Flexible Layer Research Articles

Soft Robotic Arm With Extensible Stiffening Layer

When talking about soft robots, softness is considered the most important feature, which brings dexterity and safety in interactive tasks with humans and environments. Such softness sometimes limits the real application of soft robots because load capability and rigidity are widely needed on many occasions. Often the inspiration for soft robots, soft animals such as octopus and sea cucumbers still preserve the ability to turn rigid when needed, and, more importantly, they can rapidly turn rigid at any deformed states. Intrigued by these capabilities, differently from existing stiffening technologies that do not adapt to soft robots' variety of morphological changes, we explored a new approach to create a flexible layer that can easily stiffen soft robots without affecting their original flexibility. We built an extensible chain of particles with a peculiar geometry, which can jam and stiffen. We enclosed it in a flexible membrane so that we can stiffen the structure by removing air. The overall structure is flexible enough and can withstand large elongation. So, it can be used around a soft robot arm for stiffening it in different deformed states. Experimental results show that such a stiffening layer does not affect the flexible motion of the soft robot, the stiffening function works even under the robot's 90% elongation state, and the stiffness can be increased by 15 times. By adopting the stiffening layer and different end effectors, we show that an assistive soft robot can simultaneously have the flexibility to collect food and the rigidity to hold it and help in feeding a participant. The results from this work may provide new design and application insights into the creation of soft robots with both flexible and rigid features.

Stretchable and Sensitive Strain Sensors Based on CB/MWCNTs–TPU for Human Motion Capture and Health Monitoring

Flexible strain sensors have attracted significant attention in the wearable electronic device field, owing to their exceptional ductility, sensitivity, and durability compared to rigid strain sensors. However, the limited strain detection range or sensitivity has hindered their widespread application. In this study, a flexible strain sensor is fabricated by screen-printing a conductive carbon black ink layer on a conductive flexible composite layer made of thermoplastic polyurethane and multiwalled carbon nanotubes. Both kirigami-patterned and fingerprint-patterned structures are introduced to the architecture of sensors; while the former is designed for the improvement of strain sensing range, the latter serves for the enhancement of sensitivity and interfacial adhesion. It is demonstrated that the sensor achieves high sensitivity with a gauge factor of up to 5705.53 and has a wide strain sensing range from 0 to 150%. Besides, the sensor also shows good durability (6000 stretching–releasing cycles) and a fast response time of ∼220 ms. The excellent sensor performance of the flexible strain sensor suggests promising applications in human–computer interaction, medical health monitoring, and motion capture.

Flexible All-Inorganic Perovskite Photodetector with a Combined Soft-Hard Layer Produced by Ligand Cross-Linking.

Although perovskite nanocrystals have attracted considerable interests as emerging semiconductors in optoelectronic devices, design and fabrication of a deformable structure with high stability and flexibility while meeting the charge transport requirements remain a huge challenge. Herein, a combined soft-hard strategy is demonstrated to fabricate intrinsically flexible all-inorganic perovskite layers for photodetection via ligand cross-linking. Perfluorodecyltrichlorosilane (FDTS) is employed as the capping ligand and passivating agent bound to the CsPbBr3 surface via Pb-F and Br-F interactions. The SiCl head groups of FDTS are hydrolyzed to produce SiOH groups which subsequently condense to form the SiOSi network. The CsPbBr3 @FDTS nanocrystals (NCs) are monodispersed cubes with an average particle size of 13.03nm and exhibit excellent optical stability. Furthermore, the residual hydroxyl groups on the surface of the CsPbBr3 @FDTS render the NCs tightly packed and cross-linked to each other to form a dense and elastic CsPbBr3 @FDTS film with soft and hard components. The photodetector based on the flexible CsPbBr3 @FDTS film exhibits outstanding mechanical flexibility and robust stability after 5000 bending cycles.

Large Area Flexible Thin Layer Terahertz Detector

AbstractDue to the low photon energy in the relevant frequency band, fewer materials can directly excite carriers, and an extreme lack of high‐performance, large‐area detectors, limit the promotion, and development of 6G technology. In addition, flexible detectors with the integration of 6G communication and Internet of Things technology has good prospects for application in the development of optoelectronic devices, which are also urgently needed. A large‐area flexible thin layer terahertz detector is designed that combines the advantages of excellent optoelectronic performance in the detection of electromagnetic waves with low photon energy and the localized surface plasmon (LSP) effect in a sub‐wavelength structure detector. Due to the large effective area, the spiral electrode device demonstrates the best performance at room temperature. The noise equivalent power (NEP) and photoresponsivity (RV) are achieved with 0.4 pW Hz−1/2 and 154 MV W−1 at 0.28 THz. In addition, the bending experiments show that the device has extreme advantages for flexible 6G detector applications.

Aluminum-Doped Zinc Oxide Improved by Silver Nanowires for Flexible, Semitransparent and Conductive Electrodes on Textile with High Temperature Stability.

In order to facilitate the design freedom for the implementation of textile-integrated electronics, we seek flexible transparent conductive electrodes (TCEs) that can withstand not only the mechanical stresses encountered during use but also the thermal stresses of post-treatment. The transparent conductive oxides (TCO) typically used for this purpose are rigid in comparison to the fibers or textiles they are intended to coat. In this paper, a TCO, specifically aluminum-doped zinc oxide (Al:ZnO), is combined with an underlying layer of silver nanowires (Ag-NW). This combination brings together the advantages of a closed, conductive Al:ZnO layer and a flexible Ag-NW layer, forming a TCE. The result is a transparency of 20-25% (within the 400-800 nm range) and a sheet resistance of 10 Ω/sq that remains almost unchanged, even after post-treatment at 180 °C.

Electrochemical assembly of single-walled carbon nanotube/polypyrrole/tellurium/lead telluride multi-layer nanocomposite films for room-temperature flexible thermoelectric application

With the complexity and diversification of thermoelectric (TE) application scenarios, it becomes increasingly difficult for single-component thermoelectric materials to satisfy practical demands. Therefore, recent researches have largely focused on the development of the multi-component nanocomposites, which are probably a good solution for the TE application of some materials that are not eligible when used alone. In this work, a seires of single-walled carbon nanotube (SWCNT)/polypyrrole (PPy)/tellurium (Te)/lead telluride (PbTe) multi-layer flexible composite films were fabricated via the successive electrodeposition of the flexible PPy layer with a low thermal conductivity, the ultra-thin Te induction layer, and the brittle PbTe layer with a large Seebeck coefficient over the pre-fabricated SWCNT membrane electrode with a high electrical conductivity. Through the complementary advantages between different components and the multiple synergies of the interface engineering, the SWCNT/PPy/Te/PbTe composites harvested the excellent TE performance with a maximum power factor (PF) of 929.8 ± 35.4 µW m−1 K−2 at room temperature, outperforming those of most of the electrochemically-prepared organic/inorganic TE composites reported previously. This work evidenced that the electrochemical multi-layer assembly is a feasible tactic for constructing special thermoelectric materials to meet customized requirements, which could also be applied to other material platforms.

A Transponder Aggregator With Efficient Use of Filtering Function for Transponder Noise Suppression

Colorless, directionless, and contentionless reconfigurable optical add/drop multiplexing (CDC-ROADM) provides highly flexible physical layer network configuration. Such CDC-ROADM must operate in multiple wavelength bands which are being increasingly implemented in optical transmission systems. The operation in C+L bands requires switch devices used in CDC-ROADM to also be capable of multiband operation. Recent studies on wavelength division multiplexing (WDM) systems have pointed out the impact of amplified spontaneous emission (ASE) noise generated by signals of different wavelengths, which causes OSNR degradation. Therefore, it is desirable to filter out the ASE noise from different transponders when multiplexing multiple wavelengths at the transmitter side, especially in a system with non-wavelength selective combiners such as directional couplers and multicast switches. The use of transponder aggregators with filtering functions, such as the M × N wavelength selective switch (WSS), is preferable for this filtering. However, the downside of these devices is that it is difficult to provide economical multiband support. Therefore, we propose an economical transponder aggregator configuration by allowing a certain amount of ASE superposition and reducing the number of filtering functions. In this paper, we fabricated a prototype of the proposed transponder aggregator by combining silica-based planar lightwave circuit technology and C+L band WSS, both commercially available, and verified its feasibility through transmission experiments. The novel transponder aggregator is a practical solution for a multiband CDC-ROADM system with improved OSNR performance.

AI-driven and automated MRI sequence optimization in scanner-independent MRI sequences formulated by a domain-specific language.

The complexity of Magnetic Resonance Imaging (MRI) sequences requires expert knowledge about the underlying contrast mechanisms to select from the wide range of available applications and protocols. Automation of this process using machine learning (ML) can support the radiologists and MR technicians by complementing their experience and finding the optimal MRI sequence and protocol for certain applications. We define domain-specific languages (DSL) both for describing MRI sequences and for formulating clinical demands for sequence optimization. By using various abstraction levels, we allow different key users exact definitions of MRI sequences and make them more accessible to ML. We use a vendor-independent MRI framework (gammaSTAR) to build sequences that are formulated by the DSL and export them using the generic file format introduced by the Pulseq framework, making it possible to simulate phantom data using the open-source MR simulation framework JEMRIS to build a training database that relates input MRI sequences to output sets of metrics. Utilizing ML techniques, we learn this correspondence to allow efficient optimization of MRI sequences meeting the clinical demands formulated as a starting point. ML methods are capable of capturing the relation of input and simulated output parameters. Evolutionary algorithms show promising results in finding optimal MRI sequences with regards to the training data. Simulated and acquired MRI data show high correspondence to the initial set of requirements. This work has the potential to offer optimal solutions for different clinical scenarios, potentially reducing exam times by preventing suboptimal MRI protocol settings. Future work needs to cover additional DSL layers of higher flexibility as well as an optimization of the underlying MRI simulation process together with an extension of the optimization method.

Helical polymers for dissymmetric circularly polarized light imaging.

Control of the spin angular momentum (SAM) carried in a photon provides a technologically attractive element for next-generation quantum networks and spintronics1-5. However, the weak optical activity and inhomogeneity of thin films from chiral molecular crystals result in high noise and uncertainty in SAM detection. Brittleness of thin molecular crystals represents a further problem for device integration and practical realization of chiroptical quantum devices6-10. Despite considerable successes with highly dissymmetric optical materials based on chiral nanostructures11-13, the problem of integration of nanochiral materials with optical device platforms remains acute14-16. Here we report a simple yet powerful method to fabricate chiroptical flexible layers via supramolecular helical ordering of conjugated polymer chains. Their multiscale chirality and optical activity can be varied across the broad spectral range by chiral templating with volatile enantiomers. After template removal, chromophores remain stacked in one-dimensional helical nanofibrils producing a homogeneous chiroptical layer with drastically enhanced polarization-dependent absorbance, leading to well-resolved detection and visualization of SAM. This study provides a direct path to scalable realization of on-chip detection of the spin degree of freedom of photons necessary for encoded quantum information processing and high-resolution polarization imaging.

Analysis and characterization of structural, dielectric and magnetic properties of MgxCo(0.90−x)Zn0.10Fe2O4 nanoparticles based flexible metamaterial absorber for wireless communication

The anisotropic flexible metamaterial absorber (FMA) structure comprises a resonating layer, a ferrite-based flexible substrate layer, and a back layer. The sol-gel method is used to prepare the ferrite-based nanomaterial for enhancing absorbability. The solution prepared material is according to the formula of MgxCo(0.90−x)Zn0.10Fe2O4 and specified as X25, X50, X75 for X = 25%, 50%, and 75%, respectively. Then the proposed ferrites materials are characterized by XRD, and FESEM, respectively. Moreover, the magnetic and dielectric properties of the proposed MgxCo(0.90−x)Zn0.10Fe2O4 ferrites samples were explored for microwave applications. The dielectric constants and loss tangents of the fabricated, flexible substrates are measured at a frequency range of 3–7 GHz using the DAK 3.5. The computed relative permittivity values are 2.95 for X25, 3.55 for X50, and 4.05 for X75, respectively, while the loss tangents values are 0.00295 for X25, 0.00495 for X50, and 0.00695 for X75. Whereas the permeability and magnetic loss tangent values varied from 0.875 to 1.00 and 0.00225–0.00585, respectively. Finally, the resonators of the absorber consist of a Y-style shape, square-shaped metallic plate resonator, square ring resonator (SRR), and cross-liked shape. The proposed flexible metamaterial absorber (FMA) structure is developed on MgxCo(0.90−x)Zn0.10Fe2O4 nanomaterial-based flexible substrate with dimensions of 0.110λ x 0.110λ x 0.017λ to validate the design experimentally. The electrical dimension is calculated at a 5.5 GHz resonance frequency. The simulated results from the CST Microwave Studio simulator reveal that absorption peaks at 5.5 GHz with a 99.99% absorption that includes the C band frequency that can be used in satellite applications. The proposed FMA structure achieved a 60° incident angle response for TE and TM modes. The structure has superiority with compact dimensions, lightweight, and flexibility and can be used for C band wireless communications.

Enhancing Molecular Representations Via Graph Transformation Layers.

Molecular representation learning is an essential component of many molecule-oriented tasks, such as molecular property prediction and molecule generation. In recent years, graph neural networks (GNNs) have shown great promise in this area, representing a molecule as a graph composed of nodes and edges. There are increasing studies showing that coarse-grained or multiview molecular graphs are important for molecular representation learning. Most of their models, however, are too complex and lack flexibility in learning different granular information for different tasks. Here, we proposed a flexible and simple graph transformation layer (i.e., LineEvo), a plug-and-use module for GNNs, which enables molecular representation learning from multiple perspectives. The LineEvo layer transforms fine-grained molecular graphs into coarse-grained ones based on the line graph transformation strategy. Especially, it treats the edges as nodes and generates the new connected edges, atom features, and atom positions. By stacking LineEvo layers, GNNs can learn multilevel information, from atom-level to triple-atoms level and coarser level. Experimental results show that the LineEvo layers can improve the performance of traditional GNNs on molecular property prediction benchmarks on average by 7%. Additionally, we show that the LineEvo layers can help GNNs have more expressive power than the Weisfeiler-Lehman graph isomorphism test.

Radial compression pressure estimation of carcass layers in unbonded flexible pipes based on neural networks

Radial pressure resistance is a key factor in the design of unbonded flexible pipe carcass layers because it directly affects the safe installation and use of marine flexible pipes. It is challenging to evaluate the performance of radial compression because the carcass layer, which is a large-angle helical winding structure in innermost flexible pipes with a special-shaped cross section, experiences numerous contact frictions under radial pressure. In this study, a radial pressure estimation approach based on neural networks is proposed to address the aforementioned issues. This study explores the nonlinear relationship between the limited structural responses (circumferential strain) and the overall load (radial compression pressure) from the radial compression experimental data and estimates the radial compression pressure of carcass layers. The input variables are the structural strains, and the output variable is the radial pressure. Furthermore, some examples are used to test the generalization of the method, including robustness and understanding, considering the geometric characteristics of the carcass layer. The results demonstrate that the method can accurately predict the radial pressure applied to the carcass layer and provide a theoretical framework for the design of unbonded flexible pipes in deep-sea environments.

Microstructural and Texture Evolution of Pearlite-Drawn Wires for Flexible Marine Pipelines: Investigating the Effect of Heat Treatments on Mechanical Properties

Flexible pipelines connect offshore platforms to subsea production systems due to their high flexibility, applicability, and recycling. Flexible armor layers in flexible pipelines are constructed using the parallel helical wrapping of several rectangular wires. The complex stress modes to which pipelines are subjected provide complex failure modes that are mostly unpredictable, requiring expensive pipeline integrity verification methods. This work investigates texture and microstructure evolution in pearlite-drawn wires due to different heat treatments. The material was subjected to annealing and isothermal heat treatments to obtain changes in its microstructure and texture. The changes were characterized using SEM, XRD, and EBSD techniques. Samples were subjected to tensile testing to evaluate their mechanical properties. This work revealed that annealing and isothermal treatments mainly modify the material microstructure, whereas annealing provides a material with grains with ease of deformation. In contrast, isothermal treatment provides grain growth with high internal energy and more deformation resistance. Annealing increases the intensity of all texture components, while isothermal treatment reduces intensity. These findings provide insights into the relationship between material properties and heat treatments, which can be used to optimize the design and performance of flexible pipelines, thereby reducing the need for expensive integrity verification methods.

Deep-learning-assisted low-cost flexible cotton yarn-based triboelectric nanogenerator for ultra-sensitive human-computer merging interfaces

Currently, deep-learning-assisted triboelectric nanogenerators (TENGs) have shown great potential for human-computer interaction. The Ecoflex-based polyvinyl-alcohol layer (Ecoflex/PVA) and the Ecoflex-based graphitic carbon nitride layer (Ecoflex/g-C3N4) are sequential spin-coated on the substrate of flexible cotton yarn. The Ecoflex/g-C3N4 and Ecoflex/PVA based silicone composite layer (SCL@(g-C3N4/PVA)) is designed as a high-output artificial skin in TENG. The flexible silicone composite layer plays a crucial role in enhancing output of the TENG. The silicone composite layer-based triboelectric nanogenerators (SCL-TENG) with 10 wt% PVA and 1.6 wt% g-C3N4 yielded the optimal output (720 V, 134 μA, 0.255 mW/cm2) under the pressure of 5 kPa and frequency of 8 Hz. By applying the Internet of Things technology, the single electrode mode SCL-TENG can be integrated into the intelligent sensing system to control and monitor electronic and electrical systems. In addition, the single electrode mode SCL-TENG is capable of sensing and distinguishing the instantaneous mechanical contact generated by balls with different materials, which can be high-accuracy identified with the assistant of deep-learning method of convolutional neural network-gate recurrent unit (CNN-GRU). It shows that the flexible silicone composite layer has great potential in the next generation of AI and intelligent interactive applications.

Mechano‐Triboelectric Transduction of Sliding‐Mode Nanogenerators with Magnetic Pre‐Stress

AbstractStudies on the enhancement of output performance are widely presented based on the theory of output characteristics for sliding‐mode triboelectric nanogenerators (TENGs). However, few studies are reported to investigate the electromechanical coupling of the external force onto the S‐TENG due to its fuzzy influence on electrical performance. In order to study the effect of the pre‐stress on S‐TENGs, in this work, a novel strategy of adding flexible magnet layers in the S‐TENG is first proposed to study the electrification and coupling effect induced by magnetic pre‐stress (or magnetically stresses triboelectric nanogenerator, MS‐TENG) both theoretically and experimentally. The electromechanical characteristic of the MS‐TENG is theoretically studied by introducing the magnetic pre‐stress coefficient. Considering the effect of the magnetic pre‐stress, load resistances, and sliding frequency on MS‐TENG at a low sliding speed, the relevant output performance of the MS‐TENG is derived through the equivalent circuit model. Furthermore, the corresponding experiments are conducted and the experimental results present a good agreement with the theoretical calculations, which effectively reveal the principle of the magnetic pre‐stress on the MS‐TENG. This work is aimed at providing a theoretical description with validations on the effect of external pre‐stresses on the performance enhancement of S‐TENGs.

MateJam: Multi-Material Teeth-Clutching Layer Jamming Actuation for Soft Haptic Glove.

Most existing force feedback gloves use rigid exoskeleton with large structural dimensions, making it challenging to meet the future demand of virtual reality (VR) applications for large-scale end users. There is an urgent need to develop soft and ultrathin gloves similar to daily gloves. Different from the idea of regulating the friction between layers, here we propose a multi-material teeth-clutching layer jamming (MateJam) actuator, which can achieve distinctive bending stiffness in free and constrained space. The actuator consists of multi-material layers whose modulus span three orders of magnitude, including flexible substrate layer, limiting layer, rigid micro-teeth clutching structure layer, sliding film boundary layer, and sealed shell layer. In free space, the flexible substrate, and the rigid micro-teeth array structure form flexible hinges, ensuring low resistance. In constrained space, the engagement degree of the rigid micro-teeth array is controlled by vacuum pressure to continuously adjust the output resistance. Reliable switching between free and constrained space is ensured by introducing the sliding film boundary layer and the optimized design of the micro-teeth cross-sectional shape. The force output ratio between free and constrained space reaches over 20 times (0.45N vs. 11.95N). The thickness of the actuator is as low as 3.8mm (clutched state), and the weight of the glove is 44.03g. The ultrathin formfactor and the low cost fabrication process makes the MateJam glove a promising solution for VR applications in home entertainment and social interactions.

Hierarchical pomegranate-structure design enables stress management for volume release of Si anode

Si is a promising anode material for lithium-ion batteries owing to its high theoretical capacity. However, large stress during (de)lithiation induces severe structural pulverization, electrical contact failure, and unstable solid-electrolyte interface, which hampers the practical application of Si anode. Herein, a Si-based anode with a hierarchical pomegranate-structure (HPS-Si) was designed to modulate the stress variation, and a sub-micronized Si-based sphere was assembled by the nano-sized Si nanospheres with sub-nanometer-sized multi-phase modification of the covalently linked SiO2–x, SiC, and carbon. The sub-micronized HPS-Si stacked with Si nanospheres can avoid agglomerates during cycling due to the high surface energy of nanomaterials. Meanwhile, the reasonable pore structure from SiO2 reduction owing to density difference is enough to accommodate the limited volume expansion. The Si spheres with a size of about 50 nm can prevent self-cracking. SiO2–x, and SiC as flexible and rigid layers, have been synergistically used to reduce the surface stress of conductive carbon layers to avoid cracking. The covalent bonding immensely strengthens the link of the modification with Si nanospheres, thus resisting stress effects. Consequently, a full cell comprising an HPS-Si anode and a LiCoO2 cathode achieved an energy density of 415 Wh kg−1 with a capacity retention ratio of 87.9% after 300 cycles based on the active materials. It is anticipated that the hierarchical pomegranate-structure design can provide inspiring insights for further studies of the practical application of silicon anode.

Temporal and spatial temperature predictions for flexible pavement layers using numerical thermal analysis and verified with large datasets

The pavement temperature patterns play a significant role in the performance of flexible pavements containing asphalt concrete surface layers for several reasons. Since asphalt concrete behaves as a viscoelastic material at medium to high service temperatures, the structural response of the pavement is affected by the temperature history and the magnitude and duration of the wheel loads applied on the pavement. Therefore, predicting the pavement temperature patterns in a scalable manner (i.e., daily, seasonal, annual) is vital for a more accurate simulation of pavement response and performance, particularly the permanent deformation (rutting). This can result in more effective pavement designs. A numerical model was developed in this paper to predict the temperatures in asphalt layers by using the heat transfer theory and the Finite Element method with the help of proprietary software. A model that can predict the pavement temperatures continuously for one year covering all four seasons at time intervals that allow pavement distress prediction for each axle load cycle and calibrated with high-quality measured data is the main contribution of this study. Among the parameters that significantly affect pavement temperature prediction, the convective heat transfer at the air-pavement interface is of utmost importance. Therefore, the Convection Heat Transfer Coefficient (CHTC) for the air-pavement interface was also selected as an exploratory focus area for this paper. Two approaches were used to estimate the CHTC needed for the heat-transfer model. An error analysis was conducted to assess the accuracy of the pavement temperature prediction by using a large, measured dataset from a field test. The error of temperature predictions for the whole year was found to be minimum when the value of CHTC was equal to 21 W/(m2K). The error observed in winter predictions with the wind speed model of CHTC was minimized when a constant value of 60 W/(m2K) was used for CHTC. The significance of the convective heat transfer component in the FE model was identified through the improvements obtained by adapting the value of CHTC. Correlation analysis also identified a connection between the prediction error and relative humidity. Further research is underway to improve the temperature prediction model using additional parameters (i.e., relative humidity, cloud cover) and more refined data analytics approaches.

A novel magnetic loading porous liquid absorbent for removal of Cu(II) and Pb(II) from the aqueous solution

The first example of a magnetic loading functional porous liquid (PL) by employing hollow silicon dioxide (HS) loaded with Fe3O4 as the porous guest, (3-glycidyloxypropyl) trimethoxysilane (KH560) and polyetheramine M2070 as hindered sterically solvents was demonstrated in this paper. This nanoadsorbent (Fe3O4@HS-PL) was prepared for the decontamination of aqueous solutions from Cu(II) and Pb(II) heavy metals. The hindered sterically solvents (i.e., the flexible oligomer chains) endow the Fe3O4@HS porous guest with a superior flowing behavior at ambient temperature. Various approaches were applied to confirm the morphology, structure and properties of the resultant Fe3O4@HS-PL. More significantly, the adsorption behavior of Cu(II) and Pb(II) in aqueous solution by Fe3O4@HS-PL was studied in detail. As expected, combined with the cavity structure from porous guest and amine groups from flexible organic flow layer, Fe3O4@HS-PL exhibited excellent adsorption capacities of 322.58 and 357.14 mg·g−1 for Cu(II) and Pb(II), respectively, at 298 K, pH 8 and initial concentration of 20 mg·L-1. The obtained HS and Fe3O4 in Fe3O4@HS-PL magnetic nanoadsorbent presented average particles size ascertained to be ca.200 nm and 30 nm, respectively. Moreover, the Freundlich model and pseudo-first-order model has described the adsorption process and adsorption kinetics. The parameters in thermodynamic experiment signified the spontaneity and endothermic properties of the adsorption. Moreover, magnetic loading endowed the Fe3O4@HS-PL with a superior regeneration performance. A variety of mechanisms containing coordination interaction, porous guest absorption, electrostatic interaction, steric effect and entropy are simultaneously realized in this novel adsorption system. Therefore, the introduction of PLs adsorbents into heavy metal adsorption systems through the construction of PLs materials by magnetic loading and surface amination is of great significance in the field of heavy metal adsorption, eventually starting a new time of pushing forward the development of novel adsorbents for decontamination of aqueous solutions from heavy metal ions.

Understanding the graphene-polymer interfacial mechanical behavior via coarse-grained modeling

Understanding the interfacial behavior of graphene-polymer nanocomposite is a long-standing endeavor to gain deep insight into the mechanical properties of engineered structural materials. In this study, we implement the ‘hard’ cutoff scheme to develop a 4-1 mapping coarse-grained graphene (CGGr) model and the corresponding CG potential TersoffCG(4-1), which faithfully reproduces the honeycomb structure (bond length and angle) and mechanical properties of the graphene sheet compared to experimental results. Taking the poly(methyl methacrylate) (PMMA) and graphene sheet as a representative composite system, we establish a predictive CG modeling framework to study the interfacial behavior at a molecular level. By performing the rate-dependent interfacial separation simulations, our results reveal that lower separation velocity and thicker flexible layer can facilitate the craze fibrils formation and further enhance the toughness of the composite, which attribute to the adequate response of polymer to the graphene under lower velocity and more polymers that potentially to form fibrils under thicker flexible layer. Our work demonstrates the efficacy of TersoffCG(4-1) potential in understanding the interfacial mechanical behavior of graphene-polymer nanocomposite, offering an effective modeling strategy for performance improvement by designing the interfaces.