Despite extensive research for more than 200 years, the experimental isolation of monatomic sulphur chains, which are believed to exhibit a conducting character, has eluded scientists. Here we report the synthesis of a previously unobserved composite material of elemental sulphur, consisting of monatomic chains stabilized in the constraining volume of a carbon nanotube. This one-dimensional phase is confirmed by high-resolution transmission electron microscopy and synchrotron X-ray diffraction. Interestingly, these one-dimensional sulphur chains exhibit long domain sizes of up to 160 nm and high thermal stability (~800 K). Synchrotron X-ray diffraction shows a sharp structural transition of the one-dimensional sulphur occurring at ~450–650 K. Our observations, and corresponding electronic structure and quantum transport calculations, indicate the conducting character of the one-dimensional sulphur chains under ambient pressure. This is in stark contrast to bulk sulphur that needs ultrahigh pressures exceeding ~90 GPa to become metallic.

A little addition of Cl to MAPbI 3 has been reported to improve the material stability as well as light harvesting and carrier conducting properties of organometal trihalide perovskites, the key component of perovskite solar cell (PSC). However, the mechanism of performance enhancement of PSC by Cl addition is still unclear. Here, we apply the efficient virtual crystal approximation method to revealing the effects of Cl addition on the structural, electronic, optical properties and material stability of MAPb(I 1-x Cl x ) 3 . Our ab initio calculations present that as the increase of Cl content cubic lattice constants and static dielectric constants decrease linearly, while band gaps and exciton binding energies increase quadratically. Moreover, we find the minimum of exciton binding energy at the Cl content of 7%, at which the chemical decomposition reaction changes coincidentally to be from exothermic to endothermic. Interactions among constituents of compound and electronic charge transferring during formation are carefully discussed. This reveals new prospects for understanding and designing of stable, high efficiency PSCs.

This paper considers a massive multiple-inputmultiple-output (MIMO) system with low-resolution analog-todigital converters (ADCs). In this system, inspired by supervised learning, we propose a novel communication framework that consists of channel training and data detection. The underlying idea of the proposed framework is to use the input-output relations of a nonlinear system, formed by a channel and a quantization at the ADCs, for data detection. Specifically, for the channel training, we develop implicit and explicit training methods that empirically learn the conditional probability mass functions (PMFs) of the nonlinear system. For the data detection, we propose three detection methods that map a received signal vector to one of the indexes of possible symbol vectors, according to the empirical conditional PMFs learned from the channel training. We also present a low-complexity version of the proposed framework that reduces a detection complexity by using a successive-interference-cancellation (SIC) approach. In this lowcomplexity version, a symbol vector is divided into two subvectors and then these two subvectors are successively detected using SIC. When employing the proposed framework with one-bit ADCs, we derive an analytical expression for the symbol-vectorerror probability. One major observation is that the symbolvector-error probability decreases exponentially with the inverse of the number of transmit antennas, the operating signal-tonoise ratio, and the minimum distance that can increase with the number of receive antennas. Simulations demonstrate the detection error reduction of the proposed framework compared to existing detection techniques.

This paper presents a low-complexity near-maximum-likelihood-detection (near-MLD) algorithm called one-bit-sphere-decoding for an uplink massive multiple-input multiple-output (MIMO) system with one-bit analog-to-digital converters (ADCs). The idea of the proposed algorithm is to estimate the transmitted symbol vector sent by uplink users (a codeword vector) by searching over a sphere, which contains a collection of codeword vectors close to the received signal vector at the base station in terms of a weighted Hamming distance. To reduce the computational complexity for the construction of the sphere, the proposed algorithm divides the received signal vector into multiple sub-vectors each with reduced dimension. Then, it generates multiple spheres in parallel, where each sphere is centered at the sub-vector and contains a list of sub-codeword vectors. The detection performance of the proposed algorithm is also analyzed by characterizing the probability that the proposed algorithm performs worse than the MLD. The analysis shows how the dimension of each sphere and the size of the sub-codeword list are related to the performance-complexity tradeoff achieved by the proposed algorithm. Simulation results demonstrate that the proposed algorithm achieves near-MLD performance, while reducing the computational complexity compared to the existing MLD method.

We consider a wireless device-to-device (D2D) network where n nodes are uniformly distributed at random over the network area. We let each node with storage capacity M cache files from a library of size m ≥ M . Each node in the network requests a file from the library independently at random, according to a popularity distribution, and is served by other nodes having the requested file in their local cache via (possibly) multihop transmissions. Under the classical "protocol model" of wireless networks, we characterize the optimal per-node capacity scaling law for a broad class of heavy-tailed popularity distributions including Zipf distributions with exponent less than one. In the parameter regimes of interest, we show that a decentralized random caching strategy with uniform probability over the library yields the optimal per-node capacity scaling of Θ( M/m), which is constant with n, thus yielding throughput scalability with the network size. Furthermore, the multihop capacity scaling can be significantly better than for the case of single-hop caching networks, for which the per-node capacity is Θ(M/m). The multihop capacity scaling law can be further improved for a Zipf distribution with exponent larger than some threshold > 1, by using a decentralized random caching uniformly across a subset of most popular files in the library. Namely, ignoring a subset of less popular files (i.e., effectively reducing the size of the library) can significantly improve the throughput scaling while guaranteeing that all nodes will be served with high probability as n increases.