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Wave-Powered Reverse Osmosis (WPRO) represents a promising convergence of ocean energy harvesting and advanced Reverse Osmosis (RO) desalination techniques. The significant fluctuations in pressures and flow rates within the integrated WPRO system present a critical challenge, necessitating an accurate transient model for effective performance estimation. This study presents a two-dimensional transient model based on pressure-correction algorithm to simulate channel flow with membrane boundary conditions under varying inlet conditions. The coupled dynamics of pressure, velocity, and salt concentration are addressed iteratively by decoupling and updating each term separately. The model investigates the performance of RO systems under different input conditions, including constant, sinusoidal, and irregular flow. The results indicate that constant input with higher pressure and lower flow rate achieves a better Recovery Ratio (RR). It is emphasized that for WPRO systems, a fair comparison requires choosing the same average power or pressure when evaluating different inputs. Under equivalent input power, sinusoidal waves result in a lower RR compared to constant inputs due to reduced average pressure. Conversely, under equivalent inlet pressure and flow rate, sinusoidal waves achieve a higher RR than constant inputs due to the phase difference between pressuredriven permeate velocity and diffusion-driven Concentration Polarization (CP). Specifically, sinusoidal inputs with higher frequency and higher amplitude display a higher RR. Additionally, irregular input yields a higher RR than constant inputs, as mean pressure and power can be maintained at levels comparable to those of constant input. The model’s adaptability to diverse flow regimes — from steady to sinusoidal and irregular fluctuations — highlights its potential as a critical tool for optimizing RO desalination processes powered by renewable ocean energy. 

Cellulose and pectin are the key components of plant primary cell walls (PCWs) responsible for their dynamic growth, as they transition from a flexible structure at early stages to a rigid unit at full growth. However, a fundamental understanding of the pectin-cellulose interface and interactions within PCW and the underlying mechanics of these materials remained a subject of debate, hindering progress in bioinspired and sustainable composite designs to meet the demands of emerging fields, from flexible robotics to regenerative medicine. This study presents a multiscale investigation into the CNC-pectin interface and the influence of calcium ion-mediated cross-links, integrating molecular dynamics (MD) simulations, supported by experimental data of molecular interactions via spectroscopic studies and bulk interactions via viscosity measurements. The MD simulations revealed cross-linking mechanisms of “zipper” and “egg-box”, both being present, depending on local composite properties and ionic concentrations, with the zipper model being the dominant mechanism by almost 10 times with relative insensitivity to Ca2+, thus providing deeper insights into the long-ongoing discussion on pectin Ca2+ interactions. The zipper model is driven by the coordination of Ca2+ with deprotonated carboxyl groups (–COO–), while the egg-box model involves both carboxyl and hydroxyl groups (–OH). The confirmation via spectroscopic studies, characterized by consistent shifts in Raman peaks of the carboxylate group, indicating the rearrangement of ester and carboxyl groups of HGA, and concentration-dependent peak enhancement trends of the hydroxyl group in the FTIR study involved in the two models validated the MD outcomes. Furthermore, MD predicted viscosity aligned with bulk properties provides a basis for the future extension of the work on quantifying interface energies. Overall, the study provides fundamental knowledge on Ca2+-mediated CNC-pectin interactions, helping to resolve reported experimental discrepancies and offering design guidelines for advanced pectin-based biocomposites. 

Abstract Recent experimental studies in the awake brain have identified a rule for synaptic plasticity that is instrumental for the instantaneous creation of memory traces in area CA1 of the mammalian brain: Behavioral Time scale Synaptic Plasticity. This one-shot learning rule differs in five essential aspects from previously considered plasticity mechanisms. We introduce a transparent model for the core function of this learning rule and establish a theory that enables a principled understanding of the system of memory traces that it creates. Theoretical predictions and numerical simulations show that our model is able to create a functionally powerful content-addressable memory without the need for high-resolution synaptic weights. Furthermore, it reproduces the repulsion effect of human memory, whereby traces for similar memory items are pulled apart to enable differential downstream processing. Altogether, our results create a link between synaptic plasticity in area CA1 of the hippocampus and its network function. They also provide a promising approach for implementing content-addressable memory with on-chip learning capability in highly energy-efficient crossbar arrays of memristors.