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Researchers at the University of Pennsylvania have made a microfabricated laser using semiconductor fabrication methods that can control two features of the light particles: their orbit and their spin. This allows them to make light particles with four states simultaneously. These higher-dimensional quantum bits (qudits) can store more information and better avoid noise. They can also make quantum communication more secure and faster and allow more secure communications.

We developed one-component, sequence-defined Ionizable Amphiphilic Janus Dendrimers (IAJDs) and their assemblies with mRNA. These Dendrimersome Nanoparticles (DNPs) were investigated for the delivery of mRNA for vaccines and nanotherapeutics.

The thermal properties of oxides are of interest for a number of important applications, including thermoelectrics, thermal barrier coatings, memristors, and fuel cells. MIT MRSEC researchers demonstrated, for the first time, the controllable impact of oxygen defects on the thermal conductivity of a reducible oxide. The thermal conductivity was modulated by varying the concentration of oxygen vacancies and reduced cations in oxide thin films treated under different annealing conditions.

Aligned liquid crystal elastomers (LCEs) are soft materials that exhibit reversible actuation akin to human muscles when thermally cycled above their nematic-to-isotropic transition temperature. Lewis and collaborators studied the effects of LCE ink composition, nozzle geometry, and printing parameters on director alignment.

• Amorphous oxide semiconductors (AOS) provide superior performance and lower cost for next generation displays. However, instability under illumination remains a critical issue. • In this work, the photoconductivity decay was investigated to deduce deep trap density.

In recent years there has been an increasing interest in the development of electronic devices using non-conventional soft matter materials, as a novel alternative to traditional solid-state electronic devices. Traditional solid-state devices are often fragile and usually are made of non-biocompatible materials. The possibility of producing resilient nano-electronic devices opens a new avenue to develop novel technology.  

In conventional machine learning, a computer is used to minimize a cost function that specifies a desired task, using global information about the entire network. We have now demonstrated how machine learning tasks can be learned and performed without either a computer or global information by taking advantage of physics via a scheme called coupled learning1. Specifically, twin variable-resistor networks are run under different boundary conditions and corresponding edges are compared against only each other for determining resistance updates2.

On March 21, 2015, Professor Bob Cava led an engaging prsentation for over 400 guests (two sessions) about the amazing properties of chemistry and materials science. Special guests, Kathryn Wagner, Dan Steinberg, and Professor Cava's graduate student researchers engaged children ages 9-14 and their parents with fun group activities, stimulating Q&A sessions, and exciting demonstrattions to enlighten the public about materials science research.

In October 2014, RT-MRSEC co-sponsored a “Research2Innovation” one-day workshop at Duke University, as part of Lens of the Market, a three-stage training program to provide scientists and engineers with the full range of knowledge required for successful translation of their research into commercial ventures.