Optimizing the piezoelectric response in AlN thin-films via high-throughput ab-initio perturbation theory simulations.
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In this project, we attempted to make CdTe homojunctions using single crystal p-type doped substrates and closed-space sublimation epitaxy (CSSE) n-type doped films. The project included modeling which determined the ideal thickness for the n-type layer as being <200 nm. Boules of high concentration indium-doped CdTe and CdSe 0.4 Te 0.6 (CST) were grown using modified vertical Bridgman (MVB) methods. Similarly, iodine-doped CdTe crystals were grown for the first time. Washington State University (WSU) stock phosphorus-doped CdTe was used as the p-type substrate layer. The crystals were characterized by Hall effect and time-resolved photoluminescence (TRPL, for electrical properties), photoluminescence microscopy (for uniformity), X-ray diffraction (for crystal structure), and glow discharge mass spectrometry (GDMS, for dopant and impurity concentration). CdTe:I crystals were also characterized by visible and infrared transmission measurements, and various Cd or Te heat treatments were performed to assess changes in optical and electrical properties. The grown n-type materials – CdTe:In, CdSe0.4Te0.6:In, and CdTe:I – were provided to the National Renewable Energy Laboratory (NREL) for growth of CSSE thick films for characterization by two photon TRPL (for carrier lifetime), electron back-scatter diffraction (EBSD, to assess epitaxy), and Hall effect. Several measurements of secondary ion mass spectroscopy (SIMS) were performed. Nearly 100% of the indium from the crystal was incorporated into the measured thick films, while only ~2-22% of the iodine from the crystal was incorporated. The net result of the diffusion issue is that homojunction devices created using CSSE have a buried homojunction, as indicated by the near infrared peak in the external quantum efficiency (EQE). Various parameterization of front and back contacts suggested that the poor device performance was primarily a result of this buried junction and not due to other effects. There may also be an issue with the CSSE film lifetime in addition to the dopant profile.
In evaluating the state-of-the-art spalling techniques, the key factor that is missing is the control of crack propagation, which is responsible for low quality wafers due to high surface roughness and Total Thickness Variation, TTV. This project has shown that acoustic waves can alter the conditions at the crack tip during thermal spalling, modifying the propagation speed and the stress field around it (stress intensity factor, K). Both factors are directly related to the roughness of the spalled surface, so as we control speed and stress, we can fine-tune the surface roughness through the entire wafer as suggested by Arakawa.
Transcription factors (TFs) are critical biomolecules that control and regulate gene expression in every organism. In wheat, there are 3,606 genes annotated as putative TFs which are primarily uncharacterized. In this work, using a DAP-seq approach, we have validated and characterized a select number of TFs (5bl and 5dl) belonging to GeBP family of proteins, responsible for plant cellular growth, development, and differentiation. We found that top DNA binding motifs for 5bl and 5dl are (G/T)N(T/G)GTGGT and (C/G)AA(C/G)AA respectively. These motifs/peaks are enriched in the promoter regions of the genes, which are associated with rRNA biogenesis and protein maturation pathways. This study is also a first demonstration that DAP-seq can be applied for such a complex and large genome such as wheat.
Coastal environments span large biogeochemical gradients and provide critical ecosystem services worldwide, thereby having a disproportionately larger influence on global biogeochemical cycles and greenhouse gas fluxes than their geographical extent suggests. Despite this, cohesive quantitative tools to understand and describe these environments, including scalable, standardized field and laboratory methodologies are lacking. In this work, we advanced the streamlining and standardizing of quantitative approaches to measure and understand coastal physiochemical properties and biogeochemical processes across systems and scales. Outcomes of this work includes workflows for multiple complementary and in-line analyses, from basic physiochemical datasets to targeted molecular approaches and proof of concept through manipulation and analysis of coastal soils and waters with varying salinity, minerology, and biogeochemistry.
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The objective of this project in broad terms was to address the capital equipment expense associated with Cu(In,Ga)Se 2 (CIGS) as a material for photovoltaic devices. This project has demonstrated a new method for processing CIGS using metal halides to recrystallize and enhance the quality of the final materials. A wide spectrum of metal halides were examined. Of these Cu and In chlorides and AgBr were found to be most effective; and, of these, AgBr was found to produce exemplary results. Cu and In chlorides were found to produce undesirable changes in film composition. The project demonstrated over 16% efficiency in a CIGS device deposited at 10x the standard rate of ~1 μm/hr and with a >100°C reduction in maximum temperature to 450°C. The entire deposition of the absorber layer including the recrystallization step required only 15 minutes, which is a 4x decrease in processing time.
With the advances in next generation sequencing technologies, the number of sequenced genomes is growing exponentially, resulting in a technology bottleneck for the translation of sequence information into usable hypotheses about the function of each gene. We have proposed leveraging our leadership high-performance computing (HPC) resources to help break this annotation bottleneck. Here we design an HPC-based framework to infer gene function from gene sequence by incorporating information about protein structure and interactions predicted by deep learning approaches. Accurate functional prediction and gene annotation using computational methods will facilitate breakthroughs in the genomic sciences essential to understanding and harnessing life processes in bacteria, fungi and plants. The development and applications of the state-of-the-art deep neural networks to protein structural modeling, interaction prediction, sequence comparison, and quality assessment of protein structural models will be made possible by leadership computational resources. These HPC-enabled bioinformatics and molecular modeling tools will provide powerful insights into molecular functions of genes.
Drought is the primary abiotic factors negatively affecting agriculture, causing tremendous reductions in yield world-wide. Drought responsive adaptation strategies require complex molecular responses to including hormone signaling and production of osmo-protectants, as well as developmental responses including stomatal closure, changes to leaf morphology, and altered root system architecture. A common strategy to better understand these adaptation mechanisms is to compare plants with differing drought tolerances and has been successful in multiple crop plants including wheat, rice, and maize. To better understand drought adaptation strategies, we compared the cytosolic- and organelle-enriched protein profiles of leaves from two Sorghum bicolor genotypes with differing pre-flowering drought tolerances after 8-weeks of growth under water limitation. In agreement with previous literature findings, we observed significant drought-induced changes in the abundance of multiple heat shock proteins and dehydrins in both genotypes. We also identified unique proteins that might play a role in genotype dependent drought adaptation.