The increasing integration of nanoparticles into laboratory studies and consumables, such as filters, pipette tips, test kits, and coatings, has raised critical concerns about their environmental sustainability, occupational safety, and end-of-life disposal. This review provides a comprehensive examination of the life cycle impacts of nanoparticle-based laboratory products, covering stages from synthesis and in-lab use to waste generation and disposal. Drawing upon ISO 14040/44 life cycle assessment (LCA) frameworks, we highlight methodological challenges in evaluating nano-enabled systems, including data gaps, toxicity uncertainties, boundary selection, and the influence of intrinsic nanoparticle properties (e.g., size, shape, and surface coating) on fate and impact modeling. We assess the environmental burden of nanoparticle synthesis methods, energy inputs, and hazardous byproducts, and explore their property-dependent transformation in laboratory waste streams. Comparative LCA analyses of nano-enabled versus conventional lab consumables reveal performance trade-offs with substantial implications for environmental policy and green lab practices. Furthermore, this review situates LCA within the emerging Safe and Sustainable by Design (SSbD) framework, emphasizing its role in guiding early-stage material innovation, risk prevention, and circularity strategies for nanotechnology. The review also evaluates regulatory responses and proposes safety and circular economy recommendations tailored for laboratories, manufacturers, and policymakers. By connecting LCA with nanowaste management, physicochemical risk factors, and SSbD-guided sustainability principles, this work establishes a foundational platform for safer, more sustainable adoption of nanomaterials in research environments. These findings are particularly relevant as nano-enabled products proliferate across biomedical, energy, environmental, and analytical laboratory domains. Graphical Abstract

This study explores the integration of nanomaterials and machine learning (ML) in enhancing atomization and combustion behavior of nanofuels. Nanoparticles such as TiO₂, Al₂O₃, and graphene derivatives improve fuel atomization, thermal conductivity, and emission reduction. A systematic review (2021–2025) and meta-analysis reveal short-term gains in brake thermal efficiency (+12.5%) and emission reduction (CO −12%, HC −25%, NOx −19%), though long-term stability remains limited by agglomeration and injector fouling. ML-models, including Bayesian-Ridge and Random-Forest, predict efficiency metrics effectively but underperform for emissions. The findings highlight the need for atomization descriptors and hybrid ML–CFD models for robust predictive combustion design.

High-performing cooling fluids are increasingly gaining prominence in thermal applications due to their superior heat transfer (HT) features compared to conventional coolants. This is attributed to the sub-optimal cooling and heat transfer limitations by conventional coolants. Reports reveal that HT enhancement includes different modifications to the radiator for effective heat dissipation, engineered suspensions of nanoparticles in base fluids ensures the direct deployment of existing radiators. In this study, reduced graphene oxide (rGO) nanoparticle was prepared by modified Hummer’s technique via reduction process, with graphite powder as starting material, while silicon dioxide (SiO₂) was commercially sought. A comparative study was conducted to investigate the heat transfer of de-ionised (DI) water-based rGO-SiO₂ against its individual make-ups (rGO and SiO₂). At different volumetric concentrations (0.1%–0.3%) and temperature (20-60°C), SiO₂-rGO|0.3 composite nanofluid achieved a thermal conductivity (TC) enhancement of 16.8%, 9.4%, 7.1%, and 1.8% over the DI-Water, SiO₂|0.1, rGO|0.1, and SiO₂|0.3, which suggest a quick temperature equalization and better heat dissipation potential via Brownian motion, but 0.6% lower Tc compared to rGO|0.3 nanofluid. The viscosities of SiO₂|0.1, rGO|0.1, SiO₂|0.3, rGO|0.3 and SiO₂-rGO|0.3 nanofluids increased by 4.5%, 8.8%, 12.2%, 17.0%, and 14.0%, respectively, which may lead to an increase in pumping power demand, however, the positive figure of merit (TCR>1.0) justifies the nanofluids as better alternative to the basefluid. Furthermore, the Nu values of DI-Water/rGO SiO₂|0.3 was enhanced by 27.4%, 11.6%, 13.2%, 7.6% and 3.9% over the basefluid, SiO₂|0.1, rGO|0.1, SiO₂|0.3 and rGO|0.3 nanofluids, with an observable Nu increment of 3.71% and 8.87% of SiO|0.3 and rGO|0.3 compared to SiO|0.1 and rGO|0.1, respectively. In conclusion, TC ratio of the nanofluids were above unity, indicating enhanced heat transfer capability of the nanofluids compared to the base-fluid, and the MATLAB implementation of the proposed nusselt number correlation, fitted from experimental data achieved an R2 = 0.964.

A systematic comparative hydraulic performance evaluation of liquid-cooled micro/minichannel heat exchangers and macro-scale shell-and-tube heat exchangers under unified constraints on Reynolds number, pumping power, thermal performance, and footprint over five different databases (Scopus, ScienceDirect, ASME, IEEE, and Web of Science databases from 2015 to 2025) was conducted in this study. Scale-dependent hydrodynamics are identified as the primary determinants of performance and design strategy. The systematic assessment of studies showed that liquid-cooled heat exchangers, with hydraulic diameters ranged 0.005–3 mm attained high heat-transfer coefficients and power density through micro-geometry and manifold control, but they incur steep elevated pressure losses unless flow uniformity and fabrication tolerances are tightly controlled. Shell-and-tube heat exchangers, with tube hydraulic diameters of 6–25 mm delivered robust, manufacturable gains mainly via shell-side flow management, optimizing baffle spacing/cut and controlling leakage/bypass, typically with only moderate increments in pressure drop. Coolant property effects were also scale-specific, with liquid-cooled heat exchangers being highly sensitive to viscosity-driven shifts in Reynolds number at the channel scale, whereas shell-and-tube heat exchangers respond primarily to temperature-dependent viscosity and macro-scale flow distribution. Methodologically, liquid-cooled heat exchangers predictions should bridge gaps between computational fluid dynamics and developed heat exchangers stemming from surface roughness, tolerance dispersion, and maldistribution; shell-and-tube heat exchangers use benefits from refining semi-empirical correlations with leakage-aware assessment. The resulting control is scale-tailored, to co-optimize liquid-cooled heat exchangers geometry and manifold configuration within pumping-power limits and manage shell-and-tube heat exchangers shell-side hydrodynamics and tube shaping to balance duty gains, maintainability, as well as allowable pressure drop. Overall, liquid-cooled heat exchangers suit compact, high-heat-flux applications when viscous losses are constrained, whereas shell-and-tube heat exchangers provide dependable, industrial-scale performance with broad coolant compatibility.

This study quantifies the benefit of integrating machine learning (ML) with exergy analysis for automotive waste heat-to-power (WHP) systems. A PRISMA 2020 systematic review (2015–2025) across Scopus, ScienceDirect and Web of Science screened open-access peer-reviewed articles, yielding 19 eligible studies on organic Rankine cycle (ORC), thermoelectric generator (TEG) and hybrid ORC–TEG configurations. A random-effects meta-analysis shows a pooled moderate-to-strong gain (Hedges’ g = 0.49; 95% CI: 0.31–0.67) and a symmetric funnel plot. ORC–ANN optimization is strongest (g = 0.61), followed by hybrid CFD–ML (0.55). Multi-objective ML (0.52) and TEG models (0.34–0.41) improved performance, supporting improved recovery and reduced fuel use and CO₂ emissions.