Vol. 4 No. 1 (2026)

Open Access

Article

Article ID: 3988

Density effect on erosion mechanisms in silica-phenolic solid rocket motors insulations

by Jacob Nagler

Materials Technology Reports, Vol.4, No.1, 2026;

The design of lightweight Internal Thermal Protection Systems (ITPS) for solid rocket motors is constrained by the non-linear degradation of erosion resistance at low densities. The primary motivation for this work is the discrepancy often observed between standard design models and flight data, specifically in regions of complex flow such as the aft-dome and submerged nozzle inlets. This study establishes a physics-based constitutive law to predict the transition from thermochemical ablation to mechanical spallation in silica-phenolic composites. Unlike semi-empirical correlations, we derive an Augmented Density-Erosion Model from first principles by coupling the energy conservation equation with Gibson-Ashby cellular solids mechanics. We analytically demonstrate that the mechanical erosion rate scales with density according to a power law (r˙∝ ρ^−β) , where the exponent β ≈ 1.5 corresponds to the fracture toughness scaling of open-cell porous foams. This theoretical framework resolves the "spallation gap", the under-prediction of recession by standard heat-of-ablation models (Q^∗) in low-density felts (ρ < 600 kg·m⁻³). The model is validated against historical firing data, demonstrating that the erosion mechanism shifts from energy-limited to strength-limited regimes as density decreases. Furthermore, we address the practical application of these findings by quantifying "danger zones" in density space for graded insulation architectures. This work provides propulsion designers with a rigorous methodology for determining safety margins in mass-critical motor stages, ensuring structural integrity is not compromised by the pursuit of weight reduction.

Open Access

Article

Article ID: 3985

Physics-informed surrogate modelling of finite-size scaling and Curie temperature suppression in ferroelectric perovskite nanostructures

by Aswin Karkadakattil

Materials Technology Reports, Vol.4, No.1, 2026;

Finite-size suppression of the Curie temperature (T_c) in ferroelectric perovskite nanostructures remains an important yet insufficiently resolved problem, with reported scaling exponents varying considerably across experimental and theoretical studies. Although density functional theory provides atomistic insight into size-dependent behaviour, its high computational cost limits systematic exploration across broad size ranges. Conversely, purely empirical fitting approaches often lack physical interpretability and formal uncertainty quantification. In this work, a physics-informed surrogate modelling framework is developed to investigate finite-size scaling in BaTiO₃ and KNbO₃ nanostructures using a structured dataset compiled from the literature. The model is based on thermodynamically motivated scaling behaviour, enabling extraction of physically meaningful size-dependent parameters. Bootstrap resampling is employed to quantify statistical robustness, yielding scaling exponents of 1.59 (95% confidence interval: 1.43–1.72) for BaTiO₃ and 1.40 (95% confidence interval: 1.31–1.52) for KNbO₃. Gaussian Process regression is further integrated to provide uncertainty-aware predictions across the nanoscale domain. In addition to forward prediction, the framework enables inverse estimation of the minimum particle size required to preserve ferroelectric stability at a specified operating temperature. For a threshold of 300 K, the predicted critical sizes are approximately 4.96 nm for BaTiO₃ and 2.89 nm for KNbO₃. Extension to a coupled size–strain formulation produces a two-dimensional stability map, demonstrating tunable interactions between confinement and strain. Overall, the proposed methodology provides a transparent, statistically rigorous, and computationally efficient framework for predictive analysis and rational design of nanoscale ferroelectric materials.

Open Access

Article

Article ID: 3955

Exploring the effect of graphite-coating on hexanary high entropy metal oxides towards efficient water electrocatalysis

by Shakeel Abbas, Akbar Hussain, Muhammad Asim, Tehmeena Maryum Butt, Banafsha Habib Ur Rehman, Javeria Arshad, Amina Hana, Sadia Kanwal, Muhammad Yasir, Naveed Kausar Janjua

Materials Technology Reports, Vol.4, No.1, 2026;

High-entropy oxides (HEOs) have emerged as promising electrocatalysts due to their high configurational entropy, modular electronic structures, and defect-rich multicationic lattices. However, modifying their electrochemical kinetics through conductive surface modification remains completely unknown. An Al-rich hexanary spinel, Cr, Cd, Fe, Mg, and Mn-based materials were synthesized using a sol-gel method and then modified with graphite (5–20 wt%) via rotary ball milling to improve conductivity and interfacial charge transfer, resulting in a stable spinel phase as validated by Rietveld-refined XRD. The addition of graphite significantly increased anodic activity, with the 10 wt% composite (HEO-10C) achieving a peak current density of 47.09 mA cm⁻² in 1 M KOH + methanol. This was followed by decreased charge-transfer resistance and better electron-transfer kinetics. The graphite-HEO interface allows for faster reaction pathways, as evidenced by a high diffusion coefficient (8.65 × 10⁻⁸ cm² s⁻¹), a heterogeneous electron-transfer rate constant (3.75 × 10⁻⁴ cm s⁻¹), and a low Tafel slope of 97 mV dec⁻¹. To better measure intrinsic activity, we add a new descriptor, Jη = (Jₚ (peak current density)−Jₒₙₛₑₜ (onset current density)), which represents the net operating current above onset. Jη correlates strongly with traditional kinetic measurements, highlighting the conductivity-driven performance gain in HEO-10C (44.59 mA cm⁻²), which is about 1.6× greater than the uncoated HEO. These findings confirm graphite coating as a viable method for modifying multication HEO electrodynamics and introduce a new measure for assessing advanced oxide-based electrocatalysts.

Open Access

Article

Article ID: 3999

Investigating the effects of wood ash as an alkaline additive and deflocculant in water-based mud

by Abdelaziz Belmahdi, Anas Elhederi

Materials Technology Reports, Vol.4, No.1, 2026;

The environmental impact of chemical additives used in drilling fluids has increased interest in sustainable alternatives. Wood ash, a byproduct of biomass combustion, represents a potential alkaline and rheological modifier for water-based drilling mud systems. This study investigates the performance of wood ash (45–75 μm) at concentrations of 2–8 wt% in bentonite-based water-based mud under both ambient and thermal (hot rolling) conditions. Results demonstrated a clear concentration-dependent response. Plastic viscosity and gel strength decreased progressively up to 6 wt%, indicating improved dispersion and reduced structural buildup. At 8 wt%, partial reversal of this trend was observed, suggesting excessive solids loading may counteract dispersion effects. Yield point values decreased from 6 to 3 lb/100 ft² as concentration increased, confirming enhanced flowability. Wood ash effectively increased mud pH into the desired operational range (9–11) under ambient conditions, while higher concentrations under thermal aging approached the upper alkaline limit. Mud density remained stable (~8.7 lb/gal) across all concentrations, confirming that wood ash does not adversely affect hydrostatic pressure control. Thermal aging generally reduced rheological parameters due to structural weakening of the bentonite network, although moderate concentrations maintained relatively stable performance. The findings indicate that 4–6 wt% wood ash provides an optimal balance between rheological control and alkalinity enhancement. While promising as a sustainable additive, further investigation is required to evaluate extended filtration performance and compositional variability under field conditions. 

Open Access

Article

Article ID: 4151

Automated quality control of recycled aggregates via deep learning: A unified framework for instance segmentation and mass estimation

by Jérôme Lux, Pierre-Yves Mahieux, Philippe Turcry

Materials Technology Reports, Vol.4, No.1, 2026;

The large-scale use of recycled aggregates (RA) in high-grade construction applications is currently hindered by the high variability of their physical properties. Current quality control relies on manual sorting, which is labor-intensive and limits scalability. This study presents RAMSES (Recycled Aggregates Mass estimation and Segmentation), an automated framework based on deep learning, designed to bridge the gap between high-speed production and rigorous material characterization. A central contribution of this work is the introduction of a large-scale, publicly available dataset comprising 90,000 labeled and batch-weighed aggregate instances. This extensive dataset supports a strong statistical robustness across diverse RA compositions and serves as a benchmark for automated waste characterization. Using this dataset, RAMSES performs simultaneous instance segmentation and direct mass estimation from 2D images. By integrating a dual-branch architecture, the model effectively decouples morphological features from instance-dependent density factors. The framework achieves high precision in particle identification (mean Average Precision mAP@[0.5:0.95] = 0.84, mAP@0.5 = 0.91) and a 0.3% relative error in total mass prediction, which meets industrial requirements for batch monitoring. By providing a scalable alternative to manual inspection, this approach improves the consistency of RA-based concrete mixes, directly supporting the transition to a circular construction economy.