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In recent years, many researchers have focused on designing hydrogels with specific functional groups that exhibit high affinity for various contaminants, such as heavy metals, organic pollutants, pathogens, or nutrients, or environmental parameters. Novel approaches, including cross-linking strategies and the use of nanomaterials, have been employed to enhance the structural integrity and performance of the desired hydrogels. The evolution of these hydrogels is further highlighted, with an emphasis on fine-tuning features, including water absorption capacity, environmental pollutant/factor sensing and selectivity, and recyclability. Furthermore, this review investigates the emerging topic of stimuli-responsive smart hydrogels, underscoring their potential in both sorption and detection of water pollutants. By critically assessing a wide range of studies, this review not only synthesizes existing knowledge, but also identifies advantages and limitations, and describes future research directions in the field of chemically engineered hydrogels for water purification and monitoring with a low environmental impact as an important resource for chemists and multidisciplinary researchers, leading to improvements in sustainable water management technology.

Structure-based models have been instrumental in simulating protein folding and suggesting hypotheses about the mechanisms involved. Nowadays, at least for fast-folding proteins, folding can be simulated in explicit solvent using classical molecular dynamics. However, other self-assembly processes, such as protein aggregation, are still far from being accessible. Recently, we proposed that a hybrid multistate structure-based model, multi- e GO, could help to bridge the gap toward the simulation of out-of-equilibrium, concentration-dependent self-assembly processes. Here, we further improve the model and show how multi- e GO can effectively and accurately learn the conformational ensemble of the amyloid β42 intrinsically disordered peptide, reproduce the well-established folding mechanism of the B1 immunoglobulin-binding domain of streptococcal protein G, and reproduce the aggregation as a function of the concentration of the transthyretin 105-115 amyloidogenic peptide. We envision that by learning from the dynamics of a few minima, multi- e GO can become a platform for simulating processes inaccessible to other simulation techniques.