Broadband lens and graphene/silicon photodiode for wide spectral imaging.

We designed a broadband lens along with a graphene/silicon photodiode for wide spectral imaging ranging from ultraviolet to near-infrared wavelengths. By using five spherical glass lenses, the broadband lens, with the modulation transfer function of 0.38 at 100 lp/mm, corrects aberrations ranging from 340 to 1700 nm. Our design also includes a broadband graphene/silicon Schottky photodiode with the highest responsivity of 0.63 A/W ranging from ultraviolet to near-infrared. By using the proposed broadband lens and the broadband graphene/silicon photodiode, several single-pixel imaging designs in ultraviolet, visible, and near-infrared wavelengths are demonstrated. Experimental results show the advantages of integrating the lens with the photodiode and the potential to realize broadband imaging with a single set of lens and a detector.

Boolean Computation in Single-Transistor Neuron.

Brain neurons exhibit far more sophisticated and powerful information-processing capabilities than the simple integrators commonly modeled in neuromorphic computing. A biological neuron can in fact efficiently perform Boolean algebra, including linear nonseparable operations. Traditional logic circuits require more than a dozen transistors combined as NOT, AND, and OR gates to implement XOR. Lacking biological competency, artificial neural networks require multilayered solutions to exercise XOR operation. Here, it is shown that a single-transistor neuron, harnessing the intrinsic ambipolarity of graphene and ionic filamentary dynamics, can enable in situ reconfigurable multiple Boolean operations from linear separable to linear nonseparable in an ultra-compact design. By leveraging the spatiotemporal integration of inputs, bio-realistic spiking-dependent Boolean computation is fully realized, rivaling the efficiency of a human brain. Furthermore, a soft-XOR-based neural network via algorithm-hardware co-design, showcasing substantial performance improvement, is demonstrated. These results demonstrate how the artificial neuron, in the ultra-compact form of a single transistor, may function as a powerful platform for Boolean operations. These findings are anticipated to be a starting point for implementing more sophisticated computations at the individual transistor neuron level, leading to super-scalable neural networks for resource-efficient brain-inspired information processing.

Highly Thermally Conductive and Structurally Ultra-Stable Graphitic Films with Seamless Heterointerfaces for Extreme Thermal Management.

Highly thermally conductive graphitic film (GF) materials have become a competitive solution for the thermal management of high-power electronic devices. However, their catastrophic structural failure under extreme alternating thermal/cold shock poses a significant challenge to reliability and safety. Here, we present the first investigation into the structural failure mechanism of GF during cyclic liquid nitrogen shocks (LNS), which reveals a bubbling process characterized by "permeation-diffusion-deformation" phenomenon. To overcome this long-standing structural weakness, a novel metal-nanoarmor strategy is proposed to construct a Cu-modified graphitic film (GF@Cu) with seamless heterointerface. This well-designed interface ensures superior structural stability for GF@Cu after hundreds of LNS cycles from 77 to 300 K. Moreover, GF@Cu maintains high thermal conductivity up to 1088 W m-1 K-1 with degradation of less than 5% even after 150 LNS cycles, superior to that of pure GF (50% degradation). Our work not only offers an opportunity to improve the robustness of graphitic films by the rational structural design but also facilitates the applications of thermally conductive carbon-based materials for future extreme thermal management in complex aerospace electronics.

Precise Thermoplastic Processing of Graphene Oxide Layered Solid by Polymer Intercalation.

The processing capability is vital for the wide applications of materials to forge structures as-demand. Graphene-based macroscopic materials have shown excellent mechanical and functional properties. However, different from usual polymers and metals, graphene solids exhibit limited deformability and processibility for precise forming. Here, we present a precise thermoplastic forming of graphene materials by polymer intercalation from graphene oxide (GO) precursor. The intercalated polymer enables the thermoplasticity of GO solids by thermally activated motion of polymer chains. We detect a critical minimum containing of intercalated polymer that can expand the interlayer spacing exceeding 1.4 nm to activate thermoplasticity, which becomes the criteria for thermal plastic forming of GO solids. By thermoplastic forming, the flat GO-composite films are forged to Gaussian curved shapes and imprinted to have surface relief patterns with size precision down to 360 nm. The plastic-formed structures maintain the structural integration with outstanding electrical (3.07 × 105 S m-1) and thermal conductivity (745.65 W m-1 K-1) after removal of polymers. The thermoplastic strategy greatly extends the forming capability of GO materials and other layered materials and promises versatile structural designs for more broad applications.