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Siilicon is the most scalable optoelectronic material but has suffered from its inability to generate directly and efficiently classical or quantum light on-chip. Scaling and integration are the most fundamental challenges facing quantum science and technology. We report an all-silicon quantum light source based on a single atomic emissive center embedded in a silicon-based nanophotonic cavity. We observe a more than 30-fold enhancement of luminescence, a near-unity atom-cavity coupling efficiency, and an 8-fold acceleration of the emission from the all-silicon quantum emissive center. Our work opens immediate avenues for large-scale integrated cavity quantum electrodynamics and quantum light-matter interfaces with applications in quantum communication and networking, sensing, imaging, and computing.

 

The security and performance of blockchain systems such as Bitcoin critically rely on the P2P network. This paper aims to investigate blockchain P2P networks. We explore the topologies, peer discovery, and data forwarding and examine the security and performance of the P2P network. Further, we formulate an optimization problem to study the theoretical limit of the performance and provide a solution to achieve optimal performance in a blockchain P2P network. 

With our continuing interest in the chemistry of cationic boranes, we have synthesizedthe tetrafluoroborate salt of 1-dimesitylboron-4-(N-methyl-9-acridinium)-phenylene which acts as a turn-on fluoride anion sensor, visibly changing from yellow to orange upon binding fluoride. To understand this reactivity, we spectroscopically and computationally analyzed the cation and triarylfluoroborate adduct. UV-vis spectroscopy and TD-DFT revealed the basis of the color change to be a red shift in a low-energy absorption band resulting from intramolecular charge-transfer. Electrochemical studies were undertaken to further probe this system. Cyclic voltammetry indicated a reversible one-electron reduction for the cation and a cathodic shift of -0.12 V in the first reduction wave upon fluoride binding. Chemical reduction of the cation yielded the acridine borane radical which was verified by EPR spectroscopy. 

A microsecond time-scale photonic lift-off (PLO) process was used to fabricate mechanically flexible photovoltaic devices (PVs) with a total thickness of less than 20 μm. PLO is a rapid, scalable photothermal technique for processing extremely thin, mechanically flexible electronic and optoelectronic devices. PLO is also compatible with large-area devices, roll-to-roll processing, and substrates with low temperature compatibility. As a proof of concept, PVs were fabricated using CuInSe2 nanocrystal ink deposited at room temperature under ambient conditions on thin, plastic substrates heated to 100 °C. It was necessary to prevent cracking of the brittle top contact layer of indium tin oxide (ITO) during lift-off, either by using a layer of silver nanowires (AgNW) as the top contact or by infusing the ITO layer with AgNW. This approach could generally be used to improve the mechanical versatility of current collectors in a variety of ultrathin electronic and optoelectronic devices requiring a transparent conductive contact layer. 

Next-generation scientific applications in various fields are experiencing a rapid transition from traditional experiment-based methodologies to large-scale computation-intensive simulations featuring complex numerical modeling with a large number of tunable parameters. Such model-based simulations generate colossal amounts of data, which are then processed and analyzed against experimental or observation data for parameter calibration and model validation. The sheer volume and complexity of such data, the large model-parameter space, and the intensive computation make it practically infeasible for domain experts to manually configure and tune hyperparameters for accurate modeling in complex and distributed computing environments. This calls for an online computational steering service to enable real-time multi-user interaction and automatic parameter tuning. Towards this goal, we design and develop a generic steering framework based on Bayesian Optimization (BO) and conduct theoretical performance analysis of the steering service. We present a case study with the Weather Research and Forecast (WRF) model, which illustrates the performance superiority of the BO-based tuning over other heuristic methods and manual settings of domain experts using regret analysis.