Search for: All records

Abstract Manufacturing custom three-dimensional (3D) carbon functional materials is of utmost importance for applications ranging from electronics and energy devices to medicine, and beyond. In lieu of viable eco-friendly synthesis pathways, conventional methods of carbon growth involve energy-intensive processes with inherent limitations of substrate compatibility. The yearning to produce complex structures, with ultra-high aspect ratios, further impedes the quest for eco-friendly and scalable paths toward 3D carbon-based materials patterning. Here, we demonstrate a facile process for carbon 3D printing at room temperature, using low-power visible light and a metal-free catalyst. Within seconds to minutes, this one-step photocatalytic growth yields rod-shaped microstructures with aspect ratios up to ~500 and diameters below 10 μm. The approach enables the rapid patterning of centimeter-size arrays of rods with tunable height and pitch, and of custom complex 3D structures. The patterned structures exhibit appealing luminescence properties and ohmic behavior, with great potential for optoelectronics and sensing applications, including those interfacing with biological systems. 

Organic-inorganic halide perovskite quantum dots (PQDs) constitute an attractive class of materials for many optoelectronic applications. However, their charge transport properties are inferior to materials like graphene. On the other hand, the charge generation efficiency of graphene is too low to be used in many optoelectronic applications. Here, we demonstrate the development of ultrathin phototransistors and photonic synapses using a graphene-PQD (G-PQD) superstructure prepared by growing PQDs directly from a graphene lattice. We show that the G-PQDs superstructure synchronizes efficient charge generation and transport on a single platform. G-PQD phototransistors exhibit excellent responsivity of 1.4 × 10 8 AW –1 and specific detectivity of 4.72 × 10 15 Jones at 430 nm. Moreover, the light-assisted memory effect of these superstructures enables photonic synaptic behavior, where neuromorphic computing is demonstrated by facial recognition with the assistance of machine learning. We anticipate that the G-PQD superstructures will bolster new directions in the development of highly efficient optoelectronic devices.