A non-enzymatic glucose sensor was fabricated by electrochemically depositing gold nanoparticles (Au-NPs) onto anodically grown titanium dioxide nanotubes (TiO2-NTs). The sensor’s properties were systematically tuned by varying the number of deposition cycles (10–60). Comprehensive characterization revealed that the Au-NP decoration significantly alters the material’s optical properties, inducing a tunable bandgap shift due to localized surface plasmon resonance (LSPR), with the most pronounced redshift (2.94 eV) observed at 20 cycles. Morphological and elemental analyses (SEM, TEM EDX) confirmed that increasing deposition to 60 cycles drives the lateral coalescence of Au-NPs (average size ∼33 nm) into an interconnected network. Electrochemically, this configuration triggers a mechanistic shift from porous diffusion-limited kinetics to electronic percolation-dominated charge collection, successfully offsetting partial pore choking. The sensor achieved a high sensitivity of 7.96 μA/mM cm2, a low detection limit of 15.1 µM within a linear range of 0.5–4 mM, and excellent reproducibility over 40 operational cycles with notable selectivity against common interferents. The synergy between the high-surface-area TiO2-NT scaffold and the catalytic/plasmonic Au-NPs establishes this platform as a robust and promising candidate for advanced electrochemical biosensing applications.
IntroductionGreen construction teams need a defensible way to convert product-specific concrete carbon declarations into supply decisions that recognize delivery and disruption exposure. Prediction-to-optimization workflows can obscure a critical evidence boundary: verified environmental product declaration (EPD) values can support formal supplier comparison, whereas machine-learning estimates require decision-level validation before allocation.Methods We introduce an evidence-aligned framework that separates observed EPD attributes, model-estimated screening outputs, scenario-derived A4 transport, and assumption-based disruption stress and assigns each a specific decision role. Model transportability was audited using plant, company, temporal, and geographic holdouts, followed by a direct comparison of predicted and EPD-observed rankings and allocations. Formal strength-constrained optimization minimized observed EPD A1–A3 plus scenario A4 under plant-share caps.ResultsAfter harmonization and deduplication, the analysis contained 45,739 EPD records from 663 plants. A prospective external cohort added 22 declarations from 14 plants and seven derivation-unseen companies; the frozen model reduced record-level MAE by 6.6% relative to the frozen training median, while the supplier-ranking gate retained the EPD-grounded path. Across nine city-strength cases, a 40% cap produced three-plant allocations with a mean Herfindahl–Hirschman Index of 0.36. Under a 10% independent plant-disruption stress rate, it reduced the probability of losing at least half of supply from 0.100 to 0.027; mean and median A1–A4 premiums were 14.57% and 7.76%, respectively. The benefits remained measurable across disruption probabilities of 5%–30% and pairwise correlations of 0–0.50. An OpenStreetMap routing check covered 79 plant–project pairs: median distance error was 6.39%, 17 of 18 allocation decisions were unchanged, and the remaining case had 0.026% route-evaluated objective regret.DiscussionThe contribution is a transparent carbon-concentration frontier with an empirical decision-fidelity gate: prediction supports early screening, whereas EPD values anchor formal allocation. The framework provides extensible planning evidence while preserving clear boundaries between comparative scenarios and operational procurement.
Tow-steered automated fiber placement (AFP) enables tailored laminates, but curvature and compaction can generate bending, shear, contact concentration, and deposition mismatch before inspection. In this study, we develop a Cosserat-rod reduced mechanics layer that maps programmed paths to residual-balanced deposited states and exports objective kinematics, energy, dissipation, contact response, and defect-tendency indicators. Under identical geometry, elasticity, loading, restraints, and unilateral roller contact, three states are compared with independently assembled Q4 and 43,065-DOF Hex8 models; maximum discrepancies in centerline deformation, integrated reaction, and peak of the width-averaged pressure are 0.58%, 9.69%, and 6.57%, respectively. A corrected 320-case Latin-hypercube set and a 32-case severe set all satisfy the dual convergence criterion. At N=48, a process/geometry surrogate reverses severe-case order (ρ=−0.904), whereas a Cosserat-mechanics surrogate retains ρ=0.990 and identifies all 11 high-risk cases. In a separate fixed-geometry process counterfactual, mechanics-based features recover the top-risk ordering ρ=0.918 but underpredict all four high-risk thresholds, demonstrating why surrogate selections require nonlinear reevaluation. An unseen process candidate is reevaluated, with an absolute risk discrepancy of 2.27×10−4; prewarmed surrogate screening is 4.72×104 times faster than Cosserat-rod reevaluation. The evidence supports mechanics-constrained triage, not calibrated defect prediction or AFP qualification. Absolute thresholds require machine-specific experiments or calibrated high-fidelity process FE.
In this article, the plain strain mode using LS-DYNA is conducted to investigate the coal mass damage evolution under blasting load, and a fragment evaluation method is proposed to optimize mining blasting engineering. The Riedel–Hiermaier–Thoma model is applied and verified in the single-borehole blasting model with a validation parameter. Several blasting models are simulated to reveal the effect of biaxial isobaric stresses, delay explosion, and borehole space on the crack growth pattern. The results indicate that crack growth tends to be restrained with increasing isobaric stresses; in the multi-hole model, the crack tends to fully grow with the increase in delay, and there is an optimal hole spacing for crack growth and fragment formation. Finally, a fragment evaluation method based on damage threshold is proposed to optimize the blasting design in engineering practice.
Medium manganese steels (MMnSs, 3–12 wt% Mn) are pivotal for third-generation automotive applications due to their strong strength-ductility synergy from complex phase transformations and texture evolution. This review discusses the latest findings in texture development across ferritic, austenitic, and duplex MMnSs, focusing on the influence of alloying elements, stacking fault energy (SFE), and austenite reversion during processing. Studies show that γ-fiber strengthening, cube orientation, and twinning-induced reorientation affect mechanical anisotropy and crashworthiness. Key elements like Mn, Ni, and C stabilize austenite and foster γ-fiber textures, while Al and Si decrease SFE to enhance twinning and ferritic cube components. Despite significant progress, challenges in predicting quantitative prediction of texture evolution under industrial processing conditions, the stability of reverted austenite during cyclic loading, and the integration of TRIP/TWIP mechanisms into predictive models persist. Research gaps include the need for advanced in-situ characterization, multiscale simulations linking chemistry to texture, and long-term corrosion-mechanical coupling studies. By identifying these challenges, this review provides a roadmap for alloy design and processing strategies aimed at optimizing the strength–ductility balance of MMnSs for next-generation automotive steels.
