Micro Deep Drawing of C1100 Conical-cylindrical Cups

AbstractMicro deep drawing was prompted by the rapid development of micro electro mechanical systems, electron industries, new energy, and biomedical in recent years because of its mass production, high efficiency, high precision, low cost and no pollution. However, most researches concentrated on micro cylindrical cups, few studies were reported on other shaped parts. Micro deep drawing of micro conical-cylindrical cups was investigated in this study by using a micro blanking-deep drawing multiple operation mould. The specimen material was pure copper C1100 with a thickness of 50μm which was thermally treated in vacuum condition at 723K for 1h. Micro deep drawing experiments were carried out at room temperature on a universal testing machine at a drawing velocity of 0.05mm/s with the lubrication of polyethylene (PE) film. The results showed that micro conical-cylindrical cups with internal conical bottom diameter of only 0.4mm were well formed. The drawing force and limiting drawing ratio (LDR) micro conical-cylindrical cups were also discussed at the end of this paper

Modeling of Dynamic Recrystallization Behavior of As-Extruded AM50 Magnesium Alloy during Hot Compression by a Cellular Automaton Method

The dynamic recrystallization (DRX) behavior of as-extruded AM50 magnesium alloy was modelled and simulated by a cellular automaton (CA) method. Isothermal compression experiments were conducted, and the characteristic parameters in the CA model were obtained by the testing stress–strain flow curves in a wide temperature range of 250–450 °C and strain rate range of 0.001–10 s−1. The flow stress, DRX volume fraction and DRX grain size of the as-extruded AM50 magnesium alloy were predicted by CA simulation. The results showed that the DRX behavior of the studied magnesium alloy was susceptive with the temperature and strain rate; meanwhile, the prediction results were approximate to the experimental values, indicating that the developed CA model can make a confident estimation on the DRX behavior of the as-extruded AM50 magnesium alloy in high temperature conditions

Effect on Microstructure and Performance of B4C Content in B4C/Cu Composite

In this paper, boron carbide (B4C) ceramics were added to a copper (Cu) base, to improve the mechanical properties and wear resistance of pure copper. The B4C/Cu composites with different B4C contents, were obtained by mechanical mixing and discharge plasma sintering methods. Scanning electron microscopy (SEM), energy spectrum analysis (EDS), and electron probe microanalysis (EPMA) were used, to observe and analyze the microstructures of the B4C/Cu composites. The influences of the B4C content on the hardness, density, conductivity, and wear resistance were also studied. The experimental results show that B4C has an important effect on Cu. With increasing B4C content, both the density and conductivity of the B4C/Cu composites gradually decrease. The hardness of the Cu-15 wt.% B4C composite has the highest value, 86 HBW (Brinell hardness tungsten carbide ball indenter), which is 79.2% higher than that of pure copper. However, when the B4C amount increases to 20 wt.%, the hardness decreases due to the metallic connection being weakened in the material. The Cu-15 wt.% B4C composite has the lowest volume loss, indicating that it has the best wear resistance. Analyses of worn B4C/Cu composite surfaces suggest that deep and narrow grooves, as well as sharp ridges, appear on the worn pure Cu surface, but on the worn Cu-15 wt.% B4C composite surface, the furrows become shallow and few. In particular, ridge formation cannot be found on the worn Cu-15 wt.% B4C composite surface, which represents the enhancement in wear resistance

Understanding the Role of β Recrystallization on β Microtexture Evolution in Hot Processing of a Near-β Titanium Alloy (Ti-10V-2Fe-3Al)

The present study evaluated the β recrystallization behavior and deformation microtexture evolution of TB6 titanium alloy (Ti-10V-2Fe-3Al) taking place during isothermal compression. The hot deformation tests were carried out in the temperature range below the β phase transition temperature and spanned a wide strain rate range of 0.0001~1 s−1. Microstructure evolution on β phase, including its recrystallization behavior and microtexture formation, is sensitive to the strain rates, whereas the average grain size of equiaxed α phase exhibits a slight increase with the strain rate decreasing. Moreover, β recrystallization is not homogeneous among the prior β grains, and is characterized by: (I) enriched β sub-grains, (II) sporadically or chain-like distributed recrystallized β grains with a grain size far less than the prior β grains, and (III) wave-shaped β grain boundaries. The β recrystallization is inadequate and its orientation takes on the inheritance characteristic, which makes the β microtexture significant after deformation. At a lower strain rate, the high activity of the {11−2}<111> and {12−3}<111> slip systems induced the crystal rotation around <101>, but such crystal rotation did not destroy the Burgers orientation relationship (BOR), which could be accounted for by the generation of a strong microtexture of <001>//RD. The divergences on β recrystallization fraction, the operation of slip systems, and initial crystal orientations explain the different microtexture components with varied intensities under different deformation conditions

Combinatorial signaling microenvironments for studying stem cell fate

Extracellular matrix (ECM) and growth factor signaling networks are known to interact in a complex manner. Therefore, reductionist approaches that test the cellular response to individual ECM components and growth factors cannot be used to predict the response to more complex mixtures without knowledge of the underlying signaling network. To address this challenge, we have developed a technology platform to experimentally probe the interactions of ECM components and soluble growth factors on stem cell fate. We present a multiwell microarray platform that allows 1200 simultaneous experiments on 240 unique signaling environments. Mixtures of extracellular matrix (fibronectin, laminin, collagen I, collagen III, collagen IV) are arrayed using a robotic spotter and arranged in a multiwell format. Embryonic stem (ES) cells adhere to ECM spots and are cultured in mixtures of soluble factors [wnt3a, activin A, bone morphogenetic protein-4 (BMP-4), and fibroblast growth factor-4 (FGF- 4)]. Differentiation along the cardiac lineage is monitored by myosin heavy chain--green fluorescent protein (MHC-GFP) reporter expression as compared to growth by monitoring nuclear DNA, and both signals are quantified using a confocal microarray scanner. In developing the platform, we characterized the amount of deposited protein, the fluorescent readout of GFP expression and DNA content, and the use of a laser-based scanner as compared to fluorescent microscopy for data acquisition. The effects of growth factors on growth and differentiation are consistent with previously reported literature, and preliminary evidence of interactive signaling is illuminated. This versatile technique is compatible with virtually any set of insoluble and soluble cues, leverages existing software and hardware, and represents a step toward developing the ‘systems biology’ of stem cells

Anti-cancer effects of nitrogen-containing bisphosphonates on human cancer cells.

Zoledronic acid, a potent nitrogen-containing bisphosphonate (NBP), has been extensively used to limit bone turnover in a various diseases including tumors. Recent clinical studies have demonstrated direct anti-cancer effects of zoledronic acid, in addition to its clinical benefits for skeletal-related events. Here we investigated the effects of 4 clinically available NBPs on human tumor cell proliferation. Our data demonstrate a potent anti-proliferative effect of zoledronic acid against glioblastoma (GBM) cell lines, breast cancer cells and GBM patient-derived lines. Zoledronic acid also effectively inhibited GBM tumor growth in xenograft mouse models. Zoledronic acid strongly stimulated autophagy but not apoptotic signals in all tested cells. Only one intermediate product of cholesterols synthesis pathway, geranylgeranyl diphosphate (GGPP) rescued cells from the cytotoxic effects of zoledronic acid. To further investigate the effect of GGPP, we knocked down RABGGTA, which encodes a subunit of the Rabgeranylgeranyltransferase protein. This knockdown induced an effect similar to zoledronic acid in cancer cell lines. These data are promising and suggested a potential for zoledronic acid as an anti-cancer agent, through its ablation of the function of Rab proteins

Anti-cancer effects of nitrogen-containing bisphosphonates on human cancer cells.

Zoledronic acid, a potent nitrogen-containing bisphosphonate (NBP), has been extensively used to limit bone turnover in a various diseases including tumors. Recent clinical studies have demonstrated direct anti-cancer effects of zoledronic acid, in addition to its clinical benefits for skeletal-related events. Here we investigated the effects of 4 clinically available NBPs on human tumor cell proliferation. Our data demonstrate a potent anti-proliferative effect of zoledronic acid against glioblastoma (GBM) cell lines, breast cancer cells and GBM patient-derived lines. Zoledronic acid also effectively inhibited GBM tumor growth in xenograft mouse models. Zoledronic acid strongly stimulated autophagy but not apoptotic signals in all tested cells. Only one intermediate product of cholesterols synthesis pathway, geranylgeranyl diphosphate (GGPP) rescued cells from the cytotoxic effects of zoledronic acid. To further investigate the effect of GGPP, we knocked down RABGGTA, which encodes a subunit of the Rabgeranylgeranyltransferase protein. This knockdown induced an effect similar to zoledronic acid in cancer cell lines. These data are promising and suggested a potential for zoledronic acid as an anti-cancer agent, through its ablation of the function of Rab proteins

Predictive modeling for eosinophilic chronic rhinosinusitis: Nomogram and four machine learning approaches

Summary: Eosinophilic chronic rhinosinusitis (ECRS) is a distinct subset of chronic rhinosinusitis characterized by heightened eosinophilic infiltration and increased symptom severity, often resisting standard treatments. Traditional diagnosis requires invasive histological evaluation. This study aims to develop predictive models for ECRS based on patient clinical parameters, eliminating the need for invasive biopsy. Utilizing logistic regression with lasso regularization, random forest (RF), gradient-boosted decision tree (GBDT), and deep neural network (DNN), we trained models on common clinical data. The predictive performance was evaluated using metrics such as area under the curve (AUC) for receiver operator characteristics, decision curves, and feature ranking analysis. In a cohort of 437 eligible patients, the models identified peripheral blood eosinophil ratio, absolute peripheral blood eosinophil, and the ethmoidal/maxillary sinus density ratio (E/M) on computed tomography as crucial predictors for ECRS. This predictive model offers a valuable tool for identifying ECRS without resorting to histological biopsy, enhancing clinical decision-making