Engineering topics

Ma, En

Publications and source records attributed to Ma, En.

3 records

DOE OSTI2023

An organic electrochemical transistor for multi-modal sensing, memory and processing

By integrating sensing, memory and processing functionalities, biological nervous systems are energy and area efficient. Emulating such capabilities in artificial systems is, however, challenging and is limited by the device heterogeneity of sensing and processing cores. Here we report an organic electrochemical transistor capable of sensing, memory and processing. The device has a vertical traverse architecture and a crystalline–amorphous channel that can be selectively doped by ions to enable two reconfigurable modes: a volatile receptor and a non-volatile synapse. As a volatile receptor, the device is capable of multi-modal sensing and is responsive to stimuli such as ions and light. As a non-volatile synapse, it is capable of 10-bit analogue states, low switching stochasticity and good state retention. We also show that the homogeneous integration of the devices could provide functions such as conditioned reflexes and could be used for real-time cardiac disease diagnoses via reservoir computing.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Tailoring planar slip to achieve pure metal-like ductility in body-centred-cubic multi-principal element alloys

Uniform tensile ductility (UTD) is crucial for the forming/machining capabilities of structural materials. Normally, planar-slip induced narrow deformation bands localize the plastic strains and hence hamper UTD, particularly in body-centred-cubic (bcc) multi-principal element high-entropy alloys (HEAs), which generally exhibit early necking (UTD < 5%). Here we demonstrate a strategy to tailor the planar-slip bands in a Ti-Zr-V-Nb-Al bcc HEA, achieving a 25% UTD together with nearly 50% elongation-to-failure (approaching a ductile elemental metal), while offering gigapascal yield strength. The HEA composition is designed not only to enhance the B2-like local chemical order (LCO), seeding sites to disperse planar slip, but also to generate excess lattice distortion upon deformation-induced LCO destruction, which promotes elastic strains and dislocation debris to cause dynamic hardening. Further, this encourages second-generation planar-slip bands to branch out from first-generation bands, effectively spreading the plastic flow to permeate the sample volume. Moreover, the profuse bands frequently intersect to sustain adequate work-hardening rate (WHR) to large strains. Our strategy showcases the tuning of plastic flow dynamics that turns an otherwise-undesirable deformation mode to our advantage, enabling an unusual synergy of yield strength and UTD for bcc HEAs.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Machine-Learned Structure-Property Relationship in Metallic Glasses (Final Report)

The goal of the proposed work was to gain the ability to predict properties from the known coordinates (relative positions) of atoms in a metallic glass, presuming that information about which regions of the glass are fertile for rearrangment (defects) are encoded in this structural information. This goal is important to the science of metallic glasses. This research of applied machine learning methods to automate the development of a relationship between structural measures and thermally and stress-activated events in the metallic glass. In this way, the work aimed to building a bridge between structure and properties for this class of materials. The work is expected to have timely impact on guiding the tuning of internal structures of metallic glasses for desired properties. It also represents an advance in applying machine learning techniques to discerning the properties of materials.

36 MATERIALS SCIENCE↗