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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]

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]

Triboelectric nanogenerator (TENG) is one of the most attractive candidates for providing a green energy source capable of satisfying the world's energy consumption. 3D printing is a promising route to satisfy the demands of various self-powered devices with individualized design in practical applications such as signal processing, precisely tuning circuits, active sensor networks, remote controls, and flexible electronics. This work reports an approach of fused deposition modeling (FDM), one of 3D printing methods, which enables the creation of optimized digital designs for TENG devices for the purpose of efficiently harvesting ambient vibration energy. To obtain satisfying output power and high mechanical energy conversion efficiency, various positive and negative polymers were chosen as friction layers, such as polylactic acid (PLA), nylon (PA), a mixture of polypropylene and polyethylene (PP/PE), and poly(ethylene terephthalateco-1,4-cylclohexylenedimethylene terephthalate) (PETG). By increasing the vibrational frequency from 5 Hz to 20 Hz, the output voltage of the TENG device increased from 50 V to 241 V in a TENG device using nylon (PA) and a mixture of polypropylene and polyethylene (PP/PE). The TENG device had a 0° contact angle between the two films, which also had some macroscopic patterns on their surfaces. Simultaneously, a decrease in the filling rate (from 100% to 20%) and thickness (from 0.4 to 0.2 mm) resulted in an increase in the output voltage from 57 V to 176 V and from 50 V to 241 V, respectively. To better understand the effects of the printing parameters on the output performance, we studied the factors of the filling rate, thickness, contact angle and the width of the zigzag pattern. From these results, we conclude that 3D printing based on the FDM strategy to fabricate TENG outstandingly improves the output performance by decreasing the effective Young's modulus. Additionally, the peak of the current reaches 1.52 mA in ultrashort time. The triboelectrification efficiency in this vertical contact-separation mode reaches 63.9%. Our concept of 3D printing based on FDM will stimulate further fundamental work in fabricating approaches to harvesting environmental energy. [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]