Mohammod Hafizur Rahman

Indoor formaldehyde (HCHO) vapor and aqueous hexavalent chromium (Cr(VI)) are pervasive pollutants typically addressed with distinct, purpose-specific materials. In this study, a ternary Cu–Fe–Zn oxide (CuFeZnO) was synthesized via co-precipitation and evaluated as a dual-function material for both photocatalytic HCHO degradation and Cr(VI) adsorption. The catalyst exhibited a narrowed optical band gap of 2.3 eV, compared to 3.2 eV for TiO₂, consistent with enhanced visible light absorption. Under low-intensity visible irradiation (single 18 W daylight lamp, 8.8 mW cm⁻²), 20 mg of CuFeZnO achieved 92.1% HCHO removal within 2 h, outperforming commercial TiO₂ (P25, 78.2%) and laboratory-synthesized TiO₂ (68.3%) under identical conditions, and retaining 90.8% conversion after seven reuse cycles. For Cr(VI) removal, CuFeZnO achieved 98.98% removal from a 50 mg L⁻¹ solution at pH 8 with a 0.2 g dose. Adsorption kinetics followed a pseudo-second-order model, and equilibrium data were best described by the Freundlich isotherm (R² = 0.992). A double-layer statistical physics model further captured the temperature-dependent equilibrium behavior (298–318 K) with high accuracy (R² = 0.997–0.998), yielding a near-unimolecular site occupancy (n ≈ 0.999 at 298 K) and a maximum adsorption capacity of 111.7 mg g⁻¹. The first-layer adsorption energy increased from 13.4 to 27.6 kJ mol⁻¹ with rising temperature, indicating a physisorption-dominated mechanism. Notably, CuFeZnO achieved comparable or superior HCHO conversion under substantially lower light intensity (18 W) than reported photocatalysts requiring 27–300 W sources. These results demonstrate CuFeZnO as a low-energy, reusable, and multifunctional material for simultaneous air and water remediation.

The present study investigates the thermo-hydraulic enhancement of a coaxial heat exchanger through the combined use of an Al ₂ O ₃ –ZnO/water hybrid nanofluid and optimized internal turbulator geometries under turbulent flow conditions. A hybrid nanofluid containing equal proportions of Al ₂ O ₃ and ZnO nanoparticles dispersed in water was numerically investigated for nanoparticle volume fractions ranging from 1 to 3% over a Reynolds number range of 5 × 10 ⁴ to 10 ⁵ . The results show that increasing the nanoparticle concentration significantly enhances convective heat transfer, with the 3% volume fraction providing the best overall thermo-hydraulic performance among the investigated cases. The introduction of turbulator inserts further promotes fluid mixing and thermal boundary-layer disruption, resulting in a maximum heat transfer enhancement of approximately 115% compared with the smooth tube using pure water. Although the inserts increase the friction factor, the optimal configuration maintains a favorable balance between heat transfer enhancement and hydraulic losses, achieving a maximum thermal enhancement factor (TEF) of 1.73. Among the investigated geometries, Model 5 exhibits the best thermo-hydraulic performance. These findings demonstrate that combining an optimized Al ₂ O ₃ –ZnO/water hybrid nanofluid with an appropriate turbulator geometry is an effective passive strategy for improving the efficiency of compact coaxial heat exchangers.

Fiber-reinforced polymer (FRP) composites offer high specific strength, high stiffness, and good corrosion resistance, which makes them suitable for aerospace, automotive, marine, and wind-energy structures. Their interlaminar fracture behavior is harder to pin down because delamination testing is slow and costly and the response shifts with loading mode. This study presents an explainable hybrid machine learning framework for interlaminar fracture toughness under Mode-I, Mode-II, and Mixed-Mode loading. A weighted ensemble pairs Categorical Boosting (CatBoost), a gradient-boosting method, with the Tabular Prior-Data Fitted Network (TabPFN), a pretrained transformer built for tabular data. A Tree-structured Parzen Estimator (TPE) tunes the CatBoost hyperparameters and the ensemble weight against specimen-grouped cross-validation. The work draws on the public D-STANDART experimental dataset and treats DCB, ELS, and MMB as three separate prediction problems for G_IC, G_IIC, and G_C rather than pooling them into one model. The analyzed D-STANDART specimens comprise pultruded carbon-fiber-reinforced polymer (CFRP) material based on Zoltek PX35 carbon fiber. The data are partitioned at the specimen level, so no specimen contributes records to more than one subset, and feature selection uses the training data alone. SHapley Additive exPlanations (SHAP) then measure how each input shifts the predicted toughness in each mode. The framework reached an R² of 0.9280 for DCB, 0.9870 for ELS, and 0.9627 for MMB, with RMSE values of 5.1759, 1.5041, and 2.8809 and MAE values of 1.6502, 0.5372, and 0.7000, respectively. Bringing a tabular foundation model into delamination analysis, and keeping the three loading modes apart, lets the framework report mode-specific feature behavior instead of one averaged picture.

Inadequate management of chemical industrial effluents necessitates the development of high-capacity, sustainable wastewater treatment technologies. This study investigates the adsorptive removal of hazardous Acid Violet 17 (AV17) dye using a novel biochar derived from Vachellia nilotica stems (VNBB). Characterization via HRTEM and BET analysis revealed a mesoporous, sponge-like architecture with a surface area of 50.92 m ² g ^− 1 and an average pore diameter of 8.02 nm. Batch experiments demonstrated that VNBB is highly effective, achieving a maximum removal efficiency of 97.94% at pH 6. Equilibrium data were best described by the Langmuir isotherm, yielding a superior maximum adsorption capacity ( Q _max ) of 333.33 mgg ^− 1 . Adsorption kinetics followed the pseudo-second order model, (R² = 0.995), implying strong correlation with the experimental results. From thermodynamic and statistical physics study, it was found that the adsorption mechanism followed mainly physical interaction. To provide deeper mechanistic insights, advanced statistical physics modeling was applied. A double-layer model with five parameters successfully described the adsorption isotherms. The steric parameter n ranged from 0.39 to 0.42, indicating a multi-docking mechanism where AV17 molecules maintain a horizontal orientation on the VNBB surface. Thermodynamic analysis confirmed the process is endothermic and spontaneous, with adsorption energies ( E ₁ ) between 22.16 and 24.48 kJmol ^− 1 , signifying a physical component to the binding. Furthermore, the biochar exhibited excellent stability, maintaining high performance across five regeneration cycles with less than a 2% loss in capacity. These findings position VNBB as a high-performance, cost-effective, and reusable adsorbent for the remediation of dye-contaminated industrial wastewater.

Fiber-reinforced polymer (FRP) composites offer high specific strength, high stiffness, and good corrosion resistance, which suits them to aerospace, automotive, marine, and wind energy structures. Their interlaminar fracture behaviour is harder to pin down, because delamination testing is slow and costly and the response shifts with loading mode. This study presents an explainable hybrid machine learning framework for interlaminar fracture toughness under Mode-I, Mode-II, and Mixed-Mode loading. A weighted ensemble pairs Categorical Boosting (CatBoost), a gradient boosting method, with the Tabular Prior-Data Fitted Network (TabPFN), a pretrained transformer built for tabular data. A Tree-structured Parzen Estimator (TPE) tunes the CatBoost hyperparameters and the ensemble weight against specimen-grouped cross-validation. The work draws on the public D-STANDART experimental dataset and treats DCB, ELS, and MMB as three separate prediction problems for GIC, GIIC, and GC rather than pooling them into one model. Partitioning runs at specimen level, so no coupon supplies records to more than one subset, and feature selection uses training data alone. SHAP then measures how each input shifts predicted toughness in each mode. The framework reached R² of 0.9280 for DCB, 0.9870 for ELS, and 0.9627 for MMB, with RMSE of 5.1759, 1.5041, and 2.8809 and MAE of 1.6502, 0.5372, and 0.7000. Bringing a tabular foundation model into delamination analysis, and keeping the three loading modes apart, lets the framework report mode-specific feature behaviour instead of one averaged picture.

This study provides a comprehensive assessment of Water, Sanitation, and Hygiene (WASH) conditions in selected government primary schools of Dhaka, Bangladesh, using an integrated methodology combining the WASH Index, Water Quality Index (WQI), and Heavy Metal Pollution Index (HPI). A systematic questionnaire survey, on-site observations, and laboratory analyses of water samples were conducted across the selected schools, and the collected data were thoroughly analyzed to assess the status of WASH services and identify critical gaps. Results reveal that while basic infrastructure exists, significant gaps persist in hygiene practices, facility functionality, and microbiological water safety. The WASH Index ranged from 34% to 63%, with two schools (PS1, PS5) classified as “Basic Service” and three schools (PS2, PS3, PS4) as “Limited Service”; none of the schools achieved “Advanced Service” levels, and hygiene was identified as the most critical deficiency, with two schools scoring 0% on hygiene indicators. WQI values (33–38) classified all schools as having “Good” physicochemical water quality, and HPI values (37–43) indicated low heavy metal pollution. However, E. coli was detected at 6000 and 30,000 CFU/100 mL in two schools (PS4 and PS5), far exceeding the permissible limit of 0 CFU/100 mL. In addition, lead (Pb) concentrations (0.04–0.05 mg/L) exceeded the WHO guideline value of 0.01 mg/L in all five schools, indicating potential health risks to students. The findings highlight that infrastructure provision alone is insufficient; sustainable WASH outcomes require integrated interventions encompassing behavior change, routine monitoring, fecal sludge management, and institutional governance. This study provides actionable insights to guide evidence-based policy and targeted interventions for improving WASH services in urban primary schools.

Indoor formaldehyde (HCHO) vapor and aqueous hexavalent chromium (Cr(VI)) are pervasive pollutants typically addressed with distinct, purpose-specific materials. In this study, a ternary Cu–Fe–Zn oxide (CuFeZnO) was synthesized via co-precipitation and evaluated as a dual-function material for both photocatalytic HCHO degradation and Cr(VI) adsorption. The catalyst exhibited a narrowed optical band gap of 2.3 eV, compared to 3.2 eV for TiO₂, consistent with enhanced visible-light absorption. Under low-intensity visible irradiation (single 18 W daylight lamp, 8.8 mW cm⁻²), 20 mg of CuFeZnO achieved 92.1% HCHO removal within 2 h, outperforming commercial TiO₂ (P25, 78.2%) and laboratory-synthesized TiO₂ (68.3%) under identical conditions, and retaining 90.8% conversion after seven reuse cycles. For Cr(VI) removal, CuFeZnO achieved 98.98% removal from a 50 mg L⁻¹ solution at pH 8 with a 0.2 g dose. Adsorption kinetics followed a pseudo-second-order model, and equilibrium data were best described by the Freundlich isotherm (R² = 0.992). A double-layer statistical physics model further captured the temperature-dependent equilibrium behavior (298–318 K) with high accuracy (R² = 0.997–0.998), yielding a near-unimolecular site occupancy (n ≈ 0.999 at 298 K) and a maximum adsorption capacity of 111.7 mg g⁻¹. The first-layer adsorption energy increased from 13.4 to 27.6 kJ mol⁻¹ with rising temperature, indicating a physisorption-dominated mechanism. Notably, CuFeZnO achieved comparable or superior HCHO conversion under substantially lower light intensity (18 W) than reported photocatalysts requiring 27–300 W sources. These results demonstrate CuFeZnO as a low-energy, reusable, and multifunctional material for simultaneous air and water remediation.

Silica aerogels are among the most extraordinary porous materials produced through sol–gel chemistry, distinguished by ultralow density, exceptionally high porosity, large specific surface area, and extremely low thermal conductivity. Despite these characteristics, widespread application of conventional silica aerogels has been constrained by inherent brittleness, poor mechanical strength, and moisture sensitivity. Significant research has therefore focused on silica aerogel composites, in which reinforcing or functional phases—fibers, polymers, carbon nanomaterials, metal oxides, and biopolymers—are integrated into the silica network to enhance mechanical robustness, flexibility, hydrothermal stability, electrical conductivity, catalytic activity, and multifunctionality while largely preserving the parent aerogel’s desirable properties. We review the synthesis, characterization, properties, and applications of silica aerogel composites. Sol–gel processing and drying technologies are discussed, followed by composite-formation strategies and the advanced techniques used to evaluate structural, mechanical, thermal, surface, and functional properties. The effects of reinforcing phases on mechanical performance, thermal conductivity, and hydrothermal stability are analyzed, and current and emerging applications in thermal insulation, environmental remediation, catalysis, acoustic damping, aerospace systems, biomedical engineering, and energy storage are highlighted. Finally, key challenges and future directions involving multifunctional materials, green synthesis, and data-driven materials design are discussed.

Advanced engineering applications increasingly demand high-performance polymers with exceptional mechanical and thermal properties; however, predicting their processing behavior remains challenging due to complex rheological responses and the lack of integrated experimental–simulation frameworks. This study introduces a novel integrated experimental–computational methodology that combines comprehensive rheological characterization, multi-model fitting, injection molding simulation, and multiphysics finite element analysis (FEA) to investigate the processing capabilities of Polyether Ether Ketone (PEEK) for aircraft bearing applications. Unlike conventional approaches that treat rheological analysis, processing simulation, and structural assessment separately, our framework establishes a coupled material–process–performance relationship through: (i) systematic thermal and mechanical characterization, establishing PEEK’s high melting temperature (343 °C), degradation temperature (575 °C), and tensile strength (95 MPa); (ii) comparative rheological model fitting, demonstrating that the Carreau–Yasuda model accurately predicts non-linear flow behavior with R² = 0.97, outperforming simpler Power Law and Cross models; (iii) CAD-based injection molding simulation, revealing homogeneous flow distribution and optimized pressure profiles; and (iv) thermo-mechanical FEA, coupling thermal expansion with structural stress analysis to evaluate bearing integrity under operational conditions. The key novelty lies in the seamless integration of experimental rheology with multiphysics simulation, validated through rigorous statistical analysis achieving low RMSE (0.6854 MPa for stress, 0.003220 mm for deformation) and high correlation coefficients (R² = 0.97). The results confirm a uniform flow distribution, stable structural performance, and reliable thermo-mechanical response, establishing PEEK’s suitability for high-performance aerospace components. This work contributes a comprehensive, scalable, and transferable framework that bridges experimental analysis and advanced simulation, enabling the predictive optimization of polymer processing parameters and significantly enhancing manufacturing reliability for industrial applications. The findings demonstrate the applicability of the experimental–computational analysis to the investigated PEEK bearing configuration under the specified processing and simulation conditions. Its specific contribution is the application of comparative rheological model fitting and experimentally characterized PEEK properties to the selected bearing geometry and processing conditions.

Silica aerogels are among the most extraordinary porous materials produced through sol–gel chemistry, distinguished by ultralow density, exceptionally high porosity, large specific surface area, and extremely low thermal conductivity. Despite these characteristics, widespread application of conventional silica aerogels has been constrained by inherent brittleness, poor mechanical strength, and moisture sensitivity. Significant research has therefore focused on silica aerogel composites, in which reinforcing or functional phases — fibers, polymers, carbon nanomaterials, metal oxides, and biopolymers — are integrated into the silica network to enhance mechanical robustness, flexibility, hydrothermal stability, electrical conductivity, catalytic activity, and multifunctionality while largely preserving the parent aerogel's desirable properties. This chapter reviews the synthesis, characterization, properties, and applications of silica aerogel composites. Sol–gel processing and drying technologies are discussed, followed by composite-formation strategies and the advanced techniques used to evaluate structural, mechanical, thermal, surface, and functional properties. The effects of reinforcing phases on mechanical performance, thermal conductivity, and hydrothermal stability are analyzed, and current and emerging applications in thermal insulation, environmental remediation, catalysis, acoustic damping, aerospace systems, biomedical engineering, and energy storage are highlighted. Finally, key challenges and future directions involving multifunctional materials, green synthesis, and data-driven materials design are discussed.