Thermomechanically Resilient Polyionic Elastomers with Enhanced Anti-Icing Performances.

Anti-icing gels inhibit ice formation and accretion; however, current iterations face prevalent drawbacks such as poor strength, weak substrate adhesion, and limited anti-icing properties. Herein, we propose a novel approach to address these challenges by developing a thermomechanical robust polyionic elastomer (PIE) with enhanced anti-icing properties. The PIE surface exhibits an icing delay time up to 5400 s and remains frost-free after exposure to -10 °C for 3.5 h, attributed to the inhibitory effect on ice formation by ions from ILs and the polyelectrolyte network. Moreover, the PIE exhibits remarkable anti-icing durability, with ice adhesion strengths below 35 kPa after undergoing 30 icing/deicing cycle tests at -20 °C. Following sandpaper abrasion (300 cycles), scratching, and heat treatment (100 °C, 16 h), the adhesion strength remains ca. 20 kPa, highlighting its resilience under various thermal and mechanical conditions. This exceptional durability is attributed to the low volatility of the IL and the robust ionic interactions within the PIE network. Furthermore, the PIE demonstrates favorable self-healing properties and strong substrate adhesion in both low-temperature and ambient environments, facilitated by the abundance of hydrogen bonds and electrostatic forces within PIE. This work presents an innovative approach to developing high-performance, durable, and robust anti-icing materials with potential implications across various fields.

Ice-Enabled Transfer of Graphene on Copper Substrates Enhanced by Electric Field and Cu2O.

Graphene films grown by the chemical vapor deposition (CVD) method suffer from contamination and damage during transfer. Herein, an innovative ice-enabled transfer method under an applied electric field and in the presence of Cu2O (or Cu2O-Electric-field Ice Transfer, abbreviated as CEIT) is developed. Ice serves as a pollution-free transfer medium while water molecules under the electric field fully wet the graphene surface for a bolstered adhesion force between the ice and graphene. Cu2O is used to reduce the adhesion force between graphene and copper. The combined methodology in CEIT ensures complete separation and clean transfer of graphene, resulting in successfully transferred graphene to various substrates, including polydimethylsiloxane (PDMS), Teflon, and C4F8 without pollution. The graphene obtained via CEIT is utilized to fabricate field-effect transistors with electrical performances comparable to that of intrinsic graphene characterized by small Dirac points and high carrier mobility. The carrier mobility of the transferred graphene reaches 9090 cm2 V-1 s-1, demonstrating a superior carrier mobility over that from other dry transfer methods. In a nutshell, the proposed clean and efficient transfer method holds great potential for future applications of graphene.

Risk factors of pneumothorax in computed tomography guided lung nodule marking using autologous blood: a retrospective study.

BACKGROUND: To investigate the risk factors of pneumothorax of using computed tomography (CT) guidance to inject autologous blood to locate isolated lung nodules. METHODS: In the First Hospital of Putian City, 92 cases of single small pulmonary nodules were retrospectively analyzed between November 2019 and March 2023. Before each surgery, autologous blood was injected, and the complications of each case, such as pneumothorax and pulmonary hemorrhage, were recorded. Patient sex, age, position at positioning, and nodule type, size, location, and distance from the visceral pleura were considered. Similarly, the thickness of the chest wall, the depth and duration of the needle-lung contact, the length of the positioning procedure, and complications connected to the patient's positioning were noted. Logistics single-factor and multi-factor variable analyses were used to identify the risk factors for pneumothorax. The multi-factor logistics analysis was incorporated into the final nomogram prediction model for modeling, and a nomogram was established. RESULTS: Logistics analysis suggested that the nodule size and the contact depth between the needle and lung tissue were independent risk factors for pneumothorax. CONCLUSION: The factors associated with pneumothorax after localization are smaller nodules and deeper contact between the needle and lung tissue.

Upgrading Gel Electrolytes Through Electrostatic-Induced Dual-Salt Paradigm for Superior Zn-Ion Battery Performance.

Gel electrolytes are gaining attention for rechargeable Zn-ion batteries because of their high safety, high flexibility, and excellent comprehensive electrochemical performances. However, current gel electrolytes still perform at mediocre levels due to incomplete Zn salts dissociation and side reactions. Herein, an electrostatic-induced dual-salt strategy is proposed to upgrade gel electrolytes to tackle intrinsic issues of Zn metal anodes. The competitive coordination mechanism driven by electrostatic repulsion and steric hindrance of dual anions promotes zinc salt dissociation at low lithium salt addition levels, improving ion transport and mechanical properties of gel electrolytes. Li+ ions and gel components coordinate with H2O, reducing active H2O molecules and inhibiting associated side reactions. The dual-salt gel electrolyte enables excellent reversibility of Zn anodes at both room and low temperatures. Zn||Polyaniline cells using the dual-salt gel electrolyte exhibit a high discharge capacity of 180 mAh g-1 and long-term cycling stability over 180 cycles at -20 °C. The dual-salt strategy offers a cost-effective approach to improving gel electrolytes for high-performance flexible Zn-ion batteries.

Development of composite separators by coating hydrochloric acid-treated halloysite nanotubes on polypropylene separators for lithium-ion batteries.

In this study, polypropylene/halloysite nanotube (PP/HNT) composite separators were prepared by coating HNTs treated with hydrochloric acid (HCl) of different concentrations on both sides of a PP separator. The effect of HNTs treated with hydrochloric acid (HCl) of different concentrations on the properties of PP/HNT composite separators was investigated. The results indicate that the PP/HNT composite separator exhibits higher electrolyte uptake and wettability than a commercial PP separator, resulting in a better electrochemical performance in Li/LiFePO4 cells. In particular, the PP/HNTs-1.2 M composite separator with HNTs treated with 1.2 M HCl exhibits the highest electrolyte uptake (384%) and ionic conductivity (1.03 mS cm-1). The cells assembled with a PP/HNTs-1.2 M composite separator deliver discharge capacities of 166 mA h g-1 (0.5 C) and 131 mA h g-1 (3 C) with attractive cycling performance (87.6% capacity retention after 100 cycles). HNTs treated with HCl of appropriate concentrations can significantly improve the properties of PP/HNT composite separators for application in lithium-ion batteries.

HTR+: a novel algorithm for identifying type and polycrystal of gas hydrates.

In this work, the hierarchical topology ring (HTR+) algorithm, an extension of the HTR algorithm, was developed for identifying gas hydrate types, cage structures, and grain boundaries (GBs) within polycrystalline structures. Utilizing molecular dynamics trajectories of polycrystalline hydrates, the accuracy of the HTR+ algorithm is validated in identifying sI, sII and sH hydrate types, hydrate grains, and GBs in multi-hydrate polycrystals, as well as clathrate cages at GBs. Additionally, during the hydrate nucleation and growth processes, clathrate cages, hydrate type, hydrate grains and ice structures are accurately recognized. Significantly, this algorithm demonstrates high efficiency, particularly for large hydrate systems. HTR+ algorithm emerges a powerful tool for identifying micro/mesoscopic structures of gas hydrates, enabling an in-depth understanding of the formation mechanisms and properties of gas hydrates.

Mechanical properties of amorphous CO2 hydrates: insights from molecular simulations.

Understanding physicochemical properties of amorphous gas hydrate systems is of great significance to reveal structural stabilities of polycrystalline gas hydrate systems. Furthermore, amorphous gas hydrates can occur ordinarily in the nucleation events of gas hydrate systems. Herein, the mechanical properties of amorphous carbon dioxide hydrates are examined by means of all-atom classical molecular dynamic simulations. Our molecular simulation results reveal that mechanical strengths of amorphous carbon dioxide hydrates are evidently governed by temperatures, confining pressures, and ratios of water to carbon dioxide molecules. Notably, under compressive loads, amorphous carbon dioxide hydrates firstly exhibit monotonic strain hardening, followed by an interesting distinct phenomenon characterized by a steady flow stress at further large deformation strains. Furthermore, structural evolutions of amorphous carbon dioxide hydrates are analyzed on the basis of the N-Hbond DOP order parameter. These important findings can not only contribute to our understanding of the structural stabilities of amorphous gas hydrate systems, but also help to develop fundamental understandings about grain boundaries of gas hydrate systems.

Insights into the mechanical stability of tetrahydrofuran hydrates from experimental, machine learning, and molecular dynamics perspectives.

Natural gas hydrates (NGHs) hold immense potential as a future energy resource and for sustainable applications such as gas capture and storage. Due to the challenging formation conditions, however, their mechanical properties remain poorly understood. Herein, the mechanical characteristics of tetrahydrofuran (THF) hydrates, a proxy for methane hydrates, were investigated at different ice contents, strain rates, and temperatures using uniaxial compressive experiments. The results unveil a distinct behavior in the peak strength of THF hydrates with a varying ice content, strain rate and temperature, exhibiting an increase as the strain rate and temperature decrease, in contrast to the peak strength-strain rate relationship observed in polycrystalline ice. Based on the experimental data, four machine learning (ML) models including extreme gradient boosting (XGboost), multilayer perceptron (MLP), gradient boosting decision tree (GBDT) and decision tree (DT) were developed to predict the peak strength. The XGboost model demonstrates superior predictive performance, emphasizing the significant influence of ice content and temperature on the peak strength of hydrates. Furthermore, molecular dynamics (MD) simulations were employed to gain insights into the dissociation and formation processes of clathrate cages, as well as phase transitions and amorphization occurring at grain boundaries (GBs) involving diverse unconventional clathrate cages, including 51265, 4151062, 4151064, 425861 and 425862, with 425861 and 425862 cages being predominant. This study enhances our understanding of the mechanical properties and deformation mechanisms of hydrates and provides a ML-based predictive framework for estimating the compressive strength of hydrates under diverse coupling conditions. The findings have significant implications for stability assessments of NGHs and the exploitation of NGH resources.

Thermal conductivity of fivefold twinned silicon-germanium heteronanowires.

The thermal transport properties of five-fold twinned (5FT) germanium-silicon (Ge-Si) heteronanowires (h-NWs) with varying cross-sectional areas, germanium (Ge) domain ratios and heterostructural patterns are investigated using homogeneous nonequilibrium molecular dynamics (HNEMD) simulations. The results demonstrate a distinctive behavior in the thermal conductivity (κ) of 5FT-NWs, characterized by a "flipped" trend at a critical cross-sectional area. This behavior is attributed to the hydrodynamic phonon flow, arising from the normal three-phonon scattering process in the low-frequency region. In addition, the composition ratio of 5FT-NWs has a significant impact on reducing the κ of 5FT-NWs and suppressing the hydrodynamic effect. Intriguingly, as the homogeneous element domains are separated, stronger phonon hydrodynamic flows are observed in comparison to the adjacent homogeneous element domains. By analyzing various phonon properties, including phonon dispersion, three-phonon scattering rate, and phonon mean free path, critical insights into the origin of the differential κ in different 5FT-NW structures are provided. The findings deepen the understanding of the thermal transport properties of nanomaterials and hold implications for the design and development of nanoelectronics and thermoelectric devices.

Predicting mechanical properties of CO2hydrates: machine learning insights from molecular dynamics simulations.

Understanding the mechanical properties of CO2hydrate is crucial for its diverse sustainable applications such as CO2geostorage and natural gas hydrate mining. In this work, classic molecular dynamics (MD) simulations are employed to explore the mechanical characteristics of CO2hydrate with varying occupancy rates and occupancy distributions of guest molecules. It is revealed that the mechanical properties, including maximum stress, critical strain, and Young's modulus, are not only affected by the cage occupancy rate in both large 51262and small 512cages, but also by the distribution of guest molecules within the cages. Specifically, the presence of vacancies in the 51262large cages significantly impacts the overall mechanical stability compared to 512small cages. Furthermore, four distinct machine learning (ML) models trained using MD results are developed to predict the mechanical properties of CO2hydrate with different cage occupancy rates and cage occupancy distributions. Through analyzing ML results, as-developed ML models highlight the importance of the distribution of guest molecules within the cages, as crucial contributor to the overall mechanical stability of CO2hydrate. This study contributes new knowledge to the field by providing insights into the mechanical properties of CO2hydrates and their dependence on cage occupancy rates and cage occupancy distributions. The findings have implications for the sustainable applications of CO2hydrate, and as-developed ML models offer a practical framework for predicting the mechanical properties of CO2hydrate in different scenarios.

Electric field as a crystallization switch of heterogeneous ice formation.

Using molecular dynamics simulations, we investigated the effect of external electric field on ice formation with the present of a substrate surface. It turns out that the electric field can affect the ice formation on substrate surface by altering the dipole orientation of interfacial water molecules (IWs): a crossover from inhibiting to promoting ice formation with the increase of electric field strength. According to the influence of the electric field on ice formation, the electric field strength range of 0.0 V nm-1-7.0 V nm-1can be divided into three regions. In the region I and region III, there are both ice formation on the substrate surface. While, the behavior of IWs in the region I and region III are distinguished, including the arrangements of oxygen atoms and the dipole orientation distribution. In region II, ice formation does not occur in the system within 5 × 200 ns simulations. The IWs show a disorder structure, preventing the ice formation process on substrate. The interfacial water molecular orientation distribution and two-dimensional free energy landscape reveals that the electric field can alter the dipole orientation of the interfacial water and lead a free energy barrier, making the ice formation process harder. Our result demonstrates the external electric field can regulate the behavior of IWs, and further affect the ice formation process. The external electric field act as a crystallization switch of ice formation on substrate, shedding light into the studies on the control of ice crystallization.

Accurate prediction of heat conductivity of water by a neuroevolution potential.

We propose an approach that can accurately predict the heat conductivity of liquid water. On the one hand, we develop an accurate machine-learned potential based on the neuroevolution-potential approach that can achieve quantum-mechanical accuracy at the cost of empirical force fields. On the other hand, we combine the Green-Kubo method and the spectral decomposition method within the homogeneous nonequilibrium molecular dynamics framework to account for the quantum-statistical effects of high-frequency vibrations. Excellent agreement with experiments under both isobaric and isochoric conditions within a wide range of temperatures is achieved using our approach.

Using Wool Keratin as a Structural Biomaterial and Natural Mediator to Fabricate Biocompatible and Robust Bioelectronic Platforms.

The design and fabrication of biopolymer-incorporated flexible electronics have attracted immense interest in healthcare systems, degradable implants, and electronic skin. However, the application of these soft bioelectronic devices is often hampered by their intrinsic drawbacks, such as poor stability, inferior scalability, and unsatisfactory durability. Herein, for the first time, using wool keratin (WK) as a structural biomaterial and natural mediator to fabricate soft bioelectronics is presented. Both theoretical and experimental studies reveal that the unique features of WK can endow carbon nanotubes (CNTs) with excellent water dispersibility, stability, and biocompatibility. Therefore, well-dispersed and electroconductive bio-inks can be prepared via a straightforward mixing process of WK and CNTs. The as-obtained WK/CNTs inks can be directly exploited to design versatile and high-performance bioelectronics, such as flexible circuits and electrocardiogram electrodes. More impressively, WK can also be a natural mediator to connect CNTs and polyacrylamide chains to fabricate a strain sensor with enhanced mechanical and electrical properties. With conformable and soft architectures, these WK-derived sensing units can be further assembled into an integrated glove for real-time gesture recognition and dexterous robot manipulations, suggesting the great potential of the WK/CNT composites for wearable artificial intelligence.

Interfacial mechanical properties of tetrahydrofuran hydrate-solid surfaces: Implications for hydrate management.

Understanding the interfacial mechanical properties between hydrate and solids is vital to designing and fabricating surfaces for hydrate management. Herein, the role of the surface wettability, the type of solid substrate and temperature on the interfacial adhesion properties of tetrahydrofuran (THF) hydrate and ice were examined by force analysis based shearing measurements and molecular dynamics (MD) simulations. The results showed that the adhesion strength of THF hydrate and ice on silica varies with the compositions of coating, and the adhesion strength of ice is larger than that of THF hydrate for all investigated solid substrates. Particularly, in contrast to a linear relationship between 1 + cosθr and hydrate adhesion on organic silanes/thiols/polymer surfaces, the hydrate adhesion on the coated inorganic glass surfaces is enhanced as a function of 1 + cosθr, in which θr is the receding contact angle. MD simulations uncovered that the adhesion strength of ice on solid substrates is dominated by the quasi-liquid water layer, however, that of hydrate is governed not only by the quasi-liquid layer but also newly formed unconventional clathrate cages. This study provides new insights and perspectives into the hydrate adhesion on solid surfaces, which is of help to develop hydrate-phobic coatings for advanced hydrate management.

Isotope doping-induced crossover shift in the thermal conductivity of thin silicon nanowires.

Here, using homogeneous nonequilibrium molecular dynamics simulations, we report the thermal transport characteristics of thin Si nanowires (NWs) with varying size and isotope doping ratio. It is identified that crossover in the thermal conductivity (κ) of both isotope doping-free and isotope doped Si-NWs appears at critical sizes, below whichκis enlarged with decreasing size because the hydrodynamic phonon flow predominates, above which, due to the dominant phonon boundary scattering, opposite behavior is observed. With increasing isotope doping, however, the critical size in minimizing theκis moved to small values because the phonon impurity scattering caused by isotope doping is critically involved. Moreover, there is a critical isotope doping (<50%) in the critical size motion, originating from that, above which, the critical size no longer moves due to the persistence of hydrodynamic phonon flow. This study provides new insights into the thermal transport behaviors of quasi-1D structures.

Metabolomic and transcriptomic analyses reveal the mechanism of sweet-acidic taste formation during pineapple fruit development.

Pineapple (Ananas comosus L.) is one of the most valuable subtropical fruit crop in the world. The sweet-acidic taste of the pineapple fruits is a major contributor to the characteristic of fruit quality, but its formation mechanism remains elusive. Here, targeted metabolomic and transcriptomic analyses were performed during the fruit developmental stages in two pineapple cultivars ("Comte de Paris" and "MD-2") to gain a global view of the metabolism and transport pathways involved in sugar and organic acid accumulation. Assessment of the levels of different sugar and acid components during fruit development revealed that the predominant sugar and organic acid in mature fruits of both cultivars was sucrose and citric acid, respectively. Weighted gene coexpression network analysis of metabolic phenotypes and gene expression profiling enabled the identification of 21 genes associated with sucrose accumulation and 19 genes associated with citric acid accumulation. The coordinated interaction of the 21 genes correlated with sucrose irreversible hydrolysis, resynthesis, and transport could be responsible for sucrose accumulation in pineapple fruit. In addition, citric acid accumulation might be controlled by the coordinated interaction of the pyruvate-to-acetyl-CoA-to-citrate pathway, gamma-aminobutyric acid pathway, and tonoplast proton pumps in pineapple. These results provide deep insights into the metabolic regulation of sweetness and acidity in pineapple.

GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations.

We present our latest advancements of machine-learned potentials (MLPs) based on the neuroevolution potential (NEP) framework introduced in Fan et al. [Phys. Rev. B 104, 104309 (2021)] and their implementation in the open-source package gpumd. We increase the accuracy of NEP models both by improving the radial functions in the atomic-environment descriptor using a linear combination of Chebyshev basis functions and by extending the angular descriptor with some four-body and five-body contributions as in the atomic cluster expansion approach. We also detail our efficient implementation of the NEP approach in graphics processing units as well as our workflow for the construction of NEP models and demonstrate their application in large-scale atomistic simulations. By comparing to state-of-the-art MLPs, we show that the NEP approach not only achieves above-average accuracy but also is far more computationally efficient. These results demonstrate that the gpumd package is a promising tool for solving challenging problems requiring highly accurate, large-scale atomistic simulations. To enable the construction of MLPs using a minimal training set, we propose an active-learning scheme based on the latent space of a pre-trained NEP model. Finally, we introduce three separate Python packages, viz., gpyumd, calorine, and pynep, that enable the integration of gpumd into Python workflows.

Size-dependent melting of onion-like fullerenic carbons: a molecular dynamics and machine learning study.

The melting thermodynamic characteristics of 2- to 20-layered onion-like fullerenes (OLFn) (C60@C240to C60@···@C6000···@C24000) are comprehensively explored using first-principles-based ReaxFF atomistic simulations and random forest machine learning (RF ML). It is revealed that OLFnshows lower thermal stability than the counterparts of single-walled fullerenes (SWFn). The melting point of SWFnincreases monotonically with increasing size, whereas for OLFn, an unusual size-dependent melting point is observed; OLFnwith intermediate size shows the highest melting point. For small OLFn, the melting occurs from the inner to the outer, whereas for large OLFn, it nucleates from the inner to the outer and to intermediate fullerenes. The melting and erosion behaviors of both SWFnand OLFnare mainly characterized by the nucleation of non-hexagons, nanovoids, carbon chains and emission of C2. RF ML model is developed to predict the melting points of both SWFnand OLFn. Moreover, the analysis of the feature importance reveals that the Stone-Wales transformation is a critical pathway in the melting of SWFnand OLFn. This study provides new insights and perspectives into the thermodynamics and pyrolysis chemistry of fullerenic carbons, and also may shed some lights onto the understanding of thermally-induced erosion of carbon-based resources and spacecraft materials.

Designing Anion-Derived Solid Electrolyte Interphase in a Siloxane-Based Electrolyte for Lithium-Metal Batteries.

The rational electrolyte design with weak solvation is regarded as an effective way to regulate the electrolyte/electrode interface (SEI) that profoundly affects the performance of Li-metal batteries. Herein, we propose a newly developed siloxane-based weakly solvating electrolyte (SiBE) with contact ion pairs (CIPs) or aggregates (AGGs) dominating the solution structure, which enables the dendrite-free Li deposition and long cycle stability of Li-metal batteries. By altering the combination of Li salts, the SiBE leads to the formation of an inorganic anion-derived solid electrolyte interphase, which is highly stable and Li+-conductive. Based on SiBE, the Li||LiFePO4 (LFP) full cell can stably cycle for 1000 cycles at a 2C rate with a capacity retention of 76.9%. Even with a limited Li-metal anode, it can maintain a capacity retention of 80% after 110 cycles with a high average Coulombic efficiency of 99.8%. This work reveals that siloxane can be a promising solvent to obtain weakly solvating electrolytes, which opens a new avenue for SEI composition regulation of Li-metal batteries.

Efficient mechanical modulation of the phonon thermal conductivity of Mo6S6 nanowires.

Mo6S6 nanowires are emerging as key building blocks for flexible devices and are competitive with carbon nanotubes due to easier separation and functionalization. Here, it is reported the phonon thermal conductivity (κ) of Mo6S6 nanowires via molecular dynamics simulations. It shows a large tunability of low-frequency phonon thermal conductivity (κlf)Amax from 27.2-191 W (m K)-1, an increase of around 702% via mechanical strain. Below critical tension/torsion strain, their phonon thermal conductivity monotonically reduces/enlarges; whereas above this value, an inverse trend is identified. On the other hand, Mo6S6 nanowires show unusual auxetic behavior. The transitions involved in phonon thermal conductivity are molecularly illustrated by a strain-induced crossover in bond configurations and are explained based on a competition mechanism between phonon scattering and group velocity. This study provides insights into the thermal transport and auxetic properties of low-dimensional structures and the thermal management of Mo6S6 nanowire-based systems.