Search

Abstract Based on the advantages of triply periodic minimal surface (TPMS) porous structures, extensive research on NiTi shape memory alloy TPMS scaffolds has been conducted. However, the current reports about TPMS porous structures highly rely on the implicit equation, which limited the design flexibility. In this work, novel shell-based TPMS structures were designed and fabricated by laser powder bed fusion. The comparisons of manufacturability, mechanical properties, and shape recovery responses between traditional solid-based and novel shell-based TPMS structures were evaluated. Results indicated that the shell-based TPMS porous structures possessed larger Young’s moduli and higher compressive strengths. Specifically, Diamond shell structure possessed the highest Young’s moduli of 605.8±24.5 MPa, while Gyroid shell structure possessed the highest compressive strength of 43.90±3.32 MPa. In addition, because of the larger specific surface area, higher critical stress to induce martensite transformation, and lower austenite finish temperature, the Diamond shell porous structure exhibited much higher shape recovery performance (only 0.1% residual strain left at pre-strains of 6%) than other porous structures. These results substantially uncover the effects of structural topology on the mechanical properties and shape recovery responses of NiTi shape memory alloy scaffolds, and confirm the effectiveness of this novel structural design method. This research can provide guidance for the structural design application of NiTi porous scaffolds in bone implants.

Abstract Continuous industrialization and other human activities have led to severe water quality deterioration by harmful pollutants. Achieving robust and high-throughput water purification is challenging due to the coupling between mechanical strength, mass transportation and catalytic efficiency. Here, a structure-function integrated system is developed by Douglas fir wood-inspired metamaterial catalysts featuring overlapping microlattices with bimodal pores to decouple the mechanical, transport and catalytic performances. The metamaterial catalyst is prepared by metal 3D printing (316 L stainless steel, mainly Fe) and electrochemically decorated with Co to further boost catalytic functionality. Combining the flexibility of 3D printing and theoretical simulation, the metamaterial catalyst demonstrates a wide range of mechanical-transport-catalysis capabilities while a 70% overlap rate has 3X more strength and surface area per unit volume, and 4X normalized reaction kinetics than those of traditional microlattices. This work demonstrates the rational and harmonious integration of structural and functional design in robust and high throughput water purification, and can inspire the development of various flow catalysts, flow batteries, and functional 3D-printed materials.

NiTi alloys have drawn significant attentions in biomedical and aerospace fields due to their unique shape memory effect (SME), superelasticity (SE), damping characteristics, high corrosion resistance, and good biocompatibility. Because of the unsatisfying processabilities and manufacturing requirements of complex NiTi components, additive manufacturing technology, especially laser powder bed fusion (LPBF), is appropriate for fabricating NiTi products. This paper comprehensively summarizes recent research on the NiTi alloys fabricated by LPBF, including printability, microstructural characteristics, phase transformation behaviors, lattice structures, and applications. Process parameters and microstructural features mainly influence the printability of LPBF-processed NiTi alloys. The phase transformation behaviors between austenite and martensite phases, phase transformation temperatures, and an overview of the influencing factors are summarized in this paper. This paper provides a comprehensive review of the mechanical properties with unique strain-stress responses, which comprise tensile mechanical properties, thermomechanical properties (e.g. critical stress to induce martensitic transformation, thermo-recoverable strain, and SE strain), damping properties and hardness. Moreover, several common structures (e.g. a negative Poisson’s ratio structure and a diamond-like structure) are considered, and the corresponding studies are summarized. It illustrates the various fields of application, including biological scaffolds, shock absorbers, and driving devices. In the end, the paper concludes with the main achievements from the recent studies and puts forward the limitations and development tendencies in the future.

Abstract Sample barcoding is essential in mass cytometry analysis, since it can eliminate potential procedural variations, enhance throughput, and allow simultaneous sample processing and acquisition. Sample pooling after prior surface staining termed live-cell barcoding is more desirable than intracellular barcoding, where samples are pooled after fixation and permeabilization, since it does not depend on fixation-sensitive antigenic epitopes. In live-cell barcoding, the general approach uses two tags per sample out of a pool of antibodies paired with five palladium (Pd) isotopes in order to preserve appreciable signal-to-noise ratios and achieve higher yields after sample deconvolution. The number of samples that can be pooled in an experiment using live-cell barcoding is limited, due to weak signal intensities associated with Pd isotopes and the relatively low number of available tags. Here, we describe a novel barcoding technique utilizing 10 different tags, seven cadmium (Cd) tags and three Pd tags, with superior signal intensities that do not impinge on lanthanide detection, which enables enhanced pooling of samples with multiple experimental conditions and markedly enhances sample throughput.

Time series data in practical applications always contain missing values due to sensor malfunction, network failure, outliers etc. In order to handle missing values in time series, as well as the lack of considering temporal properties in machine learning models, we propose a spatiotemporal prediction framework based on missing value processing algorithms and deep recurrent neural network (DRNN). By using missing tag and missing interval to represent time series patterns, we implement three different missing value fixing algorithms, which are further incorporated into deep neural network that consists of LSTM (Long Short-term Memory) layers and fully connected layers. Real-world air quality and meteorological datasets (Jingjinji area, China) are used for model training and testing. Deep feed forward neural networks (DFNN) and gradient boosting decision trees (GBDT) are trained as baseline models against the proposed DRNN. Performances of three missing value fixing algorithms, as well as different machine learning models are evaluated and analysed. Experiments show that the proposed DRNN framework outperforms both DFNN and GBDT, therefore validating the capacity of the proposed framework. Our results also provides useful insights for better understanding of different strategies that handle missing values. [ABSTRACT FROM AUTHOR]