We employ off-lattice Monte Carlo simulations to study lateral diffusion in lipid-sterol bilayers using a two-dimensional model system which has been designed to simulate the experimental phase diagrams of both lipid-cholesterol and lipid-lanosterol systems. We focus on the effects of varying sterol concentration and temperature on the tracer diffusion coefficient, D, which characterizes the lateral motion of single tagged lipids in a bilayer. Generally, we find that increasing the cholesterol concentration suppresses D due to an increased conformational ordering of lipid chains. We argue that this effect competes with an increase in the average free area per lipid, which favours an increase in D. At temperatures close to the main transition temperature, the competition between the two effects leads to intriguing behavior of D. Overall, the model results are in excellent qualitative agreement with available experimental results for lipid-cholesterol mixtures. Additional studies of a model lipid-lanosterol system, for which experimental diffusion results are not available, predict that the presence of lanosterol has a smaller effect than cholesterol on reducing D relative to the pure lipid system. We conclude that the molecular model employed contains the essential features required to describe many of the qualitative features of the lateral diffusion behavior in lipid-sterol systems.
Recent research achievements for flexible pressure sensors have promoted promising applications, such as human health detection and intelligent robotics. However, striking a balance between sensitivity and linear detection range is still a challenge. In this paper, a dual conductive layer dome (DCLD) structure, where a silver (Ag) nanowire layer is used as a highly conductive layer and the composite layer of carbon black nanoparticles and polydimethylsiloxane (CPDMS) serves as a low conductive layer, is fabricated with a scratch coating process followed by dip-coating and vacuum adsorption. Benefitting from the dual conductive layer design, the sensitivity of the DCLD sensor reaches 51.7 kPa −1 over an ultrawide pressure of 0.01−250 kPa, which is far better than the CPDMS single conductive layer dome (SCLD). The circuit models of DCLD and SCLD are proposed to disclose their working mechanism. The effects of carbon black nanoparticle ratio in CPDMS and the structural matching between the DCLD structure and electrode on the sensor's performance are explored. The long-cycle stability of DCLD sensors with different fabrication methods is also compared. Additionally, it has been demonstrated that the sensor is efficient in monitoring human motion, such as finger joint flexion, pulse pulses, and human breathing, showing potential in the field of human health monitoring.
mechanical energy harvesting, [3] and selfpowered sensing, [4] even in extreme scenarios such as fireground. [5] In recent years, a novel DC-TENG based on triboelectrification effect and electrostatic breakdown was reported, [6] based on which a motion vector sensor with high sensitivity was developed. [7] However, most traditional self-powered sensors usually work in direct contact mode or deformation mode, [8] which inevitably influences their sensing stability and shortens their service life due to long-term contact-induced wear. On the other hand, non-contact sensors/devices in public places, such as stations and hospitals, could effectively avoid the spread of infectious viruses/bacteria. [9] Therefore, it has huge significance and wide application prospects for developing non-contact sensors.The earliest non-contact triboelectric nanogenerator (NC-TENG) is the freerotating disk structure first proposed by Wang and his team in 2014, [10] which exhibits higher stability and durability compared to its working in contact mode. Then, Zhang et al. [11] proposed a self-powered vibration sensor with contact mode and non-contact mode, which would protect the device and the detected object in operation and the stability and repeatability could be well retained. In recent years, the Maxwell displacement current generated by the leakage field has proved to be the fundamental theoretical basis and source of TENG. [12] An electrodeless TENG based on Maxwell displacement current for wireless energy transmission was proposed, [13] Sensors as the significant units of the Internet of Things play an important role in the field of information interaction. Non-contact sensors have the advantages of flexible manipulation and a longer lifespan but it is constrained in motion detection due to their relative single detection function. Herein, a self-powered non-contact motion vector sensor (NMVS) for the multifunctional human-machine interface is reported. Based on the electrostatic induction effect, the motion vector is measured according to the output electrical signals from the non-contact triboelectric nanogenerator (NC-TENG). By simulation analysis and experimental validation, the output characteristics of NC-TENG dependence on structural and motion parameters are investigated in detail. On this basis, the resolution of NMVS is improved and exhibits for non-contact micro-vibration monitoring, rehabilitation gait detection, contactless smart lock, and the non-contact limit alarm. This work not only proposes an ingenious strategy for non-contact motion vector detection but also demonstrates the promising prospects of a multifunctional humanmachine interface in intelligent electronics, health rehabilitation, and industrial inspection.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.