K.R. Mangalam University

Cement production contributes a significant share of global CO _2 emissions, yet most laboratories can only access small concrete mix datasets, which limits the direct use of data-driven design tools. This study develops a physics-guided deep ensemble and inverse design framework to support the design of lower-carbon concrete mixtures under data scarcity. The framework first augments a 103-record concrete slump dataset with a physics-regularized conditional tabular GAN (PR-CTGAN) that enforces mass balance, water–binder bounds, and admixture dosage limits during synthetic data generation. It then trains a heterogeneous deep ensemble that combines tree-based regressors with a deep evidential regression (DER) network and a physics-regularized neural network (PRNN) that encodes an empirical slump–water–binder relation as a soft penalty in the loss. This ensemble predicts slump, flow, and 28-day compressive strength while providing uncertainty estimates for each target. Multi-objective Bayesian optimisation tunes the evidential backbone to balance accuracy and probabilistic calibration, and explainable AI tools (SHAP and Sobol sensitivity analysis) highlight how water, binder chemistry, and aggregate ratios drive fresh and hardened behaviour in a way that aligns with concrete practice. Finally, an NSGA-III-based inverse design stage searches the mix space for candidate formulations that meet workability and strength targets while lowering estimated binder-related CO _2 emissions compared with an all-cement reference mix. The framework integrates physics-guided data augmentation, uncertainty-aware evidential prediction, and eco-constrained inverse optimisation into a single pipeline for sustainable concrete mix design.

Multiferroic ceramics (1-x)BaTiO3-xNi0.7Zn0.3Fe2O4 exhibit strong correlations between structural distortion, vibrational dynamics, and magnetic behavior. X-ray diffraction, Raman, and FTIR analyses reveal a slight contraction of the BaTiO3 lattice and a blue shift in the Ti–O stretching mode, indicating increased bond energy due to spinel phase incorporation. Zn2+ substitution in the ferrite phase optimizes cation distribution, enhancing Fe3+–Fe3+ superexchange interactions and increasing saturation magnetization from 0.182 emu/g (x = 0) to 12.84 emu/g (x = 0.3) while reducing coercivity from 0.25 kOe to 0.082 kOe. Maximum polarization peaks at 57.08 µC/cm2 for x = 0.2, attributed to strong internal fields and efficient domain switching. These results demonstrate tunable ferroelectric and magnetic responses in BTO–NZFO composites, highlighting their potential for magnetoelectric sensors and multifunctional electronic devices.

Heterostructures comprised of metal nitrides and mixed metal oxides on 3D porous substrates are novel for the fabrication of high-performance binder-free electrodes. Herein, for the first time, we utilize the Ni 3D framework to grow hierarchical flakes-like nanosheets of ZnCo2O4 and thin film deposition of TiN for the fabrication of ZnCo2O4-TiN/Ni (ZCO-TiN/Ni) binder-free electrodes. Using the vacuum arc deposition technique to deposit TiN on Ni foam and further hydrothermal technique to grow flakes-like ZnCo2O4 nanosheets, the binder-free ZCO-TiN/Ni electrode exhibits a high specific capacitance of 233 mF cm-2 at a current density of 1 mA cm-2, enhanced rate capability of 80% and long cycling stability of 77.6% after 3000 cycles. Additionally, a symmetric supercapacitor assembled using ZCO-TiN/Ni electrodes shows a remarkable energy density of 19 mWh cm-2 at a current density of 0.25 mA cm-2 and a high power density of 493.5 mW cm-2. Above all, the device exhibits good cyclic stability after 2000 cycles. Benefiting from the stable hierarchical microstructure of ZnCo2O4, the ultrahigh electric conductivity of TiN, the 3D framework of current collector, and the binder-free approach, the ZCO-TiN/Ni electrode material prepared by this approach may open new opportunities for the development of promising electrodes for supercapacitors.

The Raman and Infrared (IR) phonon analysis of monolayer Ln2NiO4 (Ln = La, Pr, Nd, Eu, Gd) rare-earth Ruddlesden-Popper (RP) Nickelates in the tetragonal phase, which have potential applications as electrocatalysts for solid oxide cells, has been carried out using normal coordinates. The Ln2NiO4 (Ln = La, Pr, Nd, Eu, Gd) Ruddlesden-Popper compounds possess D4h 17 Point Group Symmetry, fall under the space group 139, and crystallize in the phase I4/mmm with a formula unit number Z = 2. The Layered Perovskite Oxides Ln2NiO4 (Ln = La, Pr, Nd, Eu, Gd) represent the initial members of the Ruddlesden-Popper Nickelates Series, which are structurally defined by the general stoichiometry Lnn+1NinO3n+1 (Ln = La, Pr, Nd, Eu, Gd) with n = 1. The theoretical analysis of the optical phonon modes in Ln2NiO4 (Ln = La, Pr, Nd, Eu, Gd) Ruddlesden-Popper Nickelates crystallizing in the I4/mmm structural phase employs a set of nine Short-Range Force Constants (SRFCs). Wilson's GF-Matrix Method has characterized and assigned the optical vibrational modes in rare-earth Ln2NiO4 (Ln = La, Pr, Nd, Eu, Gd) Ruddlesden-Popper Nickelates. The study further investigates the impact of the A-site Lanthanide cation-Ln (Ln = La, Pr, Nd, Eu, Gd) substitution on the lattice dynamics of the isostructural compounds Ln2NiO4 (Ln = La, Pr, Nd, Eu, Gd) within the framework of monolayered tetragonal structures. To elucidate the effects of cation-Ln substitution, a comparative analysis of the frequencies at the zone centre, bond lengths, and force constants is conducted. The vibrational frequencies primarily governed by the Ln-atoms (Ln = La, Pr, Nd, Eu, Gd) display unique features that change with atomic number, highlighting the significant influence of Ln-ion size on the phonon dynamics of Ln2NiO4 (Ln = La, Pr, Nd, Eu, Gd) rare-earth Nickel Oxides. Furthermore, for each normal mode in the Ln2NiO4 (Ln = La, Pr, Nd, Eu, Gd) rare-earth Ruddlesden-Popper Nickelates, the study of Potential Energy Distribution (PED) emphasizes the considerable role played by ShortRange Force Constants in shaping the wavenumbers, thereby offering a deeper insight into the lattice dynamics and interatomic interactions. Layered Perovskite Oxides Ln2NiO4 (Ln = La, Pr, Nd, Eu, Gd) exhibit key Ln-O and Ni-O phonon modes that govern oxygen ion transport, defect chemistry, and SOFC efficiency.

Accurate in-situ appraisal of concrete quality becomes difficult when supplementary cementitious materials (SCMs) and high-range water-reducing admixtures weaken standard non-destructive testing (NDT) correlations. This study builds a compact but information-rich multi-output dataset ( n=147 ) that pairs Ultrasonic Pulse Velocity (UPV) and Rebound Number (RN) with mix descriptors and physically motivated ratios. Six heterogeneous base learners—Ridge, RBF-SVR, Random Forest, Gradient Boosting, GPU-XGBoost, and a shallow MLP—undergo nested cross-validation and then combine through convex stacking with weights proportional to each model’s global mean absolute SHAP value. This SHAP-weighted ensemble aligns predictive accuracy with interpretability in a single, transparent scheme for simultaneous UPV and RN prediction. The approach attains a cross-validated RMSE of 1.58 (mean across targets) and R^2=0.81 , outperforming the best single learner by about 11