Mending cracks atom-by-atom in rutile TiO with electron beam radiolysis.

Rich electron-matter interactions fundamentally enable electron probe studies of materials such as scanning transmission electron microscopy (STEM). Inelastic interactions often result in structural modifications of the material, ultimately limiting the quality of electron probe measurements. However, atomistic mechanisms of inelastic-scattering-driven transformations are difficult to characterize.

Engineering metal oxidation using epitaxial strain.

The oxides of platinum group metals are promising for future electronics and spintronics due to the delicate interplay of spin-orbit coupling and electron correlation energies. However, their synthesis as thin films remains challenging due to their low vapour pressures and low oxidation potentials. Here we show how epitaxial strain can be used as a control knob to enhance metal oxidation.

Platinum Graphene Catalytic Condenser for Millisecond Programmable Metal Surfaces.

Accelerating catalytic chemistry and tuning surface reactions require precise control of the electron density of metal atoms. In this work, nanoclusters of platinum were supported on a graphene sheet within a catalytic condenser device that facilitated electron or hole accumulation in the platinum active sites with negative or positive applied potential, respectively. The catalytic condenser was fabricated by depositing on top of a -type Si wafer an amorphous HfO dielectric (70 nm), on which was placed the active layer of 2-4 nm platinum nanoclusters on graphene.

Alumina Graphene Catalytic Condenser for Programmable Solid Acids.

Precise control of electron density at catalyst active sites enables regulation of surface chemistry for the optimal rate and selectivity to products. Here, an ultrathin catalytic film of amorphous alumina (4 nm) was integrated into a catalytic condenser device that enabled tunable electron depletion from the alumina active layer and correspondingly stronger Lewis acidity. The catalytic condenser had the following structure: amorphous alumina/graphene/HfO dielectric (70 nm)/p-type Si.

Reconfigurable MoS Memtransistors for Continuous Learning in Spiking Neural Networks.

Artificial intelligence and machine learning are growing computing paradigms, but current algorithms incur undesirable energy costs on conventional hardware platforms, thus motivating the exploration of more efficient neuromorphic architectures. Toward this end, we introduce here a memtransistor with gate-tunable dynamic learning behavior. By fabricating memtransistors from monolayer MoS grown on sapphire, the relative importance of the vertical field effect from the gate is enhanced, thereby heightening reconfigurability of the device response.

Ohmic-Contact-Gated Carbon Nanotube Transistors for High-Performance Analog Amplifiers.

The growing demand for ubiquitous data collection has driven the development of sensing technologies with local data processing. As a result, solution-processed semiconductors are widely employed due to their compatibility with low-cost additive manufacturing on a wide range of substrates. However, to fully realize their potential in sensing applications, high-performance scalable analog amplifiers must be realized.

Spiking neurons from tunable Gaussian heterojunction transistors.

Spiking neural networks exploit spatiotemporal processing, spiking sparsity, and high interneuron bandwidth to maximize the energy efficiency of neuromorphic computing. While conventional silicon-based technology can be used in this context, the resulting neuron-synapse circuits require multiple transistors and complicated layouts that limit integration density. Here, we demonstrate unprecedented electrostatic control of dual-gated Gaussian heterojunction transistors for simplified spiking neuron implementation.