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Cellulose as the most abundant biopolymers on Earth, presents appealing performance in mechanical properties, thermal management, and versatile functionalization. Developing fabrication methods to design functional materials and open new application areas. However, cellulose is hard to be dissolved or melt due to its recalcitrant property. Herein, the recent progress of fabricating cellulose is summarized. First, the unique hierarchical structure of cellulose is fully investigated and the resulted processability is analysed in directions of down to nanocellulose, dissolution, and thermoplastic processing. Then, the reported fabrication methods are summarized in three aspects: (1) self‐assembly from nano/micro cellulose suspensions, especially the formation of cellulose nanocrystals; (2) dissolution–regeneration–drying, covering spinning and solvent infusion processing; and (3) thermoplastic processing, focusing on the setup and the morphology changes of the prepared products. In each aspect, the flowchart of the fabrication method, the mechanism, fabricated products, and effects of processing parameters are explored. Finally, this review provides a perspective on the further direction of fabricating cellulose, especially the challenges toward mass production. [ABSTRACT FROM AUTHOR]

High‐temperature heating is ubiquitously utilized in material synthesis and manufacturing, which often features a rapid production rate due to the significantly improved kinetics. However, current technologies generally provide overall and steady‐state heating, thereby limiting their applications in micro/nano‐manufacturing that require selective patterning and swift heating. Herein, significantly improved control over small‐scale heating is reported by utilizing 3D printed reduced‐graphene‐oxide (RGO) probe triggered by electrical Joule heating, which enables precise heating with high spatial (sub‐millimeter scale) and temporal (milliseconds) resolutions. The block copolymer‐modified aqueous‐based RGO ink enabled 3D printing of high‐precision structures, and a bio‐inspired cellular microstructure is constructed to achieve control of the electrical conductivity and maximize structure robustness (benefit for efficient heating and operability). In particular, a thermal probe featuring a microscale tip with excellent heating capabilities (up to ≈3000 K, ultra‐fast ramping rate of ≈105 K s−1, and durations in milliseconds) is fabricated. This thermal probe is ideal for surface patterning, as it is demonstrated for the selective synthesis of patterned metal (i.e., platinum and silver) nanoparticles on nano‐carbon substrates, which is not possible by traditional steady‐state heating. The material construction and heating strategy can be readily extended to a range of applications requiring precise control on high‐temperature heating. [ABSTRACT FROM AUTHOR]

The electromagnetic field and the droplet profile in electroslag remelting (ESR) process play a pivotal role in the quality of final steel products. Numerical simulation remains a central challenge to investigate ESR process with a high‐efficiency and high‐accuracy method. Herein, a strategy embedding an adaptive mesh refinement (AMR) method in 3D‐coupled model is proposed to speed up the simulation of ESR process. The coupling phenomena of the electromagnetic field and the droplet profile are successfully predicted using this method. Compared with the results of uniform mesh, the deviation of AMR results is less than 2.2%, whereas the computational speed of the AMR method is 10 times faster. Moreover, the effects of two key parameters in AMR method, maximum refinement level and refinement interval, on computational accuracy and speed are studied. With the increasing maximum refinement level, the computational accuracy is improved obviously first and then slightly, but the computation time increases exponentially. The increasing refinement interval has little effect on the computational accuracy but can greatly save the time of mesh operation. The proposed strategy can promote the application of numerical simulation in industrial ESR process. [ABSTRACT FROM AUTHOR]

Efficient electrocatalysts are critical in various clean energy conversion and storage systems. Polyelemental nanomaterials are attractive as multifunctional catalysts due to their wide compositions and synergistic properties. However, controlled synthesis of polyelemental nanomaterials is difficult due to their complex composition. Herein, a one‐step synthetic strategy is presented to fabricate a hierarchical polyelemental nanomaterial, which contains ultrasmall precious metal nanoparticles (IrPt, ≈5 nm) anchored on spinel‐structure transition metal oxide nanoparticles. The polyelemental nanoparticles serve as excellent bifunctional catalysts for the oxygen evolution reaction (OER) and oxygen reduction reaction (ORR). The mass catalytic activity of the polyelemental nanoparticles is 7‐times higher than that of Pt in ORR and 28‐times that of Ir in OER at the same overpotentials, demonstrating the high activity of the bifunctional electrocatalyst. This outstanding performance is attributed to the controlled multiple elemental composition, mixed chemical states, and large electroactive surface area. The hierarchical nanostructure and polyelemental design of these nanoparticles offer a general and powerful alternative material for catalysis, solar cells, and more. [ABSTRACT FROM AUTHOR]

Because of the insoluble and infusible properties of cellulose, it is difficult to directly apply cellulose in the manufacture of pure cellulose films (PCFs). Although progress has been achieved in developing eco-friendly fabrication methods including chemical modifications, wet process or thermoplastic process in recent decades, their extreme dependence on a large amount of solvents makes it impossible to attain green manufacturing due to the production of waste liquids and by-products. Herein, by recycling green ionic liquids (ILs) as cellulose plasticizers, a green manufacturing strategy was investigated, in which PCFs, which can be used as flexible cellulose electronic substrates, were successfully manufactured. The results indicated that the properties and structures of PCFs and ILs were essentially unchanged after four cycles; meanwhile, ILs were almost completely recycled and maintained high quality. Compared with the tensile strength of 84.3 MPa and the maximum transmittance of 64.8% of the original PCFs, the mechanical and optical performances of PCFs remained at 83.0 MPa and 64.4% after four cycles, respectively. Moreover, the recovery rates of ILs were higher than 95.0 wt%. Therefore, this study opens a green manufacturing window, which promotes the industrialization of cellulose processing using ILs and the cellulose electronic substrates to substitute the existing artificial polymer electronic substrates. [ABSTRACT FROM AUTHOR]

• Analyzed the enhancement mechanism of MZAP on the fracture process. • Demonstrated positive effect of MZAP on mechanical riveting structure. • Effects of MZAP feature on the joint is investigated firstly. Effectively joining dissimilar transparent thermoplastics plays an important role in lightweight design in vehicles and automotive industry. However, their different physical–chemical properties hamper the welding of thermoplastics directly with diode laser. In this study, magnesium zinc alloy particles (MZAP) are chosen as laser-absorbent to join polycarbonate (PC) and polyarylsulfone (PASF) specimens. First, the comparison of PC/PASF weldments with different laser-absorbents was performed, including adding carbon black (CB) as laser-absorbents. The morphology and mechani-cal properties show the weldments prepared with MZAP have advantages of cleaner appearance and stronger shear strength. Furthermore, the fracture surface and joint microstructure reveal that the mechanical riveting structure, composed of defused MZAP and filled thermoplastics in the gaps between adjacent MZAP, forms when the MZAP was used as laser-absorbent. Comparing with CB aggregates, the fused MZAP has a stronger ability to resist external force and leads to a higher shear strength. Finally, the analysis of factors of MZAP feature reveals that the mechanical riveting structure contributes to improving the shear strength, the defects, such as creaks, holes and bubbles should be eliminated in the welding. The results of analysis of variance illustrated the particle size and the standoff distance are statistically significant for the shear strength. This study provides a potential avenue to effective weld dissimilar thermoplastics. [ABSTRACT FROM AUTHOR]

Information extracting method from numerous measured signals is a critical technique for intelligent manufacturing application to further reduce the manpower cost and improve the productivity and workpiece quality. Manually defining signal features,as the common way, unfortunately will lose most of the information and the performance can’t be guaranteed. In the past few years, machine learning method with deep structure has been the most promising automatic feature extracting method which has made great breakthrough in computer vision and automatic speech recognition. In this paper, deep belief networksareemployed using vibration signal obtained from endmilling to build feature space for cutting states monitoring. Greedy layer-wise strategy is adopted to pre-train the network and standard samples are used for fine-tuning by applying back-propagation method. Comparisons are made with several manually defined features both in time and frequency domain, like MFCC and wavelet method. Different modeling methods are also employed in the research forcomparisons. Resultsshow that the deep learning method has similar ability to characterize the signal for cutting states monitoring compared to those manually defined features. And the modeling accuracy ismuch better thanother traditionalmodeling methods.Furthermore, benefitting fromthe potentialcapability in information fusion, deep learning method would be a promising solution for more complex applications, like tool wear monitoring, machining surface prediction et al. [ABSTRACT FROM AUTHOR]

Chatter is one of the most unexpected and uncontrollable phenomenon during the milling operation. It is very important to develop an effective monitoring method to identify the chatter as soon as possible, while existing methods still cannot detect it before the workpiece has been damaged. This paper proposes an energy aggregation characteristic-based Hilbert–Huang transform method for online chatter detection. The measured vibration signal is firstly decomposed into a series of intrinsic mode functions (IMFs) using ensemble empirical mode decomposition. Feature IMFs are then selected according to the majority energy rule. Subsequently Hilbert spectral analysis is applied on these feature IMFs to calculate the Hilbert time/frequency spectrum. Two indicators are proposed to quantify the spectrum and thresholds are automatically calculated using Gaussian mixed model. Milling experiments prove the proposed method to be effective in protecting the workpiece from severe chatter damage within acceptable time complexity. [ABSTRACT FROM AUTHOR]

High entropy alloy nanoparticles (HEA-NPs) are reported to have superior performance in catalysis, energy storage, and conversion due to the broad range of elements that can be incorporated in these materials, enabling tunable activity, excellent thermal and chemical stability, and a synergistic catalytic effect. However, scaling the manufacturing of HEA-NPs with uniform particle size and homogeneous elemental distribution efficiently is still a challenge due to the required critical synthetic conditions where high temperature is typically involved. In this work, we demonstrate an efficient and scalable microwave heating method using carbon-based materials as substrates to fabricate HEA-NPs with uniform particle size. Due to the abundant functional group defects that can absorb microwave efficiently, reduced graphene oxide is employed as a model substrate to produce an average temperature reaching as high as ∼1850 K within seconds. As a proof-of-concept, we utilize this rapid, high-temperature heating process to synthesize PtPdFeCoNi HEA-NPs, which exhibit an average particle size of ∼12 nm and uniform elemental mixing resulting from decomposition nearly at the same time and liquid metal solidification without diffusion. Various carbon-based materials can also be employed as substrates, including one-dimensional carbon nanofibers and three-dimensional carbonized wood, which can achieve temperatures of >1400 K. This facile and efficient microwave heating method is also compatible with the roll-to-roll process, providing a feasible route for scalable HEA-NPs manufacturing.

Lightweight structural materials are critical in construction and automobile applications. In past centuries, there has been great success in developing strong structural materials, such as steels, concrete, and petroleum-based composites, most of which, however, are either too heavy, high cost, or nonrenewable. Biosourced composites are attractive alternatives to conventional structural materials, especially when high mechanical strength is presented. Here we demonstrate a strong, lightweight bio-based structural material derived from bamboo via a two-step manufacturing process involving partial delignification followed by microwave heating. Partial delignification is a critical step prior to microwave heating as it makes the cell walls of bamboo softer and exposes more cellulose nanofibrils, which enables superior densification of the bamboo structure via heat-driven shrinkage. Additionally, microwave heating, as a fast and uniform heating method, can drive water out of the bamboo structure, yet without destroying the material's structural integrity, even after undergoing a large volume reduction of 28.9%. The resulting microwave-heated delignified bamboo structure demonstrates outstanding mechanical properties with a nearly 2-times improved tensile strength, 3.2-times enhanced toughness, and 2-times increased bending strength compared to natural bamboo. Additionally, the specific tensile strength of the modified bamboo structure reaches 560 MPa cm 3 g -1 , impressive given that its density is low (1.0 g cm -3 ), outperforming common structural materials, such as steels, metal alloys, and petroleum-based composites. These excellent mechanical properties combined with the resource abundance, renewable and sustainable features of bamboo, as well as the rapid, scalable manufacturing process, make this strong microwave-processed bamboo structure attractive for lightweight, energy-efficient engineering applications.