Search

Accurate single virus detection is critical for disease diagnosis and early prevention, especially in view of current pandemics. Numerous detection methods have been proposed with the single virus sensitivity, including the optical approaches and immunoassays. However, few of them hitherto have the capability of both trapping and detection of single viruses in the microchannel. Here, we report an optofluidic potential well array to trap nanoparticles stably in the flow stream. The nanoparticle is bound with single viruses and fluorescence quantum dots through an immunolabeling protocol. Single viruses can be swiftly captured in the microchannel by optical forces and imaged by a camera. The number of viruses in solution and on each particle can be quantified via image processing. Our method can trap and detect single viruses in the 1 mL serum or water in 2 h, paving an avenue for the advanced, fast, and accurate clinical diagnosis, as well as the study of virus infectivity, mutation, drug inhibition, etc. Ministry of Education (MOE) National Research Foundation (NRF) This work was supported by the Singapore National Research Foundation under the Competitive Research Program (NRFCRP13-2014-01), and the Singapore Ministry of Education (MOE) Tier 3 grant (MOE2017-T3-1-001).

Hybrid optical fibers have been widely investigated in different architectures to build integrated fiber photonic devices and achieve various applications. Here we proposed and fabricated hybrid microfiber waveguides with self-growing polymer nanofilms on the surfaces of microfibers triggered by evanescent field of light for the first time. We have demonstrated the polymer nanofilm of ∼50 nm can be grown on the microfiber with length up to 15 mm. In addition, the roughness of nanofilm can be optimized by controlling the triggering laser power and exposure duration, and the total transmission loss of the fabricated hybrid microfiber is less than 2 dB within a wide wavelength range. The hybrid polymer nanofilm microfiber waveguides have been characterized and their relative humidity (RH) responses have also been tested, indicating a potential for RH sensing. Our fabrication method may also be extended to construct the hybrid microfibers with different functional photopolymer materials.

Quantum autoencoders serve as efficient means for quantum data compression. Here, we propose and demonstrate their use to reduce resource costs for quantum teleportation of subspaces in high-dimensional systems. We use a quantum autoencoder in a compress-teleport-decompress manner and report the first demonstration with qutrits using an integrated photonic platform for future scalability. The key strategy is to compress the dimensionality of input states by erasing redundant information and recover the initial states after chip-to-chip teleportation. Unsupervised machine learning is applied to train the on-chip autoencoder, enabling the compression and teleportation of any state from a high-dimensional subspace. Unknown states are decompressed at a high fidelity (~0.971), obtaining a total teleportation fidelity of ~0.894. Subspace encodings hold great potential as they support enhanced noise robustness and increased coherence. Laying the groundwork for machine learning techniques in quantum systems, our scheme opens previously unidentified paths toward high-dimensional quantum computing and networking.

With the rapid growth of electronic chip’s performance, electric signal limits chip I/O in power consumption, reachability, and signal quality. In this letter, we propose a new chip-let architecture for chip-level optical interconnect. An optic I/O chip-let including an ultra-small optic transceiver and electronic components is implemented. Our analysis shows that the optical interconnect based on this architecture can achieve 1/3 power consumption and 1/2 area compared with traditional board-level optical interconnect in Ethernet NIC application. By adopting the architecture we proposed, the optic I/O chip-let can support any payload IC such as CPU, GPU, switch to have optic I/O.

The millimeter wave (mmWave) technology will perform an important role in supporting multi-gigabit services. To compensate for significant path loss and penetration loss, more antennas are equipped to establish narrow beams and provide sufficient antenna array gains. In the meantime, the hybrid beamforming (HBF) structure is utilized to provide acceptable cost and energy consumption, which also flexibly supports multi-user scenarios. However, when the channel varies rapidly, efficiently tracking multiple paths or beam directions still faces difficulties. In this letter, a joint multi-beam and channel tracking scheme is proposed to efficiently perform the beam management for the mmWave HBF multi-user systems. The pilot allocation is optimized and the asymptotic performance of the proposed algorithm is analyzed. Numerical results indicate that the proposed algorithm can significantly improve the tracking performance under time-varying channels.

Graphitic carbon nitride (g-CN) nanosheet is a potential material for sensing applications. The g-CN nanosheet films deposited on microfibers were first fabricated and employed for relative humidity (RH) optical sensing. The RH sensitivity of microfibers was significantly enhanced via g-CN nanosheet coating, the optical transmitted power of coated microfiber humidity sensor was changed ∼10.6 dB when the RH varied from 40% to 95%. In addition, fast response time of 0.43 s and recovery time of 0.87 s were demonstrated by monitoring human breathing. The g-CN nanosheet coated microfiber humidity sensor possesses excellent stability, repeatability, reversibility, and cost-effectivity. The experimental results validate that the microfiber combined with g-CN nanosheets is a promising platform for environmental and biological RH sensing applications.

We have demonstrated an effective approach to fabricating ultrathin lensed photonic crystal fibers (PCFs) with long working distances. The analytic function of the maximum working distance of lensed PCF has been first revealed, agreeing well with the numerical solutions. Our specific lensed PCF can be experimentally optimized in terms of the collapsed PCF length and fiber tip curvature by use of a graphite filament furnace. A maximum collimating working distance up to 4.84 mm for a lensed PCF with the diameter of 125 μm is demonstrated, which is more than four times as long as previous lensed PCFs. The ultrawide working bandwidth over 770 nm is also demonstrated. The significantly enhanced features in terms of key performance parameters such as working distances and bandwidth will accelerate a variety of applications using such ultrathin lensed PCFs.

The nanobore fiber (NBF) is a promising nanoscale optofluidic platform due to its long nanochannel and unique optical properties. However, so far, the applications of NBF have been based only on its original fiber geometry without any extra functionalities, in contrast with various telecom fiber devices, which may limit its wide applications. Here, we provide the first, to the best of our knowledge, demonstration of NBF-based fiber Bragg gratings (FBGs) introduced by either the femtosecond (fs) laser direct writing technique or the ultraviolet (UV) laser phase mask technique. Moreover, the FBG fabricated via the UV laser was optimized, achieving a high reflectivity of 96.89% and simultaneously preserving the open nanochannel. The NBF-based FBGs were characterized in terms of temperature variation and the infiltration of different liquids, and they showed high potential for nanofluidic applications.

Robust low-loss optical fiber joints are a prerequisite in all-fiber devices. Joining structurally dissimilar fibers, such as microstructured optical fibers, is a timely challenge, as they are beco...

Storage workload prediction is a critical step for fine-grained load balancing and job scheduling in realtime and adaptive cluster systems. However, how to perform workload time series prediction based on a deep learning method has not yet been thoroughly studied. In this paper, we propose a storage workload prediction method called CrystalLP based on deep learning. CrystalLP includes workload collecting, data preprocessing, time series prediction, and data post-processing phase. The time series prediction phase is based on a long short-term memory network (LSTM). Furthermore, to improve the efficiency of LSTM, we study the sensitivity of the hyperparameters in LSTM. Extensive experimental results show that CrystalLP can obtain performance improvement compared with three classic time series prediction algorithms.