Nano-Bainitic Steels: Acceleration of Transformation by High Aluminum Addition and Its Effect on Their Mechanical Properties

Additions of 3 and 5 wt.% Al have been investigated as a low-cost method for transformation acceleration in nano-bainitic steels. For both Al contents, two groups of steels with C-content in the range ~0.7 to ~0.95 wt.% were studied. Thermodynamic and physical simulations were used in alloy and heat treatment design. Characterization was performed via dilatometry, scanning and transmission electron microscopy, Synchrotron X-ray diffraction, and tensile and impact testing. Fast bainitic-transformation time-intervals ranging from 750–4600 s were recorded and tensile strengths up to 2000 MPa at a ductility of ~10 elongation percent were attainable for the 3 wt.% Al group at an austempering temperature of 265 °C. Higher Al additions were found to perform better than their lower Al counterparts as the austempering temperature is dropped. However, Al lowered the austenite stability, increased the martensite start temperature, austenitization temperatures and, consequently, the prior austenite grain size, as well as limiting the austempering temperatures to higher ones. Additionally, the lowered austenite stability coupled with higher additions of hardenability elements (here carbon) to maintain the martensite start at around 300 °C, causing the 5 wt.% Al group to have a large amount of low stability retained austenite (and consequently brittle martensite) in their microstructure, leading to a low elongation of around 5%.

CA Modeling of Microsegregation and Growth of Equiaxed Dendrites in the Binary Al-Mg Alloy

A two-dimensional model based on the Cellular Automaton (CA) technique for simulating free dendritic growth in the binary Al + 5 wt.% alloy was presented. In the model, the local increment of the solid fraction was calculated using a methodology that takes into account changes in the concentration of the liquid and solid phase component in the interface cells during the solidification transition. The procedure of discarding the alloy component to the cells in the immediate vicinity was used to describe the initial, unstable dendrite growth phase under transient diffusion conditions. Numerical simulations of solidification were performed for a single dendrite using cooling rates of 5, 25 and 45 K/s and for many crystals assuming the boundary condition of the third kind (Newton). The formation and growth of primary and secondary branches as well as the development of component microsegregation in the liquid and solid phase during solidification of the investigated alloy were analysed. It was found that with an increase in the cooling rate, the dendrite morphology changes, its cross-section and the distance between the secondary arms decrease, while the degree of component microsegregation and temperature recalescence in the initial stage of solidification increase. In order to determine the potential of the numerical model, the simulation results were compared with the predictions of the Lipton–Glicksman–Kurz (LGK) analytical model and the experimental solidification tests. It was demonstrated that the variability of the dendrite tip diameter and the growth rate determined in the Cellular Automaton (CA) model are similar to the values obtained in the LGK model. As part of the solidification tests carried out using the Derivative Differential Thermal Analysis (DDTA) method, a good fit of the CA model was established in terms of the shape of the solidification curves as well as the location of the characteristic phase transition temperatures and transformation time. Comparative tests of the real structure of the Al + 5 wt.% Mg alloy with the simulated structure were also carried out, and the compliance of the Secondary Dendrite Arm Spacing (SDAS) parameter and magnesium concentration profiles on the cross-section of the secondary dendrites arms was assessed.

Time-optimal quantum transformations with bounded bandwidth

In this paper, we derive sharp lower bounds, also known as quantum speed limits, for the time it takes to transform a quantum system into a state such that an observable assumes its lowest average value. We assume that the system is initially in an incoherent state relative to the observable and that the state evolves according to a von Neumann equation with a Hamiltonian whose bandwidth is uniformly bounded. The transformation time depends intricately on the observable's and the initial state's eigenvalue spectrum and the relative constellation of the associated eigenspaces. The problem of finding quantum speed limits consequently divides into different cases requiring different strategies. We derive quantum speed limits in a large number of cases, and we simultaneously develop a method to break down complex cases into manageable ones. The derivations involve both combinatorial and differential geometric techniques. We also study multipartite systems and show that allowing correlations between the parts can speed up the transformation time. In a final section, we use the quantum speed limits to obtain upper bounds on the power with which energy can be extracted from quantum batteries.

Interfacial Compatibility on the Crystal Transformation of Isotactic Poly (1-Butene)/Herb Residue Composite

Isotactic poly (1-butene) (iPB) has excellent properties which are recognized as a green and energy saving product. However, the most stable and valuable crystal form I had a spontaneous transformation that took as long as seven days to complete. As a special solid waste, the herb residue (HR) is rich in cellulose which has great potential to accelerate the crystal transformation of the iPB. However, the polarity of HR results in the interface compatibility with non-polar iPB. In this study, the HR was modified by silane coupling agent (KH570) to obtain KHR and the iPB/HR composite was prepared. The FTIR spectrum was indicated that the organic functional groups of KH570 successfully graft onto the surface of HR and the water contact angle test was indicated that the hydrophilicity of the KHR was greatly decreased. The complete crystal transformation time is 7 days for iPB, 6 days for iPB+5% HR but only 3 days for iPB+5% KHR. The addition of the HR and KHR improve the thermal stability of the composite and this beneficial effect is more obvious for KHR. After annealing for 5 days, the physical properties value include tensile strength, flexural strength, and HDT of iPB+5% HR reach that of pure iPB after annealing for 7 days, but only 3 days for iPB+5% KHR. The TG analysis and SEM photographs give clear evidence that the beneficial effect of KH570 modified HR on improving the interface compatibility with iPB.

An Analysis of the Water-to-Ice Phase Transition Using Acoustic Plate Waves

It is shown that, in spite of the wave radiation into the adjacent liquid, a large group of Lamb waves are able to propagate along piezoelectric plates (quartz, LiNbO3, LiTaO3) coated with a liquid layer (distilled water H2O). When the layer freezes, most of the group’s waves increase their losses, essentially forming an acoustic response towards water-to-ice transformation. Partial contributions to the responses originating from wave propagation, electro-mechanical transduction, and wave scattering were estimated and compared with the coupling constants, and the vertical displacements of the waves were calculated numerically at the water–plate and ice–plate interfaces. The maximum values of the responses (20–30 dB at 10–100 MHz) are attributed to the total water-to-ice transformation. Time variations in the responses at intermediate temperatures were interpreted in terms of a two-phase system containing both water and ice simultaneously. The results of the paper may turn out to be useful for some applications where the control of ice formation is an important problem (aircraft wings, ship bodies, car roads, etc.).

Study on Co-Crystallization of LCZ696 Using In Situ ATR-FTIR and Imaging

In situ ATR-FTIR spectroscopy and imaging and image analysis were applied to the study of the multicomponent co-crystallization process involving S-valsartan and sacubitril in which LCZ696 crystals were formed. LCZ696 is a combination drug for use in heart failure that was approved by the FDA in 2015 following development by Novartis Pharmaceuticals. Though much work was reported on LCZ696 about its pharmacokinetic and pharmacodynamic effects in the evaluation and clinical testing, less attention was paid to study on the co-crystallization process. LCZ696 crystals have shown difficulties in filtration mainly due to the small particle size. In this work, LCZ696 crystals were prepared successfully by S-valsartan and sacubitril, and characterized by SEM, XRPD, TG-DSC and ATR-FTIR. ATR-FTIR and imaging and image analysis were used to monitoring solution concentration and investigating the co-crystallization mechanism. It revealed that the nucleation process was very slow compared with the transformation process, which is indication that the co-crystallization was controlled by nucleation. LCZ696 crystals are composed of very thin hexagonal plates, which seems indicating that LCZ696 crystals grow mainly in two size dimensions. Stirrer speed and crystal seeds were found to have noticeable effect on the induction time, transformation time and crystal size distribution. The Johnson-Mehl-Avrami equation was found to be able to describe the co-crystallization process.

Novel Framework for Quality Retention on Educational Big Data

Adoption of big data concept towards managing increasing data from the educational sector will have various benefits in terms of knowledge sharing process. The existing research approaches towards managing educational data in terms of high end analytics is either missing or is quite narrowed that doesn’t encapsulate the concept of big data implementation with practical problem. Therefore, this paper presents a discussion of a distributed framework that is capable of performing transformation of raw educational data arriving from distributed sources followed by applying a novel mining approach in order to ensure the final data with highest quality. The term quality interprets as a dynamic educational big data to be highly structured and free from any form of artifacts. Result analysis shows that proposed system offer faster transformation time and better data purity over synthetic educational big data.