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In-situ 3D X-ray Tomography and Analysis of Reverse Osmosis Membranes Under Compaction
... Traditional analysis methods such as molecular dynamics simulation and compaction modeling and prediction are timeconsuming and prone to error [3]. We previously showcased an imaging and analysis workflow using non-destructive X-ray computed tomography (XCT) and AI-segmentation to compare SWRO and HPRO membrane microstructures and analyze performance under high pressures [4]. Recently, we have built on this workflow by implementing our newly developed in-situ compression stage to achieve higher resolution X-ray tomography with more precise control over variable pressures. ...
Imaging-based characterization of polymeric drug-eluting implants can be challenging due to the microstructural complexity and scale of dispersed drug domains and polymer matrix. The typical evaluation via real-time (and accelerated in vitro experiments not only can be very labor intensive since implants are designed to last for 3 months or longer, but also fails to elucidate the impact of the internal microstructure on the implant release rate. A novel characterization technique, combining multi-scale high resolution three-dimensional imaging, was developed for a mechanistic understanding of the impact of formulation and manufacturing process on the implant microstructure. Artificial intelligence-based image segmentation and imaging analytics convert “visualized” structural properties into numerical models, which can be used to calculate key parameters governing drug transport in the polymer matrix, such as effective permeability. Simulations of drug transport in structures constructed on the basis of image analytics can be used to predict the release rates for the drug-eluting implant without running lengthy experiments. Multi-scale imaging approach and image-based characterization generate a large amount of quantitative structural information that are difficult to obtain experimentally. The direct-imaging based analytics and simulation is a powerful tool and has potential to advance fundamental understanding of drug release mechanism and the development of robust drug-eluting implants.
Water scarcity, expected to become more widespread in the coming years, demands renewed attention to freshwater protection and management. Critical to this effort is the minimization of freshwater withdrawals and elimination of wastewater discharge, both which can be achieved via zero liquid discharge (ZLD), an aggressive wastewater management approach. Because of the high energetic cost of thermal desalination, ZLD is particularly challenging for high-salinity wastewaters. In this review, we discuss the potential of high pressure reverse osmosis (HPRO) (i.e., reverse osmosis operated at hydraulic pressure greater than ~100 bar) to efficiently desalinate hypersaline brines. We first discuss the inherent energy-efficiency of membrane processes as compared to conventional thermal processes for brine desalination. We then highlight the opportunity of HPRO to reduce energy requirements for desalination of key high-salinity industrial wastewaters. The current state of membrane materials and processes for hypersaline brine desalination is also discussed, emphasizing several process-design considerations unique to HPRO. Lastly, we discuss the most pressing research needs for the development of HPRO, notably the development of membranes and modules suitable for high pressures as well as fundamental studies of compaction and transport at HPRO conditions.
Iterative reconstruction has been shown to reduce image noise compared with traditional filtered back projection with quantum denoising software (FBP/QDS+) in CT imaging but few comparisons have been made in the same patients without the influence of interindividual factors. The objective of this study was to investigate the impact of adaptive iterative dose reduction in 3-dimensional (AIDR 3D) and FBP/QDS+-based image reconstruction on image quality in the same patients.
We randomly selected 100 patients enrolled in the coronary evaluation using 320-slice CT study who underwent CT coronary angiography using prospectively electrocardiogram triggered image acquisition with a 320-detector scanner. Both FBP/QDS+ and AIDR 3D reconstructions were performed using original data. Studies were blindly analyzed for image quality by measuring the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Image quality was assessed qualitatively using a 4-point scale.
Median age was 63 years (interquartile range [IQR]: 56–71) and 72% were men, median body mass index 27 (IQR: 24–30) and median calcium score 222 (IQR: 11–644). For all regions of interest, mean image noise was lower for AIDR 3D vs. FBP/QDS+ (31.69 vs. 34.37, P ≤ .001). SNR and CNR were significantly higher for AIDR 3D vs. FBP/QDS+ (16.28 vs. 14.64, P < .001 and 19.21 vs. 17.06, P < .001, respectively). Subjective (qualitative) image quality scores were better using AIDR 3D vs. FBP/QDS+ with means of 1.6 and 1.74, respectively (P ≤ .001).
Assessed in the same individuals, iterative reconstruction decreased image noise and raised SNR/CNR as well as subjective image quality scores compared with traditional FBP/QDS+ in 320-slice CT coronary angiography at standard radiation doses.
A recent article in Science by Culp, Kumar, Gomez, and colleagues introduces a new approach to characterizing reverse osmosis thin film composite membranes to provide critical insights into understanding how polymer membrane microstructure affects water permeability. Such approaches offer a degree of microstructure characterization that has up until now been unavailable to the membrane science community. Combined with modeling of water diffusion coefficient in the polymer membrane itself, this effort has begun to answer decades-old questions about how polyamide reverse osmosis membranes function.