Identifying overpotential components of electrochemical systems enables quantitative analysis of polarization contributions of kinetic processes under practical operating conditions. However, the inherently coupled kinetic processes lead to an enormous challenge in measuring individual overpotentials, particularly in composite electrodes of lithium-ion batteries. Herein, the full decomposition of electrode overpotential is realized by the collaboration of single-layer structured particle electrode (SLPE) constructions and time-resolved potential measurements, explicitly revealing the evolution of kinetic processes. Perfect prediction of the discharging profiles is achieved via potential measurements on SLPEs, even in extreme polarization conditions. By decoupling overpotentials in different electrode/cell structures and material systems, the dominant limiting processes of battery rate performance are uncovered, based on which the optimization of electrochemical kinetics can be conducted. Our study not only shades light on decoupling complex kinetics in electrochemical systems, but also provides vitally significant guidance for the rational design of high-performance batteries.

Ultrahigh rate performance of active particles used in lithium-ion battery electrodes has been revealed by single-particle measurements, which indicates a huge potential for developing high-power batteries. However, the charging/discharging behaviors of single particles at ultrahigh C-rates can no longer be described by the traditional electrochemical kinetics in such ion-intercalation active materials. In the meantime, regular kinetic measuring methods meet a challenge due to the coupling of interface reaction and solid-state diffusion processes of active particles. Here, we decouple the reaction and diffusion kinetics via time-resolved potential measurements with an interval of 1 ms, revealing that the classical Butler-Volmer equation deviates from the actual relation between current density, overpotential, and Li+ concentration. An interface ion-intercalation model is developed which considers the excess driving force of Li+ (de)intercalation in the charge transfer reaction for ion-intercalation materials. Simulations demonstrate that the proposed model enables accurate prediction of charging/discharging at both single-particle and electrode scales for various active materials. The kinetic limitation processes from single particles to composite electrodes are systematically revealed, promoting rational designs of high-power batteries.

Material extrusion additive manufacturing is one of the widely used rapid prototyping technology, which produces parts with complex shapes and structures by continuous deposited strands. In previous studies, the influence of process parameters on product performance was experimentally studied, while morphological evolutions of strands, particularly considering the temperature variation, were not well understood. In this study, the mesostructure formed by parallel strands during continuous non-isothermal deposition flows was comprehensively studied by numerical simulations and experimental methods. The numerical model simulated the complete process of flow, deposition, bonding, and heat transfer for the representative materials, polyether ether ketone (PEEK), and polylactic acid (PLA). The quantitative comparison of the strands cross-sectional size measured by experiments and simulations was presented, and the results were consistent. The results showed that increasing the reheating temperature can considerably improve the strand-to-strand bonding. The effects of gap distance, printing speed, and strand-to-strand distance on the mesostructures of PEEK and PLA were comprehensively investigated and compared. Additionally, the simulation and experiment results provided detailed information regarding the porosity and bonding degree, which significantly affects product performance.

Constrained by the existing scaffold inability to mimic limbal niche, limbal bio-engineered tissue constructed in vitro is challenging to be widely used in clinical practice. Here, a 3D nanofiber-aerogel scaffold is fabricated by employing thermal cross-linking electrospinned film polycaprolactone (PCL) and gelatin (GEL) as the precursor. Benefiting from the cross-linked (160 °C, vacuum) structure, the homogenized and lyophilized 3D nanofiber-aerogel scaffold with preferable mechanical strength is capable of refraining the volume collapse in humid vitro. Intriguingly, compared with traditional electrospinning scaffolds, the authors' 3D nanofiber-aerogel scaffolds possess enhanced water absorption (1100-1300%), controllable aperture (50-100 µm), and excellent biocompatibility (optical density value, 0.953 ± 0.021). The well-matched aperture and nanostructure of the scaffolds with cells enable the construction of limbal bio-engineered tissue. It is foreseen that the proposed general method can be extended to various aerogels, providing new opportunities for the development of novel limbal bio-engineered tissue.

Efficient manufacturing cellulose is a promising solution to solve severe environmental problems resulted from non-degradable plastics. Herein, we propose a plasticizing-rolling technique to manufacture cellulose featuring high cellulose ratios (50 wt%) and low temperature (65 C). Plasticization effect from plasticizers (BmimCl and LiCl) uniformly plasticizes cellulose while shear effect from rolling destroys the crystal area of cellulose, both increasing the mobility of cellulosic molecular chains. SEM images and XRD analysis reveal prepared films are all cellulose composite (ACC), cellulose I in the core area and cellulose II in the surface area. ACC films have high transparency (81 %), tensile strength (73.6 +/- 4.8 MPa), and thermal stability. The relationship of structure and properties of ACC films was also discussed and a particle-matrix composite model was proposed to analyze high transparency of ACC films. In conclusion, the proposed method presents a potential to efficiently produce highperformance cellulose materials at large scale.

摘要: 注射成型是塑料产品成型的最主要工艺,工艺参数是影响成型产品外观、尺寸与性能的关键因素之一。工艺参数的设置与优化属于弱理论、强经验的问题,迫切需要发展科学化、系统化的方法。针对产品缺陷修正中人工经验依赖性强的问题,构建知识的统一模糊化规则形式,建立工艺优化知识表示和推理于一体的Takagi-Sugeno-Kang(TSK)模糊规则网络模型。进一步,提出从工艺数据集自动发现工艺参数优化规则的学习方法,采用Dropout策略与Bagging集成学习策略缓解高维工艺数据下工艺知识库增长出现的规则数量爆炸等问题。分析了模糊规则网络参数、结构对知识表示和推理的影响,建立模型的参数学习与结构优化的双重进化方法。提出基于经验回放的工艺数据增量学习方法,建立数据的增量学习策略。在注射成型工艺数据集上的结果表明,模型的规则数量和长度降低了50%,具有高可解释性以及增量学习稳定性。

Herein, we report a remotely controlled soft robot employing a photoresponsive nanocomposite synthesized from liquid crystal elastomers (LCEs), high elastic form-stable phase change polymer (HEPCP), and multiwalled carbon nanotubes (MWCNTs). Possessing a two-stage deformation upon exposure to near-infrared (NIR) light, the LCE/HEPCP/MWCNT (LHM) nanocomposite allows the soft robot to exhibit an obvious, fast, and reversible shape change with low detection limitations. In addition to the deformation and bending of the LCE molecular chains itself, the HEPCP in the composite material can also be triggered by a reversible solid-liquid transition due to the temperature rise caused by MWCNTs, which further promotes the change of the LCE. In particular, the proposed photodriven LHM soft robot can bend up to 180° in 2 s upon NIR stimulation (320 mW, distance of 5 cm) and generate recoverable, dramatic, and sensitive deformation to execute various tasks including walking, twisting, and bending. With the capacity of imitating biological behaviors through remote control, the disruptive innovation developed here offers a promising path toward miniaturized untethered robotic systems.

The integrated circuits industry has been continuously producing microelectronic components with ever higher integration level, packaging density, and power density, which demand more stringent requirements for heat dissipation. Electronic packaging materials are used to pack these microelectronic components together, help to dissipate heat, redistribute stresses, and protect the whole system from the environment. They serve an important role in ensuring the performance and reliability of the electronic devices. Among various packaging materials, epoxy-based underfills are often employed in flip-chip packaging. However, widely used capillary underfill materials suffer from their low thermal conductivity, unable to meet the growing heat dissipation required of next-generation IC chips with much higher power density. Many strategies have been proposed to improve the thermal conductivity of epoxy, but its application as underfill materials with complex performance requirements is still difficult. In fact, optimizing the combined thermal-electrical-mechanical-processing properties of underfill materials for flip-chip packaging remains a great challenge. Herein, state-of-the-art advances that have been made to satisfy the key requirements of capillary underfill materials are reviewed. Based on these studies, the perspectives for designing high-performance underfill materials with novel microstructures in electronic packaging for high-power density electronic devices are provided.

In this work, a novel type of acoustic-driven nanogenerator (ANG) that integrates piezoelectric and triboelectric effects is reported for broadband sound energy harvesting. With an internal structure combining beam-like porous multi-walled carbon nanotubes/polyvinylidene fluoride-trifluoroethylene (MWCNTs/PVDF-TrFE) aerogel and polydimethylsiloxane (PDMS) tympanum, the integrated MWCNTs/PVDF-TrFE/PDMS (MCPP) ANG achieves efficient collection of acoustic energy through the enhanced vibration and friction generated internally. Besides, by eliminating the need for a contact separation structure or an external resonator cavity, this design greatly simplifies the structure and contributes to the flexibility and durability of the device. The MCPP ANG, with 20 wt% sodium carboxymethyl cellulose (SCMC), 2 wt% MWCNTs and 5% w/v PDMS, reaches an optimal output of 34.4 V and 1.74 mu A under 150 Hz and 115 dB sound stimulation, corresponding to an areal power density of 11.62 mW/m2. It possesses a broad working bandwidth from 110 Hz to 400 Hz, and can directly illuminate 7 light-emitting diodes (LEDs) in series. Employing a novel, simple and effective structural design, the ANG proposed here achieves flexible and stable acoustic collection, offering a promising path toward sound spectrum analysis, noise detection and power supply for various low-power sensors.

Entanglement network is an important structural feature in concentrated polymer solutions and polymer melts, which has a great influence on the transient rheological behavior and molecular configuration evolution. However, the existing constitutive models have limitations in describing the influence of dynamic entanglement behavior on molecular chain motion, resulting in inaccurate descriptions of the transient rheological behavior. Thus, a molecular configuration evolution model for polymer solutions considering the dynamic entanglement effect is proposed by introducing an intermolecular force that changes with the orientation of the molecular chain in this work. The intermolecular force is introduced by considering the friction coefficient as anisotropic, and the orientation effect is considered by introducing an excluded volume dependent anisotropic diffusion. The proposed model can better describe the stress relaxation, stress growth, and dielectric anisotropy of polymer solutions compared with the anisotropy FENE model and FENE model. In addition, the influence of different model parameters on the transient and steady shear response of polymer solution is investigated, and the results show that the influence of volume loss on the friction anisotropy factor kσ increases as the solution concentration increases.

Optimal parting curve contributes to simplify mold structure, reduce the cost of mold manufacturing and injection production. As it depends on a series of technique, such as undercut feature recognition, mold piece region recognition, parting curve assessment, and etc., optimal parting curve generation is still a complicated and time-consuming task. A hybrid hint-based and fuzzy comprehensive evaluation method is proposed in this paper to recognize mold piece region and generate optimal parting curve for 3D CAD model in injection mold design. The hint topological regions are innovatively defined and classified into three categories: concave-edge region, inner loop region, and isolated surface. Based on this, potential mold piece region is defined and recognized by the surface visibility. Exact region is recognized from the potential mold piece region by the hint derived from adjacency relationship of topological regions. Inexact region is recognized from these topological regions that are not exact regions through their adjacent information. Candidate mold piece region set is then constructed by the combination of exact region and inexact region. Boundary edges of each candidate mold piece region are extracted to form the parting curve scheme set. By using the parting curve evaluation model which is based on fuzzy comprehensive evaluation method, the parting curve with the best assessment result is selected as the optimal scheme. Finally, case study is provided to validate the feasibility and effectiveness of the proposed approach.

In the paper, a novel method to improve the uniformity of the temperature distribution on the surface of the hot-plate is presented. Firstly, the effect of magnetic flux density under coupling of the electromagnetic and heat transfer on target surface temperature is studied numerically by using the commercial simulation software COMSOL Multiphysics. To evaluate the uniformity of the temperature distribution on the target surface, the temperature nonuniformity index on the target surface was firstly employed in terms of a specific designed electric coil. Secondly, the principal components analysis combined with the orthogonal test method is employed to analyze the shape parameters and obtain optimized temperature distribution at the target surface of the hot-plate. The simulated results show that the uniformity of temperature can be greatly improved by appropriately adjusting distribution of magnetic flux and the uniform temperature distribution can be achieved on a heating surface of 130 mm in length, 130 mm in width, and 30 mm in height. Finally, the optimizations of target surface temperature on hot-plate with different target temperatures were studied as well.

Solid-state thermo-stamping of fabric reinforced composites has attracted extensive attention for its efficiency and potential energy saving in recent years. In this paper, a hybrid lamination model is proposed to describe the forming behavior of woven fabric reinforced thermoplastic composites (WFRTP) during solid-state thermo-stamping process. The woven carbon fiber (CF)/ Polyetheretherketone (PEEK) sheet/prepreg is modelled as laminate structure with woven reinforcement layers (shell elements) embedded in thermoplastic resin layer (solid elements). More specifically, a hypoelastic constitutive model is used to represent the anisotropic mechanical behavior of fabric reinforcement under large deformation, while the fabric model is calibrated and validated by uniaxial bias extension (UBE) test and tensile test of woven fabric. A phenomenological model is adopted to describe the viscoelastic-plastic deformation behavior of thermoplastic resin, while the resin model is calibrated by tensile tests of PEEK. The applicability of the hybrid model is demonstrated through comparing numerical results of UBE tests and Erichsen tests with their corresponding experiment results. The hybrid lamination model provides a theoretical foundation for numerical simulation of WFRTP solid-state thermo-stamping.

Optical polymer materials are widely used in aerospace, electronics, and other engineering fields, which have strict requirements on optical properties such as the birefringence of products. The present birefringence calculation method is based on the stress-birefringence law and is still difficult in accurate prediction, owing to extremely difficulty in predict residual stress. Actually, the birefringence of polymer optical products is essentially caused by the molecular chain orientation. Thus, a quantitative mathematical model between molecular orientation and birefringence is proposed in this study. The coupling simulation of the macroscopic flow field and microscopic orientation is realized through internal stress. The accuracy of the proposed model is verified by the polarized Raman and birefringence distribution experimental results. Compared with the stress-birefringence method, the proposed model and numerical method can more accurately simulate molecular orientation and birefringence. Meanwhile, the simulated average orientation degree in the thickness direction under different melt and mold temperatures are compared with the measured results, which verifies the effectiveness of the proposed model and simulation method at different process conditions.

Ion transport kinetics is identified as the major challenge of thick electrode design for high-energy-density lithium-ion batteries. The introduction of vertically-oriented structure pores, which provide fast transport pathways for Li+ , can maximize the rate-performance of electrodes while holding a high energy density. To overcome the harsh manufacturing requirements of traditional template-based methods for the oriented-pore electrodes, a template-free strategy is developed to meet the large-scale fabrication demand, in which controllable oriented microchannels are facilely constructed by vertically aggregated bubbles generated from thermal decomposition. The proposed method is demonstrated to be applicable for different active materials and compatible with industrial roll-to-roll manufacturing. The oriented-pore electrodes exhibit a seven times higher capacity at 5C rate and show double the power density relative to the state of the art while maintaining a high level of energy density. The balance between the ion transport kinetics through the channels and in the matrix manifests an optimal design of the electrode structures, enabling the desired superior performance of the electrodes toward practical applications.

Improving the tradeoff between accuracy and interpretability is essential for the problem of handling high-dimensional data in Takagi–Sugeno–Kang (TSK) fuzzy systems and providing insights into real-world tasks. However, the TSK fuzzy system becomes complex and challenging to interpret as the data dimension increases. Here, we report an ensemble classifier, which is an enhanced adaptive network-based fuzzy inference system (ANFIS) integrating improved bagging and dropout to build concise fuzzy rule sets. First, the high-dimensional feature space is decomposed into a series of low-dimensional feature subsets using the bagging and random subspace method to train multiple ANFISs. An improved dropout strategy is then applied in training ANFISs by temporarily disabling rules in each training epoch and deleting rules after training to obtain sparse rulesets with high-quality rules. These sub-models are subsequently aggregated to perform the fuzzy inference. Results on high-dimensional benchmark datasets confirm that both the bagging and dropout strategies are effective, providing high interpretability by reducing the co-firing degrees and rules of sub-models while guaranteeing accuracy at the same time.

The wide application of 3D models in computer-aided engineering (CAE) has created an urgent need for 3D model retrieval systems in manufacturing. However, recent methods are mainly based on the shape similarity of models, limiting reuse of the manufacturing process information associated with retrieved models. In this paper, we present a novel 3D model retrieval method for plastic injection molding involving process-related features. An effective feature is proposed to characterize both the geometry and process information of the 3D model, using the pressure profile based on the molding process. A variational autoencoder (VAE) is utilized to refine process-related features through unsupervised learning to improve retrieval efficiency. A 3D model database containing 120 models in actual production was built for validation experiments. The experimental results show that the proposed encoded pressure feature outperforms conventional methods with an accuracy of 86.61% compared to 78.57% using shape distribution and 73.21% using numerical features. A retrieval application proves that the information of retrieved models can be reused by a new product through the proposed method. There is considerable potential for utilizing the proposed method in similar manufacturing fields.

Restricted by the inherent property of low power density, acoustic energy can hardly be effectively captured by conventional piezo- or triboelectric nanogenerators for powering miniature electronics. Herein, a novel piezo-tribo hybrid nanogenerator employing nanoporous polyvinylidene fluoride (PVDF) hollow fiber and polydimethylsiloxane (PDMS) valve, which can mimic the eardrum, has been advocated for efficient acoustic harvesting. The nanoporous, hollow, and valve structure design, together with the effective combination of piezo- and triboelectricity, make the nanoporous PVDF hollow fiber and PDMS valve based acoustic harvester (PHVAH) a promising candidate for acoustic-electric conversion. With an optimal output of 105.5 V and 16.7 μA and a power density of 0.92 W m-2 under the sound stimulation of 117.6 dB and 150 Hz, it can not only recognize audio signals but also convert the sound into electrical energy to light up seven LED bulbs in series. Exhibiting excellent durability and stability, the disruptive innovation proposed here is an effective method for hunting the ubiquitous sound energy in the environment, which provides great potential and impetus for using acoustic-electric conversion to power various low-power-consumption sensors.

Core-pulling mechanism plays a vital role in producing plastic products with complex shapes. However, the design of core-pulling mechanism is still excessively depended on experience. As a result, low intelligent input often leads to high costs and long lead-time. In this paper, we propose a similarity-based approach for the variant design of core-pulling mechanism in injection mold design. An assemble model supporting variant design is established as the foundation of the case repository of core-pulling mechanism. A hierarchical assessment algorithm, which calculate the synthetic similarity of four characteristic parameters, viz., core-pulling type, core-pulling force, core-pulling distance, and shape description, is proposed to evaluate the similarity among undercut features. On the basis of the retrieved case with the highest similarity to the undercut feature of new plastic product, geometric model modification is performed by fine-tuning and head feature generation algorithm, and assemble tree modification is finished by the structural descriptions for assemble frame and assemble information of parts. Finally, case study is provided to validate the feasibility and effectiveness of the proposed approach.

Almost all flows of polymers are accompanied by evolution of the molecular configuration, which has a great influence on material properties. However, the existing molecular configuration evolution models are mainly limited to the configuration evolution of a single molecular chain in a dilute solution ignoring intermolecular forces; it is impossible to predict the evolution of the molecular configuration of polymer melt with a complex entanglement network. Thus, we propose a non-isothermal compressible viscoelastic molecular configuration evolution model for polymer melt using non-equilibrium irreversible thermodynamic methods. The proposed model can more accurately describe the rheological properties of polymer melts compared with the commonly used XPP model, especially at high shear rates. The predicted molecular configuration in rotary Couette flow agrees well with the measured dielectric anisotropy results. Finally, the present model and a simulation method are successfully applied to predict the evolution of polymer molecular configuration and birefringence in three-dimensional injection compression molding, and the predicted birefringence is consistent with the measured results. A typical skin-core structure, resulting from the competition of the flow field and the molecular Brownian thermal motion in the injection molding, is investigated. (C) 2020 Published by Elsevier Inc.