Suppressed Size Effect in Nanopillars with Hierarchical Microstructures Enabled by Nanoscale Additive Manufacturing.

Studies on mechanical size effects in nanosized metals unanimously highlight both intrinsic microstructures and extrinsic dimensions for understanding size-dependent properties, commonly focusing on strengths of uniform microstructures, e.g., single-crystalline/nanocrystalline and nanoporous, as a function of pillar diameters, D. We developed a hydrogel infusion-based additive manufacturing (AM) technique using two-photon lithography to produce metals in prescribed 3D-shapes with ∼100 nm feature resolution. We demonstrate hierarchical microstructures of as-AM-fabricated Ni nanopillars (D ∼ 130-330 nm) to be nanoporous and nanocrystalline, with d ∼ 30-50 nm nanograins subtending each ligament in bamboo-like arrangements and pores with critical dimensions comparable to d. In situ nanocompression experiments unveil their yield strengths, σ, to be ∼1-3 GPa, above single-crystalline/nanocrystalline counterparts in the D range, a weak size dependence, σ ∝ D-0.2, and localized-to-homogenized transition in deformation modes mediated by nanoporosity, uncovered by molecular dynamics simulations. This work highlights hierarchical microstructures on mechanical response in nanosized metals and suggests small-scale engineering opportunities through AM-enabled microstructures.

Standard Deviation Effect of Average Structure Descriptor on Grain Boundary Energy Prediction.

The structural complexities of grain boundaries (GBs) result in their complicated property contributions to polycrystalline metals and alloys. In this study, we propose a GB structure descriptor by linearly combining the average two-point correlation function (PCF) and standard deviation of PCF via a weight parameter, to reveal the standard deviation effect of PCF on energy predictions of Cu, Al and Ni asymmetric tilt GBs (i.e., Σ3, Σ5, Σ9, Σ11, Σ13 and Σ17), using two machine learning (ML) methods; i.e., principal component analysis (PCA)-based linear regression and recurrent neural networks (RNN). It is found that the proposed structure descriptor is capable of improving GB energy prediction for both ML methods. This suggests the discriminatory power of average PCF for different GBs is lifted since the proposed descriptor contains the data dispersion information. Meanwhile, we also show that GB atom selection methods by which PCF is evaluated also affect predictions.

Neural Physiological Model: A Simple Module for Blood Glucose Prediction.

Continuous glucose monitors (CGM) and insulin pumps are becoming increasingly important in diabetes management. Additionally, data streams from these devices enable the prospect of accurate blood glucose prediction to support patients in preventing adverse glycemic events. In this paper, we present Neural Physiological Encoder (NPE), a simple module that leverages decomposed convolutional filters to automatically generate effective features that can be used with a downstream neural network for blood glucose prediction. To our knowledge, this is the first work to investigate a decomposed architecture in the diabetes domain. Our experimental results show that the proposed NPE model can effectively capture temporal patterns and blood glucose associations with other daily activities. For predicting blood glucose 30-mins in advance, NPE+LSTM yields an average root mean square error (RMSE) of 9.18 mg/dL on an in-house diabetes dataset from 34 subjects. Additionally, it achieves state-of-the-art RMSE of 17.80 mg/dL on a publicly available diabetes dataset (OhioT1DM) from 6 subjects.