Ex-vivo cellular MRI with b-SSFP: quantitative benefits of 3 T over 1.5 T

INTRODUCTION: The use of MRI with iron-based magnetic nanoparticles for imaging cells is a rapidly growing field of research. We have recently reported that single iron-labeled cells could be detected, as signal voids, in vivo in mouse brains using a balanced steady-state free precession imaging sequence (b-SSFP) and a customized microimaging system at 1.5 T. METHODS: In the current study we assess the benefits, and challenges, of using a higher magnetic field strength for imaging iron-labeled cells with b-SSFP, using ex vivo mouse brain specimens imaged with near identical systems at 1.5 and 3.0 T. RESULTS: The substantial banding artifact that appears in 3 T b-SSFP images was readily minimized with RF phase cycling, allowing for banding-free b-SSFP images to be compared between the two field strengths. This study revealed that with an optimal 3 T b-SSFP imaging protocol, more than twice as many signal voids were detected as with 1.5 T. CONCLUSION: There are several factors that contributed to this important result. First, a greater-than-linear SNR gain was achieved in mouse brain images at 3 T. Second, a reduction in the bandwidth, and the associated increase in repetition time and SNR, produced a dramatic increase in the contrast generated by iron-labeled cells.     

Additive Manufacturing of Batteries

Additive manufacturing, i.e., 3D printing, is being increasingly utilized to fabricate a variety of complex‐shaped electronics and energy devices (e.g., batteries, supercapacitors, and solar cells) due to its excellent process flexibility, good geometry controllability, as well as cost and material waste reduction. In this review, the recent advances in 3D printing of emerging batteries are emphasized and discussed. The recent progress in fabricating 3D‐printed batteries through the major 3D‐printing methods, including lithography‐based 3D printing, template‐assisted electrodeposition‐based 3D printing, inkjet printing, direct ink writing, fused deposition modeling, and aerosol jet printing, are first summarized. Then, the significant achievements made in the development and printing of battery electrodes and electrolytes are highlighted. Finally, major challenges are discussed and potential research frontiers in developing 3D‐printed batteries are proposed. It is expected that with the continuous development of printing techniques and materials, 3D‐printed batteries with long‐term durability, favorable safety as well as high energy and power density will eventually be widely used in many fields.     

Using Deep Machine Learning to Understand the Physical Performance Bottlenecks in Novel Thin‐Film Solar Cells

There is currently a worldwide effort to develop materials for solar energy harvesting which are efficient and cost effective, and do not emit significant levels of CO₂ during manufacture. When a researcher fabricates a novel device from a novel material system, it often takes many weeks of experimental effort and data analysis to understand why any given device/material combination produces an efficient or poorly optimized cell. It therefore takes the community tens of years to transform a promising material system to a fully optimized cell ready for production (perovskites are a contemporary example). Herein, developed is a new and rapid approach to understanding device/material performance, which uses a combination of machine learning, device modeling, and experiment. Providing a set of electrical device parameters (charge carrier mobilities, recombination rates, trap densities, etc.) in a matter of seconds thus offers a fast way to directly link fabrication conditions to device/material performance, pointing a way to further and more rapid optimization of light harvesting devices. The method is demonstrated by using it to understand annealing temperature and surfactant choice and in terms of charge carrier dynamics in organic solar cells made from the P3HT:PCBM, PBTZT‐stat‐BDTT‐8:PCBM, and PTB7:PCBM material systems.     

Oxidation resistance of β-Sialon/TiN composites: an ion beam analysis (IBA) study

The oxidation resistance of β-Sialon processed with Y₂O₃ sintering additive and β-Sialon/TiN composites containing 1–10 wt% TiN was studied using ion beam analysis (IBA) techniques, augmented by XRD and SEM measurements. Rutherford backscattering spectrometry was used to monitor the diffusion of Y and Ti in the oxidised samples, and the diffusion of oxygen and nitrogen was observed by particle-induced gamma emission and nuclear reaction analysis. These techniques showed that in the Sialon control sample without TiN, oxygen was the first element to migrate at 1000 °C, followed by Y and N at 1100 °C. At 1200 °C, a N-poor, Y- and O-rich oxidised layer was formed, containing crystalline Y₂Si₂O₇. In the TiN-containing samples, Si, Al, Y and Ti were very mobile even at 1000 °C and the surface nitrogen was depleted by 1250 °C. The combined presence of yttrium aluminium garnet (YAG) and TiN protects the β-Sialon phase by forming an oxygen-rich crystalline barrier layer. The oxidation products of TiN in these composites are TiO₂ and Y₂Ti₂O_7. The details of the oxidation mechanism of the β-Sialon/TiN composites provided by these IBA studies (movement of yttrium and titanium, replacement of nitrogen by oxygen in the glassy yttrium phase and major crystalline and chemical changes in an outer oxidised layer) could not readily have been obtained by any other techniques, and illustrate the value of IBA for oxidation studies of non-oxide ceramics.     

Metabolic footprinting as a tool for discriminating between brewing yeasts

The characterization of industrial yeast strains by examining their metabolic footprints (exometabolomes) was investigated and compared to genome-based discriminatory methods. A group of nine industrial brewing yeasts was studied by comparing their metabolic footprints, genetic fingerprints and comparative genomic hybridization profiles. Metabolic footprinting was carried out by both direct injection mass spectrometry (DIMS) and gas chromatography time-of-flight mass spectrometry (GC-TOF-MS), with data analysed by principal components analysis (PCA) and canonical variates analysis (CVA). The genomic profiles of the nine yeasts were compared by PCR-restriction fragment length polymorphism (PCR-RFLP) analysis, genetic fingerprinting using amplified fragment length polymorphism (AFLP) analysis and microarray comparative genome hybridizations (CGH). Metabolomic and genomic analysis comparison of the nine brewing yeasts identified metabolomics as a powerful tool in separating genotypically and phenotypically similar strains. For some strains discrimination not achieved genomically was observed metabolomically.     

A Methodology to Predict the Effects of Quench Rates on Mechanical Properties of Cast Aluminum Alloys

The mechanical properties of age-hardenable Al-Si-Mg alloys depend on the rate at which the alloys are cooled after the solutionizing heat treatment. Quench factor analysis, developed by Evancho and Staley, was able to quantify the effects of quenching rates on the as-aged properties of an aluminum alloy. This method has been previously used to successfully predict yield strength and hardness of wrought aluminum alloys. However, the quench factor data for aluminum castings is still rare in the literature. In this study, the time-temperature during cooling and hardness were used as the inputs for quench factor modeling. The experimental data were collected using the Jominy end quench method. Multiple linear regression analysis was performed on the experimental data to estimate the kinetic parameters during quenching. Time-temperature-property curves of cast aluminum alloy A356 were generated using the estimated kinetic parameters. Experimental verification was performed on a cast engine head. The predicted hardness agreed well with that experimentally measured. The methodology described in this article requires little experimental effort and can also be used to experimentally estimate the kinetic parameters during quenching for other aluminum alloys. This article is based on a presentation made in the symposium “Simulation of Aluminum Shape Casting Processing: From Design to Mechanical Properties” which occurred March 12–16, 2006, during the TMS Spring meeting in San Antonio, Texas, under the auspices of the Computational Materials Science and Engineering Committee, the Process Modeling, Analysis and Control Committee, the Solidification Committee, the Mechanical Behavior of Materials Committee, and the Light Metal Division/Aluminum Committee.     

Subirrigation Systems to Minimize Nitrate Leaching

Nitrate leaching from corn production systems and the subsequent contamination of ground and surface waters is a major environmental problem. In field plots 75 m long by 15 m wide, the writers tested the hypothesis that subirrigation and intercropping will reduce leaching losses from cultivated corn and minimize water pollution. Nitrate leaching under subirrigation at a depth of either 0.7 m or 0.8 m below the soil surface was compared with leaching under free drainage. The cropping systems investigated were corn (Zea mays L.) monoculture and corn intercropped with annual Italian ryegrass (Lolium multiflorum Lam. cv. Barmultra). The effects of three fertilizer application rates (0, 180, and 270 kg N ha?1) on leaching were investigated in the freely drained plots. The greatest annual loss of NO3?-N in tile drainage water (21.9 kg N ha?1) occurred in freely draining, monocropped plots fertilized with 270 kg N ha?1. Monocropped plots fertilized with 270 kg N ha?1, with subirrigation at 0.7 m depth, resulted in annual nitrate losses into tile drainage of 6.6 kg N ha?1, 70% less than under free drainage. Annual soil denitrification rates (60 kg N ha?1) with subirrigation at 0.7 m were about three-fold greater than under free drainage. Intercropping under free drainage resulted in a 50% reduction in tile drainage loss of NO3?-N compared with monocropping. Off-season (November 1, 1993, to May 31, 1994) tile drainage losses of NO3?-N (7.8 kg N ha?1) from freely draining monocropped plots accounted for 30% of the annual tile drainage losses.     

Nanofabrication of 30 nm devices incorporating low resistance magnetic tunnel junctions

In this paper the electron-beam lithography conditions and the nanofabrication process are described for current-perpendicular-to-plane (CPP) pillar devices with 30 nm critical dimensions. This work combines a RAITH-150 tool with a negative e-beam resist (AR-7520) so that dense nanopillar arrays are patterned fast into large area samples. The resist dilution and coating conditions are optimized, aiming at its thickness reduction down to 80 nm. The exposure parameters are tuned for different geometries and dimensions, so that features down to 30 nm are exposed with good accuracy (+/- 1.9 nm) and reproducibility. The complete integration of these nanoelements into CPP devices involved electron beam lithography, ion milling for pattern transfer and chemical-mechanical polishing (CMP). Results on devices incorporating very low resistance-area (R x A) MTJ films deposited by Ion beam assisted deposition are shown, for MTJ stacks with R x A down to 0.8 omega x microm2. Device characterization includes electrical measurement of the pillar resistance and the transfer curves under dc magnetic fields (TMR up to 40%).