Healthcare must balance safety, efficiency, and effectiveness with affordability and accessibility. Microfluidic devices offer low-cost, portable solutions for point-of-care testing, miniaturizing lab functions on chips through microchannels for quick diagnostics, retaining resolution and sensitivity with minimal reagent use. However, their environmental sustainability is uncertain, with concerns about production scale-up, risks from disposability, and the impact of alternative raw materials or manufacturing techniques compared to traditional soft lithography based on polydimethylsiloxane (PDMS). We conducted a cradle-to-grave life-cycle assessment (LCA) of three glucose-detection devices, a PDMS device via soft lithography, a paper device via wax stamping, and a polylactic acid (PLA) device via 3D printing, for both laboratory-scale and commercial-scale production. For lab-scale production, the paper device had the lowest environmental impact across most impact categories, while the PLA device had the highest. However, for commercial-scale production, by transitioning from 3D printing to injection molding, the PLA device performed best overall, while PDMS performed the worst. For both scales, material and energy use were key contributors, with minimal impact from the use phase. This study highlights the importance of considering environmental impacts at multiple scales and shows the added value of using LCA to guide design and production for early-stage technologies.

Owing to their excellent biocompatibility and antibacterial properties, the global annual production of silver nanoparticles (AgNPs) is estimated at 400-800 tons. Therefore, developing a green and safe approach for AgNPs synthesis is urgently required. However, the conventional AgNPs manufacturing methods required toxic reductant and higher energy consumption, which makes the reaction system harmful to humans and environment. In this study, an eco-friendly ultrasound-intensified continuous system employing minimal toxic reagents was developed. The AgNPs formation process was intensified by cavitation instead of using toxic and strong reductants. Several analytical methods were conducted to systematically investigate the mechanism and performance of as-synthesized AgNPs, such as particle size distribution, zeta potential, UV-visible absorption, morphology, yield calculation, as well as in vitro photothermal, and antibacterial. The results clearly indicated that the physicochemical properties of AgNPs could be tuned by adjusting process parameters, including temperature, feeding rate, and ultrasonic power. Specifically, the AgNPs prepared at 90 ℃, 600 W and 50 µL/min exhibited smaller size (2-5 nm) with monodisperse, spherical shape, higher yield, enhanced photothermal, and antibacterial activities. Furthermore, life-cycle assessment (LCA) and machine learning (ML) were employed to evaluate the process sustainability and identify the key influencing parameters. Comprehensive consideration of safety, experimental results, and LCA outcomes demonstrated that the ultrasound-intensified continuous method proposed in this study is superior to the traditional NaBH₄-based batch synthesis at the laboratory scale.

This study presents a comparative cradle-to-gate environmental and economic evaluation of nine synthetic routes for Molnupiravir (EIDD-2801), a broad-spectrum antiviral drug. Integrating Life Cycle Assessment (LCA) with Life Cycle Costing (LCC), we quantified the environmental impacts alongside capital and operational costs, net present costs, and break-even prices of each route. The results reveal significant variation across the schemes, with Schemes #1 and #2 exhibiting the highest impacts and costs due to intensive use of chloroform and diethyl ether, while Scheme #8 demonstrated the lowest impacts and costs, benefitting from higher batch output, greener solvents, and process intensification. Scenario analyses showed that solvent recovery improves sustainability only when high recovery rates are combined with clean electricity; otherwise, the additional energy demand may offset benefits. Scale-up analysis confirmed that higher production scales substantially reduce per-gram impacts and costs, supporting economies of scale. These findings highlight the critical role of solvent management, energy sourcing, the use of sustainability assessment at the earliest stages of innovation, and process design in aligning green chemistry with economic viability. The proposed integrated LCA-LCC framework provides transferable insights to guide sustainable pharmaceutical manufacturing and supports the development of evidence-based policy for greener antiviral drug production.

Continuous human population growth, industrialization, and technical progress have increased the demand for a new design and synthesis of chemical compounds. Developing eco-friendly chemical compounds has been a priority for fostering a sustainable and healthy environment, which is directly linked to human well-being. In this context, green chemistry and circular economy principles have been applied to generate valuable new chemicals, such as surfactants, with high market value. Surfactants play a crucial role in various products for both domestic and industrial applications, leading to their large-scale production a diverse array of chemical structures. However, the advantages of their use must be balanced against their negative environmental impact as pollutants. Thus, there is an increasing demand for the development of new eco-friendly surfactants. Additionally, life cycle assessment (LCA) studies of new surfactants are essential for evaluating their environmental impact, enhancing energy efficiency and facilitating the transition toward sustainable energy resources. In this work, we present the chemical synthesis of oligomeric and polymeric thiophene-based surfactants with potential applications in biosensors, organic transistors, and various other fields. The newly synthesized oligomeric and polymeric thiophene-based surfactants demonstrated medium-to-high biodegradation potential and showed no significant ecotoxicological effects on bacterial communities. However, the LCA of their synthesis revealed a negative impact on the environment and human health, particularly concerning polymeric thiophene-based surfactants. The LCA identified specific chemical steps that could be optimized to develop a new generation of eco-friendly surfactants.

Recent research has emphasized the need to adapt life cycle inventories and life cycle impact assessments to account for changes in future scenarios. This is mostly achieved by using Integrated Assessment Models (IAMs), which combine environmental and economic data to develop prospective life cycle inventories (LCIs). However, running complex IAMs to simulate scenarios is computationally expensive, and the resulting data is not always aligned with the geographical scope of background inventories. This study explores the potential of machine learning (ML) to create prospective water-scarcity characterisation factors by forecasting the AWARE factor for ~ 9700 watersheds globally. Historical time series of water-scarcity characterisation factors are generated using the global freshwater model WaterGAP v2.2d and the AWARE method. Several ML models are trained on these historical datasets and benchmarked using symmetric mean absolute percentage error (sMAPE). The best-performing model, N-Beats, is then used to forecast AWARE values through 2032. The results demonstrate that ML can produce spatially and temporally explicit forecasts with reasonable accuracy (median sMAPE ~ 28%). However, the models primarily capture seasonal patterns rather than long-term structural trends and the results are sensitive to the quality and representativeness of the training data. This study highlights both the potential and the limitations of time series forecasting for developing prospective characterisation factors in life cycle assessment. Graphical abstract