This paper reviews the application of molecular dynamics simulations on mechanical and tribological properties of polymer composites reinforced by carbon nanotubes and graphene sheet as ...reinforcements. A variety of simulation studies on modelling, calculation and analysis on enhanced elastic, tensile, fracture properties of carbon nanotubes and graphene sheet/polymer composites are introduced and reviewed. The capabilities of molecular dynamics simulations on exploring inherent mechanisms on improved tribological properties of carbon nanotubes and graphene sheet/polymer composites from atomic views are particularly discussed. Different methods of surface modifications of the two nano reinforcements on further enhancing the strength of polymer composites are summarized. Summary and recommendations for potential researches are also provided. This review is intended to provide a state-of-the-art and better understanding on applications of carbon nanotubes and graphene sheet for enhancing mechanical and tribological properties of polymer composites by molecular dynamics simulations, and inspire future efforts in this area.
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To compare enhancements of mechanical properties of polymer composites reinforced by carbon nanotubes and graphene sheet, molecular models of polymer matrix reinforced by the same weight percentage ...of carbon nanotubes and graphene sheet are developed. Pull-out process and strain constant method are applied to find mechanical properties of the nano-composites by examining the interfacial interactions between nano-reinforcements and polymer matrices. The results show that about 18% higher in Young's modulus, 8.7% higher in tensile strength, and 5% higher in surface crack energy are obtained for the composites by incorporation of graphene sheet than those by incorporation of carbon nanotubes. Graphene sheet is found to play a better role in delaying the propagations of cracks. To explore the mechanisms on the enhanced tensile and fracture properties, the interfacial interaction energy and shear forces between the nano-reinforcements and polymer matrices, and total van der Waals energy of the polymer composites are examined and interpreted accordingly.
•Orthogonal design was combined with ideal overlap model to ascertain key parameters.•Cooling rate and solidification velocity were the reason for morphological features.•The influence rules of ...critical parameters on morphology and properties were deduced.•The characteristics of morphology determine the anisotropy of mechanical properties.
Laser Metal Deposition Shaping (LMDS) is a new rapid manufacturing technology, which can build fully-dense metal components directly from the information transferred from a computer file by depositing metal powders layer by layer with neither mould nor tool. Typically, performed with stainless steel (SS) 316 powder, the orthogonal experiments combining with the ideal overlapping model were applied to ascertain the optimal processing parameters. Then the characteristics of microstructure, composition and phase of as-deposited cladding layers were analyzed through Scanning Electron Microscope (SEM) and X-ray diffraction (XRD), as well as relative model. Furthermore, the cooling rate and the solidification velocity during LMDS were evaluated based on empirical method. With the optimal parameters, some parts were fabricated without obvious defects, and then the mechanical properties of them were tested. Finally, the influencing regularities of critical parameters on microstructure and properties were concluded by comparison. The results prove that the microstructure of SS 316 deposits is composed of the slender dendrites growing epitaxially from the substrate, the mechanical properties are favorable and anisotropic, and the composition is uniform. Besides, the microstructure morphology and the mechanical properties are affected by the varied processing parameters at different degrees. Among them, the scanning speed shows the most remarkable effects on microstructure morphology, characteristic microscale, mechanical properties, as well as geometric shape of as-deposited parts.
Molecular cross-linked models of pure epoxy matrix and carbon nanotubes reinforced epoxy composites with initial single edged crack are developed. Tensile processes are conducted by applying the ...constant strain method to examine the enhanced fracture behaviors of the epoxy composites by introduction of carbon nanotubes as reinforcements. The results showed that increases of 24.8% and 34.3% in the tensile strength and elongation at break of the carbon nanotubes/epoxy composites can be obtained. The J-integral expressions are derived and an increase of 35.7% in the energy release rate of the carbon nanotubes/epoxy composites is achieved. The mechanisms of the enhanced fracture properties and crack growth behaviors of the epoxy composites are explored and interpreted from an atomic view by monitoring the variations of the non-bond interaction energy and radius distribution function between carbon nanotubes and epoxy matrix.
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Animal manure application as organic fertilizer does not only sustain agricultural productivity and increase soil organic carbon (SOC) stocks, but also affects soil nitrogen cycling and nitrous oxide ...(N2O) emissions. However, given that the sign and magnitude of manure effects on soil N2O emissions is uncertain, the net climatic impact of manure application in arable land is unknown. Here, we performed a global meta‐analysis using field experimental data published in peer‐reviewed journals prior to December 2015. In this meta‐analysis, we quantified the responses of N2O emissions to manure application relative to synthetic N fertilizer application from individual studies and analyzed manure characteristics, experimental duration, climate, and soil properties as explanatory factors. Manure application significantly increased N2O emissions by an average 32.7% (95% confidence interval: 5.1–58.2%) compared to application of synthetic N fertilizer alone. The significant stimulation of N2O emissions occurred following cattle and poultry manure applications, subsurface manure application, and raw manure application. Furthermore, the significant stimulatory effects on N2O emissions were also observed for warm temperate climate, acid soils (pH < 6.5), and soil texture classes of sandy loam and clay loam. Average direct N2O emission factors (EFs) of 1.87% and 0.24% were estimated for upland soils and rice paddy soils receiving manure application, respectively. Although manure application increased SOC stocks, our study suggested that the benefit of increasing SOC stocks as GHG sinks could be largely offset by stimulation of soil N2O emissions and aggravated by CH4 emissions if, particularly for rice paddy soils, the stimulation of CH4 emissions by manure application was taken into account.
The uncertain manure effects on N2O emissions constrain evaluation of the net climatic impact of manure application in arable lands. A global meta‐analysis was performed to quantify the overall responses of N2O emissions to manure application relative to synthetic N fertilizer in agricultural soils. Manure application on average significantly increased N2O emissions by 32.7% as compared to synthetic N fertilizer alone, and the sign and magnitude of N2O emissions were dependent on manure characteristics, climate, and soil properties. The benefit of C sequestration could be largely offset by stimulation of soil N2O emissions and aggravated by CH4 emissions if, particularly for rice paddy soils, the stimulation of CH4 emissions by manure application was taken into account.
Polymer composites reinforced by pristine and functionalized graphene are investigated to identify the improvement of tribological properties of polymer composites. A pull-out process is conducted to ...study the interfacial interactions between polymer matrices and graphene reinforcements using molecular dynamics simulations. Molecular layer models containing Fe atoms as the top nano-layer are built to study the enhancement of tribological properties of the polymer composites by sliding the top Fe nano-layer on the surface of the polymer matrix. The simulation results show that decreases of about 13% and 42.3% in the average friction coefficient and abrasion rate of the functionalized graphene/polymer composites can be achieved. In order to provide the understandings of the findings, the interfacial interaction energy, RDF values between graphene and polymer matrix are particularly calculated and discussed.
Most scheduling problems are required to follow rigid metrics, such as the maximum completion time, earliest deadline first, etc., ignoring the flexibility of manufacturing services (MSs) and the ...effects of their historical data hidden in millions of manufacturing activities. The historical data serves as a powerful basis for describing the comprehensiveness or credibility of the MS itself with the help of Industrial IoT enabling all equipment to communicate and take preventive actions. Similar to the bank credit for individuals, MSs should also have their own referential credit values when choosing the most suitable service for a specific manufacturing task. This paper summarizes the MS attributes from six aspects with sufficient sub-attributes. The fuzzy Analytic Network Process combined with the Cross-Entropy method is employed to evaluate the credit of MSs in the complex manufacturing network system. Such service scoring mechanism (SSM) can personify a comprehensive credit evaluation of services, where, a smart service configuration mode based on credit is proposed for carrying out the supply–demand matching with the help of the data-security technology. Subsequently, a credit-based manufacturing mode is derived under SSM. Numerical examples are carried out to demonstrate the validity of the matching mode. The result may assist manufacturers to allocate their manufacturing tasks in real time in a “credit” way and make quicker decisions in exceptional circumstances, while making the chosen service truly competent enough to finish the work, so as to further improve the customer satisfaction.
Grit, viz. a combination of perseverance and passion for long-term goals, is a psychological variable that has recently attracted scholarly attention. Research along this psychological line is ...significant because it complements the rich on-going research on cognitive variables. The seminal research by Plonsky (2018) and colleagues has validated an instrument to measure what they call ‘L2 grit’ in the Iranian EFL context. As a partial replication of the above research in the Chinese context, the present study confirmed the two-fold structure of L2 grit identified in the seminal research, and found that the selected socio-biographical variables (e.g. multilingualism, L2 joy, age, and gender) were linked to L2 grit to varying degrees. Additionally, this study proposes providing a range of effect sizes for each predictor in hierarchical regression as a more refined data analysis approach. Suggestions (e.g. considering a wider multilingual population) are made for future research.
•This study was the first (partial) replication of the very first L2 grit study.•It confirmed the factorial structure of the L2 grit scale in the Chinese context.•The influence of seven sociobiographical variables on L2 grit was examined.•Language competence, L2 joy, and age statistically significantly predicted L2 grit.•A more refined data analysis method based on hierarchical regression was proposed.
Biomaterials is an exciting and dynamic field, which uses a collection of diverse materials to achieve desired biological responses. While there is constant evolution and innovation in materials with ...time, biomaterials research has been hampered by the relatively long development period required. In recent years, driven by the need to accelerate materials development, the applications of machine learning in materials science has progressed in leaps and bounds. The combination of machine learning with high‐throughput theoretical predictions and high‐throughput experiments (HTE) has shifted the traditional Edisonian (trial and error) paradigm to a data‐driven paradigm. In this review, each type of biomaterial and their key properties and use cases are systematically discussed, followed by how machine learning can be applied in the development and design process. The discussions are classified according to various types of materials used including polymers, metals, ceramics, and nanomaterials, and implants using additive manufacturing. Last, the current gaps and potential of machine learning to further aid biomaterials discovery and application are also discussed.
The advancement of machine learning (ML) in materials science has progressed in leaps and bounds and has made a big impact into biomaterials research, ranging from discovery of bioactive chemical moieties, screening and optimization of material properties, to developing materials that interface better with biological systems. There is still untapped potential to integrate with ML for the next frontier in biomaterials.
Exploring earth-abundant electrocatalysts with high activity and low-cost for oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) is vital to energy harvesting and storage, such as ...fuel cell and effective overall water splitting. Herein, novel heterostructures (Ni3S2/VS2) without nitrogen (N) and with N doping are reported as superior electrocatalysts for the OER and HER, respectively. The heterostructure without doping shows enhanced OER performance with an extremely low overpotential (227 mV at 10 mA/cm2) due to increased active sites and fantastic interfaces as well as unique structure. Moreover, N-doped Ni3S2/VS2 (N-Ni3S2/VS2) shows high electrocatalytic HER performance with a low HER overpotential (151 mV at 10 mA/cm2), because the N-doping greatly improves conductivity and increases large amounts of catalytic active sites. Finally, we construct a two-electrode electrolyzer system (Ni3S2/VS2//N-Ni3S2/VS2) and it achieves a current density of 10 mA/cm2 at a low cell voltage of 1.648 V. Our findings demonstrate that structure design and doping can effectively improve the catalytic activities of nanomaterials for OER and HER.
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•We propose a novel heterostructure Ni3S2/VS2 with abundant active sites and interfaces.•The Ni3S2/VS2 exhibits a low OER overpotential of 227 mV at 10 mA/cm2.•The N-Ni3S2/VS2 demonstrates a low HER overpotential of 151 mV at 10 mA/cm2.•A two-electrode system shows good performance on overall water-splitting with an overpotential of 1.648 V at 10 mA/cm2.
