Topological grain boundary segregation transitions.

Engineering the structure of grain boundaries (GBs) by solute segregation is a promising strategy to tailor the properties of polycrystalline materials. Solute segregation triggering phase transitions at GBs has been suggested theoretically to offer different pathways to design interfaces, but an understanding of their intrinsic atomistic nature is missing. We combined atomic resolution electron microscopy and atomistic simulations to discover that iron segregation to GBs in titanium stabilizes icosahedral units ("cages") that form robust building blocks of distinct GB phases.

Grand canonically optimized grain boundary phases in hexagonal close-packed titanium.

Grain boundaries (GBs) profoundly influence the properties and performance of materials, emphasizing the importance of understanding the GB structure and phase behavior. As recent computational studies have demonstrated the existence of multiple GB phases associated with varying the atomic density at the interface, we introduce a validated, open-source GRand canonical Interface Predictor (GRIP) tool that automates high-throughput, grand canonical optimization of GB structures. While previous studies of GB phases have almost exclusively focused on cubic systems, we demonstrate the utility of GRIP in an application to hexagonal close-packed titanium.

Almost free of calibration for SSVEP-based brain-computer interfaces.

. Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) is a promising technology that can achieve high information transfer rate (ITR) with supervised algorithms such as ensemble task-related component analysis (eTRCA) and task-discriminant component analysis (TDCA). However, training individual models requires a tedious and time-consuming calibration process, which hinders the real-life use of SSVEP-BCIs.

A data-driven framework for structure-property correlation in ordered and disordered cellular metamaterials.

Extracting the relation between microstructural features and resulting material properties is essential for advancing our fundamental knowledge on the mechanics of cellular metamaterials and to enable the design of novel material systems. Here, we present a unified framework that not only allows the prediction of macroscopic properties but, more importantly, also reveals their connection to key morphological characteristics, as identified by the integration of machine-learning models and interpretability algorithms. We establish the complex manner in which strut orientation can be critical in determining effective stiffness for certain microstructures and highlight cellular metamaterials with counterintuitive material behavior.

Blended fabric with integrated neural network based on attention mechanism qualitative identification method of near infrared spectroscopy.

Near Infrared spectroscopy (NIRS) qualitative analysis technology has shown excellent development potential in the field of blend fabrics. However, the qualitative detection method based on the convolutional neural network (CNN) is difficult to accurately extract the feature of the spectral data, which will lead to missing detection or false detection; when using deep learning to build a qualitative detection model, due to interference of the external environment and other factors, the spectral data collected may have outliers, this means that the knowledge generalization on anomalous testing data, which may have a different distribution of that of the training set, is not trivial, which will also lead to missing detection or false detection. To solve the above problems, this paper proposes a novel qualitative detection neural network by analyzing the near infrared spectral data of blend fabrics.

Stretching DNA origami: effect of nicks and Holliday junctions on the axial stiffness.

The axial stiffness of DNA origami is determined as a function of key nanostructural characteristics. Different constructs of two-helix nanobeams with specified densities of nicks and Holliday junctions are synthesized and stretched by fluid flow. Implementing single particle tracking to extract force-displacement curves enables the measurement of DNA origami stiffness values at the enthalpic elasticity regime, i.

Transferable Kinetic Monte Carlo Models with Thousands of Reactions Learned from Molecular Dynamics Simulations.

Molecular dynamics (MD) simulation of complex chemistry typically involves thousands of atoms propagating over millions of time steps, generating a wealth of data. Traditionally these data are used to calculate some aggregate properties of the system and then discarded, but we propose that these data can be reused to study related chemical systems. Using approximate chemical kinetic models and methods from statistical learning, we study hydrocarbon chemistries under extreme thermodynamic conditions.

Gold nanorods as plasmonic nanotransducers: distance-dependent refractive index sensitivity.

Owing to the facile tunability of the localized surface plasmon resonance wavelength (LSPR) and large refractive index sensitivity, gold nanorods (AuNR) are of high interest as plasmonic nanotransducers for label-free biological sensing. We investigate the influence of gold nanorod dimensions on distance-dependent LSPR sensitivity and electromagnetic (EM) decay length using electrostatic layer-by-layer (LbL) assembly of polyelectrolytes. The electromagnetic decay length was found to increase linearly with both nanorod length and diameter, although to variable degrees.