous reporting was handled through a consistent preprocessing pipeline (training-only imputation, categorical encoding, and scaling). Eleven regression algorithms were evaluated, covering linear baselines, regularized regression, neighborhood learning, single trees, bagging and boosting ensembles, kernel regression, and a feedforward neural network. Performance was assessed using $R^2$, RMSE, and MAE on training/validation/test splits, and engineering credibility was supported through explainability-based diagnostics to verify mechanically plausible sensitivities. Results show that ensemble-tree models (Extra Trees and Random Forest) provide the most reliable accuracy–robustness balance across both targets, consistently outperforming linear models and the tested SVR configuration and exhibiting stable validation-to-test behavior. The explainability audit confirms physically meaningful separation of governing controls: stiffness is primarily stress-controlled (${\sigma }_m^{\prime }$ dominant for $G$), whereas damping is primarily strain-controlled ($\gamma$ dominant for $D$). The proposed framework supports practical deployment as a fast surrogate for generating $G\hspace{0.17em}-\hspace{0.17em}\gamma$ and $D\hspace{0.17em}-\hspace{0.17em}\gamma$ curves within the training domain and for guiding targeted laboratory test planning in carbonate settings. Full article

Adiabatic demagnetization refrigeration (ADR) has attracted considerable attention as an effective approach to reach ultra-low temperatures required for fundamental physics and quantum technologies. Here we directly characterize the cryogenic magnetocaloric performance of the rare-earth-based double-perovskite oxide Gd₂CuTiO₆ (GCTO) through quasi-adiabatic demagnetization measurements. Magnetization measurements show no long-range magnetic transition above 1.8 K and indicate dominant antiferromagnetic (AFM) interactions, consistent with an AFM ordering temperature of $T_{\mathrm{N}}\approx 1.15$ K reported previously. Notably, the isothermal magnetization $M\left(H\right)$ at 1.8 K deviates from an ideal single-ion Brillouin response and is better described by a molecular-field correction for the Gd sublattice, suggesting correlated paramagnetism persisting above $T_{\mathrm{N}}$. In contrast to previous studies that inferred cooling performance from thermodynamic estimates, we directly validate the achievable sub-Kelvin cooling in GCTO through quasi-adiabatic measurements. In the quasi-ADR process starting from $T_0$∼2 K, demagnetization fields of 4, 6, and 9 T yield minimum temperatures of $T_{\min }=761.5$, 452.4, and 289.2 mK, respectively, well below $T_{\mathrm{N}}$. After complete removal of the magnetic field, the sample temperature remains highly stable for at least several tens of minutes, demonstrating a long hold time under quasi-adiabatic conditions. Moreover, the $T\left(H\right)$ curves reveal a characteristic field scale around $H_{\mathrm{c}}$∼1 T, implying a field-induced modification of the low-temperature magnetic-entropy landscape that is relevant to the cooling behavior during demagnetization. These results highlight GCTO as a promising magnetic refrigerant for sub-Kelvin ADR applications and underscore the role of correlated magnetism in optimizing cryogenic magnetocaloric performance. Full article

In recent years, with the widespread application of shortwave infrared (SWIR) spectroscopy in mineral identification and hydrothermal alteration studies, an increasing number of studies have attempted to integrate SWIR spectral data with machine learning approaches to fully exploit mineralization-related discriminative information embedded in high-dimensional spectral datasets. In this study, the Zhunuo porphyry copper deposit in Tibet was selected as the research target. SWIR drill core spectral data were systematically acquired, and a random forest (RF) machine learning model was applied to full-band SWIR spectra (1300–2500 nm) to conduct integrated analyses of copper grade regression and mineralization discrimination. A total of 2140 drill core samples were measured, with three replicate measurements per sample, yielding 6420 spectra. After standardized preprocessing and interpolation resampling, a unified spectral feature dataset was constructed for regression and classification analyses. SWIR spectral data are characterized by a large number of bands, strong inter-band correlations, and relatively limited sample sizes; under such conditions, model generalization ability and stability become critical factors in method selection. Based on ensemble learning, the random forest model constructs multiple decision trees and aggregates their predictions through voting or averaging, effectively reducing model variance and mitigating overfitting, and is therefore well suited for high-dimensional, small-sample, and highly correlated geological spectral datasets. In porphyry copper systems, the spectral characteristics of hydrothermal alteration minerals and mineralization intensity commonly exhibit complex nonlinear relationships, which can be effectively captured by random forest models without requiring predefined functional forms. The regression results indicate that accurate quantitative prediction of copper grade based solely on SWIR spectral data remains limited. In contrast, when a threshold-based binary classification was introduced using an industrial cutoff grade of 0.2% Cu, the model achieved an overall accuracy of 75%, an F1 score of 0.69, and an area under the ROC curve (AUC) of 0.80, demonstrating strong mineralization discrimination capability and stability. Overall, the integration of SWIR spectroscopy with machine learning methods provides an efficient, reliable, and geologically interpretable technical approach for early-stage exploration and detailed drill core interpretation in porphyry copper deposits. Full article

Background/Objectives: Per- and polyfluoroalkyl substances (PFAS) are persistent environmental pollutants that accumulate in humans through everyday exposure pathways, raising concern about long-term metabolic health effects in exposed populations. This study aimed to characterize PFAS-associated serum metabolic alterations in a Flemish population residing within a 3 km radius of a PFAS production facility using untargeted metabolomics and lipidomics. Methods: A cohort of 82 adults was stratified into high-exposure (n = 41, median total PFAS = 162.0 ng/mL) and low-exposure (n = 41, median total PFAS = 7.2 ng/mL) groups. Serum metabolic profiling was performed using four liquid chromatography–high-resolution mass spectrometry (LC-HRMS)-based platforms. Univariate and multivariate statistics were conducted to identify metabolites that were differentially expressed between both exposure groups. Results: The analysis revealed 38 altered metabolites. Overall, high PFAS exposure was characterized by upregulation of phosphatidylglycerols (PG), phosphatidylinositols, phosphatidylethanolamines (PE), and triacylglycerols (TG) and downregulation of sphingomyelins, with differential regulation of ceramides, hexosylceramides (HexCer), and phosphatidylcholines. Glycerophospholipid metabolism as well as sphingolipid metabolism pathways were identified as perturbed. Seven lipids and one amino acid showed weak-to-strong correlations (|r|= 0.23–0.61) with PFAS levels. A panel of five metabolites was selected to explore whether they collectively form a potential metabolic signature associated with PFAS exposure. This panel, including L-aspartic acid, PG 18:0_18:2, HexCer (d18:1/14:0), PE 16:0_18:3, and TG 16:0_20:5_22:6, showed moderate discrimination between residents with high and low PFAS levels (area under the curve, AUC = 0.753). Conclusions: This study identifies coordinated lipid metabolic changes associated with PFAS exposure and highlights a small, exploratory metabolite panel that may provide complementary insight into the biological effects of PFAS. Full article

Background: Pulmonary inflammation is a widely recognized characteristic of active tuberculosis (TB). Although standard TB treatment is effective, a substantial proportion of mycobacteriologically cured TB patients experience persistent pulmonary inflammation, which can lead to long-term lung impairment, post-tuberculosis lung disease (PTLD) and potentially TB recurrence. Methods: We conducted a case–control study to compare host serum biomarker profiles in individuals with minimal (TLG < 50 SUVbw*mL, n = 37) versus extensive (TLG ≥ 50 SUVbw*mL, n = 34) persistent lung inflammation following completion of standard drug-sensitive TB treatment. Lung inflammation was measured by 18F-FDG PET/CT scan using total lung glycolysis (TLG) as a surrogate marker. All participants had negative sputum cultures at four months of TB treatment, and blood samples were collected at treatment completion (month six). A Luminex^® multiplex assay performed on the Bio-Plex^® 200 platform was used to analyze 48 host serum biomarkers involved in cytokine/chemokine signaling. Results: Following multiple t-test analysis, fifteen biomarkers were significantly elevated (p < 0.05) in participants with extensive persistent lung inflammation compared to those with minimal inflammation. Among these, 14 demonstrated potential as discriminatory markers, with area under the curve (AUC) values ranging from 0.707 to 0.806, sensitivities ranging from 47.06% to 73.53%, and specificities ranging from 70.27% to 83.78%. Notably, 13 of these 16 candidate biomarkers significantly correlated with TLG values, further supporting their potential clinical utility. Conclusion: We report associations between serum inflammatory mediators and persistent pulmonary inflammation following mycobacterial clearance in TB patients, highlighting their potential as diagnostic biomarkers that could potentially meet the target product profile (TPP) criteria. Full article

Objectives: Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is an autoimmune encephalitis that can lead to severe neurological impairments, particularly in pediatric patients. Effective biomarkers for diagnosis and prognosis are crucial for improved treatment outcomes. To evaluate the potential of soluble Triggering Receptor Expressed on Myeloid cells 2 (sTREM2) in cerebrospinal fluid (CSF) and serum as diagnostic and prognostic biomarkers in pediatric patients with anti-NMDAR encephalitis. Methods: The study included 21 children diagnosed with anti-NMDAR encephalitis and 27 children with non-inflammatory neurological disorders (OND) as controls. CSF and serum samples were collected from each patient. sTREM2 levels were measured using enzyme-linked immunosorbent assay (ELISA). Statistical analyses, including Mann–Whitney U test and ROC curve analysis, were performed to assess the diagnostic and prognostic value of sTREM2. Results: sTREM2 levels in CSF and serum were significantly higher in children with anti-NMDAR encephalitis compared to the OND group (p < 0.001). CSF sTREM2 levels showed a positive correlation with modified Rankin Scale (mRS) scores and a negative correlation with Glasgow Coma Scale (GCS) scores, suggesting an association with disease severity. ROC curve analysis demonstrated that CSF sTREM2 had a high diagnostic accuracy (AUC = 0.887, p < 0.001), while serum sTREM2 showed a slightly lower diagnostic accuracy (AUC = 0.848, p < 0.001). Patients with better prognoses had significantly lower CSF sTREM2 levels than those with poorer outcomes (p = 0.029). Conclusions: Elevated CSF sTREM2 levels were associated with increased neuroinflammation and poorer clinical outcomes in children with anti-NMDAR encephalitis. These findings suggest that CSF sTREM2 may serve as a valuable biomarker for the diagnosis and prognosis of pediatric anti-NMDAR encephalitis. Full article

Background/Objectives: Hypertensive disorders of pregnancy (HDP) remain a significant cause of maternal and perinatal morbidity worldwide. In some healthcare settings, access to angiogenic testing is limited, underscoring the need for affordable biomarkers to guide risk assessment. NT-proBNP, a marker of myocardial wall stress and cardio-renal dysfunction, may offer complementary prognostic value to the angiogenic sFlt-1/PlGF ratio. Methods: In this prospective multicenter observational study, we enrolled 180 pregnant women and categorized them into preeclampsia (PE, n = 95), non-PE HDP (gestational or chronic hypertension, n = 25), and healthy controls (n = 60). NT-proBNP and sFlt-1/PlGF levels were measured at enrollment, after 20 weeks of gestation, predominantly during the second and third trimesters. Associations with proteinuria, uric acid, creatinine, and maternal–fetal complications were examined using multivariable logistic regression adjusted for maternal age, BMI, and gestational age. Discrimination was assessed using receiver operating characteristic (ROC) curve analysis, and the incremental value of NT-proBNP beyond the sFlt-1/PlGF ratio was evaluated using ΔAUC and net reclassification improvement (NRI). Results: Median NT-proBNP levels were significantly higher in PE compared with non-PE HDP and controls (p < 0.01). NT-proBNP ≥200 pg/mL independently predicted maternal–fetal complications (adjusted OR 3.12, 95% CI 1.41–6.90, p = 0.005) and correlated with proteinuria (r = 0.47), creatinine (r = 0.43), and uric acid (r = 0.40) (all p < 0.001). sFlt-1/PlGF alone yielded an AUC of 0.84 (95% CI 0.77–0.89), while NT-proBNP alone demonstrated an AUC of 0.78 (0.71–0.84). Combining both biomarkers improved discrimination (AUC 0.88, 95% CI 0.82–0.92), with a ΔAUC of 0.04 (p = 0.02) and a continuous NRI of 0.21 (p = 0.03). The 200 pg/mL threshold for NT-proBNP achieved 80% sensitivity and 71% specificity (p < 0.001). Conclusions: NT-proBNP provides independent and complementary prognostic value to the sFlt-1/PlGF ratio in predicting maternal–fetal complications in HDP. A practical threshold of 200 pg/mL aids risk assessment, and integrating NT-proBNP into angiogenic models improves prediction. Further multicenter studies are needed to validate multimarker strategies and their cost-effectiveness. Full article

Breast cancer subtypes are known to have important pathobiological and clinical features. For example, triple-negative breast cancer (TNBC) remains one of the most aggressive and treatment-resistant breast cancer subtypes, lacking hormone and HER2 targets. Increasing evidence suggests that circular RNAs (circRNAs) and their N6-methyladenosine (m⁶A) modifications play critical roles in cancer biology through the regulation of gene expression, stability, and signaling networks. This study aimed to identify m⁶A methylation patterns in circRNAs among breast cancer subtypes, explore their potential biological functions, and assess their diagnostic and prognostic relevance compared with luminal breast cancer subtypes. Genome-wide profiling of m⁶A-modified circRNAs was conducted in TNBC and luminal breast tumor samples using methylated RNA immunoprecipitation followed by microarray analysis. Differential methylation and expression analyses were integrated with pathway enrichment, survival correlation, and receiver operating characteristic (ROC) curve assessments to identify subtype-specific and clinically relevant circRNA candidates. Distinct m⁶A circRNA methylation signatures were identified across breast cancer subtypes, with TNBC showing enrichment in pathways related to Wnt/β-catenin, CDC42 GTPase signaling, and cytoskeletal remodeling. Several circRNAs, including those derived from ZBTB16, DOCK1, METTL8, and VAV3, exhibited significant hypermethylation and high diagnostic accuracy (AUC > 0.80). Survival analyses revealed associations between circRNAs from key host genes and overall or relapse-free survival, suggesting prognostic potential. These findings uncover subtype-specific m⁶A circRNA methylation landscapes that may contribute to tumor aggressiveness and heterogeneity. Identified circRNAs represent candidates for investigation as biomarkers for subtype classification and prognosis and may inform future research into epigenetic and post-transcriptional therapeutic targets in breast cancer. Full article

The mechanical performance of rail fasteners plays a crucial role in the track–structure interaction of high-speed railways. A reasonable lateral stiffness of the fastener system can enhance the stability and safety of train operation and prevent derailment accidents. Under seismic action, adjacent bridge spans undergo reciprocating displacement, causing the rail-fastener system near the beam ends to be subjected to lateral cyclic forces. To investigate the mechanical and deformation behavior of the WJ-8B fastener system under lateral loading, low-cycle reciprocating loading tests were conducted on the rail-fastener system considering different bolt torques. The load–displacement curves and torque–rotation curves of the fastener system were obtained, and formulas for calculating the characteristic values of the mechanical properties of the WJ-8B fastener system were fitted, which show good agreement with the experimental results. The results indicate that the lateral mechanical behavior of the WJ-8B fastener exhibits significant nonlinear characteristics, marked by three distinct inflection points in the load–displacement curve that delineate five stages: initial stage, rail shearing stage, rail sliding stage, rail contact stage, and three-point contact. The bolt torque is positively correlated with the lateral stiffness of the fastener system. Increasing the torque from 115 N·m to 190 N·m enhances the lateral bearing capacity by 29.06% in the push direction and by 38.74% in the pull direction. Meanwhile, the system torque decreases by 21.45% in the push direction and increases by 21.14% in the pull direction. Full article

Indocyanine green near-infrared fluorescence (ICG-NIRF) imaging is widely used to assess tissue perfusion, yet its subjective interpretation limits correlation with postoperative parathyroid function. To address this, the Workflow model for ICG-angiography integrating Standardization and Quantification (WISQ) was developed. This exploratory prospective multicenter study evaluated the reproducibility of WISQ in adults undergoing total thyroidectomy at two Dutch university centres. Patients with contraindications to ICG or prior neck surgery were excluded. Intraoperative imaging used standardized camera settings with blood volume-adjusted ICG dosing, and perfusion curves were analyzed using predefined regions of interest. Eighty patients were included. Significant inter-centre variability was observed in maximum fluorescence intensity, inflow slope, and outflow slope (n = 30). At the lead centre, outflow was the most promising predictor of postoperative hypoparathyroidism (HPT) (median −0.33 [IQR −0.49–−0.15] a.f.u./s for HPT vs. −0.68 [−0.91–−0.41], n = 17, p = 0.08), although no parameter significantly predicted HPT. Repeated ICG injections consistently produced lower maximal intensities irrespective of injection rate, and reproducible curves were achieved only when ICG was freshly dissolved at 0.5 mg/mL instead of 2.5 mg/mL. These findings indicate that ICG concentration and injection technique influence perfusion kinetics and underscore the need to update WISQ with standardized injection dilution to improve its clinical utility. Full article

Accurate and real-time state of charge (SOC) monitoring is critical for the safe, efficient, and stable long-term operation of vanadium redox flow batteries (VRFBs). Traditional monitoring methods are susceptible to errors arising from side reactions, cumulative drift, and electrolyte imbalance. This study develops a non-invasive optical sensor module for the negative electrolyte (anolyte), utilizing the favorable spectral properties of V(II)/V(III) ions at 850 nm for real-time SOC tracking. A fifth-order polynomial model was employed for calibration, successfully managing the non-linear optical response of highly concentrated electrolytes and achieving exceptional accuracy (adjusted R² > 0.9999). The optical sensor reliably tracked capacity degradation over 50 galvanostatic cycles, yielding a degradation curve that showed a high correlation with the conventional coulomb counting method, thus confirming its feasibility for assessing battery’s state of health. Contrary to initial expectations, operating at higher current densities resulted in a lower capacity degradation rate (CDR). This phenomenon is primarily attributed to the time-dependent nature of parasitic side reactions. Higher current densities reduce the cycle duration, thereby minimizing the temporal exposure of active species to degradation mechanisms and mitigating cumulative ion imbalance. This mechanism was corroborated by physicochemical analysis via UV-Vis spectroscopy, which revealed a strong correlation between the severity of spectral deviation and the CDR ranking. This non-invasive optical technology offers a low-cost and effective solution for precise VRFB management and preventative maintenance. Full article