Switchable β-lactoglobulin (BLG) adsorption on protein resistant oligo (ethylene glycol) (OEG) self-assembled monolayers (SAMs)

Unveiling the spatiotemporal dynamics of membrane fouling: A focused review on dynamic fouling characterization techniques and future perspectives

Membrane technology has emerged as a crucial method for obtaining clean water from unconventional sources in the face of water scarcity. It finds wide applications in wastewater treatment, advanced treatment, and desalination of seawater and brackish water. However, membrane fouling poses a huge challenge that limits the development of membrane-based water treatment technologies. Characterizing the dynamics of membrane fouling is crucial for understanding its development, mechanisms, and effective mitigation. Instrumental techniques that enable in situ or real-time characterization of the dynamics of membrane fouling provide insights into the temporal and spatial evolution of fouling, which play a crucial role in understanding the fouling mechanism and the formulation of membrane control strategies. This review consolidates existing knowledge about the principal advanced instrumental analysis technologies employed to characterize the dynamics of membrane fouling, in terms of membrane structure, morphology, and intermolecular forces. Working principles, applications, and limitations of each technique are discussed, enabling researchers to select appropriate methods for their specific studies. Furthermore, prospects for the future development of dynamic characterization techniques for membrane fouling are discussed, underscoring the need for continued research and innovation in this field to overcome the challenges posed by membrane fouling.

Neural network analysis of neutron and X-ray reflectivity data incorporating prior knowledge

Due to the ambiguity related to the lack of phase information, determining the physical parameters of multilayer thin films from measured neutron and X-ray reflectivity curves is, on a fundamental level, an underdetermined inverse problem. This ambiguity poses limitations on standard neural networks, constraining the range and number of considered parameters in previous machine learning solutions. To overcome this challenge, a novel training procedure has been designed which incorporates dynamic prior boundaries for each physical parameter as additional inputs to the neural network. In this manner, the neural network can be trained simultaneously on all well-posed subintervals of a larger parameter space in which the inverse problem is underdetermined. During inference, users can flexibly input their own prior knowledge about the physical system to constrain the neural network prediction to distinct target subintervals in the parameter space. The effectiveness of the method is demonstrated in various scenarios, including multilayer structures with a box model parameterization and a physics-inspired special parameterization of the scattering length density profile for a multilayer structure. In contrast to previous methods, this approach scales favourably when increasing the complexity of the inverse problem, working properly even for a five-layer multilayer model and a periodic multilayer model with up to 17 open parameters.

Machine learning for scattering data: strategies, perspectives and applications to surface scattering

Machine learning (ML) has received enormous attention in science and beyond. Discussed here are the status, opportunities, challenges and limitations of ML as applied to X-ray and neutron scattering techniques, with an emphasis on surface scattering. Typical strategies are outlined, as well as possible pitfalls. Applications to reflectometry and grazing-incidence scattering are critically discussed. Comment is also given on the availability of training and test data for ML applications, such as neural networks, and a large reflectivity data set is provided as reference data for the community.