Recent progress in stimuli-responsive polymeric micelles for targeted delivery of functional nanoparticles.

Stimuli-responsive polymeric micelles have emerged as a revolutionary approach for enhancing the in vivo stability, biocompatibility, and targeted delivery of functional nanoparticles (FNPs) in biomedicine. This article comprehensively reviews the preparation methods of these polymer micelles, detailing the innovative strategies employed to introduce stimulus responsiveness and surface modifications essential for precise targeting. We delve into the breakthroughs in utilizing these micelles to selectively deliver various FNPs including magnetic nanoparticles, upconversion nanoparticles, gold nanoparticles, and quantum dots, highlighting their transformative impact in the biomedical realm. Concluding, we present an insight into the current research landscape, addressing the challenges at hand, and envisioning the future trajectory in this burgeoning domain. Join us as we navigate the exciting confluence of polymer science and nanotechnology in reshaping biomedical solutions.

Fast Water Desalination with a Graphene-MoS2 Nanoporous Heterostructure.

Energy-efficient water desalination is the key to tackle the challenges with drought and water scarcity that affect 1.2 billion people. The material and type of membrane in reverse osmosis water desalination are the key factors in their efficiency. In this work, we explored the potential of a graphene-MoS2 heterostructure membrane for water desalination, focusing on bilayer membranes and their advantages over monolayer counterparts. Through extensive molecular dynamics simulation and statistical analysis, the bilayer MoS2-graphene was investigated and compared to the monolayer of graphene and MoS2. By optimizing the heterostructure membrane, improved water flux was achieved while maintaining a high ion rejection rate. Furthermore, the study delves into the physical mechanisms underlying the superior performance of heterostructure nanopores, comparing them with circular bilayer and monolayer pores. Factors investigated include water structure, hydration shell near the membrane surface, water density, energy barrier using the potential of mean force, and porosity within the nanopore. Our findings contribute to the understanding of heterostructure membranes and their potential in enhancing the water desalination efficiency, providing valuable insights for future membrane design and optimization.

Machine Learning in Membrane Design: From Property Prediction to AI-Guided Optimization.

Porous membranes, either polymeric or two-dimensional materials, have been extensively studied because of their outstanding performance in many applications such as water filtration. Recently, inspired by the significant success of machine learning (ML) in many areas of scientific discovery, researchers have started to tackle the problem in the field of membrane design using data-driven ML tools. In this Mini Review, we summarize research efforts on three types of applications of machine learning in membrane design, including (1) membrane property prediction using ML, (2) gaining physical insight and drawing quantitative relationships between membrane properties and performance using explainable artificial intelligence, and (3) ML-guided design, optimization, or virtual screening of membranes. On top of the review of previous research, we discuss the challenges associated with applying ML for membrane design and potential future directions.

Large-Scale Pretraining Improves Sample Efficiency of Active Learning-Based Virtual Screening.

Virtual screening of large compound libraries to identify potential hit candidates is one of the earliest steps in drug discovery. As the size of commercially available compound collections grows exponentially to the scale of billions, active learning and Bayesian optimization have recently been proven as effective methods of narrowing down the search space. An essential component of those methods is a surrogate machine learning model that predicts the desired properties of compounds. An accurate model can achieve high sample efficiency by finding hits with only a fraction of the entire library being virtually screened. In this study, we examined the performance of a pretrained transformer-based language model and graph neural network in a Bayesian optimization active learning framework. The best pretrained model identifies 58.97% of the top-50,000 compounds after screening only 0.6% of an ultralarge library containing 99.5 million compounds, improving 8% over the previous state-of-the-art baseline. Through extensive benchmarks, we show that the superior performance of pretrained models persists in both structure-based and ligand-based drug discovery. Pretrained models can serve as a boost to the accuracy and sample efficiency of active learning-based virtual screening.

A main chain biodegradable polyurethane with anti-protein adsorption and anti-bacterial adhesion performances.

Biofilms are initially formed by substances such as proteins secreted by bacteria adhering to a surface. To achieve a durable antibacterial material, biodegradable dihydroxyl-terminated poly[(ethylene oxide)-co-(ethylene carbonate)] (PEOC(OH)2) with anti-protein adsorption properties was synthesized in this study. Further polycondensation of PEOC(OH)2 and isophorone diisocyanate (IPDI) led to biodegradable polyurethane (PEOC-PU) with PEOC as the soft segment. For comparison, polyurethanes with polyethylene glycol (PEG-PU) and polypropylene glycol (PPG-PU) as soft segments were also synthesized. The chemical structures of the polyurethanes were characterized by 1H NMR and FTIR. The biodegradation behavior of PEOC-PU promoted by lipase due to the presence of ethylene carbonate units was also studied. Their resistance to proteins was studied using quartz crystal microbalance with dissipation (QCM-D) and the results revealed that PEOC-PU exhibited excellent nonspecific resistance to proteins. The colonization of microorganisms on PU in the liquid culture medium was further examined and the results showed that PEOC-PU exhibited excellent antibacterial adhesion performance due to its protein resistance and biodegradation.

Neural network predicts ion concentration profiles under nanoconfinement.

Modeling the ion concentration profile in nanochannel plays an important role in understanding the electrical double layer and electro-osmotic flow. Due to the non-negligible surface interaction and the effect of discrete solvent molecules, molecular dynamics (MD) simulation is often used as an essential tool to study the behavior of ions under nanoconfinement. Despite the accuracy of MD simulation in modeling nanoconfinement systems, it is computationally expensive. In this work, we propose neural network to predict ion concentration profiles in nanochannels with different configurations, including channel widths, ion molarity, and ion types. By modeling the ion concentration profile as a probability distribution, our neural network can serve as a much faster surrogate model for MD simulation with high accuracy. We further demonstrate the superior prediction accuracy of neural network over XGBoost. Finally, we demonstrated that neural network is flexible in predicting ion concentration profiles with different bin sizes. Overall, our deep learning model is a fast, flexible, and accurate surrogate model to predict ion concentration profiles in nanoconfinement.

MOFormer: Self-Supervised Transformer Model for Metal-Organic Framework Property Prediction.

Metal-organic frameworks (MOFs) are materials with a high degree of porosity that can be used for many applications. However, the chemical space of MOFs is enormous due to the large variety of possible combinations of building blocks and topology. Discovering the optimal MOFs for specific applications requires an efficient and accurate search over countless potential candidates. Previous high-throughput screening methods using computational simulations like DFT can be time-consuming. Such methods also require the 3D atomic structures of MOFs, which adds one extra step when evaluating hypothetical MOFs. In this work, we propose a structure-agnostic deep learning method based on the Transformer model, named as MOFormer, for property predictions of MOFs. MOFormer takes a text string representation of MOF (MOFid) as input, thus circumventing the need of obtaining the 3D structure of a hypothetical MOF and accelerating the screening process. By comparing to other descriptors such as Stoichiometric-120 and revised autocorrelations, we demonstrate that MOFormer can achieve state-of-the-art structure-agnostic prediction accuracy on all benchmarks. Furthermore, we introduce a self-supervised learning framework that pretrains the MOFormer via maximizing the cross-correlation between its structure-agnostic representations and structure-based representations of the crystal graph convolutional neural network (CGCNN) on >400k publicly available MOF data. Benchmarks show that pretraining improves the prediction accuracy of both models on various downstream prediction tasks. Furthermore, we revealed that MOFormer can be more data-efficient on quantum-chemical property prediction than structure-based CGCNN when training data is limited. Overall, MOFormer provides a novel perspective on efficient MOF property prediction using deep learning.

The diffusion mechanism of water in conductive metal-organic frameworks.

Water is one of the most important guest molecules in metal-organic frameworks (MOFs) since it often serves as a solvent for ions and other molecules. Studying the diffusion mechanism of water molecules in conductive MOFs (c-MOFs) is fundamental to harnessing the potential of c-MOFs in designing next generation energy storage devices. In this work, using molecular dynamics simulations, we show that water follows the Fickian-type of diffusion mechanism in different types of c-MOFs. We investigate the effect of the stacking and metal center type on the water diffusion coefficient in c-MOFs. Water in c-MOFs with eclipsed stacking is shown to have 21.5% higher diffusion coefficient than in c-MOFs with slipped-parallel stacking, and 4-8% higher diffusion coefficient than in bulk water. The physical reasons behind the reduced water diffusion coefficient in slipped-parallel stacking c-MOFs are the higher number of hydrogen bonds near the inner surface and the zig-zag geometry. This work provides a molecular insight into the water dynamics and water structure inside multiple types of c-MOFs.

Which 2D Material is Better for DNA Detection: Graphene, MoS2, or MXene?

Despite much research on characterizing 2D materials for DNA detection with nanopore technology, a thorough comparison between the performance of different 2D materials is currently lacking. In this work, using extensive molecular dynamics simulations, we compare nanoporous graphene, MoS2 and titanium carbide MXene (Ti3C2) for their DNA detection performance and sensitivity. The ionic current and residence time of DNA are characterized in each nanoporous materials by performing hundreds of simulations. We devised two statistical measures including the Kolmogorov-Smirnov test and the absolute pairwise difference to compare the performance of nanopores. We found that graphene nanopore is the most sensitive membrane for distinguishing DNA bases. The MoS2 is capable of distinguishing the A and T bases from the C and G bases better than graphene and MXene. Physisorption and the orientation of DNA in nanopores are further investigated to provide molecular insight into the performance characteristics of different nanopores.

Prediction of GPCR activity using machine learning.

GPCRs are the target for one-third of the FDA-approved drugs, however; the development of new drug molecules targeting GPCRs is limited by the lack of mechanistic understanding of the GPCR structure-activity-function relationship. To modulate the GPCR activity with highly specific drugs and minimal side-effects, it is necessary to quantitatively describe the important structural features in the GPCR and correlate them to the activation state of GPCR. In this study, we developed 3 ML approaches to predict the conformation state of GPCR proteins. Additionally, we predict the activity level of GPCRs based on their structure. We leverage the unique advantages of each of the 3 ML approaches, interpretability of XGBoost, minimal feature engineering for 3D convolutional neural network, and graph representation of protein structure for graph neural network. By using these ML approaches, we are able to predict the activation state of GPCRs with high accuracy (91%-95%) and also predict the activation state of GPCRs with low error (MAE of 7.15-10.58). Furthermore, the interpretation of the ML approaches allows us to determine the importance of each of the features in distinguishing between the GPCRs conformations.

Simultaneous Electrochemical Exfoliation and Covalent Functionalization of MoS2 Membrane for Ion Sieving.

Transition metal dichalcogenide membranes exhibit good antiswelling properties but poor water desalination property. Here, a one-step covalent functionalization of MoS2 nanosheets for membrane fabrication is reported, which is accomplished by simultaneous exfoliating and grafting the lithium-ion-intercalated MoS2 in organic iodide water solution. The lithium intercalation amount in MoS2 is optimized so that the quality of the produced 2D nanosheets is improved with homogeneous size distribution. The lamellar MoS2 membranes are tested in reverse osmosis (RO), and the functionalized MoS2 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 MoS2 membranes are attributed to the suitable channel widths that are tuned by iodoacetamide. Furthermore, the stability of the functionalized MoS2 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 MoS2 layers but a double layer between the ethanol-functionalized MoS2 (MoS2 -ethanol) layers, which indicates that a less packed structure of water between the MoS2 -ethanol layers leads to lower hydrodynamic resistance and higher permeation.

Ozark Graphene Nanopore for Efficient Water Desalination.

A nanoporous graphene membrane is crucial to energy-efficient reverse osmosis water desalination given its high permeation rate and ion selectivity. However, the ion selectivity of the common circular graphene nanopore is dependent on the pore size and scales inversely with the water permeation rate. Larger, circular graphene nanopores give rise to the high water permeation rate but compromise the ability to reject ions. Therefore, the pursuit of a higher permeation rate while maintaining high ion selectivity can be challenging. In this work, we discover that the geometry of graphene nanopore can play a significant role in its water desalination performance. We demonstrate that the ozark graphene nanopore, which has an irregular slim shape, can reject over 12% more ions compared with a circular nanopore with the same water permeation rate. To reveal the physical reason behind the outstanding performance of the ozark nanopore, we compared it with circular, triangular, and rhombic pores from perspectives including interfacial water density, energy barrier, water/ion distribution in pores, the ion-water RDF in pores, and the hydraulic diameter. The ozark graphene nanopore further explores the potential of graphene for efficient water desalination.

DNA Detection with Single-Layer Ti3C2 MXene Nanopore.

Nanopore based sequencing is an exciting alternative to the conventional sequencing methods as it allows for high-throughput sequencing with lower reagent costs and time requirements. Biological nanopores, such as α-hemolysin, are subject to breakdown under thermal, electrical, and mechanical stress after being used millions of times. On the contrary, two-dimensional (2D) nanomaterials have been explored as a solid-state platform for the sequencing of DNA. Their subnanometer thickness and outstanding mechanical properties have made possible the high-resolution and high-signal-to-noise ratio detection of DNA, but such a performance is dependent on the type of nanomaterial selected. Solid-state nanopores of graphene, Si3N4, and MoS2 have been studied as potential candidates for DNA detection. However, it is important to understand the sensitivity and characterization of these solid-state materials for nanopore based detection. Recent developments in the synthesis of MXene have inspired our interest in its application as a nanopore based DNA detection membrane. Here, we simulate the metal carbide, MXene (Ti3C2), with single stranded DNA to understand its interactions and the efficiency of MXene as a putative material for the development of a nanopore based detection platform. Using molecular dynamics (MD) simulations, we present evidence that a MXene based nanopore is able to detect the different types of DNA bases. We have successfully identified features to differentiate the translocation of different types of DNA bases across the nanopore.

Advanced Wearable Microfluidic Sensors for Healthcare Monitoring.

Wearable flexible sensors based on integrated microfluidic networks with multiplex analysis capability are emerging as a new paradigm to assess human health status and show great potential in application fields such as clinical medicine and athletic monitoring. Well-designed microfluidic sensors can be attached to the skin surface to acquire various pieces of physiological information with high precision, such as sweat loss, information regarding metabolites, and electrolyte balance. Herein, the recent progress of wearable microfluidic sensors for applications in healthcare monitoring is summarized, including analysis principles and microfabrication methods. Finally, the challenges and opportunities for wearable microfluidic sensors in practical applications are discussed.

Water Desalination with Two-Dimensional Metal-Organic Framework Membranes.

Providing fresh and drinkable water is a grand challenge the world is facing today. Development in nanomaterials can create possibilities of using energy-efficient nanoporous materials for water desalination. In this work, we demonstrated that ultrathin conductive metal-organic framework (MOF) is capable of efficiently rejecting ions while giving access to high water flux. Through molecular dynamic simulation, we discovered perfect ion rejection rate by two-dimensional (2D) multilayer MOF. The naturally porous structure of 2D MOF enables significantly 3-6 orders of magnitude higher water permeation compared to that of traditional membranes. Few layers MOF membranes show 1 order of magnitude higher water flux compared to that of single-layer nanoporous graphene or molybdenum disulfide (MoS2) without the requirement of drilling pores. The excellent performance of 2D MOF membranes is supported by water permeation calculations, water density/velocity profiles at the pore, and the water interfacial diffusion near the pore. Water desalination performance of MOF offers a potential solution for energy-efficient water desalination.

The Codelivery of siRNA and QDs by pH-Responsive Micelle for Hepatoma Cancer Cells.

Recently, RNA interfering (RNAi) has become a promising approach for cancer therapy. However, the application of RNAi for clinics is still hindered due to the lack of safe and efficient carriers. In this study, a pH-responsive micelle based on polycaprolactone-block-poly 2-(dimethylamino)ethyl methacrylate (PCL-PDEM) cationic copolymer was developed to carry short interfering RNA (siRNA) for silencing interleukin 8 (IL-8) gene in hepatoma cancer cells. The transfection efficiency of the PCL-PDEM-siRNA/quantum dots (QDs) nanoplex has reached about 70%, and the expression level of IL-8 decreased about 63%. Furthermore, the codelivery of QDs and siRNA has been realized, which is beneficial to visualize the process of siRNA delivery. No considerable cytotoxicity from the nanoparticles has been observed, indicating that our responsive cationic micelle is potential in clinical trial for hepatoma cancer therapy.

Biomimetic Self-Renewal Polymer Brushes with Protein Resistance Inspired by Fish Skin.

Inspired by fish skin, biomimetic self-renewal poly[(ethylene oxide)-co-(ethylene carbonate)] (PEOC) brushes with protein resistance had been prepared via surface-initiated ring-opening polymerization (ROP). The results of hydrolytic degradation indicated that the PEOC brushes could degrade in artificial seawater. Ellipsometry, X-ray photoelectron spectrometry, and contact angle results demonstrated that the PEOC brushes degrade uniformly. By using a quartz crystal microbalance with dissipation, we studied the protein adsorption on the surfaces in artificial seawater at different degradation times. After 24, 48, 96, and 168 h of degradation, the PEOC surfaces showed nearly zero Δf and ΔD for bovine serum albumin, lysozyme, and fibrinogen. More importantly, there was a notably lower density of microorganisms adhered to the surface modified with PEOC compared with that of the surface without PEOC in natural seawater. The current study showed that the PEOC brushes exhibit a self-renewal property with persistent protein resistance and prevent the adhesion of microorganisms. Such a biomimetic polymer had a great potential in marine antibiofouling.

Near-Field Communication Sensors.

Near-field communication is a new kind of low-cost wireless communication technology developed in recent years, which brings great convenience to daily life activities such as medical care, food quality detection, and commerce. The integration of near-field communication devices and sensors exhibits great potential for these real-world applications by endowing sensors with new features of powerless and wireless signal transferring and conferring near field communication device with sensing function. In this review, we summarize recent progress in near field communication sensors, including the development of materials and device design and their applications in wearable personal healthcare devices. The opportunities and challenges in near-field communication sensors are discussed in the end.

Mimicking enzymatic systems: modulation of the performance of polymeric organocatalysts by ion-specific effects.

Like natural enzymatic systems, our study has demonstrated that the activity of the polymeric organocatalysts can be modulated by ion-specific effects via the combination of anion-specific salting-in/out effects and anion-specific polarization of hydrogen bonding induced stabilization of the transition state.

Dynamics of polyzwitterions in salt-free and salt solutions.

Dynamics of polyzwitterions remains largely unclear. We have prepared zwitterionic poly[1-(3-sulphopropyl) betaine-2-vinylpyridinium] (PSB) and investigated its dynamics in aqueous solution as a function of added salt (NaCl) concentration (Cs) by dynamic laser light scattering (DLS) and sedimentation velocity (SV) in analytical ultracentrifugation (AUC). A fast and a slow mode can be observed by DLS in a salt-free and low-salt solution, where the latter exhibits a maximum intensity at Cs ∼ 10(-3) M. SV measurements demonstrate that the fast mode corresponds to the diffusion of individual chains and the slow mode arises from the dynamic inhomogeneity due to interchain electrostatic repulsion. As Cs increases, the sedimentation coefficient exhibits a maximum at Cs ∼ 0.1 M whereas the diffusion coefficient has a minimum at Cs ∼ 10(-3) M and a maximum at Cs ∼ 0.1 M. Namely, PSB shows a complex dynamics in a salt-free and low-salt solution and anti-polyelectrolyte behavior in a high-salt solution. Our studies reveal that the dynamics of polyzwitterions is mediated by the long range interchain electrostatic repulsion and short range intrachain attraction, which are determined by effective charges on the chains.