Performance Improvement of Membrane Energy Exchanger using Ultrasound for HVAC Application

Air conditioning (AC) systems consume the maximum proportion of the total electricity used in the building sector. The demand of AC systems is expected to increase exponentially in the coming years due to various reasons such as climate change, increasing affordability and increase in living floor space. Membrane-based liquid desiccant AC system along with energy recovery ventilating equipment is considered as a prospective alternative to the conventional air conditioning system (CACS) and has the potential to meet the increasing current and future AC demand in a sustainable manner. Its efficiency and energy saving potential with respect to CACS depends on the performance of the membrane-based dehumidifier, regenerator and energy recovery ventilating equipment which are commonly referred to as membrane energy exchangers (MEEs). MEE is an indirect exchanger type in which the working streams are separated by a porous membrane. This intermediate membrane creates an additional resistance for the heat and mass transfer process in the MEE. To reduce the resistance, this study experimentally and numerically investigates the influence of ultrasound on the performance of the MEE for dehumidification, humidification (applicable for membrane-based evaporative cooling and desiccant regeneration devices) and energy recovery processes. It is found that the vibration due to ultrasound has the potential to improve the effectiveness of the MEE by 55% in the dehumidification process and by 65% in the humidification and energy recovery processes.

A continuum membrane model predicts curvature sensing by helix insertion

Protein domains, such as ENTH (Epsin N-terminal homology) and BAR (bin/amphiphysin/rvs), contain amphipathic helices that drive preferential binding to curved membranes. However, predicting how the physical parameters of these domains control this ‘curvature sensing’ behavior is challenging due to the local membrane deformations generated by the nanoscopic helix on the surface of a large sphere. To overcome this challenge, we here use a deformable continuum model that accounts for the physical properties of the membrane and the helix insertion to predict curvature sensing behavior and is in good agreement with existing experimental data. Specifically, we show that the insertion can be modeled as a local change to the membrane’s spontaneous curvature,. Using physically reasonable ranges of the membrane bending modulus к, and a of ∼0.2-0.3 nm-1, this approach provides excellent agreement with the energetics extracted from experiment. For small vesicles with high curvature, the insertion lowers the membrane energy by relieving strain on a membrane that is far from its preferred curvature of zero. For larger vesicles with low curvature, however, the insertion has the inverse effect, de-stabilizing the membrane by introducing more strain. The membrane energy cannot be directly predicted analytically, due to shape changes from surface relaxation around the anisotropic insertion. We formulate here an empirical expression that captures numerically calculated membrane energies as a function of both basic membrane properties (bending modulus к and radius R) as well as stresses applied by the inserted helix ( and area Ains). We show that the shape relaxation energy has a similar magnitude to the insertion energy, with a strong nonlinear dependence on . We therefore predict how these physical parameters will alter the energetics of helix binding to curved vesicles, which is an essential step in understanding their localization dynamics during membrane remodeling processes.

Minimizing isotropic and deviatoric membrane energy – An unifying formation mechanism of different cellular membrane nanovesicle types

Tiny membrane-enclosed cellular fragments that can mediate interactions between cells and organisms have recently become a subject of increasing attention. In this work the mechanism of formation of cell membrane nanovesicles (CNVs) was studied experimentally and theoretically. CNVs were isolated by centrifugation and washing of blood cells and observed by optical microscopy and scanning electron microscopy. The shape of the biological membrane in the budding process, as observed in phospholipid vesicles, in erythrocytes and in CNVs, was described by an unifying model. Taking the mean curvature h and the curvature deviator d of the membrane surface as the relevant parameters, the shape and the distribution of membrane constituents were determined theoretically by minimization of membrane free energy. Considering these results and previous results on vesiculation of red blood cells it was interpreted that the budding processes may lead to formation of different types of CNVs as regards the compartment (exo/endovesicles), shape (spherical/tubular/torocytic) and composition (enriched/depleted in particular kinds of molecules). It was concluded that the specificity of pinched off nanovesicles derives from the shape of the membrane constituents and not primarily from their chemical identity, which explains evidences on great heterogeneity of isolated extracellular vesicles with respect to composition.

Diverse scientific benchmarks for implicit membrane energy functions

Energy functions are fundamental to biomolecular modeling. Their success depends on robust physical formalisms, efficient optimization, and high-resolution data for training and validation. Over the past 20 years, progress in each area has advanced soluble protein energy functions. Yet, energy functions for membrane proteins lag behind due to sparse and low-quality data, leading to overfit tools. To overcome this challenge, we assembled a suite of 12 tests on independent datasets varying in size, diversity, and resolution. The tests probe an energy function’s ability to capture membrane protein orientation, stability, sequence, and structure. Here, we present the tests and use the franklin2019 energy function to demonstrate them. We then present a vision for transforming these “small” datasets into “big data” that can be used for more sophisticated energy function optimization. The tests are available through the Rosetta Benchmark Server (https://benchmark.graylab.jhu.edu/) and GitHub (https://github.com/rfalford12/Implicit-Membrane-Energy-Function-Benchmark).