Atomic layer deposition of aluminum phosphate based on the plasma polymerization of trimethyl phosphate

Aluminum phosphate thin films were deposited by plasma-assisted atomic layer deposition (ALD) using a sequence of trimethyl phosphate (TMP, Me3PO4) plasma, O-2 plasma, and trimethylaluminum (TMA, Me3Al) exposures. In situ characterization was performed, including spectroscopic ellipsometry, optical emission spectroscopy, mass spectrometry and FTIR. In the investigated temperature region between 50 and 320 degrees C, nucleation delays were absent and linear growth was observed, with the growth per cycle (GPC) being strongly dependent on temperature. The plasma polymerization of TMP was found to play an important role in this process, resulting in CVD-like behavior at low temperatures and ALD-like behavior at high temperatures. Films grown at 320 degrees C had a GPC value of 0.37 nm/cycle and consisted of amorphous aluminum pyrophosphate (Al4P6O21). They could be crystallized to triclinic AlPO4 (tridymite) by annealing to 900 degrees C, as evidenced by high-temperature XRD measurements. The use of a TMP plasma might open up the possibility of depositing many other metal phosphates by combining it with appropriate organometallic precursors

On probabilistic aspects in the dynamic degradation of ductile materials

Dynamic loadings produce high stress waves leading to the spallation of ductile materials such as aluminum, copper, magnesium or tantalum. The main mechanism used herein to explain the change of the number of cavities with the stress rate is nucleation inhibition, as induced by the growth of already nucleated cavities. The dependence of the spall strength and critical time with the loading rate is investigated in the framework of a probabilistic model. The present approach, which explains previous experimental findings on the strain-rate dependence of the spall strength, is applied to analyze experimental data on tantalum.Comment: 28 pages, 13 figures, 3 table

Reply With: Proactive Recommendation of Email Attachments

Email responses often contain items-such as a file or a hyperlink to an external document-that are attached to or included inline in the body of the message. Analysis of an enterprise email corpus reveals that 35% of the time when users include these items as part of their response, the attachable item is already present in their inbox or sent folder. A modern email client can proactively retrieve relevant attachable items from the user's past emails based on the context of the current conversation, and recommend them for inclusion, to reduce the time and effort involved in composing the response. In this paper, we propose a weakly supervised learning framework for recommending attachable items to the user. As email search systems are commonly available, we constrain the recommendation task to formulating effective search queries from the context of the conversations. The query is submitted to an existing IR system to retrieve relevant items for attachment. We also present a novel strategy for generating labels from an email corpus---without the need for manual annotations---that can be used to train and evaluate the query formulation model. In addition, we describe a deep convolutional neural network that demonstrates satisfactory performance on this query formulation task when evaluated on the publicly available Avocado dataset and a proprietary dataset of internal emails obtained through an employee participation program.Comment: CIKM2017. Proceedings of the 26th ACM International Conference on Information and Knowledge Management. 201

Antigen-driven T-cell turnover.

A mathematical model is developed to characterize the distribution of cell turnover rates within a population of T lymphocytes. Previous models of T-cell dynamics have assumed a constant uniform turnover rate; here we consider turnover in a cell pool subject to clonal proliferation in response to diverse and repeated antigenic stimulation. A basic framework is defined for T-cell proliferation in response to antigen, which explicitly describes the cell cycle during antigenic stimulation and subsequent cell division. The distribution of T-cell turnover rates is then calculated based on the history of random exposures to antigens. This distribution is found to be bimodal, with peaks in cell frequencies in the slow turnover (quiescent) and rapid turnover (activated) states. This distribution can be used to calculate the overall turnover for the cell pool, as well as individual contributions to turnover from quiescent and activated cells. The impact of heterogeneous turnover on the dynamics of CD4(+) T-cell infection by HIV is explored. We show that our model can resolve the paradox of high levels of viral replication occurring while only a small fraction of cells are infected