Research on additive manufacturing of metallic glass alloy

The required rapid cooling has limited the dimension of the Bulk Metallic glasses (BMGs) produced by traditional method, and hence has seriously limited their applications, despite their remarkable mechanical properties. In this present project, a detailed study is conducted on the methodology and understanding of manufacturing large Zr- based metallic glass part by laser based additive manufacturing technology, which breaks the size limitation. The first research issue proposes and develops a new additive manufacturing technology, named Laser-Foil-Printing (LFP). Sheet foils of LM105 (Zr52.5Ti5Al10Ni14.6Cu17.9 (at. %)) metallic glass are used as feed stork. Fully amorphous 3D structures are successfully achieved. The second research issue focus on investigating the temperature evolution during laser-BMG interaction. By combining experimental measurement and mathematical model, the dynamic temperature field evolution during laser-BMG interaction is simulated. The developed model not only optimizes the LFP process, but also helps us in further understanding the fundamental crystallization mechanism during laser-BMG interaction. The crystalline phase evolution during laser-MG interaction is studied in the third research issue, using the classical nucleation theory (CNT). Combining with the temperature model, the volume fraction of crystallized BMG is calculated. The result successfully explains the observation of crystalline phase within both the fusion zone (FZ) and the heat-affected-zone (HAZ) of laser processed BMG. This thesis is a proven road map for developing metallic glass structures using additive manufacturing technology. The research results of this dissertation can benefit a wide range industry, such as 3D printing, material science, medical and aerospace --Abstract, page iv

Building Zr-Based Metallic Glass Part on Ti-6Al-4V Substrate by Laser-Foil-Printing Additive Manufacturing

Through using Zr intermediate layers, Zr52.5Ti5Al10Ni14.6Cu17.9 metallic glass (MG) parts are successfully built on Ti-6Al-4V substrates by laser-foil-printing (LFP) additive manufacturing technology in which MG foils are laser welded layer-by-layer onto the substrate. The printed MG part is free of porosity, cracking and crystallization; additionally, its glass transition temperature, crystallization temperature, micro-hardness, and tensile strength are very similar to the original MG material. The Zr intermediate layers are aimed at preventing direct interaction between the first layer of MG foil and the Ti substrate; otherwise, the welded MG foils would peel off from the substrate due to the formation of hard and brittle intermetallic compounds. With the use of Zr intermediate layers, the bonding strength between the printed MG part and the Ti substrate can reach 758 MPa owing to the formation of α-Zr phase

Mechanical Properties of 304L Parts Made by Laser-Foil-Printing Technology

Laser-Foil-Printing (LFP) is a novel laminated object manufacturing process for metal additive manufacturing. It fabricates three-dimensional metal parts by using a dual-laser system to weld and cut metal foils layer by layer. A main advantage of LFP is the higher cooling rate compared to powder-based laser additive manufacturing processes due to the thermal conductivity difference between foil and powder. This study focuses on the mechanical properties of 304L stainless steel parts built by the LFP process. The experimental results indicate that the yield strength and ultimate tensile strength of LFP fabricated 304L SS parts are higher by 9% and 8% in the longitudinal direction, and 24% and 25% in the transverse direction, respectively, in comparison to the parts fabricated by the selective laser melting process. X-ray diffraction and electron backscattered diffraction are used to obtain the lattice structure and the grain size of the fabricated parts

Light-induced dynamic frequency shifting of microwave photons in a superconducting electro-optic converter

Hybrid superconducting-photonic microresonators are a promising platform for realizing microwave-to-optical transduction. However, the absorption of scattered photons by the superconductors leads to unintended microwave resonance frequency variation and linewidth broadening. Here, we experimentally study the dynamics of this effect and its impact on microwave-to-optics conversion in an integrated lithium niobate-superconductor hybrid resonator platform. We unveiled an adiabatic frequency shifting of the intracavity microwave photons induced by the fast photo-responses of the thin-film superconducting resonator. As a result, the temporal and spectral responses of electro-optics transduction are modified and well described by our theoretical model. This work provides important insights on the light-induced conversion dynamics which must be considered in future designs of hybrid superconducting-photonic system

Photonic link from single flux quantum circuits to room temperature

Broadband, energy-efficient signal transfer between cryogenic and room-temperature environment has been a major bottleneck for superconducting quantum and classical logic circuits. Photonic links promise to overcome this challenge by offering simultaneous high bandwidth and low thermal load. However, the development of cryogenic electro-optic modulators -- a key component for photonic readout of electrical signals -- has been stifled by the stringent requirements of superconducting circuits. Rapid single flux quantum circuits (RSFQ), for example, operate with a tiny signal amplitude of only a few millivolts (mV), far below the volt-level signal used in conventional circuits. Here, we demonstrate the first direct optical readout of an RSFQ circuit without additional electrical amplification enabled by a novel superconducting electro-optic modulator (SEOM) featuring a record-low half-wave voltage V{\pi} of 42 mV on a 1 m-long SEOM. Leveraging the low ohmic loss of superconductors, we break the fundamental V{\pi}-bandwidth trade-off and demonstrate electro-optic bandwidth up to 17 GHz on a 0.2 m-long SEOM at cryogenic temperatures. Our work presents a viable solution toward high-bandwidth signal transfer between future large-scale superconducting circuits and room-temperature electronics

Surviving an infectious disease outbreak: How does nurse calling influence performance during the COVID‐19 fight?

Aim: To assess the performance of frontline nurses, who believed they were living out their calling, during the Coronavirus Disease 2019 (COVID-19) pandemic. Background: Although as a profession nursing generally requires high levels of performance, the disruption arising from an infectious disease outbreak increases the work stress and decreases the performance of frontline nurses. How this situation can be improved has yet to be thoroughly examined. Method: We used a snowball sampling technique to recruit 339 nurses who were originally from outside of Hubei but volunteered to join medical teams going to Hubei to tackle COVID-19. Results: Drawing on the theory of work as a calling, we found that living a calling had a positive effect on frontline nurses’ performance through the clinical and relational care they provided. Perceived supervisor support strengthened these mediated relationships. Conclusion: Our findings indicate that despite the constraints associated with pandemics, frontline nurses who are living a calling are able to provide better clinical and relational care to infected patients, which in turn improves their performance. Implications for Nursing Management: The findings of this study suggest that hospitals can introduce career education interventions to enhance nurses’ ability to discern and live out their calling to improve their performance

Cross-Platform Comparison of Microarray-Based Multiple-Class Prediction

High-throughput microarray technology has been widely applied in biological and medical decision-making research during the past decade. However, the diversity of platforms has made it a challenge to re-use and/or integrate datasets generated in different experiments or labs for constructing array-based diagnostic models. Using large toxicogenomics datasets generated using both Affymetrix and Agilent microarray platforms, we carried out a benchmark evaluation of cross-platform consistency in multiple-class prediction using three widely-used machine learning algorithms. After an initial assessment of model performance on different platforms, we evaluated whether predictive signature features selected in one platform could be directly used to train a model in the other platform and whether predictive models trained using data from one platform could predict datasets profiled using the other platform with comparable performance. Our results established that it is possible to successfully apply multiple-class prediction models across different commercial microarray platforms, offering a number of important benefits such as accelerating the possible translation of biomarkers identified with microarrays to clinically-validated assays. However, this investigation focuses on a technical platform comparison and is actually only the beginning of exploring cross-platform consistency. Further studies are needed to confirm the feasibility of microarray-based cross-platform prediction, especially using independent datasets

Baichuan 2: Open Large-scale Language Models

Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most powerful LLMs are closed-source or limited in their capability for languages other than English. In this technical report, we present Baichuan 2, a series of large-scale multilingual language models containing 7 billion and 13 billion parameters, trained from scratch, on 2.6 trillion tokens. Baichuan 2 matches or outperforms other open-source models of similar size on public benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Furthermore, Baichuan 2 excels in vertical domains such as medicine and law. We will release all pre-training model checkpoints to benefit the research community in better understanding the training dynamics of Baichuan 2.Comment: Baichuan 2 technical report. Github: https://github.com/baichuan-inc/Baichuan

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead