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Transition metal dichalcogenide membranes exhibit good antiswelling properties but poor water desalination property. Here, a one-step covalent functionalization of MoS 2 nanosheets for membrane fabrication is reported, which is accomplished by simultaneous exfoliating and grafting the lithium-ion-intercalated MoS 2 in organic iodide water solution. The lithium intercalation amount in MoS 2 is optimized so that the quality of the produced 2D nanosheets is improved with homogeneous size distribution. The lamellar MoS 2 membranes are tested in reverse osmosis (RO), and the functionalized MoS 2 membrane exhibits rejection rates of >90% and >80% for various dyes (Rhodamine B, Crystal Violet, Acid Fuchsin, Methyl Orange, and Evans Blue) and NaCl, respectively. The excellent ion-sieving performance and good water permeability of the functionalized MoS 2 membranes are attributed to the suitable channel widths that are tuned by iodoacetamide. Furthermore, the stability of the functionalized MoS 2 membranes in NaCl and dye solutions is also confirmed by RO tests. Molecular dynamics simulation shows that water molecules tend to form a single layer between the amide-functionalized MoS 2 layers but a double layer between the ethanol-functionalized MoS 2 (MoS 2 -ethanol) layers, which indicates that a less packed structure of water between the MoS 2 -ethanol layers leads to lower hydrodynamic resistance and higher permeation., (© 2022 Wiley-VCH GmbH.)

A longstanding challenge for accurate sensing of biomolecules such as proteins concerns specifically detecting a target analyte in a complex sample (e.g., food) without suffering from nonspecific binding or interactions from the target itself or other analytes present in the sample. Every sensor suffers from this fundamental drawback, which limits its sensitivity, specificity, and longevity. Existing efforts to improve signal-to-noise ratio involve introducing additional steps to reduce nonspecific binding, which increases the cost of the sensor. Conducting polymer-based chemiresistive biosensors can be mechanically flexible, are inexpensive, label-free, and capable of detecting specific biomolecules in complex samples without purification steps, making them very versatile. In this paper, a poly (3,4-ethylenedioxyphene) (PEDOT) and poly (3-thiopheneethanol) (3TE) interpenetrating network on polypropylene-cellulose fabric is used as a platform for a chemiresistive biosensor, and the specific and nonspecific binding events are studied using the Biotin/Avidin and Gliadin/G12-specific complementary binding pairs. We observed that specific binding between these pairs results in a negative Δ R with the addition of the analyte and this response increases with increasing analyte concentration. Nonspecific binding was found to have the opposite response, a positive Δ R upon the addition of analyte was seen in nonspecific binding cases. We further demonstrate the ability of the sensor to detect a targeted protein in a dual-protein analyte solution. The machine-learning classifier, random forest, predicted the presence of Biotin with 75% accuracy in dual-analyte solutions. This capability of distinguishing between specific and nonspecific binding can be a step towards solving the problem of false positives or false negatives to which all biosensors are susceptible.

The reaction-diffusion system is naturally used in chemistry to represent substances reacting and diffusing over the spatial domain. Its solution illustrates the underlying process of a chemical reaction and displays diverse spatial patterns of the substances. Numerical methods like finite element method (FEM) are widely used to derive the approximate solution for the reaction-diffusion system. However, these methods require long computation time and huge computation resources when the system becomes complex. In this paper, we study the physics of a two-dimensional one-component reaction-diffusion system by using machine learning. An encoder-decoder based convolutional neural network (CNN) is designed and trained to directly predict the concentration distribution, bypassing the expensive FEM calculation process. Different simulation parameters, boundary conditions, geometry configurations and time are considered as the input features of the proposed learning model. In particular, the trained CNN model manages to learn the time-dependent behaviour of the reaction-diffusion system through the input time feature. Thus, the model is capable of providing concentration prediction at certain time directly with high test accuracy (mean relative error <3.04%) and 300 times faster than the traditional FEM. Our CNN-based learning model provides a rapid and accurate tool for predicting the concentration distribution of the reaction-diffusion system.

The beta-1 adrenergic receptor (ADRB1) is a promising therapeutic target intrinsically involved in the cognitive deficits and pathological features associated with Alzheimer's disease (AD). Evidence indicates that ADRB1 plays an important role in regulating neuroinflammatory processes, and activation of ADRB1 may produce neuroprotective effects in neuroinflammatory diseases. Novel small molecule modulators of ADRB1, engineered to be highly brain permeable and functionally selective for the G protein with partial agonistic activity, could have tremendous value both as pharmacological tools and potential lead molecules for further preclinical development. The present study describes our ongoing efforts toward the discovery of functionally selective partial agonists of ADRB1 that have potential therapeutic value for AD and neuroinflammatory disorders, which has led to the identification of the molecule STD-101-D1. As a functionally selective agonist of ADRB1, STD-101-D1 produces partial agonistic activity on G protein signaling with an EC50 value in the low nanomolar range, but engages very little beta-arrestin recruitment compared to the unbiased agonist isoproterenol. STD-101-D1 also inhibits the tumor necrosis factor α (TNFα) response induced by lipopolysaccharide (LPS) both in vitro and in vivo, and shows high brain penetration. Other than the therapeutic role, this newly identified, functionally selective, partial agonist of ADRB1 is an invaluable research tool to study mechanisms of G protein-coupled receptor signal transduction.

The advancement of nanoscale electronics has been limited by energy dissipation challenges for over a decade. Such limitations could be particularly severe for two-dimensional (2D) semiconductors integrated with flexible substrates or multilayered processors, both being critical thermal bottlenecks. To shed light into fundamental aspects of this problem, here we report the first direct measurement of spatially resolved temperature in functioning 2D monolayer MoS 2 transistors. Using Raman thermometry, we simultaneously obtain temperature maps of the device channel and its substrate. This differential measurement reveals the thermal boundary conductance of the MoS 2 interface with SiO 2 (14 ± 4 MW m -2 K -1 ) is an order magnitude larger than previously thought, yet near the low end of known solid-solid interfaces. Our study also reveals unexpected insight into nonuniformities of the MoS 2 transistors (small bilayer regions) which do not cause significant self-heating, suggesting that such semiconductors are less sensitive to inhomogeneity than expected. These results provide key insights into energy dissipation of 2D semiconductors and pave the way for the future design of energy-efficient 2D electronics.

Materials that can serve as long-lived barriers to biofluids are essential to the development of any type of chronic electronic implant. Devices such as cardiac pacemakers and cochlear implants use bulk metal or ceramic packages as hermetic enclosures for the electronics. Emerging classes of flexible, biointegrated electronic systems demand similar levels of isolation from biofluids but with thin, compliant films that can simultaneously serve as biointerfaces for sensing and/or actuation while in contact with the soft, curved, and moving surfaces of target organs. This paper introduces a solution to this materials challenge that combines (i) ultrathin, pristine layers of silicon dioxide (SiO 2 ) thermally grown on device-grade silicon wafers, and (ii) processing schemes that allow integration of these materials onto flexible electronic platforms. Accelerated lifetime tests suggest robust barrier characteristics on timescales that approach 70 y, in layers that are sufficiently thin (less than 1 μm) to avoid significant compromises in mechanical flexibility or in electrical interface fidelity. Detailed studies of temperature- and thickness-dependent electrical and physical properties reveal the key characteristics. Molecular simulations highlight essential aspects of the chemistry that governs interactions between the SiO 2 and surrounding water. Examples of use with passive and active components in high-performance flexible electronic devices suggest broad utility in advanced chronic implants., Competing Interests: The authors declare no conflict of interest.

A fully controllable nano-electro-mechanical device that can pump fluids at nanoscale is proposed. Using molecular dynamics simulations, we show that an applied electric field to an ion@C60 inside a water-filled carbon nanotube can pump water with excellent efficiency. The key physical mechanism governing the fluid pumping is the conversion of electrical energy into hydrodynamic flow with efficiencies as high as 64%. Our results show that water can be compressed up to 7% higher than its bulk value by applying electric fields. High flux of water (up to 13,000 molecules/ns) is obtained by the electro-mechanical, piston-cylinder-like moving mechanism of the ion@C60 in the CNT. This large flux results from the piston-like mechanism, compressibility of water (increase in density of water due to molecular ordering), orienting dipole along the electric field and efficient electrical to mechanical energy conversion. Our findings can pave the way towards efficient energy conversion, pumping of fluids at nanoscale, and drug delivery.

The droplet interface bilayer (DIB)--a method to assemble planar lipid bilayer membranes between lipid-coated aqueous droplets--has gained popularity among researchers in many fields. Well-packed lipid monolayer on aqueous droplet-oil interfaces is a prerequisite for successfully assembling DIBs. Such monolayers can be achieved by two different techniques: "lipid-in", in which phospholipids in the form of liposomes are placed in water, and "lipid-out", in which phospholipids are placed in oil as inverse micelles. While both approaches are capable of monolayer assembly needed for bilayer formation, droplet pairs assembled with these two techniques require significantly different incubation periods and exhibit different success rates for bilayer formation. In this study, we combine experimental interfacial tension measurements with molecular dynamics simulations of phospholipids (DPhPC and DOPC) assembled from water and oil origins to understand the differences in kinetics of monolayer formation. With the results from simulations and by using a simplified model to analyze dynamic interfacial tensions, we conclude that, at high lipid concentrations common to DIBs, monolayer formation is simple adsorption controlled for lipid-in technique, whereas it is predominantly adsorption-barrier controlled for the lipid-out technique due to the interaction of interface-bound lipids with lipid structures in the subsurface. The adsorption barrier established in lipid-out technique leads to a prolonged incubation time and lower bilayer formation success rate, proving a good correlation between interfacial tension measurements and bilayer formation. We also clarify that advective flow expedites monolayer formation and improves bilayer formation success rate by disrupting lipid structures, rather than enhancing diffusion, in the subsurface and at the interface for lipid-out technique. Additionally, electrical properties of DIBs formed with varying lipid placement and type are characterized.

Droplet interface bilayer (DIB) networks are considered for the development of stimuli-responsive membrane-based materials inspired by cellular mechanics. These DIB networks are often modeled as combinations of electrical circuit analogues, creating complex networks of capacitors and resistors that mimic the biomolecular structures. These empirical models are capable of replicating data from electrophysiology experiments, but these models do not accurately capture the underlying physical phenomena and consequently do not allow for simulations of material functionalities beyond the voltage-clamp or current-clamp conditions. The work presented here provides a more robust description of DIB network behavior through the development of a hierarchical multiscale model, recognizing that the macroscopic network properties are functions of their underlying molecular structure. The result of this research is a modeling methodology based on controlled exchanges across the interfaces of neighboring droplets. This methodology is validated against experimental data, and an extension case is provided to demonstrate possible future applications of droplet interface bilayer networks.

Efficient desalination of water continues to be a problem facing the society. Advances in nanotechnology have led to the development of a variety of nanoporous membranes for water purification. Here we show, by performing molecular dynamics simulations, that a nanopore in a single-layer molybdenum disulfide can effectively reject ions and allow transport of water at a high rate. More than 88% of ions are rejected by membranes having pore areas ranging from 20 to 60 Å(2). Water flux is found to be two to five orders of magnitude greater than that of other known nanoporous membranes. Pore chemistry is shown to play a significant role in modulating the water flux. Pores with only molybdenum atoms on their edges lead to higher fluxes, which are ∼ 70% greater than that of graphene nanopores. These observations are explained by permeation coefficients, energy barriers, water density and velocity distributions in the pores.

Nanopore-based DNA sequencing has led to fast and high-resolution recognition and detection of DNA bases. Solid-state and biological nanopores have low signal-to-noise ratio (SNR) (< 10) and are generally too thick (> 5 nm) to be able to read at single-base resolution. A nanopore in graphene, a 2-D material with sub-nanometer thickness, has a SNR of ∼3 under DNA ionic current. In this report, using atomistic and quantum simulations, we find that a single-layer MoS2 is an extraordinary material (with a SNR > 15) for DNA sequencing by two competing technologies (i.e., nanopore and nanochannel). A MoS2 nanopore shows four distinct ionic current signals for single-nucleobase detection with low noise. In addition, a single-layer MoS2 shows a characteristic change/response in the total density of states for each base. The band gap of MoS2 is significantly changed compared to other nanomaterials (e.g., graphene, h-BN, and silicon nanowire) when bases are placed on top of the pristine MoS2 and armchair MoS2 nanoribbon, thus making MoS2 a promising material for base detection via transverse current tunneling measurement. MoS2 nanopore benefits from a craftable pore architecture (combination of Mo and S atoms at the edge) which can be engineered to obtain the optimum sequencing signals.

Encapsulation of a single water molecule in a buckyball (C60) can provide fundamental insights into the properties of water. Investigation of a single water molecule is feasible through its solitary confinement in C60. In this paper, we performed a detailed study of the properties and dynamics of a single water molecule in a buckyball using DFT and MD simulations. We report on the enhancement of rotational diffusion and entropy of a water molecule in C60, compared to a bulk water molecule. H2O@C60 has zero translational diffusion and terahertz revolution frequency. The harmonic, high amplitude rotation of a single water molecule in C60 is compared to stochastic behavior of bulk water molecules. The combination of large rotational and negligible translational motion of water in C60 creates new opportunities in nanotechnology applications.

A fundamental understanding of chemical sensing mechanisms in graphene-based chemical field-effect transistors (chemFETs) is essential for the development of next generation chemical sensors. Here we explore the hidden sensing modalities responsible for tailoring the gas detection ability of pristine graphene sensors by exposing graphene chemFETs to electron donor and acceptor trace gas vapors. We uncover that the sensitivity (in terms of modulation in electrical conductivity) of pristine graphene chemFETs is not necessarily intrinsic to graphene, but rather it is facilitated by external defects in the insulating substrate, which can modulate the electronic properties of graphene. We disclose a mixing effect caused by partial overlap of the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) of adsorbed gas molecules to explain graphene's ability to detect adsorbed molecules. Our results open a new design space, suggesting that control of external defects in supporting substrates can lead to tunable graphene chemical sensors, which could be developed without compromising the intrinsic electrical and structural properties of graphene.

We investigate the spatial variation of the axial, radial, and tangential diffusion coefficients in carbon nanotubes (CNTs) of various diameters. The effect of confinement and CNT wall on the diffusion coefficient is studied. On the basis of the spatial variation of the diffusion coefficient, the diffusion mechanisms in different regions of the nanotube are identified. The dependence of the diffusion coefficient on the carbon-water interaction parameter is investigated. The average diffusion coefficient in the nanotube as a function of the nanotube diameter is computed, and the diffusion mechanisms, including the transition regimes, are identified. The results are analyzed via hydrogen bonds and water orientations.