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Development of pH sensitive biocompatible block copolymer polymersomes, which are stable in physiological conditions, is enabling the intracellular delivery of water soluble drugs and proteins. As a result, it is becoming increasingly important to develop robust production methods to enhance the polymersome encapsulation efficiency. One way that this could be achieved is through production in microfluidic devices that potentially offer more favourable conditions for encapsulation. Here a flow focussing microfluidic device is used to induce self-assembly of poly(2-(methacryloyloxy)ethyl phosphorylcholine)-poly(2-(diisopropylamino)ethyl methacrylate) (PMPC-b-PDPA) block copolymer by changing the pH of the flows within the microchannels. The laminar flow conditions within the device result in a pH gradient at either interface of the central flow, where diffusion of hydrogen ions enables the deprotonation of the PDPA block copolymer and results in self-assembly of polymersomes. Dynamic light scattering reveals hydrodynamic diameters in the range of 75-275 nm and double membrane structures visualized using transmission electron microscopy indicate that polymersome nanostructures are being produced. The encapsulation efficiency for Bovine Serum Albumin (BSA) was calculated by measuring the spectroscopic absorbance at 279 nm and indicates that the encapsulation efficiency produced in the microfluidic device is equivalent to the standard in solution production method. Critically, the microfluidic system eliminates the use of organic solvents, which limit biological applications, through the pH induced self-assembly process and offers a continuous production method for intracellular delivery polymersomes.

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Estimates of biophysical and biochemical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC) are a fundamental requirement for effectively monitoring and managing forest environments. With its red-edge bands and high spatial resolution, the Multispectral Instrument (MSI) on board the Sentinel-2 missions is particularly well-suited to LAI and CCC retrieval. Using field data collected throughout the growing season at a deciduous broadleaf forest site in Southern England, we evaluated the performance of two hybrid retrieval algorithms for estimating LAI and CCC from MSI data: the Scattering by Arbitrarily Inclined Leaves (SAIL)-based L2B retrieval algorithm made available to users in the Sentinel Application Platform (SNAP), and an alternative retrieval algorithm optimised for forest environments, trained using the Invertible Forest Reflectance Model (INFORM). Moderate performance was associated with the SNAP L2B retrieval algorithm for both LAI (r2 = 0.54, RMSE = 1.55, NRMSE = 43%) and CCC (r2 = 0.52, RMSE = 0.79 g m−2, NRMSE = 45%), while improvements were obtained using the INFORM-based retrieval algorithm, particularly in the case of LAI (r2 = 0.79, RMSE = 0.47, NRMSE = 13%), but also in the case of CCC (r2 = 0.69, RMSE = 0.52 g m−2, NRMSE = 29%). Forward modelling experiments confirmed INFORM was better able to reproduce observed MSI spectra than SAIL. Based on our results, for forest-related applications using MSI data, we recommend users seek retrieval algorithms optimised for forest environments.